Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems

The selection of appropriate equipment maintenance strategies is essential for avoiding accidents and unnecessary operating costs in companies. This paper aims to propose a methodology which makes use of Decision-Making Grids (DMGs) for classifying and prioritizing the manufacturing assets according to their criticality and, accordingly, selecting the most appropriate maintenance strategy for the analyzed assets components. The ability to support the assignment of maintenance strategies at the component level distinguishes the proposed methodology from the related studies found in the literature, as they define a unique type of strategy for the equipment as a whole or strategies focused on its functional failures. The assets classification and prioritization is performed considering the failure impact and quantitative criteria related to their maintenance history. Therefore, the methodology can also be used as a performance measurement tool for identifying the need of redefining the maintenance plan of specific assets or implementing equipment improvement initiatives. The methodology was successfully applied in a case study performed in a company that produces wood-based panels. Its application enabled to assign maintenance strategies to the six components with higher frequency of replacement of one safety critical equipment in a quick manner. The case study has also shown that the effort and resources required for applying the methodology are low, since it uses mainly data recorded by the current management systems.

Condition monitoring (CM) of machines and robots is vital to improve operational reliability and to avoid occupational incidents. Recently, deep learning (DL) has become popular in CM literature for its outstanding ability of learning fault patterns. However, due to the black box and non-intuitive nature of its layers, the logic behind its decisions is hard to understand. This shortcoming hinders the DL implementation in many critical applications where the user needs to ensure the reliability of the classifier. Hence, in this paper, a new framework for DL-based CM systems is proposed, which consists of four steps (1) Feature extraction (2) Fault diagnosis (3) eXplainable Artificial Intelligence (XAI)-based model optimization (4) Interpretation system. The experimental evaluations on two real-world datasets demonstrate that the proposed XAI interpreter was able to visualize the contributing patterns to fault types. The feature engineering block not only makes it easier for the operator to only observe the contributing features, but also it helps the model optimizer to speed up the runtime. The results show that the proposed model achieved a slightly better accuracy than the other state-of-the-art models.

In the current competitive markets, the possibility to obtain relevant and accurate data and information can make the difference between achieving success or not. A company which has several maintenance technicians in the final stage of their career and plants spread through different countries, can benefit from a collaborative platform that aggregates the maintenance information and knowledge to support decision making. The more accurate the data, the more reliable will be the analyzes and better decisions can be made. This paper presents a study performed in a wood derivatives company which intends to see the knowledge of its most experienced maintenance technicians and all the information related to maintenance department being captured, retained, and disseminated by all elements of the maintenance teams from the different plants. To capture and disseminate the relevant information by all the plants, a collaborative platform and its information architecture were proposed. The platform aims to obtain accurate records for supporting failure diagnosis and maintenance interventions, as well as process the data to carry out benchmarking actions and defining improvement actions. This paper presents the test results of the collaborative platform prototype, developed to validate its specification and information architecture, and the benefits that the company can obtain through its implementation. These benefits are the reduction of the interventions duration, promoting internal Benchmarking actions and establishing priorities in problems solving to improve performance. The proposed novel platform provides helpful information for both technicians and decision-making agents, and considers future integration with holographic solutions.

The project described in this paper was developed in a rubber area of a cork company, where maintenance activity was weak or non-existent. The main objective was to implement a maintenance philosophy, based on the principles of Total Productive Maintenance (TPM). The methodology adopted was based on action-research. The autonomous maintenance (AM) and preventive maintenance (PM) plans created and implemented are described and the results are presented considering the number of intervention requests and the availability of critical equipment before and after the implementation. Intervention requests were reduced by 40% and the availability of critical equipment increased with this project due to an efficient implementation of TPM principles.

This article describes the work carried out with the objective of analyzing and improving the maintenance activity in the molding process of a company in the cork sector. Regarding the research design, an analysis of the production and maintenance process was carried out, which allowed an initial diagnosis with the identification of existing problems and the calculation of main maintenance Key Performance Indicators (KPIs). After, improvement actions were identified, planned and implemented in the molding process. A set of standards and best practices was established, and maintenance documentation updated. Additionally, equipment was changed, and the principles of Total Productive Maintenance (TPM) were incorporated, according to the Lean Production philosophy, promoting collaboration between the production and maintenance teams and autonomous maintenance. The post-implementation results revealed an improvement trend in the previously analyzed KPIs.

Process mining (PM) and digital twin (DT) present synergy in the context of Industry 4.0 era, as data-driven approaches are leading the way. With this relation, some design principles of DT are accomplished by applying PM techniques in order to extract knowledge from industrial processes. This knowledge can range from singular metrics of indicators through complex process analyzes (comprehension of paths, deviations, frequency, simulation and among others). In this context, the motivation of the work comes from using factory floor process data in a process’ digital twin environment as inputs in the decision layer to make assertive choices in the industrial and/or maintenance environment considering different scenarios of actions. Therefore, the main goal of this paper aims at integrating those fields of study (i.e., process mining, process’ digital twin and decision-making) in an industrial maintenance area to choose or indicate a strategic maintenance action that best fits the evaluated scenario.

Industry 4.0 (I4.0) refers to recent technological advances where the internet and supporting technologies serve as communication for integration between systems, thus raising the level of maturity of the company. This context is intrinsically linked to Industrial Maintenance, which is greatly impacted by the characteristic of distribution and volume of information imposed by the requirements of I4.0, suggesting a review those models and strategies for a more predictive characteristic based on reliability. In this direction, the Digital Twin (DT) concept emerges expressively, which encompasses functional and technological scopes associated with Industrial Maintenance. Based on these elements, a diagnostic evaluation can suggest Industrial Master Plans, allowing a clearer view of the elements involved, restrictions, risks and better application of resources. Under this focus, the interoperability assessment, a strong requirement of I4.0, represents a relevant diagnostic tool guiding both the requirements and specifications aimed at the integration of supervision and industrial maintenance platforms. The objective of this article is to present a diagnostic evaluation model of interoperability in maintenance, under the requirements of I4.0, through the use of multicriteria decision support methods (MCDM) and to provide an evaluative and directional basis to implement the DT concept.

The commoditization initiatives triggered in manufacturing companies the concern with the rigidity of their offers, and the appeal of their offers to their customers, leading to servitization strategies. This research highlights the importance of preventive maintenance, and builds on the extraction and processing of data for a PSS business model. Data from https://www.kaggle.com was used to analyze the performance of a production process with three products with differentiated levels of quality. Data analysis was of exploratory nature using descriptive statistics and Pearson’s correlation through, using Python via Google Collaboratory. The main failures were exposed allowing the elaboration of a preventive maintenance plan. Through multivariate statistical analyses, we demonstrate that in a PSS business model, having machine suppliers at your disposal that combine the sale of the item with the provision of predictive maintenance services is a differential that will help to make assertive a preventive d skillful decision, avoiding maintenance unnecessary and waste of time.

In the manufacturing industry, anomalies are an unfortunate but inevitable reality. If left unaddressed, they can lead to costly production defects and halted production lines. However, with the rise of Industry 4.0, many industrial machines are now equipped with sensors that can be used to detect anomalous behaviors, allowing for early identification and prevention of defects. Therefore, this study presents a solution using a Long Short-Term Memory (LSTM) autoencoder to detect abnormal behavior in an industrial machine temperature sensor dataset. The algorithm is compared with conventional methods, further demonstrating its capabilities in anomaly detection. Additionally, an implementation architecture is proposed using InfluxDB and Telegraf software, providing a simulated real-world application of the proposed solution.

Nowadays, and regardless of the activity sector, ensuring product quality and customer satisfaction is the main focus of organizations. In this sense, and since measuring and monitoring equipment (MME) are essentially used in product conformity assessment, an efficient management of the calibration process of these instruments becomes crucial for organizational performance. Although there are several methods for defining calibration intervals, there is a lack of guidelines for selecting the most appropriate method for each situation. Therefore, this paper has as its main goal to present an approach to assist in the management of calibration intervals for MME. This approach is intended to identify, considering a set of factors associated with the characteristics of the measuring instrument and its condition and use environment, which method for setting calibration intervals has the greatest potential for each case. A case study was performed, using data from a set of measuring instruments of an organization that produces cork stoppers, which allowed conclusions to be drawn for each method under study. In the end, with the development and application of the approach, it was concluded that an appropriate choice of the method for defining calibration intervals allows a more efficient management of the measuring and monitoring equipment, in terms of aptitude and adequacy of its metrological capacity within the productive sector.

Industry 4.0 is directly related to the digital transformation of organizations. One of the main characteristics of this transformation is the ability to analyze and manage data to obtain relevant and previously unknown information from it. Specifically, in the context of operational risk management, an efficient analysis of the data that typically come from occurrence records, can support decision-making. This paper presents a new algorithm to identify dependencies between events in productive environments, such as equipment failures, defects, or safety and environmental incidents, supporting the decision-making process in the context of analyzing and managing operational risks. The algorithm was developed based on association rule mining algorithms, then tested with data from a Portuguese metalworking company, and finally validated. The test results show that the developed algorithm is capable of identifying dependencies, which have to be confirmed by specialized staff to conclude if they are causal relationships.

Demand Driven Material Requirements Planning (DDMRP) assumes that a production order is generated for replenishment when the inventory position, given by the net flow equation, is below a given level. Literature on this production planning and control system suggests prioritizing open orders on the shop floor based on the inventory buffer status. However, the performance of buffer-oriented priority dispatching largely remains unknown. Using discrete event simulation, this study suggests that buffer-oriented dispatching based on the net flow equation outperforms due date-oriented dispatching rules and first-come-first-served. The performance impact depends, however, on the reorder quantity associated with the production orders. These results have important implications for industrial practice.

Recently, closed-loop supply chains (CLSCs) are receiving growing attentions by the academic world since they represent a sustainable network for circular economies. However, the impact of production control policies (PCPs) on the performance of CLSCs is still unexplored. Then, this paper investigates the role of the Demand-Driven Material Requirements Planning (DDMRP) as a PCP in two-product CLSCs. The normal flow of the CLSC consists of factory and retailer nodes. The factory is characterized by a production line subject to failure events and product changeovers. The retailer issues orders to the factory through the Smoothing Order-Up-To policy and meets the customer demand. The reverse flow is represented by the remanufacturer that collects and restores the two product types coming from the customer. DDMRP is adopted by the factory node to decide what product type has to be processed. A full-factorial Design of Experiments was defined, and ANOVA was carried out to investigate the impact of remanufacturer and control parameters of DDMRP on the fill rate of the whole closed-loop supply chain. This paper allows investigating how the parameters regulating the DDMRP bias the responsiveness of two-product CLSC with capacity restrictions.

This paper describes the development of a Production Planning and Control (PPC) method implemented in a company that produces shower screens, to respond to problems associated with overproduction. In this system, the daily workload is directly influenced by the productivity presented in the bottleneck. The bottleneck was identified, a database was built to support the calculation of the bottleneck’ productivity and the determination of the workload for each day. It is foreseeable that the system will bring considerable improvements in the productivity values. The data collected before and after the implementation of PPC system allowed identifying an increasing trend in the productivity values of the bottleneck resulting from the implementation.

Changes in customer demand and technological advances increase production scheduling complexity. A solution to handle the increased complexity is to facilitate machine learning. Current reviews of the research of scheduling algorithms focus on summarizing existing methods. They lack the characteristics of practical implications arising from the increased complexity of the scheduling problem by Industry 4.0 and do not consider how the scheduling algorithms cope with the added complexity. Therefore, we address these issues and synthesize the current literature in a comprehensive literature review and taxonomy. We discuss how they address changeovers, buffers before and after machines, prioritization of jobs, dynamics of production, and transportation on the shop floor. Afterward, we summarize the concepts of scheduling approaches and derive five research gaps.

The present study brings forward a simulation-based study of the production process of a Portuguese bakery. The main goal is to analyse different production processes and propose improvements, through the use of discrete event simulation. A relevant set of data was collected, and four productive processes were selected to be modelled using Simio software (Simulation Modelling based on Intelligent Objects). The analysis of the developed models highlighted the need for improvements and different scenarios were created to this purpose. Among the obtained results, it was found that the adoption of mixed production scenarios allowed the increase of the production level while maintaining the current existing resources. In conclusion, this study highlighted the ability of the simulation technique to analyse manufacturing processes, throughout the creation of different scenarios, providing insights on the production process optimising the companies’ productive performance.

This paper presents a kaizen case study that aims to create a smooth flow in both machining and non-machining processes, while addressing the inventory problem that arises in these processes. To accomplish this, we developed a model that analyzes the production requirements of various products in the machining process and the cleaning process of machined parts in the non-machining process. The machining and set-up times for each product differ depending on the CNC machine types used, and set-up labor resources are required whenever a machine switches to produce a different product. Meanwhile, the non-machining process involves cleaning batches of machined parts before they are delivered. In this study, we focus on creating an optimal flow between these two processes by considering different batch sizes and their impact on overall inventory. Our results demonstrate that by implementing a leveling and small lot sizing strategy, we can achieve a more efficient and streamlined production process that reduces inventory levels and improves productivity.

This paper presents an application of computational simulation to a production problem on unrelated parallel machines and dependent setup times, aiming at the reduction of jobs’ total processing time (makespan). The scenarios studied by Rabadi, et al. (2014) were used, along with the SIMIO simulation software, to model and conduct experiments to study the impact of a set of sequencing rules. Based on the computational study carried out, it was possible to realize that simulation is a powerful tool to support scheduling processes, as it allows to reach generally good results in a reasonable time to properly support decision-making in the resolution of production problems. These problems are highly complex and dynamically changing in nature, being usually hard to solve. Thus, simulation tools are of particular interest in this field. Moreover, as these problems are characterized by a more or less extensible set of factors, parameters and conditions, it continues to be of primer importance to further explore its resolutions, namely based on simulation tools. This study relies on the base for being used in industrial practice in the context of iFixturing Project.

New digital technologies are integrated to improve production. The Lean manufacturing paradigm is the foremost approach to creating efficient processes in the industry. In small-lot production, improvements include optimizing the process and decision-making for controlling the quality of the final product, reducing production time, and saving energy. This research overviews existing combinations of Lean manufacturing and Industry 4.0 technologies by literature review and discusses the potential of the digital twins in lean manufacturing. The objective of this paper is to investigate the potential of digital twins, as a state-of-the-art technology in industry 4,0, in small-lot production in the metal-cutting industry. As a result, a digital twins framework with five layers is proposed to use in the product design and manufacturing phase of a product lifecycle with a lean waste paradigm. The tasks of these layers are to transfer data from the physical space to the virtual space, extract information and knowledge, analyses them to select the appropriate manufacturing parameters, evaluate the results, and predict the status of the production. The framework enables the users to optimize the process planning and make decisions based on the updated data from the physical shop floor.

Flexible manufacturing systems in high-mix low-volume segments offer many challenges in terms of planning and scheduling. The complexity of these systems often requires a systemic approach for which humans are the perfect actors. However, computer systems can support scheduling tasks more effectively due to their capacity to synthesize large amounts of data. This paper describes a system developed for the flexible manufacturing of wooden doors with a wide range of product configurations. This paper proposes a rule-based scheduling method for high-mix production. The method was applied and validated at a wooden door producer. Based on the company's production schedule for a given week, a scheduling program was developed that suggested minor rearrangements for production leveling. As the rule-based approach makes the decisions verifiable, the program was validated at the producer, through a case study of production leveling under volume constraints. The results include the complete elimination of changeovers and the stabilization of throughput, which improved the precision of the delivery time. The producer is integrating the program into their production planning and control system. Thus, the results suggest that the proposed method can be useful for scheduling high-mix low-volume production, and merits further validation in similar environments.

In this paper, we address the chemotherapy outpatient scheduling problem with the aim of reducing the total patient waiting time. Since the problem is characterized by several sources of uncertainty, a stochastic approach is adopted. A simulation model based on discrete-time recursive equations, which includes all the steps of the oncology process, was developed. In particular, the simulation model emulates the therapy preparation and transportation process. Since the oncology and pharmacy can be located in different buildings, the therapies are collected in batches and delivered by a courier service. The simulation model is embedded in a stochastic metaheuristic algorithm to evaluate the candidate solution for the chemotherapy outpatient scheduling problem. A novel metaheuristic algorithm, namely Self-Adaptive Harmony Search (SAHS), is here proposed and its effectiveness is demonstrated through an experimental comparison with a well-known Harmony Search (HS) and Greedy Randomized Adaptive Search Procedure (GRASP) algorithms. Specifically, non-parametric tests revealed that the difference between the performance of SAHS and HS is not statistically significant. Then, SAHS is preferable since it avoids the time-consuming calibration phase.

Setting labor standards is an important topic to operational and strategic planning which requires the time studies establishment. This paper applies the statistical method for the definition of a sample size in order to define a reliable cycle time for a real industrial process. For the case study it is considered a welding process performed by a single operator that does the load and unload of components in 4 different welding machines. In order to perform the time studies, it is necessary to collect continuously data in the production line by measuring the time taken for the operator to perform the task. In order to facilitate the measurements, the task is divided into small elements with visible start and end points, called Measurement Points, in which the measurement process is applied. Afterwards, the statistical method enables to determine the sample size of observations to calculate the reliable cycle time. For the welding process presented, it is stated that the sample size defined through the statistical method is 20. Thus, these time observations of the task are continuously collected in order to obtain a reliable cycle time for this welding process. This time study can be implemented in similar way in other industrial processes.

Increasing the flexibility of production environments and the resilience of value chains are major challenges of Industry 4.0. Multi-enterprise manufacturing networks can be a solution to create dynamic supply chains and to make product manufacturing more flexible. To enable the concept of manufacturing networks, vendor-independent and data-secure solutions are required. This includes automated bidding procedures and cross-company interpretation of information. Existing approaches to data exchange between companies are based on proprietary concepts. An industrial data space should support vendor-independent solutions to facilitate data exchange, participation in multiple data spaces, and offers the freedom not to be dependent on a single software company or marketplace provider. The paper focuses on existing technologies and describes a Shared Production scenario based on the decentralized GAIA-X architecture, the Asset Administration Shell and the Industry 4.0 language. In this way, uniform semantics can be achieved, the Industry 4.0 language defines the structure of text messages as well as the communication flow, and GAIA-X provides technologies for secure and data-sovereign information exchange. The focus is on the possible identification of supply chains in the machining industry.

Mass customization approach requires low-volume, high-mix production schemes, leading to the creation and industrial uptake of highly flexible and modular production lines capable of processing a wide range of products. Towards that end, flexibility of reconfiguration and responsiveness of production plants needs to be increased. Digital technologies can make a significant contribution in making production systems more flexible for modular production. Reconfigurability and interoperability between automated industrial systems and Cyber Physical Systems (CPS) can be achieved by implementing a Digital Twin based on Asset Administration Shell (AAS) and formal definitions found in the I4.0, IIoT and IDTA standards. The AAS modelling containing a machine-readable, semantically unambiguous self-description metamodels needs to be supported by a software infrastructure capable to orchestrate the different assets. This paper presents an implementation of AAS modelling based on the relation of the main production assets –Product, Process and Resources (PPR model)– allowing the dynamic creation of process chains and reconfigurations, as well as up-to-date virtual representation of the Product by deploying a Digital Thread able to trace and orchestrate the data generated along the product lifecycle.

To face the challenge of increasing productivity while having flexibility, collaborative robots can help manufacturing or assembly systems. However, it is essential to assign and schedule tasks between humans and robots to provide a successful Human-Robot Collaboration while considering the agents’ potential and limitations. Thus, the main goal and contribution of this paper is to present an algorithm inspired in the GRASP metaheuristic for the assignment and scheduling of tasks in a collaborative workspace composed of a human worker and a collaborative robot. The algorithm is first tested with a fabricated data instance and then using data from six practical jobs from a published paper. The comparison with the dataset from the literature showed that the developed algorithm can provide good-quality solutions.

Porta KMI Poland S.A. company has implemented a fully automated intelligent technological line TechnoPORTA for customized mass-production of technical door leaves. Each door leaf is provided with a unique QR code. It allows the line modules to individually adjust the machining parameters to the currently processed element according to the IT controlling system. Before entering the TechnoPorta line, the door leaves are not sorted, although the high capacity characteristic of mass production is required. Therefore, the aim of this short test was to verify the influence of leaf segregation by colour on the efficiency of the TechnoPorta line. Three groups of door leaves – white, black anthracite and mixed – were tested. The test included measurements of the time interval between successive door leaves, which is the tact of the line. The test showed that each time the machining parameters are changed due to colour changes, the tact of the line is approximately twice as long. The colour segregation of door leaves influences the efficiency of the production line. In the case of a single-color set, the tact of the line is half that of a mixed set. The less frequent the colour changes, the greater the production time savings.

This work was carried out in an industrial context though the adoption of action-research. The main objective was two-fold: (i) to implement a pilot project for improving the method for calculating the cost of ceramic products, and (ii) to achieve improvements in the production process by cost reduction though lean principles. Organizational intervention and direct observation were applied to carry out this work. This could deliver an update method for calculating the cost of products, which fulfilled the needs of a company from the ceramic sector. Improvements were achieved both in the costing method and in the organization management. The results included enhancement of a previous outdated costing method (main barrier) that enabled better management and control of a specific type of product (‘blank’ ceramic). Lean manufacturing applied to the production process contributed to achieving a standardized process with less waste. Future work involves expanding the company pilot project in medium and long term. Work limitations include an intervention in a single unit of analysis and a low scientific contribution.

This work was developed to improve the costing process of new products within the Product Development Department of a furniture manufacturer. It consisted of creating a parametric cost estimation model based on applying simple and multiple linear regressions, considering the existing data of the products produced and their respective costs. The proposed model considers the cost estimation of creating a product that covers the materials and operations costs. The suitability of the different independent variables was studied by applying simple and multiple linear regressions. A set of functions that return an estimate of the cost as a function of these predictor variables was obtained. The model built with the functions obtained provides the materials and operations cost estimation. The results indicated that 75% of the tests performed show an estimation error of less than 2% in the total cost of a product. Incorporating this model in a tool with the purpose of cost estimation brings the ability to predict prices faster, improving the internal process of obtaining costing and enhancing the analytical capacity of the team in the relentless pursuit of cost minimization and value creation.

The Dynamic Facility Layout Problem (DFLP) is concerned with finding an optimal facility design considering changes in the planning horizon. Since DFLP belongs to the non-polynomial hard class problem, different solutions have been used to find an optimal solution. However, a correct performance evaluation is needed to validate and compare the results with others. This performance evaluation refers to using both statistical and computational tests. When searching the literature on related papers, none consider these tests. The lack of such information and the constant evolution of algorithms motivated this work. The current document reviews the solution methodologies applied to solve DFLP and the manner the performance evaluation is done. In addition, the methods used to mix solution methodologies, called hybrid approaches, are included. This work was carried out using the Barbara Kitchenham methodology, in which studies from 2015 to 2022 were considered. A sample of 59 articles was analyzed, all about DFLP. As a result, this study identified two commonly used categories when solving DFLPs: hybrid and metaheuristic approaches. Furthermore, performance evaluation is done using different statistical methods in some cases, comparisons of some numerical results obtained from the algorithm output, and studies without comparisons. Finally, the results do not find any instances in which a methodology is applied to compose the algorithm when a hybrid approach is used. To the best of our knowledge, this work is the first in which performance evaluation is considered.

The paper aims to highlight the lack of usage of knowledge-based expert systems in purchasing decisions in the context of hybrid corporate reality. We use the transdisciplinary approach in our work, which is essential to examine the problem that occurs in the reality. While reviewing publications containing the keywords “Cognitive bias” and “Supplier selection”, we focused on the methods used. The examined methods in the pooled papers are mainly based on arithmetic and rank the possibilities without considering the available expert knowledge then and there. Afterwards, we propose a solution beyond analyzing the data measured in the past; in addition, the decision-maker, their mental model, and their knowledge is considered. We assume that the effects of cognitive biases are more readily identifiable when using expert systems in considering the decision-maker’s opinions in connection with the actually applied rules in making decisions. In addition to seemingly objective solutions, in our experiment, we propose that by using past cases with known results, complex rules, which are based on the expert’s knowledge, can be simplified without changing the results of decisions in purchasing.

This paper presents a novel framework for developing Circular Business Models based on a strategic approach adopted from lean production. Building on existing theory in both the circular economy and the lean production research domain, the framework was developed from the findings of a longitudinal single case study based on a mixed research design of case study and action research. The research was carried out over a three-year period as part of a larger research project that explored the connection between lean thinking and circular economy. The aim of the research was to help close the gap on the integration of lean and green and to guide academics and practitioners who seek to accelerate the transition to a circular economy.

This paper presents the improvements in the internal logistical flows of material and information on the welding section of a company, specializing in the production of sterilizers for laboratories and hospitals. Based on the diagnostics phase, associated with the Action Research Methodology applied in this project, the main problems identified were the inadequate quantities of material at workstations, leading to production stoppages, lack of standardized material supply routines, and lack of stock management methods to “trigger” purchase orders. The line-side inventory of the workstations was dimensioned, based on the 2-bin system, resulting in a constant volume of production. The quantities in the containers of the supermarket, and in the containers of the line-side inventory should be the same, allowing the direct replacement of empty containers for full containers. After the implementation, a route was defined to replenish the workstations once a week, reducing considerably the number of travels each worker performed to resupply his workstation and preventing unnecessary production stoppages. A study on the references bought externally was conducted, resulting in 53% of the references will be replenished every three weeks, 22% of the references will be replenished every two weeks, and 25% of references weekly. At the supermarket, a purchase order should be placed with an amount equivalent to the Reorder Point when the material is removed from the last (second) container. To signal this request, the kanban system was introduced in these containers, facilitating the management of the developed system and reducing stockouts.

The construction management literature has made significant strides in studying productivity in construction projects. However, it is challenging to examine the general characteristics of the growing body of literature relevant to this topic because of its diversity. The objectives of this paper are (1) to examine the trends and evolution of research on quantitative analysis of construction productivity; and (2) to suggest a roadmap for future research on quantifying productivity in construction projects. To achieve these objectives, this research performed a systematic literature review, resulting in identifying 84 substantively relevant articles. Subsequently, it conducted a bibliometric analysis to explore the general characteristics of the selected articles, such as conceptual and social structures, year of publications, citations, and journal specialty. This research contributes to the literature by providing useful information about research capacity and how the quantitative research on productivity in construction projects has progressed, in turn assisting construction management scholars and practitioners in shaping the future directions on this topic.

This paper studies a real world dedicated parallel machine scheduling problem with sequence dependent setups, different machine release dates and additional resources (PMSR). To solve this problem, two previously proposed models have been adapted and a novel objective function, the minimisation of the sum of the machine completion times, is proposed to reflect the real conditions of the manufacturing environment that motivates this work. One model follows the strip-packing approach and the other is time-indexed. The solutions obtained show that the new objective function provides a compact production schedule that allows the simultaneous minimisation of machine idle times and setup times. In conclusion, this study provides valuable insights into the effectiveness of different models for solving PMSR problems in real-world contexts and gives directions for future research in this area using complementary approaches such as matheuristics.

The maritime industry is an important sector of the world economy, facing many challenges. Lean tools can have a significant impact on reducing waste, time and costs, and increasing efficiency. This paper aims to identify and categorise the different lean practices implemented in the maritime industry and the main barriers and benefits of their implementation through a systematic literature review. This categorisation is intended to improve the understanding of lean practices and their benefits and to contribute to the decision making of stakeholders for their implementation. The analysis of the selected articles suggests that the implementation of these practices is effective in the context of the maritime industry, having as main benefits the increase of efficiency, and cost reduction. It was also possible to see that the vast majority of studies do not specify the lean tools used. The lack of knowledge, know-how, training of workers, and the lack of management involvement were identified as the main barriers to the implementation of these initiatives. However, further research is needed in this industry, namely in quantifying the impacts and identifying best practices for the implementation of these initiatives.

The automotive industry deals with complex processes. Becoming aware of the importance of agile management they are contributing to a creative fusion known as “leagile”. However, this concept needs further study and investigation. This work presents a systematic literature review on Lean Agile implementation in the automotive industry. Thirty-three publications were reviewed and characterized according to the year of publication, country of origin, industrial sector, used tools and their contributions to the automotive sector. The results show that 50% of the articles were published after 2018. The countries with the most publications are India, Portugal, and United Kingdom. The most cited tools are Value Stream Mapping (VSM), Just in Time (JIT) and 5S (23%). This study confirms the growing use of “leagile” in the automotive industry and the growing potential for research development in the area.

The Pharma sector is increasingly under pressure to improve the sustainability of supply chains, as consumers and regulators require greater transparency, efficiency, and accountability. Although Lean 4.0 has created a lot of buzz in the organization, the pharmaceutical sector faces challenges in implementing it. The main goal of this literature review is to identify the challenges of Lean 4.0 for the sustainability of the pharma supply chains (PSCs). A series of papers extracted from the most relevant scientific databases, including the Web of Science, Scopus, Google Scholar, and ProQuest was analyzed and synthesized from 2007 to 2022. 31 articles were used in the study. The findings of the study indicate that challenges include financial, staff experience and specialization, ongoing maintenance, resources for generating new skills and experiences, employees, and partners’ resistance to changes in regulations, and cyber-hacking of key information. The results will facilitate future work by practitioners and researchers and make an important contribution to existing knowledge.

The current paper aims to fill this gap through case-based research, understanding from empirical data the application of Lean 4.0 in four UK companies. The findings showed that Lean 4.0 represents an excellent strategy to improve the manufacturing systems’ performance, mainly productivity, flexibility, real-time data accessibility, and quality control. Also, it was possible to confirm that financial resources, adversity to change, and process complexity are the main barriers to Lean 4.0.

Industry 4.0 is being pushed by Industry 5.0 to put the human factor at the centre of technological innovation by fostering engagement practices, namely through participatory design and communication and knowledge sharing. Kaizen Teian, or suggestion systems, are tools that empower employees with voice behaviour, giving them the possibility to foster innovation in the organisation by fostering in them this feeling of involvement and/or belonging. This paper brings a prototype of a suggestion system, co-created with employees from three multi-sector companies that integrates the PDCA cycle with the Disney strategy, a Neuro-linguistic Programming technique used in Coaching. The Disney strategy aims at supporting innovation through a process for the realisation of ideas, giving them a mature character. This system fosters two of the predictors of work engagement, which are participative design and knowledge sharing, and brings transparency to suggestions, enabling the employee to monitor them. The promotion of sustainable innovation through the Disney strategy, the improvement of labour engagement and the sharing of knowledge, are all enhancing factors of organisational competitive advantage, not only by retaining the workforce, but also by preserving organisational knowledge. This is a digital tool adapted to the current challenging technological context, where Lean must be preserved and coaching tools applied.

Recently, sustainability has been tackled several times due to the impending climate change the earth is facing. Numerous techniques have been applied to reverse the direction companies were going into. In this paper, it is explained the importance that Lean Manufacturing tools and Industry 4.0 technologies can have on the sustainable side of a company. The aim of this work is to fill the scientific gap related to studies deepening the combination of these different paradigms, Industry 4.0-Lean-Green, which have been scarcely investigated together. Thus, a Systematic Literature Review has been performed to detect which were the key variables of these three fields and then, it was studied what their interaction was. This study is giving the opportunity to understand the main variables of Industry 4.0 and Lean manufacturing on which companies have to act in order to have an impact on green variables and their overall sustainability.

The presented work is focused on applying continuous improvement tools to reduce waste and process variability throughout an air-conditioned pipe production line (PL), to determine the line’s capacity and improve efficiency so that the desired productivity levels can be achieved. The line’s processes were analyzed, together with the integrated application of continuous improvement and Lean tools to determine and improve its production capacity. Thus, a variability study was carried out, focused on balancing the PL, to reduce the main bottlenecks. After balancing, several improvements were performed in the line layout, improving the line efficiency, and reducing unnecessary employees’ movements. Simultaneously, the Constant Work-In-Progress (CONWIP) methodology was implemented facilitating the management of the component’s production before their entry into the PL. Other modifications were also implemented, both mechanically and in terms of documentation, to support production and contribute to the line’s increase in efficiency, quality, and safety. The results obtained are relevant, with an increase in the capacity of the line and its productivity and efficiency, from 28.81% to 47.21%, and an increase of 18% in the Overall Equipment Effectiveness (OEE).

Although Lean was developed for manufacturing, it was quickly expanded to other areas, thus resulting in Lean Office (LO), which can be defined as a system of standardized service operations. It consists of activities to generate value for the customer and am to meet their expectations of quality and price while focusing on intangibles. This study evaluated the applicability of LO concepts in the management of labor grievances and commercial matters in the legal department of a network of retailers. The data was collected through unstructured interviews, observations, and database analysis. The study was divided into diagnosis, application of Lean tools, and the implementation of indicators. As a result, the internal mapping was developed, and new flows were established, eliminating waste and activities that had not added value. Following the Lean philosophy, a new management system was defined and implemented, which increased processes efficiency and reduced the expenses in lawsuits.

The market is increasingly competitive and innovative. In this way, it is necessary for companies to find tools that help them to innovate and manage to reduce costs. It should also be noted that companies need to have good strategic management with a focus on continuous improvement of processes. Companies can only remain in the market when they differentiate themselves from their peers, for which it is necessary to invest in the improvement of production processes. Safety and health at work is also a very important factor in the success of companies. This theme has been gaining more and more importance, because in their work activities, employees are exposed to various risks that affect their safety and health. Regarding accidents at work and occupational diseases, in addition to the damage to employees, they also generate high costs for companies and society. Lean is an innovative and revolutionary methodology with positive impacts in many areas. Currently, it is implemented by several companies, leading them to reinvent themselves to be more competitive and secure.

Industry 4.0 opens new business opportunities and allows companies to gain a competitive advantage by introducing digital technologies in their processes. However, the digitalization process can be critical and, if poorly executed, could lead to worse performance than at the beginning. It requires a great deal of analysis, but most importantly, it must be seen by all stakeholders to be successful and valuable to the firms. The following study explains how Lean Management practices can facilitate the success of Digitalization projects. Overall, Lean practices have been recognized as a fundamental driver for driving the process of digitalization. In particular, hard Lean practices facilitate the removal of waste from the process before the digitalization and a fair selection of the investments. On the other hand, soft practices such as the operators’ involvement are critical for overcoming the barriers to digital transformation and achieving the overarching company’s goal.

Industry has a key role in leading the digital and green transitions for the economic and societal transformations that we are experiencing. The approach towards a sustainable, human-centric, and resilient European industry, so-called Industry 5.0, complements the existing “Industry 4.0” approach by focusing on a circular, human-oriented, and durable industry. Thus, industrial systems need to have a focus shift from technology to human so that technology will serve human and not vice versa. In this way, human play a central role between technology and organizations. This paper seeks the challenge of this focus shift and how lean thinking and practicing soft lean tools such as Toyota Kata may contribute to the development of a human-centric approach in the industry. A literature review was conducted to answer these questions, and a conceptual modified Toyota Kata methodology, the so-called Human-centric Kata, was suggested to assist industry in reaching its goals towards a human-centric green-digital era.

This paper presents the effectiveness of Kamishibai audits use in restructuring the audit system on a daily basis in the aeronautical sector. This lean tool has a strong visual component allowing to verify if the numerous processes and activities are in accordance with the company’s standards, triggering improvement opportunities in all audited areas. The paper objective is to present how this tool was developed and implemented, being described the audit framework, requirements to be audited, the many cards guiding the auditor, the operating method and how useful this tool can be even in large productive process audits. In order to demonstrate this tool effectiveness, an application example to four pilot areas in the company is presented, taking into account some pre-established restrictions. There is a practical motivation due to the existence of numerous non-conformities registered in previous audits. With this tool, company gains were: less audits, audit reduced cost of 5.000 €/year, more non-conformities corrected, reduced time performing the audits. These audits can be successfully used to assess industry standards and increase internal control.

Lean Six Sigma (LSS) is a comprehensive and powerful strategy for processes improvement and products. There is a cornucopia of tools for its implementation and 37 among them were selected to carry out an evaluation based on three factors, namely: Frequency of use of the tool; Difficulty in implementing; Importance and impact of the tool in the implementation of LSS. An online survey was conducted with Portuguese consultants and it included questions on the profile, and the companies they worked, as well as the degree of impact of the tools used. Consultants were asked to choose ten tools, ranking them in order of importance. The frequencies with which each tool had been cited were counted. A procedure was then developed to identify the know-how of consultants to establish a ranking of LSS tools. It was created an ordering list of tools, which emphasized in: Honshin Kanri, VOC, VSM. The results presented are particularly relevant when is considered the importance of understanding the requirements for a successful implementation of Lean Six Sigma management system in the organizations.

Traditional lean tools such as value stream mapping (VSM) do not account for environmental benefits. Simultaneously, life cycle assessment (LCA) does not account for improving manufacturing performance. Given the advent of the concept of sustainable development as an approach to fulfill the needs of the world's population without destroying the planet, integration of environmental benefits and manufacturing performance presents the next frontier of the industrial improvement. LCA can be integrated with VSM to ensure a sustainable development of the manufacturing industry. This paper presents a review of literature on integration of the tools-LCA and VSM. The reviews show that the main motivation factor is to enhance sustainable development. Further, a key finding is the benefit as a guide for decision making, fostering of a systematically improvement focus and increased engagement expanding the focus to include the sustainable aspect in addition to traditional lean measures. This is enabled through a systematically approach with assessing and visualization of the production process performance from a sustainable viewpoint.

Electric machines are an essential element to the electrification of industry and mobility for achieving a sustainable economy. Particularly important are the resource-efficient production of stator and rotor sheet packages and the reduction of iron losses during operation. The screen printing technology enables the production of very thin magnetic sheet laminations in near net shape geometry and thereby minimization of material waste in production. Simultaneously the efficiency of electric motors in use will be increased by reduced eddy current and hysteresis losses due to decreasing sheet thickness and avoided mechanical stress in the manufacturing process. In addition, screen printing enables multi-material components and variable alloy compositions. This paper presents the requirements of the screen printing process for magnetic sheets and identifies the relevant influencing factors and printing parameters to achieve the product specifications in multiple iterations and using universal statistical design of experiments methods. The target parameters investigated are cycle-time, green part weight, roundness, and layer thickness.

The paper presents the possibility of implementation the Principle Component Analysis (PCA) and logistic regression to develop the decision tool supporting quality of oil inserts for candles production process. The research methodology was divided into three stages: collecting data from the production process, using the PCA method to reduce the dimensionality of the production process parameters, and using logistic regression to develop a predictive model for assessing the quality of products. The results of the research showed that it was enough to have five main components to keep about 84% of the information on variability. In the third stage the logistic regression to develop the predictive model of product quality in production company was used. The developed model explain the impact of the production parameters on product quality. In addition, this decision tool will help the managers in the company identification the improvement actions.

Robotic Process Automation (RPA) emerged as a technology that promises revolutionary gains through the automation of transactional processes, based on specific rules. Through the application of RPA, organizations aim to increase their operational efficiency. The objective of the project described in this article was to develop a automatized procedure to allow the implementation of RPA, in the enrolment process in existing training courses in the “Improve Our Work (IOW)” department of Sonae Academy. After analyzing the current process through the value stream design tool, waste was identified in this process, namely, too much time in the function of data processing inherent to the training enrolment registrations. Then, improvement proposals were suggested and, subsequently, implemented, which allowed the reduction of time spent by the coworker allocated to this function, in approximately 95%. This value revealed the impact that the implementation of RPA has on the digital processes of organizations.

Process quality planning should establish a Quality Control Plan (QCP) to achieve the desired quality level with minimum Cost of Quality (CoQ). This plan establishes the critical quality variables, control stations in the process, and control method at each control station. The purpose of this study is to, through a simulation approach, determine a QCP for a manufacturing process, which minimizes the CoQ. The inputs to the simulation model are inspection and repair/replace costs, proportion of defectives at process output, type I and Type II inspection errors, alternative control methods (no control or 100% inspection), and the cost of delivering defective units to the customer. The proposed model was developed in Simulation Modelling Based on Intelligent Objects (SIMIO) software to estimate the total CoQ and number of compliant units delivered. This model allows to determine the CoQ of the alternative scenarios and to define a QCP that minimizes the total CoQ. An illustrative example based on a manufacturing process demonstrates the applicability of this model, and the results indicate that the best QCP, amongst defined alternatives, may vary when the model parameters are updated. As future research directions, the simulation models could be relevant when defining digital twins of manufacturing processes and models’ results can support process improvements.

In this study, a comprehensive framework for assessing the readiness of production systems for Zero Defect Manufacturing (ZDM) has been developed and presented. The framework includes four pillars of ZDM readiness, namely Personnel, Procedures, Infrastructure, and Company Culture, to help companies understand their level of readiness and plan for successful implementation of ZDM. We argue that a manufacturing company will be better equipped to embrace ZDM if it performs well in these four areas. We propose a tool that uses yes/no questionnaires to assess a manufacturing system’s readiness for ZDM. The results of the questionnaire will objectively show the true level of cultural readiness for ZDM adoption, and the level of investment required for implementation will depend on the level of readiness. This tool can help companies gain a clear understanding of their readiness and create a plan for implementing ZDM. Overall, our framework and tool can help manufacturers improve the quality of their products and be ready for ZDM adoption.

Faced with paradigm shifts in the global manufacturing context promoted by the Fourth Industrial Revolution, many organizations are seeking to meet customer needs through the integration of Lean Manufacturing (LM) philosophy principles with Industry 4.0 (I4.0) technologies. When there is the integration of technological enablers from I4.0 and deep advances in efficiency and productivity with LM, these systems tend to offer enhanced and more assertive results, since they are complementary concepts. The main goal of this paper is the selection of I4.0 technologies to support the LM system, considering the perspectives and barriers of Enterprise Interoperability (EI) and using multicriteria methods (MCDM) to support decision-making. Using the DEMATEL multicriteria method, it was possible to develop a diagnostic evaluation, analyze the existing influences between the elements of the LM, and support the elicitation of weights in the decisional evaluation, with the FITradeoff method. In this way, the decisional evaluation indicated as the I4.0 technology that must be implemented as a priority to raise the level of organizational maturity in LM is Big Data Analytics. Big Data integrated with Business Analytics (BA) can offer several advantages, such as assertiveness in decision-making; Keeping the company updated about the market; Indicating risks and improving data security; Promotes alignment between marketing and sales, among others.

Recent advances in digitalization have been causing changes in manufacturing and service processes. Impacting production processes and human resources within organizations, tools such as Big Data, Artificial Intelligence, Machine Learning and the Internet of Things enable improvements in communication and support I 4.0 (Industry 4.0) and Q 4.0 (Quality 4.0).Traditional quality managers, in particular, must do an effort to adapt their profile according to digitalization demands. The present paper relates to the quality manager skills that will be required at short and medium term as result of digitalization expansion. Using a focus group and semi-structured interviews, the skills needed to Q 4.0 managing were identified. In order to make a final comparison, the focus group contributions and interviewee responses were categorized.Summarizing the main skills needed to the adaptation of quality managers to the digital era reality, the conclusions highlight the relevance of this study for the target groups.

The advent of Industry 4.0 has created a need for more flexible and adaptable manufacturing systems. This paper proposes the integration of AAS (Asset Administration Shells), SBM (Skill-based manufacturing) and OPC UA (Open Platform Communications Unified Architecture), to enable more flexible manufacturing systems. The integration of these concepts provides a solution for achieving faster and easier dynamic reconfiguration in manufacturing systems, which is essential for fulfilling the demand of customization and flexibility in modern production systems. An Asset Administration Shell provides a standardized structure for describing assets and their administration, while Skill-based manufacturing enables the deployment of task-oriented machines that can self-configure, self-diagnose, and self-optimize their performance. The use of OPC UA as a communication protocol ensures that these systems can communicate with one another in a secure and reliable way. This paper presents a conceptual framework for the integration of these three open technologies. This framework contributes to having a single interface and source of information for every asset, which can lead to increased efficiency by reducing changeover times, thus reducing the overall cost in flexible manufacturing system scenarios. Future work will focus on the implementation and validation of this framework in a real-world manufacturing setting.

The industrial environment of the past years has been characterized by a high rate of change, pushing the industry to implement innovative technologies to satisfy market needs. Unmanned aerial vehicles (UAVs) and Reinforcement Learning (RL) are being implemented in the manufacturing industry to meet changing market demands for efficiency. This work focuses on using RL for optimal part dispatching in Flexible Manufacturing Systems (FMS) using UAVs. A virtual discrete events model is used to represent the shop floor state and a reward function is defined to maximize production. Proximal Policy Optimization (PPO) is employed to train the RL agent. Results show a production increase of up to 145.16% compared to traditional heuristic rules.

Today, Korea is facing a time when it is essential to develop new manufacturing technologies and strategies to lead new changes, such as smart factories and manufacturing innovation 3.0, and achieve continuous development of the domestic manufacturing industry. Therefore, many manufacturing companies are promoting process automation using collaborative robots (co-bot) to respond to the paradigm of multi-item, small-volume production. The emergence of co-bots improves the space utilization of production facilities and opens up the possibility of introducing robots without modifying the existing production line. This study aims to conduct primary research on a robot that recognizes and acts on its environment using reinforcement learning to determine its work movements and perform tasks without specific instructions from human experts. In this study, we propose a collaborative robot control methodology using a deep reinforcement learning algorithm. In addition, for the practical application of the HRC system, which is challenging to apply to the production of a single product, the problem of data sharing between collaborative robots and workers based on a process model was addressed. The system proposed in this study is designed to optimize process variables through artificial intelligence-based data learning and is expected to contribute to product and process quality optimization of human-robot collaborative processes in the future.

One of the cornerstones of Lean manufacturing, according to academic research, is the 5S+1, which introduces quality in manufacturing. This study aims to demonstrate how computer-based vision and object detection algorithms, can assist in the implementation of safety as 6th S in 5S+1 by monitoring and identifying employees who disregard accepted safety procedures, like wearing Personal Protective Equipment (PPE). The research evaluated the performance indicators of a detection technique and reviewed and analyzed it. To confirm workers’ PPE compliance, the suggested model used the You-Only-Look-Once (YOLO v7) architecture. A deep learning technique was subsequently applied to confirm the safety helmets and safety vests. This strategy is determined to be the most effective when using the VGG-16 algorithm, achieving an 80% F1 score and processing 11.79 frames per second (FPS), making it ideal for real-time detection.

This paper assesses the supporting function of a Machine-based Identification system (MBID) via Optical Character Recognition (OCR) in a Lean manufacturing paradigm. The objective of this paper is to also explore the use of MBID to enable a competitive manufacturing process in a Lean 4.0 environment. Furthermore, a MBID via OCR model is proposed to extract the printed identification number of packages from images captured by a fixed camera in an industrial environment. The method considers different digital image processing techniques to deal with the significant lighting and printing variation observed, followed by a segmentation process that extracts and aligns the characters. Experiments were carried out on a data set consisting of 200 images and achieved an overall detection accuracy of 95% with a very low Character Error Rate (CER) value of 0.0041, clearly supporting the validity and effectiveness of the proposed method.

The 4.0 revolution is leading to increasingly automated, flexible, and intelligent manufacturing systems that require greater complexity to manage during maintenance and process control. In this context the optimization of the human-machine interaction plays a crucial important role in the design of modern industrial systems. Virtual Reality (VR) offers realistic simulation environments where users can be involved to replicate specific human tasks, detecting and solving problems before they occur. The paper proposes a human-centric digital design methodology that integrates VR technologies with human data analysis tools to support the design or redesign of complex industrial systems. Different wearable devices have been used to collect data about physical and mental user conditions to provide an early assessment of the operators’ workload, while comparing different design solutions into the virtual space. An industrial use case related to the redesign of packaging automated machines was used to validate the proposed method and tools: a preliminary correlation between physiological parameters and machines interactions was found.

Plastic injection molds are assembled manually because of the high variety and low volume operation that require frequent change and comprehensive mold knowledge. The lack of skilled workers and ergonomic problems in the mold assembly requires a certain level of automation to improve the working condition and operating performance. However, the knowledge within mold assembly application is not systematically organized to automate the operation. The use of two non-identical collaborative robots is considered to assist and collaborate with a human worker during the mold assembly operation. Data containing information on components in the collaborative assembly must be collected and represented in a form that can be interpreted and understandable by all resources. Besides, relations of entities within a specific context also must be included to extract correct and useful information for every decision-making step. This study aims to provide a systematic ontology model to acquire and represent knowledge of mold assembly operation with human-robot collaboration implementation that enables expansion, learning, and generalization of created ontologies in future practical applications. A simple trial use of created ontology for positioning parts into regions in the workspace is included. This proposed conceptual context modeling acts as a stepping stone to developing a context-aware system for human-robot collaborative mold assembly operations using multiple cobots.

The concept of Industry 4.0 promises to transversally revolutionise industries. Simulation, as one of the main pillars of Industry 4.0, allows improvements in the organisational and production processes of companies. This research work develops a decision support tool based on system dynamics, that address the problem of car dealership sales forecast and evolution depending on the commercial strategies adopted. This decision support tool considers main variables that are expected to influence car sales in Portugal. To develop this tool several interviews were conducted with the people responsible for the commercial sector of different dealerships while considering existing literature on the subject. This allowed us to parameterize a system dynamics model with the most influential sales factors. The developed tool is expected to contribute to car dealerships to evaluate their commercial policies and define adjustments to these to improve profitability.

This study aims to explore various domains within Industry 4.0 (I4.0) and energy productivity in Australian context. In doing this, the study analyses I4.0 technologies & services, barriers to I4.0 for energy productivity, regulatory framework, multiple benefits of I4.0 technologies, relevant business models, and roadmap to I4.0. The findings show that inadequate IT infrastructure, cyber security, complex supply chain and contracting, uncertainty about return on investment (RoI) are significant barriers. When it comes to productivity benefits, this study has highlighted macroeconomic impact, industrial sector impact, public budget impact, health and well-being impact, and energy delivery impact stemming from I4.0 technologies. In terms of regulatory framework, this study finds that given the near-ubiquitous data generation and use across the digital economy, it is quite difficult to exhaustively address all legal and regulatory issues relating to I4.0 technologies in the energy sector. However, there are several business models (e.g. X-as-a-service or pay per X) which can be applied to adopt I4.0 technologies. The study concludes with a roadmap to I4.0 with future research directions.

The industry worldwide is undergoing continual changes owing to the emergence of new technologies and the globalization. Industry 4.0 is transforming the way things are done at the industrial level, and combined with the advancements in communication networks and the internet, it enables complete digitization of manufacturing processes. Nevertheless, the lack of appropriate management procedures and methodologies is hindering the implementation of digitization solutions, making it difficult to simplify requirements analysis and the incorporation of improvements using I4.0. Based on a literature review, the most crucial characteristics of migration projects have been identified, which are based on the I4.0 enablers. Currently, any technological system migration requires the use of an appropriate management methodology, which either replaces or complements the conventional. In this research proposes a new agile concurrent methodological procedure that relies on concurrent engineering and adapted Scrum to execute technology migration projects based on I4.0 enablers. It will be easy to applied in three level of management, and giving a faster and more efficient technology migration. Other benefits are a short periodic feedback for error correction, cross-functional teams, and simple implementation.

Collaborations between Industry partners and University researchers are common to address Industry 4.0 research projects. Such collaboration facilitate access to expertise across domains. However, there is often a mismatch of expectations around language, processes and deliverables. One of the reasons is that academics treat these type of research projects as traditional Knowledge Seeking Research. Treating the research as Solution Seeking Research is a better approach, which ensures that the needs of all participants in the project are met. Solution Seeking Research can be implemented using a Design Science approach. Collaboration must be encouraged, which requires effort around communication, coordination and cooperation. Using a Framework for Evaluation ensures that all guidelines of Design Science are achieved. In this paper, we outline how we managed successful Industry 4.0 collaboration projects. We extend the framework for evaluation by recommending suitable research methods for ex-ante and ex-post evaluation. In doing so, we provide a model for future researchers to deliver collaborative research projects to the satisfaction of all partners involved.

Industry 4.0 brought modernization to the productive system through the network integration of the constituent entities that, combined with the evolution of information technologies, allowed an increase in productivity, product quality, production cost optimization, and product customization to customer needs. In this paper a model was created using the open-source tool Knime that, based on a set of data provided by Bosch, parameterized the model with several pre-processing techniques, resource selection, and minimization of over-fitting, allowing the development of a final improved model for internal product failure prediction at Bosch production line. The study shows that model efficiency improved with the application of resource selection and reduction techniques, with Logistic Regression and PCA resource selection techniques standing out, obtaining a Recall of 100% and precision and accuracy, both with 99.43%.

Sustainability has become a critical issue for manufacturing companies in recent years, as they strive to meet government-mandated targets. With the rise of Industry 4.0 (I4.0) in the digital age, companies are turning to artificial intelligence (AI) techniques to optimise production processes. In order to support the concepts of I4.0, AI and sustainability, major databases such as Web of Science and Elsevier's Scopus were consulted. The aim of this study is to investigate the relationship between I4.0, AI and sustainability by highlighting the direct and indirect links between these concepts. The study shows that the adoption of digital approaches can significantly improve the sustainability of production systems, provided the right balance is struck. This requires investment in both technology and sustainability measures to achieve a holistic, globally sustainable production system. The study therefore concludes that manufacturers must continue to invest in innovative technologies, including I4.0 and AI, while ensuring that these investments are aligned with their sustainability goals. The research highlights the importance of a balanced approach to digitalisation that optimises production processes, while ensuring that sustainability is not compromised. Ultimately, this research provides valuable insights for managers seeking to navigate the complex relationship between I4.0, AI and sustainability in their manufacturing operations.

Compared to emerging countries, European companies must digitally transform their organization to be competitive. Industry 4.0 concepts have been developed to encourage this company’s transformation and ensure the improvement of their performance. Indeed, these concepts have been successfully implemented in large companies, but their deployment in SMEs has to be increased. Some of the barriers to this implementation can be solved with the addition of sustainability as proposed by the European commission through industry 5.0. This paper focuses on the development of a framework for implementing digital twin as an industry 5.0 concept in the SME efficient transformation. Digital twin is one of the industry 4.0 pillar that can be easily combined with sustainability to increase the company performance. This article presents the industry 5.0 methodology developed for the SME digital transformation, the intelligent system designed to support the company changes and to elaborate the digital twin that will facilitate the enterprise sustainable transformation. An example based on a carpentry manufacturing SME is exposed to validate the results of the concepts and tool that have been presented.

Design patterns are widely adopted in software engineering and can be defined as “general design solutions to recurrent problems”. We extend this approach to improve the modeling activities required for addressing optimization problems, specifically those coming from production planning in manufacturing firms. We propose a general framework for optimization patterns, including general, problem and solver specific patterns as well as data connection patterns used to collect and effectively use the large data sets that can be provided by firms that adopt Industry 4.0 paradigm, or also the newer and complementary 5.0 paradigm. We then provide a vertical application of such patterns to address a strategic planning problem inspired by a real application in a manufacturing company operating in Italy. The case study shows all the main activities that begin with the problem description, continues through data definition and optimization patterns selection, finally leading to a prototypical solution to the problem.

Technological developments and methodologies for manufacturing processes have changed the understanding and use of technology and the paradigm of human-machine interaction. Industry 4.0 has exacerbated the problems of human-machine interaction, especially in terms of interaction between humans and robots. The concept of Industry 5.0 overcomes the challenges of human-robot interaction by focusing on improving the usability, ergonomics, and accessibility of the machine, considering human needs. The article analyzes current trends in the development of collaboration technologies that can bridge the gap in understanding between humans and robots. The interaction of man and robots is considered from different points of view, which makes it possible to identify the main aspects of such interactions, problems, and possible benefits. The relationship between interaction problems, aspects of perception, and possible technologies that allow successful cooperation is also revealed. It was revealed that the key technologies in this direction are implementing artificial intelligence, machine learning, and digital twins. At the same time, there remain problems in interaction, such as ethical problems, which cannot be solved only through the development of technologies.

Change management represents a challenge in most organizations, especially when implementing fundamental tools for transitioning to an Industry 5.0 context, such as a newer and more modern enterprise resource planning (ERP). The current paper documents and investigates the change management while implementing an ERP in a plant for industrializing food in Brazil. This approach was phenomenological and focused on the users’ perspective, applying a questionnaire after the training and implementation of an ERP. The findings suggest users declared that they were ready to put energy into this change. However, at the same time, they also stated that they see no need to change. This raises doubts about how this change was implemented. The energy and desire might be there for the change, but it needs to be directed to the necessary targets through adequate guidance. Practical orientations for applications in similar contexts and further research directions are pointed out.

Successful survival and competitive advantages are the greatest challenges societies and organizations face. A cultural and strategic paradigm shift is needed, particularly in work relationships and the organizational environment. Industry 4.0 remains too linked to technology, even if the biggest challenge seems to be related to people. Higher Education Institutions are key players in this transformation process and the present study aims to understand the perception of higher technical professional students just before entering their final internship. Quantitative research of descriptive nature was used, and a questionnaire was conducted at a Polytechnic Institute in Portugal between November and December 2021. The results show that students who have heard about Industry 4.0, perceive it mainly from a technological perspective, even if they have developed leadership skills, communication, decision-making facility, and analytical skills in the courses they attend. The results achieved within the questionnaire provide a basis for improvements and innovations in Higher Education, which can be a key point for the development of Industry 4.0 strategies in the future, namely regarding the redesign of some curricula or pedagogies to overcome challenges and prepare students to be successful in the future.

Logistics chains are being increasingly developed due to several factors, among which the exponential growth of e-commerce. Crossdocking is a logistics strategy used by several companies from varied economic sectors, applied in warehouses and distribution centres. In this context, it is the objective of the “CrossLog – Automatic Mixed-Palletizing for Crossdocking Logistics Centers” Project, to investigate and study an automated and collaborative crossdocking system, capable of moving and managing the flow of products within the warehouse in the fastest and safest way. In its scope, this paper describes the concept and architecture envisioned for the crossdocking system developed in the scope of the CrossLog Project. One of its main distinguishing characteristics is the use of Autonomous Mobile Robots for performing much of the operations traditionally performed by human operators in today’s logistics centres.

Digital transformation is having an increasing impact on industries. Previous studies have explored how enterprises implement digital transformation using technology. The perspective on organizational management continues to evolve. Small and medium-sized enterprises often face resource constraints and the dynamic ability to integrate resources is important for enterprises to implement digital transformation. This study uses case study on Taiwan’s textile industry and propose the important dynamic capabilities of launching digital transformation strategies. The results suggest that collaborating with external knowledge experts and leveraging employees’ knowledge and experience in digital transformation are crucial for promoting innovation and transformation. This study provides useful managerial implications for small and medium-sized enterprises seeking to launch digital transformation initiatives.

European industrial value chains and manufacturing companies have recently faced critical challenges imposed by disruptive events related to the pandemic and associated social/political problems. Many European manufacturing industries have already recognized the importance of digitalization to increase manufacturing systems’ autonomy and, consequently, become more resilient to adapt to new contexts and environments. Augmented reality (AR) is one of the emerging technologies associated with the European Industry 5.0 initiative, responsible for increasing human-machine interactions, promoting resilience through decision-making, and flexibility to deal with variability and unexpected events. However, the application and benefits of AR in increasing manufacturing resilience are still poorly perceived by academia and by European Manufacturing companies. Thus, the purpose of this paper is to contribute to the state of the art by relating the application of AR with current industrial processes towards manufacturing systems resilience. In order to cope with this objective, the industrial resilience and augmented human worker concepts are first presented. Then, through an exploratory study involving different manufacturing companies, a list of relevant disruptive events is compiled, as well as a proposal with specific ideas and functionalities on how AR can be applied to address them. In conclusion, this research work highlights the importance of AR in coping mainly with disruptive events related to Human Workforce Management and Market/Sales Management. The AR application ideas shared a common thread of availability and delivery of information to the worker at the right time, place, and format, acting on the standardization and flexibility of the work to support manufacturing resilience.

Factory technologies have evolved to incorporate a great deal of manufacturing flexibility. Programmable automation in the form of Computer Numerical Control (CNC) and (Programmable Logic Control (PLC) coupled with hardware and process innovations (quick-change tooling, for example) enable a high level of shop-floor flexibility. Possibly, the most inflexible part of a factory is the manufacturing information system. In this paper, we develop an approach to a flexible and extendible architecture for shopfloor information systems. The Operating System for Cloud Manufacturing (OSCM) is a full-stack, distributed platform that tracks and facilitates the interaction between manufacturing jobs and resources (physical machines, humans or software apps). It uses an event-based architecture and a message exchange/broker to enable flexible and configurable distribution of to capture, distribute, curate and store information about shop floor events. The event-based architecture makes it easy to provide context to data emanating from the shop floor, while the message broker gives it flexibility, scalability and extendibility. This paper describes the architecture of the operating system and its services. Further, it demonstrates how shop-floor data can be flexibly routed to manufacturing apps that need it before drawing up conclusions.

The objective of this study is to systematically evaluate peer-reviewed literature and analyze the current research on the thematic area “Simulation-Based Approaches to Enhance Operational Decision-Support in Healthcare 5.0” using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. This systematic review was conducted in compliance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) statement and implemented with the aid of the StArt software. Appropriate keywords were used in a search of the Science Direct, SCOPUS, and Web of Science databases. A total of 357 papers were retrieved from the databases and evaluated automatically. Eventually, 79 papers were analyzed according to their title, keywords and abstract. After the full-text analysis, 33 papers successfully met the inclusion criteria in the study. The review showed that healthcare providers and decision-makers can be guided by applying the Key Performance Indicators (KPIs) obtained from the simulation-based platforms to ensure quality and resilient health system.

Manufacturing industry is experiencing another revolution towards the digitalization of industrial processes. Different value chain actors must share specific and sensitive data according to business and data requirements. Digital architectures must ensure seamless and comprehensive communications between actors according to agreed-upon vocabularies. The digital representation of machines and other types of equipment, including crucial information about their static and dynamic operational data, is made possible by the ontological modelling of Asset Administration Shells (AAS), which is proposed in this paper as modular and semantically interoperable resources. These Cognitive Digital Twins are herein defined with de facto domain ontologies that model the semantics of the current operation, status and configurations of assets. This paper reports a proof-of-concept technical implementation that demonstrates an innovative digital architecture that connects and communicates active and modular Digital Twin of a machine in a bi-directional, connecting this asset to a digital manufacturing service provider.

Up to 31% of food goods, especially fresh and perishable ones, are wasted during the transportation and storage. Products’ innate susceptibility to spoilage, unexpected issues, and shifting conditions post-strategy commencement are among the manifold reasons for distribution strategy failure in the food supply chain (FSC). Dynamically optimised distribution (DOD) methods can help distribution strategies to adapt and respond to unforeseen changes in today’s complex, connected and interdependent FSC. Building on Digital Twins (DTs) superior connectivity and actuation capabilities, this paper proposes a new DT-based method for DOD in the FSC, from DT design to modelling and evaluation in AnyLogic. Initial simulation results from a UK-based case study show that DT-enabled DOD could bring savings of more than £25,000 in direct costs and 150 tonnes of CO2 per shipment in a typical 20-tonne transport refrigeration unit (TRU). Benefits are compounded by potential fuel savings of up 132 L per year and TRU due to DT-enabled DOD dynamic carrier re-routing capabilities.

This paper presents a framework for automatically building a product data repository, overcoming the limitations of machine understanding and the time-consuming, costly nature of manual human annotation. In this study, we focus on Personal Protective Equipment (PPE), specifically respirator mask product categories, as a case study. First, we extract product specifications from the details and sub-details sections of product pages and build < attribute, attribute-values > metadata in dictionary form. Second, to process the data, we represent numerical data with a special token [NUM] and use hierarchical clustering based on Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity for categorical data. Third, we propose an algorithm-based method to annotate product attributes and attribute values in product descriptions using the metadata, while minimizing human efforts. Additionally, we employ BIO tagging to extract location information for attribute values, streamlining the overall annotation process. This paper contributes to the field by presenting a method for automatically generating product-related datasets through an algorithm that enables the construction of metadata without any human intervention. This approach is a crucial step toward implementing a knowledge repository in a Cloud Manufacturing (CM) environment. Particularly, by addressing time and effort concerns in the dataset construction process, this method can significantly contribute to the creation of large-scale datasets.

Sudden and unexpected economic changes associated with market competition and short product life cycles have generated significant organizational challenges. Thus improvements in processes and product characteristics can directly interfere with their production capacity. To identify improvements in the process, it is fundamental to analyze how the process is developing and, based on that, make favourable decisions. Today, several companies analyze their data, but most of these are done by manually collected data, which brings much uncertainty and can delay the analysis and decision-making process. The present work aims to propose a digital architecture to calculate the OEE (Overall Equipment Effectiveness) for machines in the manufacturing industry. The relationship between the behaviour parameters of the machines allows for diagnosing critical factors for the production system, namely performance, availability, and quality. The machine parameters presented in a short period allow faster action by operators in cases of failures in the activity, and greater security in the planning of the production process, as well as in the development of products and maintenance schedules, directly impacting the production cost. The project's innovation is carried out through digital data modelling based on the ETL context with parameters derived from the behaviour of a machine, where they are dynamically processed and correlated in the Desktop software to illustrate the calculated OEE value.

In a paradigm increasingly marked by the emergence and use of new technologies, economic flourishing is no longer enough to fill all of society’s concerns. The three dimensions of sustainability are increasingly in people’s consciousness. In parallel, the United Nations (UN) has launched a set of 17 sustainable development goals that aim to provide a more prosperous future for all societies. Future digital switchover projects should not be blind to this “sustainable” impact. In this sense and given that several works have already appeared that expose the technological benefits considering the 17 UN goals, this work aims to provide a systematization of the main impacts of the most popular emerging technologies on the SDG, to help in the planning of the digitalization processes. Predictive capability by Artificial Intelligence, the ability to connect and provide real-time data, achieved by the Internet of Things, and data security achieved by the Blockchain prove to be advantages in terms of sustainability and can be pillars of support for the digital transition.

Business demand and scientific associations are studying related to applications of the Digital Twin simulations in the Supply Chain. This article analyzes technological breakthroughs regarding these concepts, research questions, and perspectives linked to the essence of the Digital Twin in the Supply Chain. The research method provided a literary review of qualitative approaches, presenting an overview of the researched domain, culminating in developing a framework for simulating applications based on the Digital Twin concept and SCOR Methodology. This model offers a vision and an approach that focuses on business processes within the Supply Chain, relating 4 phases of the chain in 5 different classes and 5-step behaviors with 36 inputs/outputs in the Supply Chain. The results of this research are related to better development of applications in the Supply Chain towards Digital Twin configurations, as companies demand better decision-making and performance due to the effects of the globalized digital transformation.

This paper aims at identifying the main challenges associated with the adoption of blockchain and smart contract technologies as potential solutions for cybersecurity in an Industrial Internet of Things environment. Initially, an introduction to IIoT, blockchain and smart contracts is made, highlighting their core concepts as well as explaining their characteristics and properties. Subsequently, based on a literature review, the various benefits and challenges for their implementation are identified. The latter are presented through a hierarchical approach that first categorizes them in architecture and privacy related challenges, with each category being further divided into more specific challenges to emphasize the complex nature of the problem. Some typical use cases are also mentioned to showcase the feasibility of these technologies. Finally, a conceptual framework for IIoT cybersecurity is proposed in terms of future work directions.

Humans have often been perceived as a leading cause of error in Zero-defect manufacturing (ZDM) processes. There is thus a reduction of human interventions in the deployment of industry 4.0 (I4.0) technologies used for ZDM such as Machine Learning (ML). However, as manufacturing (e.g., I4.0 context) is often placed within a socio-technological context involving the co-integration of humans and technology, the manufacturing processes are now more vulnerable to cyber risk and threats. System vulnerabilities also derive from limitations associated with ML. This paper highlights three challenges associated with ML: explainability, data privacy, and security for ZDM. We argue that due to the high level of data complexity and lack of flexibility in ML models, humans play a critical role in ZDM decision-making. The paper explores the concept of security culture as an enabler for transformative resilience and zero-defect manufacturing and contributes to rethinking the human-centered approach in ZDM. The paper stresses a need to enhance contextual and empirical understanding of transformative resilience and security culture in ML/ZDM environments to better address adverse events such as cyber threat situations.

Industry 5.0 is the inclusion of humans in the production line. This is because human is now considered a prerequisite for any industry to enhance production performances. However, most of the technological enablers still do not consider the human factor in the production system and only a few articles expose the impact of human-centric approaches and digitalization on production performance. Therefore, this study applied a questionnaire-based survey approach to map out the level of digitalization and human-centric in companies of different sizes and identify the impact on production flexibility and throughput performances on the production systems. The survey was conducted using Google Forms. One of the key findings is the level of digitalization should go hand in hand with human centricity. Both the production performances of product throughput and process flexibility have an S-shape tendency where a high level of human-centric can have a positive impact on both production performances. Respondents from different sizes of enterprises in different regions of the world and job nature can have a different perception of both the degree of human-centric and digitalization. Companies should consider different human roles like managers and engineers in the human-centric framework to enhance human-centric in a more holistic approach.

Industry 5.0 is a concept that represents a human-centered, resilient, and sustainable manufacturing system. With the increase in the importance of the human role within the system, there is a need to adapt standard ergonomic methods to ensure safety within the work environment. The human-centered approach extends the limits of work safety to well-being principles while the new digital technologies which seek human interaction demand enhanced cognitive functions. In this paper, the principles of cognitive ergonomics will be presented as their current use in manufacturing. Due to digitalization, the human operators' role demands changes to the principles of Industry 5.0 ergonomics. The novel framework of cognitive ergonomics implementation in Industry 5.0 will be presented based on a literature review. Results will be used in a future work to implement the novel and innovative digital production and process planning system in the local stove manufacturing company.

The evolution from Industry 4.0 moves to Industry 5.0 and Society 5.0, which will transform the way industries operate through digital technologies, appreciation of the human, and sustainability. With this, there is a need for research on the worker and Industry 5.0. This paper aims to identify the main themes driving research on the worker inserted in the context of Industry 5.0. As methodology, a systematic literature review was performed in the Scopus database, obtaining a portfolio of 298 articles, which were identified as the following main areas in the articles: System, Data, Technologies, Production, Process, Human, Work, obtaining for each area, research driving themes.

Lean philosophy has been widely introduced in many manufacturing and service industries, allowing for increased operational gains and creating a relationship between the work environment and the workers’ well-being. This study aims to understand the relationship between Lean and workers' well-being on a shop floor. A questionnaire based on the Job Demands-Resources (JD-R) model was conducted in an industrial company in Brazil, and a total of 165 responses were collected. Through an exploratory factor analysis (EFA), eight factors were identified (soft Lean practices, Lean training, the pace of work, small group problem solving, interdependencies, problem-solving work demands, engagement and exhaustion). Posteriorly, a cluster analysis allowed us to identify 3 distinct groups in the sample, cluster 1 – includes those employees with a high level of engagement and exhaustion: cluster 2 – employees with a moderate level of engagement and exhaustion; and cluster 3 – employees with a low level of engagement and exhaustion. Additionally, using multiple linear regression analysis, it can be concluded that soft Lean practices (0.46) and the pace of work (−0.23) are the variables that most influence the engagement, and the same can be observed regarding exhaustion, where soft Lean practices (−0.36) and pace of work (0.40) also present statistically significant results.

Data-driven Zero Defect Manufacturing (ZDM) system gathers and organizes data from different sources, integrating and analyzing the data using different tools, with the purpose to react on potential quality issues before they happen with adequate levels of data accuracy and precision. This paper discusses the role of humans in the data driven ZDM loop, considering the context of four manufacturing companies, from the EU H2020 project DAT4Zero which has also funded this study. These companies represent distinct manufacturing environments, each with specific industrial challenges and requirements, which were studied to map, analyze, and exemplify the potential role of humans in the data driven ZDM framework in real manufacturing environments.

Manufacturing companies around the world are under constant pressure to perform effectively and sustainably. Incidental processes, such as Quality Inspection (QI), are needed to achieve Zero-Defects Manufacturing (ZDM). This study aimed to identify the Human Factors and Ergonomics (HF&E) in the design of advanced automation, QI systems, and ZDM through selected papers and empirical observations. Our presented model is built around the six main dimensions, i.e., top management, manager (project owner), designers, engineers (internal and suppliers), and operators. The commitment of top management, the openness of the manager, the design-friendly nature of the technological system, and the constant updating of knowledge by engineers are important for the success of ZDM. Researchers need to be familiar with cognitive and organisational human factors to align theory with specific cases. Operators face physical and cognitive challenges, and their environment and health must be considered for their successful contribution to the design of advanced QI systems.

The recent Industry 4.0 paradigm is disruptively changing the manufacturing landscape. Where fully automated settings are not feasible or economically viable, Industrial Internet of Things sensors are gaining traction due to their flexibility and affordable costs. In such a scenario emerges crescent attention to digitizing the human factor. Based on this, this manuscript proposes an integrated digital architecture in which a radio-frequency-based indoor positioning system is adopted to anonymously tag human operators. The highly unbalanced spatio-temporal dataset is fed into a recurrent neural network architecture that classifies without overfitting the manual picking/deposit activities in products’ stocking areas of a real and low standardized manufacturing job shop with a performance of 0.93.

Achieving Innovation is a complex challenge, involving or depending on several factors or causes. It is recognised as being crucial to economic growth, creating new industries and tackling societal challenges. There are several tools to measure innovation management but assessing innovation rests a challenge, as current valuation tools still blend results and process indicators. This study is an exploratory and descriptive study of a quantitative nature. It was carried out, in March-April 2021, to companies in the North of Portugal. Data were collected through a questionnaire. The results seem to indicate the main areas affecting innovation culture from the employees’ perspective, as well as the relevance of the leadership role in the paradigm shift.

Given the growing concern for social sustainability, companies have adopted practices to address stakeholders’ pressures and take responsibility for their suppliers. Although the social dimension of sustainability generates interest in academic and industrial communities, most published papers focus their research on emerging economies. This paper aims to address the degree of implementation of social sustainability practices - basic and advanced - in companies located in Portugal. The development of a survey allowed the collection of 142 answers. The findings show that both basic and advanced practices have a reduced level of implementation in the Portuguese industrial context. Regarding basic social sustainability practices, Portuguese industries should stimulate collaboration with their suppliers. At the level of advanced sustainability practices, there is a need to improve the involvement of NGOs and community groups in supply chains. Furthermore, the Kruskal-Wallis test shows that the companies’ size influences the implementation level of social sustainability practices.

Contemporary manufacturing organizations formulate strategies towards smart manufacturing. However, strategies often merely regard technological improvements of working processes and activities and pay limited attention to human-centric perspectives. This study addresses the complex phenomenon of reaching socially sustainable smart manufacturing by exploring the human-centric perspectives in the eras of Industry 4.0 and Industry 5.0. Data were collected through an explorative qualitative case study with focus groups applying the history wall approach to document informants’ choices of activities that impact digitalization. To investigate informants’ interpretations and experiences of digital initiatives and prospects, the history wall approach was coupled with the analytical lens of the co-workership wheel, with its four conceptual pairs: trust and openness, community spirit and cooperation, engagement and meaningfulness, responsibility, and initiative. A total of 17 informants from different organizational levels at a case company participated. Activities, impacting digitalization, brought forward were grouped into technology, organization, and external impact. Results showed that human-centric and intangible perspectives surfaced as prerequisites when navigating industrial digitalization. Further, digital initiatives and prospects risk drowning in re-occurring organizational changes making successful implementation difficult. Thus, organizations cannot rely solely on technology, but must consider activities related to organizational aspects and impacts from the external environment, when introducing digital initiatives. Intrinsically, recognition of the co-workership concept, emphasizing human-centricity, can support the foundation necessary for bridging the gap towards socially sustainable smart manufacturing and strengthening the emerging I5.0 research.

Advances in manufacturing technology made plastics comparatively inexpensive, light, mouldable and durable. The great success of plastics comes along with a strong negative environmental impact and their accumulation in landfills and leakage into the natural environment is now recognized as a global environmental crisis. The circular economy approach to plastics provides a feasible solution to the prevailing linear system and aims to raise the proportion of plastic that is reused or recycled back into the system. The transition towards a circular economy, cannot be achieved solely through changes within the waste-handling system but must be combined with changes in other parts of the value chain, including the design, the manufacturing, etc. Plastic manufacturing companies need support in the transition. Therefore, this study aims to provide knowledge to plastics companies to move from linear towards circular manufacturing processes. We conduct a systematic literature review examining current practices and research needs in circularity within the plastics industry. This study contributes to the literature by mapping circularity strategies in plastics, explaining innovative circular plastic materials, and highlighting current circular manufacturing technologies such as additive manufacturing and the chemical transformation of waste plastics into various value-added chemical feedstocks, which can replace petrochemicals. Additionally, circular pathways are illustrated to support practitioners in identifying their current position in the value chain and understanding pathways to increase circularity. One of the key conclusions is that circular plastic value chains are still deficient in the implementation of R-strategies (such as rethinking, reducing, reusing, etc.) besides recycling.

Despite the high economic importance of the plastic industry worldwide and the capacity of plastic to be recycled multiple times due to its ability to preserve its value and properties, the plastic recycling sector still needs to be developed and more cohesive. Thus, there is a need to understand what is hindering plastic recycling. To that end, this paper presents a systematic literature review of the barriers to implementing the circular economy principles in plastic industries, investigating 30 articles retrieved from the SCOPUS database and published between 2008 and 2022. The systematic literature review identified forty-one barriers clustered into seven categories: social, organisational, economic, supply chain, technological and informational, institutional, and market barriers. This study aids practitioners and managers in identifying the main barriers that may be hindering the adoption of circular economy practices within their businesses.

Sustainability has been gaining focus in recent years in companies of different segments and sizes in order to create competitive advantages and differentiation in the market to maximize profit and opportunities. Sustainable innovation corroborates this concept in order to create products that have the theme of sustainability in their development. However, in markets where the source of raw material comes from commodity products, this practice is not trivial. Thus, in an attempt to differentiate itself from competitors, a company developed a product with a “sustainable” vision, using polymer originating from ethanol (sugarcane). However, is this perception of sustainability transmitted to customers, partners and society? Therefore, this study uses the AHP multicriteria decision-making method to quantify the qualitative analyzes carried out. The results indicate that the sustainability message is transmitted, but at different levels (percentage of confirmation), and therefore, it is suggested that improvements/focuses be implemented in the marketing informational materials.

Wire arc additive manufacturing (WAAM) is a metal AM process that uses metal wire as feedstock and an electric arc to melt the feedstock wire. WAAM has gained attention in the industry due to its benefits like better material efficiency and higher deposition rates. However, WAAM needs post-processing operations like machining due to its poor surface finish. Therefore, the sustainability potential of WAAM can’t be presumed and needs to be proven quantitatively. Fewer studies that conduct an environmental or economic assessment of WAAM are present and do not comprehensively study the effect of post-processing on WAAM’s performance. Therefore, the main objective of this study is to carry out an integrated and quantitative environmental and economic assessment of the WAAM process, including the effect of its post-processing operation. A case study of a product: a marine propeller made by WAAM and CNC milling technologies is presented. It is observed that the WAAM approach causes a 60% lower environmental impact compared to CNC milling. However, the WAAM approach is found to be slightly more cost-efficient (by 7%) than CNC milling. The effect of post-processing on WAAM’s environmental and economic performance is also studied. WAAM is found to be more economical and ecological than CNC milling when the post-processing machining allowance of 4 mm or lower is left across the surface area of the propeller considered. This study can be helpful to AM practitioners in understanding the factors affecting WAAM’s sustainability and in decision-making on sustainable process selection.

Increased global temperature has dictated the need to reduce carbon emissions in the manufacturing sector, while it is expected to both improve sustainability and maintain the quality of manufacturing processes. Green strategies, such as Zero Defect Manufacturing, Circular Economy and Sustainability have been proposed, with different key performance indicators (KPIs) with unidentified relationship between those KPIs and process parameters. Up to now, no study has identified this relationship, or tried to combine different green strategies and KPIs. This study aims to investigate the impact of specific process parameters (process power & speed) for the case of welding on KPIs for Zero Defect Manufacturing (defect rate), Circular Economy (energy consumption) and Sustainability (carbon footprint). The methodological approach was based on previously published framework, while a specific case study was used to showcase the complexity of relationship between the process parameters and the relevant KPIs. The main outcome of this study is the non-linear relationship shown in most cases, such as in the case of the impact of power and speed on the defect rate and on the carbon footprint. In addition, KPIs from one green strategy could be applicable to the other, via defining the link between quality of welding, energy consumption and carbon emissions. The latter could be used to define a holistic strategy incorporating different strategies and KPIs for improving sustainability in manufacturing.

Residual agroforestry biomass, a byproduct of agricultural and forestry practices, can be used as feedstock in various industries, including bioenergy, pharmaceuticals, and cosmetics. In addition to its potential to contribute to the bio-circular economy, it can also help to reduce fuel loads in forests and mitigate the risk of forest fires. This study conducts a bibliometric analysis to identify the current state-of-the-art on drivers and barriers to the valorization of residual agroforestry biomass. A total of 194 articles from Scopus and Web of Science were analyzed using VOS viewer and R programming tool biblioshiny. The results revealed that only 4% of the articles focused specifically on residual agroforestry biomass, with the majority of them centered on the use of biomass for bioenergy production. The most influential authors, top-cited papers, and top journals in the field were also identified. This study represents the first step towards a more comprehensive systematic review of the literature on residual agroforestry biomass valorization.

The chemical industry is considered indispensable for improving both the economy and the well-being of the population in many countries. However, despite making significant contributions to the global economy and society’s welfare, the chemical industry also has adverse effects on the environment and human health and safety. To minimize its negative impacts and ensure a sustainable future, it is critical to prioritize sustainable production in the chemical industry. It is therefore imperative to comprehend the consequences of safety on sustainable production and the specific mechanisms through which safety can influence sustainable production performance. This paper reports an ongoing study to scrutinize the relationship between safety performance and sustainable production performance. The study comprises a systematic literature review to produce an initial theoretical framework and two phases of data collection to validate the initial theoretical framework. The first phase has been done, providing improvements for the framework, which will be further analyzed in the second phase.

The Brazilian territorial extension made it difficult to structure a reverse chain to deal with issues related to Waste Electrical and Electronic Equipment (WEEE). This is due to government requirements for the management of this waste by electronics manufacturers and importers. To compete with external competitors, companies need to use the circular economy, which further increases the complexity of the project. Thus, this research identified that the industries in this sector have difficulties in organizing themselves for the reverse logistics operation of electrical and electronic equipment. The objective of this study is to propose a structure for the reverse chain in the management of electronic waste in Brazil. The methodology used was a semi-structured interview with open questions with specialists in WEEE reverse logistics. As a result, the construction of a conceptual model for the reverse logistics of WEEE with the promotion of the Circular Economy. As a limitation, as it uses a semi-structured interview with specialists, it uses an exploratory approach and, therefore, cannot generalize the results.

Legislation and stakeholders increasingly urge European original equipment manufacturers (OEM) to disclose and reduce greenhouse gas (GHG) emissions of their operations and supply chains. However, a sector-specific reporting standard is not yet available for manufacturing industry. This study takes a stepwise approach to identify the GHG emission sources and intensities in further detail. The primary data of a case company, a Finnish gear manufacturer, was supplemented with secondary data from the literature. Using the proposed approach, an OEM share of the total emissions was estimated at 15%. Evaluating emissions of specific suppliers is more difficult as there is little access to the suppliers’ data. The study gives insights for the manufacturers on how to get started with GHG emissions accounting in the factories and supply chains and to show their responsible actions in the climate change mitigation.

Energy efficiency and environment decarbonization are domains of growing concern. There is an emphasis in the energy rehabilitation of buildings and in the study and research of improvement measures aimed to make the building an NZEB (Nearly Zero Energy Building). The regulatory framework issued by the member states of the European Union, establish different objectives and provide methodologies for the calculation of energy performance, establishing minimum requirements for new buildings.This work addresses the implementation of the energy certification in a service building, applying the calculation methods to a case study. Improvement proposals are presented for the energy performance improvement that lead to a reduction in energy consumption.The purpose is to demonstrate that the proposed measures for the rational use of energy, outlined in the energy certificate, contribute to increased energy efficiency. These measures foster the reduction of energy consumption and consequently the reduction of greenhouse gases emissions.

Nowadays, for companies to assert themselves in the markets, the focus on sustainability and social responsibility is preponderant and supported by academic research. For this, certification is considered an essential means to achieve high levels of social responsibility and sustainable development. In this study, we investigate the Geo distribution of the B Corp certification, a certification aligned with the main assumptions of the triple bottom line. Given its novelty, the B Corp certification phenomenon diffusion/evolution, namely its degree of implementation in different countries, has yet to be researched in depth. Hence, this study, supported by data gathered through the B Corporation certification system database, descriptive statistics, and geo-reference interactive maps, maps B Corp certified organizations’ geographical distribution and worldwide growth. The sample refers to a total of 2262 companies certified between the period 2017 and March 2021. The result showed a strong presence in the United States and appreciable growth in Europe, allowing for a better understanding of the B Corp diffusion patterns and supporting companies and certification bodies worldwide to emphasize social responsibility and sustainable development.

Traditional agricultural practices negatively impact the environment, and they usually don’t allow flexible crop production. Vertical farming has been discovered to be an effective remedy for these problems. It has higher yields, and it can be more flexible and performant than open-field agriculture, because of its structural and technological characteristics. To get the best performance from vertical farming systems, it is necessary to consider several KPIs, which assess productivity, quality and sustainability aspects. The aim of this work is to identify both the input variables and the KPIs that must be considered in a vertical farming system. Furthermore, the experimental design of an aeroponic chamber is proposed, for conducting experiments varying the input variable values and collecting the related KPI. The analysis of such data will allow to find correlations between the input variables and the KPIs.

This paper describes a research project to improve airport retail waste management, particularly in terms of waste segregation and effective processing according to current waste management practices. The Double Diamond design process was employed as the research methodology to identify and challenge these practices. On-site observations were conducted to begin the project by collecting data on the most recent airport waste management procedures and this was the demonstration of the Discover and problem identification phase of the Double Diamond methodology. Airports may apply this in the Develop and Delivery phases to boost recovery rates and handle waste more effectively and economically, lowering the waste transported to landfills or for incineration, and achieving their net-zero goals. This paper contributes to the body of knowledge on global waste management methods, especially in the aviation sector where little academic discourse exists on this issue. Practitioners can benefit from the Double Diamond’s design structure to provide guidance for implementing and assessing airport waste management practices.

While firms in different industries face increasing pressure from society and regulatory bodies to address sustainability challenges, critical questions remain unanswered regarding how supply chains should prioritise processes, allocate resources, and integrate with other supply chains to develop bio-packaging products. We explore those questions by testing a Serious Game (SG) with stakeholders of the bioplastics supply chain in the UK. We find that SG facilitates the configuration of the BSC bio-packaging supply chain (BSC) for compostable products by prioritising the implementation of downstream processes integrated with the biofuel and agriculture supply chain and closed-loop business models (i.e., integration of production, commercialisation, and end-of-life) supported by normative regulations, communication and education with the consumers and investments at the end-of-life. In addition, under crisis scenarios (i.e., extended pandemic, climate change, and economic crisis), attention must be given to solutions that address local needs, downstream processes, and cost-effective bio-packaging.

The current globalization faces environmental and social challenges in lean manufacturing processes while simultaneously ensuring a sustainable evolution, even though there is a high margin in the lean approach. To deal with this challenge, this research aims to investigate some of the main extended versions of lean techniques such as sustainable (Sus-VSM) and environmental value stream mapping (E-VSM) simultaneously integrating them with lifecycle assessment (LCA) principles for better comparative analysis of environmental performance due to an indefinite number of approaches to evaluate sustainability. Moreover, the research tries to identify tradeoff methods of environmental, economic, and social indicators in the lean E/Sus-VSM tool for better decision-making, whereas there is no common suggested method. For this purpose, the method for the project initiates a search and a literature review of scientific sources to find which features of LCA are integrated with different E/Sus-VSMs and what tools are used in balanced decision-making by using the matrix table. The results are that many elements or similar steps of LCA are used in E/Sus-VSMs and the tradeoff tools to make decisions are various or new approaches are emerging. Finally, a conclusion is made for identifying and suggesting further research and limitations.

Despite the potential environmental benefits of bioplastics, consumers remain confused regarding this innovative packaging material. One possible solution to mitigate these concerns is to introduce ecolabels to communicate this bioplastics material. This study aims to investigate the influence of ecolabel signals and other factors on the adoption and proper disposal of bioplastics packaging among consumers in Indonesia, as seen from the perspective of stakeholders in the bioplastics industry. Signaling theory is utilized as a theoretical lens to build a conceptual model to observe this phenomenon. A total of 17 case studies were collected from stakeholders in various sectors, including production, consumption, waste management, and academia. The findings reveal that while ecolabels can play a role in shaping consumer behavior towards bioplastic packaging, they cannot be the sole determining factor. Other factors such as packaging price, pro-environmental attitude, habitual practice, and access to appropriate waste management facilities also significantly influence consumer adoption and proper disposal of bioplastics. The study contributes as one of the earliest research efforts in this area. Furthermore, the study addresses gaps in bioplastics consumer communication research by highlighting the effect of ecolabels.

Additive manufacturing is one of the most prominent technologies seen as an enabler for circular economy strategies, including the development of additive symbiotic networks. There is a gap in the literature regarding the understanding of how additive symbiotic networks can be developed, namely in identifying the wastes and by-products and additive manufacturing technologies that can be used to valorize these resources. This study aims to review the existing literature on the topics of industrial symbiosis and additive manufacturing. A sample of 83 documents was reached, with only 25 highlighting the potential for developing industrial symbiosis networks in the additive manufacturing context. Results show that the most used technologies to incorporate recycled materials included fused deposition modelling, fused filament fabrication and selective laser melting. Regarding waste or by-products exchanges, plastic polymers, biological wastes and metal powders are some of the most frequently exchanged materials. This makes evidence of the additive manufacturing industry’s potential to develop additive symbiotic networks in which wastes and by-products from other industries may be used as material inputs for additive manufacturing processes and technologies.

This paper deals remanufacturing method for the reuse of adhesive bonded SMD components on stretchable textile ribbons for integration in e-textiles and its risk analysis. Nowadays, e-waste is an increasing problem in the world, and reducing this problem by using circular economy principles and reusing components is beneficial. In our research, the samples were submitted for accelerated ageing by dry heat and their quality was evaluated by the electrical resistance measurement. The recycling procedure was realized four times, and the mentioned risk analysis was realized before and after the experiment. The two main goals of our research were determined. The first goal was to demonstrate the applicability of our recycling procedure and the quality of samples with reused components after accelerated climatic ageing by dry heat. The second goal was to create and verify the risk analysis of our method and identify actions to mitigate the high and very high risks. The experiment showed that our recycling method is applicable several times without decreasing product quality. The results also present that reused samples are functional like new even after samples dry heat ageing. The risk analysis after the experiment showed seven high risks, which is necessary to solve primarily. These risk factors and actions to mitigate them are described in the paper.

Nowadays, the importance of social media and its influence on customers for customer behavior transformation has been highlighted. This has introduced the importance of analyzing the impact of social media on Product Lifecycle Management (PLM) phases and how to use it for more efficient product development cycles. The paper has shown through the literature review, the lack of contributions for benefiting from the potentials of social media platforms for PLM refinement. Especially, the capabilities of social media analytics for extracting customers’ emotions and perceptions are neglected. Moreover, the paper has highlighted the importance of perceptive quality and has and investigated it with a focus on the Omnichannel concept. Customer satisfaction and perceptual quality as two crucial factors which directly can be influenced by social media platforms have been considered. Using the system dynamics approach, a cause-and-effect model for demonstrating the relations and effects of these factors in product lifecycle management frameworks has been developed. Also, a social media data analytics model has been developed to formulate customer satisfaction and perceptual quality. The approach applies sentiment natural language processing and analysis to provide a basis for relating the mentioned factors. The findings have provided insights for embedding social media analytics in PLM analytical frameworks.

Governments and the market have stimulated replacing fossil fuel vehicles with electric mobility. As a direct effect, the demand for Lithium-ion batteries (LIB) has risen significantly. This technology has several advantages compared with other storage systems, especially the high energy density, capacity, long lifetime, and cycles. On the other hand, LIB is still expensive and is suitable for vehicles when its remaining LIB lifetime is above 80%. After that, the batteries do not satisfy the demands. Since recycling is complex and takes a long time, LIB is used as second-life batteries (SLB) for stationary systems and low-speed vehicles. Although the SLB has low performance, they need smart mechanisms to fit the application, operate under safe conditions, and perform the maximum possible cycle time. Therefore, this work presents an intelligent Battery Management System (BMS) to prolong the discharge time (DT) of a mini-packing of four cells, one of which is used as a reserve. Two configurations were present to guarantee the longest discharge cycle time and avoid cell failures. The first version could improve the DT by 22%, while the second increased to 72%. Therefore, the proposed mechanism is suitable for prolonging the DT of LIB for SLB applications such as stationary solutions or low-speed vehicles.

Circular economy (CE) and sustainability are two interrelated concepts that are increasingly gaining attention in the fields of business, economics, and environmental studies. Although both concepts share similarities as they both aim to reduce the negative impact of human activities on the environment and promote sustainable development, they have predominantly been addressed separately as two independent areas of knowledge, and continue to be ambiguous. Their relationship in literature has not been clarified, which may be obfuscating their overall usage. The aim of this article is to clarify the concepts of circular economy and sustainability, to examine how these two constructs have evolved in the last decades, and to identify their similarities and differences. In this regard a specific literature review was performed. For this purpose, 48 publications were identified, and a content analysis was conducted. Based on the aforementioned literature review, key findings relatively to circular economy and sustainability are presented.

As an intermediary step to enable solving the facility layout problem with reinforcement learning (RL), this paper presents an algorithm to automatically derive a path network from a given factory layout. It is represented as a graph with additional measures like pathwidth, segment length and segment orientation annotated as edge properties. The information can be utilized in subsequent processing steps to allow programmatic evaluation of the quality of generated layouts to aid in reward calculation for reinforcement learning. This is necessary since the material flow as a widely used evaluation criterion for algorithms solving the layout problem is not expressing the factory design goals clearly enough for a RL agent to learn. Material flow is typically computed based on transport costs and transport distance, which is abstracted as Euclidean distance between centre points of workstations. Using the path network derived by the presented algorithm enables a calculation of transport distances closer to the true distances travelled in the factory, thus enabling a more accurate reward signal.

As sustainability requirements are growing, more and more companies are falsely claiming to supply sustainable products, thus creating an unfair playing field for companies that do comply. The Textile and Clothing (TC) industry is one of the least sustainable and transparent industries, often manufacturing products in low-cost countries with inadequate working conditions and environmental standards. The purpose of this study is to investigate how social sustainability assessments can be conducted, to increase the reliability of sustainability claims. The paper proposes a concept of a Social-Life Cycle Assessment (S-LCA) service that is based on site-specific primary data and is grounded in the international Social Accountability (SA) 8000 certification system, to increase the reliability of sustainability claims. United Nations recommends the SA8000 in their S-LCA guidelines. The S-LCA service is also enabled by Blockchain to secure that critical data remains unaltered. The concept and service are being developed through the Design Science methodology, combining: i) case studies in an EU project, to understand the practical problem, ii) a S-LCA literature study, and iii) action research, to iteratively apply the service to the cases and refine it with project contributors representing the entire TC value chain. The concept consists of a workflow diagram, preliminary user interface, data collection template, and an overview of critical data to be secured by Blockchain. To the best of our knowledge, this is the first research paper about a concept of a site-specific S-LCA service that is integrated with an international certification system and a Blockchain-enabled platform.

This work aims to establish assertive inventory policies for the raw materials in a Portuguese bakery. The demand behaviour of some items was analysed, using the ABC classification, and 14 main items were prioritised. Using Holt-Winters’ additive method, the demand for these items was forecast until the end of 2023. With these forecast data, the paper presents a simulation and comparison between two different inventory policies, the continuous review model and the periodic review model, and also presents an estimate of current inventory costs. As expected, the periodic review model presented higher inventory costs than the continuous review model, due to the higher safety stocks needed. The results of this paper are a starting point for the company to decide what inventory management method fits better.

The transshipment and location problem (TLP) has been recently gaining increased attention in the literature, much due to organizations need to improve their logistical processes and distribution network. This Bibliometric analysis aims to understand which authors, countries, journals, research areas, and keywords are most relevant to the TLP. In addition, it will also be looked at the perspective of the theme's growth over time. Through the Bibliometric analysis, it was possible to identify that the interest in this problem has been growing over the years and that optimization tools and approaches are mostly used. It was also observed which countries and authors most helped in the development of the theme, and what are their quantitative participations related to citation. This research brings an overview of the development of TLP and its importance in the logistics environment. In addition to showing the main authors who address the topic and their respective countries.

This paper presents an optimization case study developed for a leather company, which aims to improve operational performance at the technical team level in routing and apply improvements that impact it. This project aims to create a route optimization model for what were initially intuitive routes. Since the assignment of work hours by the central team varies, there is a fluctuation in the material shipped to each customer. Considering that this fluctuation is relatively small and only happens every quarter, the fixed route system can be considered as functional, needing updating when there is a new time allocation. Thus, the objective is to minimize the total transportation cost, which in this case is provided by the cost per kilometer. To this end, five scenarios, obtained by a decision support tool, were studied. The VRPTW model, together with decisions concerning the routes, namely cross-docking in Orléans, allowed the reduction of costs by more than 30%, maintaining the customer's and company's requirements regarding the service level. These gains refer to one of the scenarios studied, in which it was possible to guarantee all last-mile distribution with three vans, within expected time windows and demands. Although the scenario with the greatest margin for gains is the one that presents a distribution center according to the gravity method, it also requires renting a warehouse in the Perrusson area (area indicated by the gravity method).

The temperature-controlled supply chain in Portugal is on a par with the most developed cold logistics markets. With a growing and increasingly demanding market dynamics, it is essential that companies in the sector adopt strategies aimed at increasing the efficiency of their operations, promoting an increase in the level of service and a decrease in their operating costs. The need to decrease operational costs should not have any impact on quality, and it is expected that the focus will be on the continuous improvement of internal logistics operations. A study of the outgoing movements of products from a temperature-controlled warehouse of a logistics and transport services company was developed. An integer linear programming model was built and applied, in order to minimize the costs of the logistics operator with the operation of picking and storage. It was possible to obtain savings of about 30.9% compared to the initial scenario.

This paper analyses how the harmonization between supply and demand uncertainty and supply chain responsiveness (SC fit) impacts business performance. The study analyses data obtained from a sample of 179 manufacturing companies from Portugal. The business performance of companies with different types of SC fit (high-high fit and low-low fit) and misfit (positive and negative) were analyzed and discussed. The results indicate that SC fit is positively related to business performance, economic and productivity, and commercial performance separately. This study advances the literature as the results indicate that SC fit positively affects both commercial and economic, and productivity performance. In contrast, previous empirical studies have mainly addressed the impact only on financial and operational performance.

The economic inflation that the whole world is currently experiencing and particularly Europe, in addition to globalization and the versatility of customer demand, encourage the companies to find innovative solutions to be more competitive. Indeed, for this competitiveness, industry 4.0 concepts have been successfully implemented in large companies supply chains as a tool to increase the company global performance. These concepts of digital transformation require to be adapted to SMEs expectations. The integration of sustainability aspects contributes to accelerate the concepts integration in SMEs by providing evolutive solutions appropriated to the SME singularity. This paper focuses on the design and development of an intelligent decision aided tool for well managing the sustainable digital transformation of SMEs. In this paper, the concepts and tools of the literature review, based on the supply chain 4.0 context and sustainability, industry 4.0, artificial intelligence and decision aided tools, have been briefly presented. Then, the paper next sections focus on the general approach and the decision intelligent system that are elaborated. An example is shown to validate these concepts and tools.

For companies to remain competitive in the current market, they need to find ways to reduce as much as possible their operational costs. To achieve this, one area that is often the focus of improvement is internal logistics. This paper is focused on the restructuring of the internal logistics process in a case study company that has plans for doubling its production levels within a year. To do so, a new production supermarket and a milk run routing plan will be designed with lean concepts in mind. What makes this research different from most existing literature is the use of carts as a way of storing parts in the supermarket, instead of the common shelves. The strategy developed is able to reduce the number of workers required and is estimated to bring up to 145 000 euros in profit, depending on the future kitting time, considering a production growth of 100%.

This paper presents a meta-analysis regression of efficiency and productivity papers on energy. The objective is to know if there are differences in the value of technical efficiency (TE) by applying different estimation methods. In the energy sector, over the recent decades, many studies have been published that analyse firms’ performance, using as a measure of technical efficiency the estimated values from parametric and non-parametric methods. In the literature there is no study that addresses whether different estimation methods can influence the value obtained for TE. The analysis of these factors is an important instrument for performance evaluation, by enabling firms to adopt more precise lines and benchmarking techniques. The data consist of 162 models, reporting on 63 scientific articles which empirically analyze the level of efficiency and productivity in the energy sector, from 1979 to 2013. The meta-regression model enables an understanding of the effects that different specifications of the models may have on the estimated values for efficiency. The results identify positive and negative effects on efficiency, but the most important is that neither method is preferable to the other.

The automotive industry is one of the most competitive branches of industry worldwide, due to its demand for a better product. The concept of quality and conformity is extremely important to get an edge over other competitors in the market, while continuous improvement is one of the keys to adapting and improving in an industry where costs are as important as innovation for new and better products. This paper presents a new End-of-Line (EOL) method developed to enhance the quality control of Exhaust Gas Recirculation (EGR) valve. The new method is based on Hysteresis Current loop with two primary settings (open and closed). Through the analysis of the hysteresis current loop, the new method identifies the existence of non-conformities that affect the mechanical functionality of the EGR valves. The method recently implemented at the company involved in this study is having a significant impact on improving its quality control at an irrelevant appraisal cost.

Nowadays, decision making is one of the most important and influential aspects of everyday life, and the application of metaheuristics and heuristics facilitates the process. Thus, this paper presents a performance analysis of the combination of constructive heuristics used to generate initial solutions for metaheuristics applied to scheduling problems. Namely, Nawaz, Enscore, and Ham Heuristic (NEH), Palmer Heuristic and Campbell, Dudek, and Smith Heuristic (CDS) with Cuckoo Search, Firefly Algorithm and Simulated Annealing. The aim is to compare the performance of these combinations to analyse the efficiency, effectiveness and robustness of each. All combinations were analysed in an in-depth computational study and then subjected to a statistical study to support an accurate analysis of the results. The results of the analysis show that the Firefly Algorithm associated with NEH, despite having a high runtime, performs better than the other combinations. However, the best effectiveness-efficiency ratio corresponds to SA-Palmer and SA-CDS.

Federated learning is a collaborative machine learning approach that allows multiple parties to train a model without exchanging sensitive data. In manufacturing, where different parties may have proprietary or sensitive data that cannot be shared, this is especially useful. However, traditional federated learning approaches (as proposed by McMahan et al.) do not consider the differences in data and computing resources across different parties, leading to sub-optimal model performance. Personalized federated learning addresses this issue by allowing each party to contribute to the model training according to its specific data and resources. Furthermore, most common approaches only consider a limited set of data and a short period of time, without considering the system’s long-term usefulness. It is important to consider the integration of new clients and the continuous change of data, which could result in the addition of new classes. This paper will explore the potential of federated learning in manufacturing and present a flexible and expandable approach, focusing on mapping newcomer clients based on activation strength of weights.

The widespread use of the internet, combined with the prevalence of cybersecurity threats such as botnets, has resulted in significant economic losses for manufacturing enterprises. An AI-powered network intrusion detection system is required to address the growing number of botnet attacks caused by increased machine-to-machine communication. Several Machine Learning (ML) and Deep Learning (DL) algorithms were used in this study to detect botnet attacks on seven IoT devices, with the goal of developing highly secure and accurate models for detecting security threats. After comparing its performance to other models, the proposed model was found to be highly performant, accurate, and robust for threat detection. After implementing different models, 2-CNN model demonstrated the highest accuracy level of 99.95% in DANMINI Video Door Phone Doorbell Hands-free Wireless Intercom. Furthermore, it was noted that the performance of the Deep Belief Network (DBN) model was inferior to that of other Deep Learning (DL) models in identifying Gafgyt botnet attacks.

The manual monitoring of refilling stations in industrial environments can lead to inefficiencies and errors, which can impact the overall performance of the production line. In this paper, we present an unsupervised detection pipeline for identifying refilling stations in industrial environments. The proposed pipeline uses a combination of image processing, pattern recognition, and deep learning techniques to detect refilling stations in visual data. We evaluate our method on a set of industrial images, and the findings demonstrate that the pipeline is reliable at detecting refilling stations. Furthermore, the proposed pipeline can automate the monitoring of refilling stations, eliminating the need for manual monitoring and thus improving industrial operations’ efficiency and responsiveness. This method is a versatile solution that can be applied to different industrial contexts without the need for labeled data or prior knowledge about the location of refilling stations.

Material excavated in underground mines must be hoisted to the surface level for processing and distribution. The capacity of these hoisting facilities, known as skips, can be a limiting factor that determines the rate at which excavated material can be removed to the surface. This paper presents the application of discrete event simulation to study the operations of an underground coal mining system to evaluate the impact of increasing skips capacity, together with adding buffers ahead of skips, and any bottlenecks that will be encountered. Different scenarios are considered to evaluate any variation in performance based on the area of the mine being excavated. The results reveal that adding a new skip facility can help increase monthly production output by more than 16% compared to the current levels, indicating a very favorable return on the investment. Findings also show that overall utilization of the skip facilities declines and that expected output is driven by the effective carrying capacity of the belt conveyors. Further research can be conducted to determine the effective buffer capacity and also the optimal effective belt conveyor carrying capacities to maximize production output.

The success of digital technology in mathematics education includes the design of digital tools. In this article, we will describe two tools that are available for teaching and learning mathematics: the MathCityMap system, which includes the MathCityMap@home, and the ASYMPTOTE system. These systems consist of two components, namely a web portal for teachers and a smartphone App for students. We will feature the Digital Classroom which allows the teacher to monitor students’ work and progress in real time. In addition, it allows students to interact not only with the teacher but also with each other. These systems meet the requirements of online and distance learning using smartphones.MathCityMap and ASYMPTOTE systems were designed for the educational en-vironment, but they can be used in a variety of contexts. For example, in busi-nesses, decision-making.

Production shifted from a product-centered perspective (mass production of one article) to a customer-centered perspective (mass customization of product variants). It also happens in energy-intensive industries, such as steel production. Mass customization companies face a challenge in accurately estimating the total costs of an individual product. Furthermore, 20% to 40% of the costs related to steel products come from energy. Increasing the product variety can cause an inevitable loss of sustainability. This paper presents machine-learning approaches to improve the sustainability of the steel production industry. It is done by finding the most accurate way to predict the power consumption and the costs of customized products. Moreover, this research also finds the most energy-efficient machine mix based on the predictions. The method is validated in a steel manufacturing Small Medium Enterprise (SME). In this research, experiments were conducted with different machine learning models, and it was found that the most accurate results were achieved using regularization-based and random forest regression models. Explainable AI (XAI) is also used to clarify how product properties influence process costs and power consumption. This paper also discusses scenarios on how the prediction of costs and power consumption can assist production planners in performing workstation selection. This research improves the production planning of customized products by providing a trustable decision support system for machine selection based on explainable machine learning models for process time and power consumption predictions.

Digital sensing technologies are essential for realizing Industry 4.0, as they enhance productivity, assist with real-time decision-making, and provide flexibility and agility in manufacturing factories. However, implementing these technologies can be a significant challenge due to the need to consider various factors in manufacturing factories, such as heterogeneous equipment, fragmented knowledge, customization requirements, multiple alternative technologies, and the substantial costs involved in the trial-and-error process. A Knowledge Graph (KG) approach is proposed to streamline the implementation of the factory movement tracking system. The KG approach utilizes a knowledge representation reference model that integrates manufacturing objective, activity, resource, environment, factory movement, data, infrastructure, and decision support system. This reference model aids in classifying key phrases extracted from research abstracts and establishing knowledge relationships among them. A synthesized KG, created by analyzing thirty research abstracts, has correctly answered search queries about implementing the factory movement tracking system. This approach establishes a pathway for developing a software system to support movement tracking implementation through automatic interpretation, reasoning, and suggestions.

This paper presents a project in the automotive company whose aim was to standardize processes inherent to the use of a project management tool. This tool was developed internally with the purpose of spreading throughout the organization a methodology based on their own philosophy of lean thinking. During a deep analysis of the current procedures, some problems were identified regarding the lack of adequate use of the tool and its potentialities for project management, due to its complexity, specificity, and lack of training by the project managers. This project’s main goal was to ensure that all project managers in the logistics area use this tool in a standardized way. To accomplish this, a checklist, a training plan, work instructions and a responsibility matrix were developed. An assessment was conducted in three different projects to assess the impact of these solutions. This assessment was created by the company to evaluate the way that projects were managed according to the company’s philosophy on a scale of 1 to 4 and is composed of some criteria. The results indicate an improvement in several of these criteria, consequently improving the level from 2 to 3, because of the implementation of standardized procedures for the use of the tool and taking advantage of all its potentialities for project management.

Total evacuation time constitutes an important factor in the safety of any building. It is thus essential to devise an emergency evacuation plan, which will enable the safe evacuation of all the occupants in the shortest possible time. The main objective of this article was to examine and improve the evacuation process of a 4-star hotel located in the city of Porto, Portugal. To this end, one looked into 6 scenarios, by means of PathFinder simulation software, so as to determine the shortest total evacuation time and identify possible bottlenecks and congestion. The simulation model developed was tested to analyze the evacuation of 429 people from the hotel, based on the availability of the 3 accessible exit doors (central exit, side exit, spa exit) and elevators. Strategy 4 presented the shortest total evacuation time, with 536.0 s. Two other strategies which showed very similar times were 5 and 6, with 537.0 s and 537.5 s, respectively.