Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems

In recent years, there is a growing focus on the role of small and mediumsized enterprises (SMEs), and their development towards Industry 4.0. One way of supporting SMEs in this effort, is by utilizing the method of strategic roadmapping. This article presents a) a theoretical framework for the use of strategic roadmapping towards industry 4.0, and b) insights from a validation of the framework in four pilots - Norwegian manufacturing companies. The framework offers a systemic view of the company by focusing on five dimensions: business and strategy, product, customers and suppliers, production processes, and factory and infrastructure. Simultaneously, the framework offers a stepwise method to look at these five dimensions from a strategic perspective in a holistic way. The empirical data from the pilot companies offer insights on how the companies take up the strategic roadmapping method into their strategic operations, as well as which topics related to Industry 4.0 get integrated into their future vision, strategies and plans. The results indicate that the use of the strategic roadmapping method supports companies in seeing diverse routes towards Industry 4.0 and provides support in prioritizing relevant projects and activities.

Despite advances in Industry 4.0 technologies, supply chains have fallen short in enabling agile supply chain responsiveness. Conceptually, the enablers of connectivity, visibility, and transparency are well defined, yet their operationalization remains a challenge. In this paper, we analyse a successful case study in the domestic electrical machinery industry and derive from it a proposal for data integration lifecycle phases and socio-technical domains to structure the challenges that need to be overcome as a prerequisite for digital supply networks’ visibility towards transparency and predictability.

Small- and medium- sized enterprises (SMEs) face exceptional challenges in implementing smart manufacturing solutions. Specifically, SMEs often struggle with understanding advanced technologies well enough to implement them and reap the benefits. In this paper, we discuss one specific instance of this problem, namely implementation of data standards for effective business-to-business communications. We propose a possible solution to aid in lowering barriers for SMEs to access and apply technologies for data standardization, a vital part of effective business-to-business communications. Our solution takes a gamified approach by working to conceptualize the SMEs’ data into a story with fill-in-the-blanks, similar to a Mad Lib™. We believe that the development and implementation of this tool would provide numerous benefits including, but not limited to, boosting morale, making new technology and standards more approachable, and improving the learning experience.

The digitalization of manufacturing is the essence of Industry 4.0 realization. Many large manufacturers have developed ambitious digitalization strategies, and most have taken the first steps towards digital transformation. Unfortunately, the same cannot be said for small and medium-sized enterprises (SMEs). At the same time, SMEs contribute on average with more than 50% of the value to the economy in the European Union and with almost 100 million employees, represent approximately 70% of the European workforce. This makes the onset of Industry 4.0 and the accompanying digitalization of manufacturing a fundamental challenge for most SMEs, many of which already struggle to remain competitive in a rapidly evolving business climate. As such, in this paper, we aim to present an SME-friendly approach to Industry 4.0 implementation. We share practical insights from three SME case studies that enable us to propose the lean first … then digitalize approach to Industry 4.0 implementation in SMEs.

Digital Transformation (DT) represents a real challenge for companies worldwide, not only because of its complexity due to technology’s fast evolution, but also because of the lack of appropriate guidance. Available approaches are judged generic as they do not take into account the specific context of companies. In this sense, this work explores the influence of context in DT success and introduces a performance indicator to measure the impact of the company features that represent its specific context on the dimensions involved in a DT. As the second phase in a research project aimed to build a quantitative model that explains this relationship, this paper focuses on the application of the Impact Level (IL) factor in a real case scenario. The goal is to validate a previous theoretical analysis and also to identify changes in the results with a different characterization of company features. Relevant findings confirm the critical importance of Culture (f3) and R&D investment (f9) for DT success, but many differences arise from the comparative analysis that reveals the DT process as highly contextual. Future work will be focused on translating the insights of both studies into a quantitative model that presents the IL as an aggregator but also with the possibility to provide enough detail for better decision-making during the DT process.

During the last decades, Information Sharing has gained a global attention among academic researchers. It has been widely demonstrated that such strategy improves the supply chain management and mitigates the bullwhip effect. In this research, the information distortion and its impact on a Fast Fashion Supply Chain has been modeled. The aim of this paper is to analyze how false data can affect the system performance. A simulation model has been developed in order to reproduce the behavior of the players. Then, different scenarios with different levels of digitalization and distortion have been tested. Results show that both upstream and downstream distortions have a disruptive impact on the system and a strong ripple effect. Moreover, negative effects result to be not linear, and small distortions already show great disruptive effects.

As the maturity of digital integrated supply chains grows, the amount of operations which are tracked or which are directly controlled by a computerised system has seen a rapid increase with the emergence of the Industry 4.0 paradigm. This is notably desired in high-cost countries where having an overview on the supply chain is crucial in ensuring the delivery dependability for the customer. However, the manual tasks are overlooked to a certain extent. Most digitalisation initiatives have the worker interact manually with the manufacturing execution systems (MES) using terminals placed around the shop floor. Two scenarios in which the worker has to interact with a MES are given in this paper and a digital solution is proposed to solve the implied shortcomings concerning the interface between production planning and shop-floor production. A solution comes under the form of an open-source, freely available smartwatch app designed to be used by the workers for fast and easy interaction with the MES and enterprise resource planning system while at the same time serving as a task deployment method. The solution proposal is aligned with extant initiatives of obtaining end-to-end supply chain digitalisation while enabling the worker’s fast responsiveness upon task deployment.

Cash flow bullwhip effect (CFB) is the amplification of working capital variance along a supply chain. High CFB is a sign of inefficient working capital (WC) management and can lead to a significant reduction in financial resilience. CFB can be used as a measure of a company’s ability to manage operational risks and corresponding resilience. We investigate the existence of CFB and the traditional bullwhip effect (BWE) in a sample of 238 semiconductor companies over 2010-Q1 to 2020-Q4. These companies' average CFB and BWE are 3.95 and 2.77, respectively. We find that CFB and BWE of a semiconductor company are negatively associated with company size, degree of seasonality in demand, and company’s payment policy conservativeness; and positively associated with procurement and payment lead times.

The key objective of the research is to report the results of testing of a two stage stochastic linear programming (SLP) model with recourse using a multi scenario, multi period, menu driven user friendly DSS in a North American steel company. The SLP model and the DSS is generic which can be applied to any process industry. It is capable of configuring multiple materials, multiple facilities, multiple activities and multiple storage areas. The DSS is developed using 4th Dimension programming language, and the SLP model was solved using the IBM CPLEX solver. The value of the SLP solution derived from the experimentation of the DSS with a real-world instance of one steel mill is 1.61%, which is equivalent to a potential benefit of US$ 24.61 million.A set of experiments were designed based on the potential joint probability scenarios, and the demand distributions expected skewness. The research reports a few interesting patterns emerged from optimization results when the volatility in demand of finished steel rises and the distribution of the demand skewness changes from left to right tail. The academic contribution of this research is two folds. Firstly, the depicting potential contribution to profit in a steel company using a SLP based DSS under probabilistic demand scenarios. Secondly, the optimization experiments confirm that the value of SLP solution increases with the increase in demand uncertainty. The research has applied implications that the practicing managers would be encouraged to look for more optimization based business solutions, and the prescriptive analytics discipline will fetch more scholarly and industry attention.

This research is based on the recent endeavors of the manufacturers to achieve improvements in supply chain resiliency by implementing Industry 4.0 standards. The large enterprises, in today’s scenario, are pursuing their supplier organizations to incorporate information and communication technology (ICT) based advancements to their production systems. But successful implementation of such technologies in the resource deficient supplier organizations depends on several factors that need due consideration of the decision makers. In this paper, we have identified 14 such critical success factors from extant literature for implementation of technologies conforming to Industry 4.0. Further, through an empirical investigation comprising of 222 micro, small and medium enterprises (MSMEs) from the automotive manufacturing sector in India, we have made use of exploratory factor analysis and structural equation modelling to identify the hidden constructs and examine the influencing relationships between them.

Efficiency and resilience of the clinical trials supply chain are of particular prevalence in the current global context. The unique characteristic of the reverse logistics flow in the supply chains for clinical trials is the foundation for the digital transformation framework presented in this paper. This paper proposes a novel framework that integrates internet of things (IoT) and blockchain technology for the reverse logistics supply chain for clinical trials. The framework is implemented in a Contract Research Organisation operating clinical trials in Europe and North Africa and results are discussed. The main contribution of the proposed novel framework is the integration and interaction of both IoT and blockchain in a reverse logistics process.

Transportation and logistics management are critical to the economic growth of a country. Smart transportation is becoming possible thanks to the advancement of digital technology implementation. To decrease the vulnerability of digitized systems from cyber-attacks, Blockchain has recently become one of the most commonly adopted technologies for free, decentralized, and trustworthy intelligent transportation networks. By analyzing the potential application of Blockchain technologies in the smart transportation of cargo by air, this study hopes to contribute to the area of logistics management. For efficient and safe air cargo movement, we propose a distributed blockchain layered framework that helps in transforming air logistics operations in national and international trades and we validated our framework by mathematically comparing the cost with and without the implementation of Blockchain.

Events such as Covid-19 have revealed the vulnerabilities that companies face due to low visibility. Consequently, companies experience impact on their supply chains in terms of disruptions of material supply, deliveries, productivity and revenue. Thus, the importance of Supply Chain Visibility (SCV) in global and competitive markets with increasing sustainability demands has received widespread recognition. Yet, the literature provides limited understanding of requirements to consider when developing a SCV system. Addressing the gap, this study presents the findings from a case study during the first months of 2021 at a global manufacturing company developing a SCV system to improve their inbound flow. Using a system engineering perspective, this study presents requirements highlighted during early stage for a SCV system. The results indicate the importance of ensuring SCV system requirements to enable data collection, handling and usage for decision making leading to both supply chain sustainability and resilience. This study contributes to the understanding of SCV by presenting and categorizing requirements considered for real-time case at a manufacturing company when developing a SCV system.

For single-liner ship service, this study optimizes containerized cargo revenue minus ship operating costs. The decisions to optimize the ship fleet and shipment plan are included in this proposed model. This optimization problem is formulated as a mixed-integer non-linear programming model and solved it using LINGO. The proposed model is applied to a liner service route provided in the computational study and the results are analyzed in case of different scenarios of container shipment demand and different freight rates.

In the recent decade, state-of-the-art techniques of maintenance in manufacturing firms have evolved. Redefining itself to come up with a whole new perspective by including a regime of digitization. From inter-compatibility to intra-network communication between hardware to highly interactive user interfaces have made the managing of necessary procedures extremely transparent. Even complex inclusions are easy to monitor following the current trends and digital transformation. Data generated through sources is big and unmanageable with a lack of filtering technologies to identify useful processable content. The proposed framework helps notify end-users by monitoring and identifying certain user-based settings and business functions. Suggested findings used machine learning (ML) algorithms surpass any previous claimed results. The modeling approach ensures consistent and reliable performance. Inclusive integration of notifying tools into trending smart devices has been tested and validated in this study. The coupling of multidiscipline open-source web-based technologies with minimum expense has been in focus for designing such applications. The best-identified set of tools that help enable the management of workflow multitasks, and their semantic arrangement through the latest state-of-the-art and scientific tools for generic work environments is covered in this study.

Digital transformation and servitization have been merging in a coalescing paradigm called Digital Servitization, changing not only companies’ business model but also their portfolio and thus their business. This hybrid paradigm is increasingly overwhelming manufacturing companies, compelling them to change their business model and provide more complex solutions to survive. Indeed, first a business model shift is needed (bringing to cope with organizational/managerial aspects), and then a suitable new technology stack has to be implemented. In the extant literature it is not clear how companies can define which are the improvements to implement on Smart Connected PSSs. These modifications on the physical products, if flanked by a concurrent definition and structuring of data requirement on the database on the cloud, would also lead to a better comprehension of the solution functioning, enabling to know which are the causes leading to breakdowns and performance and quality losses during the use phase. To address this, the paper proposes a method combining the Failure Mode and Effect Analysis with the Overall Equipment Effectiveness framework.

Digital technologies are disrupting servitization in manufacturing firms. In the last decade, manufacturing firms transform their business models from traditional offers of physical goods to digital solutions for their customers. In this paper, we investigate the transformation of digital servitization in manufacturing firms. We challenge relations between traditional and digital service portfolio offered by applying linear regression on the data obtained from 690 manufacturing firms from the Republic of Serbia from 2015 to 2020. The results show that firms significantly increase the offer of traditional services from 2015 to 2018. Moreover, results demonstrate a rapid growth of digital services in the period from 2018 to 2020. The application of traditional and digital services in manufacturing firms increased by 30% in five years.

Enterprise Resource Planning (ERP) systems offer firms a wealth of readily available transactional data. However, deriving insights from such data often demands the examination of multiple issues simultaneously. In this paper we use simple data mining to analyze ERP data from 27 service shops over a period of 35 months. The data has been used to provide valuable business performance insights to the service shop managers. Though the granular ERP data needed to be supplemented by further data in some instances, we found it has the potential to provide real insights into a firm's performance. Such simple data mining approaches can be standardized and automated across service centers for insights that can be used to drive continuous improvement activities within and across sites. We also suggest that this initial, exploratory study opens exciting avenues for further research into business analytics and, business intelligence pipelines.

Since data becomes more and more important in industrial context, the question arises on how data-driven added value can be measured consistently and comprehensively by manufacturing companies. Currently, attempts on data valuation are primarily taking place on internal company level and qualitative scale. This leads to inconclusive results and unused opportunities in data monetization. Existing approaches in theory to determine quantitative data value are seldom used and less sophisticated. Although quantitative valuation frameworks could enable entities to transfer data valuation from an internal to an external level to take account of progress in digital transformation into external reporting. This paper contributes to data value assessment by presenting a four-part valuation framework that specifies how to transfer internal, qualitative to external, quantitative data valuation. The proposed framework builds on insights derived from practice-oriented action research. The framework is finally tested with a machine tool manufacturer using a single case study approach. Placing value on data will contribute to management’s capability to manage data as well as to realize data-driven benefits and revenue.

Companies are more and more seeking for external partners in order to manage new solutions at their development and use phases, especially when the type of these solutions is Product-Service Systems (PSS). PSS have some organizational particularities which increase the complexity of collaboration processes. In this context, collaborating efficiently with the different partners is a key aspect to reduce the risk of failure of PSS projects, and is influenced by various organizational factors and practices. This paper proposes a methodology in four steps to build a decision-aid framework supporting collaboration assessment and management in the presented context. Important factors and performance indicators are identified based on literature review and industrial practices. Then, Fuzzy techniques as well as decision trees are used to build the assessment systems. Three case studies are conducted to explore industrial practices and to confront the different elements of the proposed framework, and finally to validate the assessment framework.

The COVID-19 Pandemic has caused an economic breakdown, especially in the manufacturing industry. Manufacturing companies have used Digital service to stay in contact with their customers and as a source of revenue even during the general lockdown An exploratory focus group has been carried out to understand the problems and opportunities manufacturing companies went and are going through and to suggest open research questions that both research and industry should explore further. To deeply analyses the content of the discussion, Latent Dirichlet Allocation (LDA) was used to identify the main research topics. 4 topics were identified as the most relevant to be investigated: digitalization and collaboration emerged as the most interesting trends that will characterize the new normal. For each of the topics, insights, critical points and research questions are presented and discussed, reporting the main evidence of the focus group discussion.

Maintenance service delivery constitutes one of the most problematic tasks for companies offering such service. Besides dealing with customers expecting to be served as soon as possible, companies must consider the penalties they are incurring if the service is delivered later than the deadline, especially if the service suppliers want to establish long and lasting relationships with customers. Despite being advisable to use appropriate tools to schedule such activity, in many companies, planners rely only on simple tools (e.g., Excel sheets) to schedule maintenance interventions. Frequently, this results in a suboptimal allocation of the interventions, which causes customer satisfaction problems. This paper, contextualised in the Balance Systems case study, proposes an optimisation model that can be used by planners to perform the intervention allocation. The optimisation model has been developed in the context of the Dual-perspective, Data-based, Decision-making process for Maintenance service delivery (D3M) framework, which aims to improve the maintenance service delivery by making a proper use of real-time and historical data related to the asset status and the service resources available. The proposed model tries to cope with the current problems present in the company’s service delivery process by proposing the introduction of a mathematical instrument in support of the planner. Being strongly influenced by the contextual setting, the model discussed in this paper originates from the D3M framework logic and is adapted to the company necessities.

Reduction of resource consumption is essential to the Sustainable Development Goals. One key strategy for achieving this is product life-extension. Products can be maintained or remanufactured to a condition that is better than new. A fragmented literature uses terms such as remanufacturing, maintenance, repair, and overhaul (MRO), or simply service, to refer to various forms of product life-extension. Even though different terms are used, these operations share common characteristics and challenges that are distinctive compared to traditional manufacturing. Addressing the ambiguous use of terms for product life-extending operations and the fragmented body of knowledge, we introduce Aftermarket Production Systems (AmPS) as an umbrella term for industrialized product life-extending operations. The purpose of this paper is to provide an overview of AmPS-related literature and to discuss future directions for research on industrialized product life-extending operations.

Landscaping services industry is estimated to be about $100 billion in the US. These services tend to be labor-intensive and are varied in scales ranging from single-family homes to large hospitality and leisure enterprises such as resorts and golf courses. From a management perspective the three main objectives of landscaping services are maintaining aesthetics, pest control, and lowering cost. Some of the major activities in landscaping include mowing lawns, pruning shrubs, clearing leaves, trimming hedges, and mulching. Operating cost depends on staffing level, frequency of activities, and associated fuel consumption, which have been investigated in several studies. The focus of this paper is to make landscaping services smarter by using decision-support models for managing them. Specifically, this paper proposes a two-stage optimization model for lawn mowing. The first-stage model assigns appropriate pieces of equipment and staff to various areas to minimize both operating costs and labor costs. The second-stage model optimizes the schedule of activities based on the desired due times for various areas. A numerical study is used for demonstrating the application of the decision-support model. Future direction for smart landscaping through better decision-making based on data from IoT sensors for monitoring growth, soil conditions, and weather data is also proposed.

This paper uses an integrative literature review to explore the contracting of advanced services based on digitally-enabled product-service systems (PSS). The need for this study was derived from studies that have highlighted the difficulty of selling and buying advanced services with a digital element. Other studies confirmed that firms had challenges with obtaining the value expected from such advanced service contracts. The integration of digital into a PSS value proposition increases the complexity and the potential application of advanced services. The emergence of digitally-enabled PSS (based on “Smart Products” in many cases) suggests a need to understand the sales and contracting process better. An integrative literature review was chosen, as the literature was fragmented between different fields. Forty-eight papers were selected as relevant, and 14 were then considered key to creating an initial model for the contracting process. It was identified that there were limited examples of contracts with high degrees of value-co-creation and that the ability of manufacturing firms to translate a value proposition successfully into a binding contract was weak. The contract negotiation process was found to be well defined, yet the governance of such contacts over their duration was again weak. For these reasons, a model based on the lifecycle was proposed. The model should be further integrated into the contracting process for services. This is an initial study, and it is recommended that further research should test the model.

The objective of this study was to investigate the methodological theoretical framework of the Lean approach in overcrowded emergency services with the implementation of Fast Track. With the integrative review method, it was defined the guiding question: Which Lean principle is related to the application of Fast Track in overcrowded emergency services? The following database were used: Web of Science, PubMed, and Scopus in which 268 records were collected and 14 articles were selected. It was verified that Fast Track is a fast flow that favors the application of the principle of Continuous Flow, essential in emergency services with overcrowding, as it aims to reduce the average length of stay of patients with lower acuity. It was identified in the methodological theoretical framework the demand for more in-depth studies; and that Fast Track is an approach to reducing overcrowding in emergency services associated with the Continuous Flow principle. It was concluded that Fast Track proved to be an effective management strategy in reducing the average length of stay and waiting time of patients, which resulted in improvements in the flow of care and increased patient satisfaction, consequently providing improved performance in the emergency services.

The objective is to identify the management of information, communication and knowledge in Emergency Care Units (ECU). The method was a lexical and semantic analysis using VOSViewer, categorization of the results for a qualitative and cross analysis of the approaches on the subject. With the results of the practical application of the Lean Project in the ECU in 50 units it was possible to establish criteria and a parameter for knowledge management in this health area. The results categorize the publications in four central themes in healthcare: leadership and governance; quality in health; technology and information; Lean. The information flow is presented as a means, not an end activity and this is confirmed when the articles limit themselves to highlight the means of communication, applications or devices used for the dissemination of information, without further deepening of steps such as data collection, validation and valuation of information in health environments. The human factor is the main agent of change, because it needs clarity that the use of information and communication technologies do not make its final activity impossible, but it is a facilitating, agile and effective tool in the conduct of their work activities.

The management of risks and failures in health systems is considered one of the most efficient solutions for providing care to patients. In practice, this activity also makes it possible to join new corporate opportunities, because it allows preventive measures resulting from social, scientific, and technological developments to be constantly evolving throughout the organization. The aim of the article is to present an application of the failure modes and their health effects analysis model (HFMEA) integrated with Fuzzy logic in three Emergency Care Units (UPAs) in Brazil. This mapping model analyzes and analyzes how failures occurred in the supply chain of UPAs. The methodology used included dividing the study into 4 stages, which were used to prepare the proposed model through the evaluation of failure modes integrated with Fuzzy logic. This work reports as one of the roots of the problems in the studied UPAs: the predominance of processing and creativity waste. These, associated with the factor Management and Leadership for a Lean environment, demonstrate that failures in leadership are linked to the dissatisfaction of management support professionals. In addition, a hierarchical analysis of the failure probabilities and evaluation of the eight Lean wastes found in the UPAs was presented.

Six Sigma comprises DMAIC which is a methodology that has attracted the attention of researchers in the field of Healthcare. This study aims to understand the motivators to apply DMAIC in process in the context of Six Sigma and Lean Six Sigma to be implemented in processes focused on direct patient care. The article covers literature on DMAIC from 1997 to October 2020. For such a purpose, a systematic literature review has been carried out based on empirical studies in Hospitals and Emergency units. A total of 21 articles were reviewed, and the topic Motivators were addressed in this article with the aim of performing DMAIC through Six Sigma/Lean Six Sigma. Based on the analyzed articles, the most frequent motivator to implement Six Sigma/Lean Six Sigma DMAIC in organizations lies in reducing process time (such as length of stay) and improving the quality of service. It was also noticed that DMAIC has been used in several health services, such as patient discharge and surgeries (femur surgeries, for instance. An application of DMAIC in 50 emergency care units (UPAs) corroborated the main motivators indicated in the literature.

The fast-track is an important strategy for the management and control of overcrowding, which is increasingly used in hospital emergency services. This review article aims to identify the main approaches and criteria for analyzing and deciding on the implementation of the fast-track strategy in the Emergency Department. The bibliographic review considered articles published in the Scopus and MedLine databases during the period 2010 and 2020. In general, the bibliographic research showed: the relative scarcity of scientific communication, regarding the implementation of fast-track strategies in hospital management systems; the diversity of the implantation purposes; the preference for modelling and simulation techniques; the difficulty of ex-ante and ex-post evaluation from a social perspective. The evaluation of the effectiveness of the implementation of fast-track strategies, when operational criteria are used, is based on quantitative models and techniques. In Brazil, the efficient management of Emergency Care Units, UPAs 24-h, is essential because the UPAs are a fundamental part of the Brazilian health system.

In this paper an adaptation of the Adaptive Large Neighborhood Search (ALNS) to a patient’s care planning problem is proposed. We formalize it as an RCPSP problem that consists of assigning a start date and medical resources to a set of medical appointments. Different intensification and diversification movements for the ALNS are presented. We test this approach on real-life problems and compare the results of ALNS to a version without the adaptive layer, called ( $$\lnot $$ ¬ A)LNS. We also compare our results with the ones obtained with a 0–1 linear programming model. On small instances, ALNS obtains results close to optimality, with an average difference of 1.39 of solution quality. ALNS outperforms ( $$\lnot $$ ¬ A)LNS with a gain of up to 18.34% for some scenarios.

The objective of this paper is to identify ways of managing capacity in healthcare settings and apply them to Emergency Care Units. The methodology used is based on three pillars: 1. lexical and content analysis of existing scientific articles in the Scopus database; 2. quantify and qualify the existence of publications by identifying the correlations between authors using VOSviewer; 3. proposed application of capacity management in 50 Emergency Care Units. The results showed that with Capacity Management it is possible to generate activities that add more value to the infrastructure and control service delivery, eliminating queues and bottlenecks in the healthcare environment, thus contributing to effective service through continuous flow in the urgency and emergency of 24 h ECUs.

The objective of this study was to evaluate the degree of organizational maturity in three Emergency Care Units (a.k.a. UPA in Brazilian acronym) in a city in the interior of the state of São Paulo - Brazil, with the following fundamental criteria observed: the utility, feasibility, and specialized technical knowledge of the health unit contextualized in this study. The method used was the Health Company Diagnostic Method (MDE-S), which is an approach to study and qualify the level and organizational maturity, that has the fundamental characteristic of providing a cycle of continuous improvement in the company. Among the most relevant results, the study demonstrated, in comparison with a study conducted in Basic Health Units in the interior of the state of Rio de Janeiro, an existent lack of knowledge in health management tools and basic administration fundamentals of the evaluated units. Finally, the study recommends that a strategic system be created for the management and deployment of goals and guidelines, based on Hoshin Kanri. It is considered that, despite the challenges that will certainly occur, this small-scale and well-developed diagnostic system will certainly be useful and may become an important step towards a continuous practice of efficient management in the investigated health unit.

The objective of this article was to map the state of the art, by means of bibliometric research, of how the scientific evolution of Kaizen has been occurring in theory, in practice and, especially, in the health area around the world, in order to guide future research in the same, as, for example, kaizen has been contributing to humanization within health centers. A broad structured search was performed using keywords and Boolean logic, in the PubMed, ISI and Scopus data bases. The identified publications were cataloged in EndNote and NVIVO 10 software for bibliometric analysis. Through these, 1,467 publications were identified, in the three databases, excluding the repeated publications and those without relevant information. The main key terms identified and in common in all publications were: “management”, “lean”, “quality” and “kaizen”; and the authors who publish the most were Van Aken, E.M., Farris, J. A., Suárez-Barraza, M.F. and Glover, W. J; and when kaizen applied to healthcare was evaluated, the most cited article among the 1,467 selected was the “Lean in healthcare: The unfilled promise?”, by Radnor, ZJ, Holweg, M. and Waring, J., which has 316 citations in Scopus. It was evidenced that kaizen applied to health still has much potential to expand.

This research aims the identification of the theoretical benefits of Toyota Kata implementation. This analyze was done through a theoretical, combined, descriptive and bibliographic research, carried out through a literature review. To clarify these benefits, improvement kata and coaching kata was described according to the literature review. As this is a literature review of a subject of general implementation, the macro results of this research suggest as benefits, the creation of knowledge and organizational culture having as secondary results the efficiency improving and waste reduction. The results obtained will culminate in operational results such as the increase in the quality of services, growth in production volume, besides the tasks optimization and success in solving problems, which are typical characteristics of Toyota Kata, for involving people and routines, promoting a better engagement. This research showed also, when applied in its essence, Toyota Kata is an effective continuous improvement mechanism within organizations.

SimUSanté is one of the biggest European simulating and training centers, proposing training sessions for all involed in healthcare: professionals, students, patients. This paper presents the timetabling problem encountered by SimUSanté with regard to the quality objectives and the time and resource constraints. To solve it, SimUACO-LS which is the hybridization of the Min-Max Ants Colony Optimization algorithm SimUACO with the variable neighborhood search SimULS [3], is presented. SimULS, SimUACO and SimUACO-LS are compared in a set of representative instances [2], newly generated and derived from those of the Curriculum-Based Course Timetabling problem [1]. SimUACO-LS always improves both results of SimULS and SimUACO by respectively 3.84% and 2.97%.

The indicators are used to quantify how the results of the public emergency departments (ED) can be classified, with regard to the efficiency and quality of the services provided to its users. In this sense, the objective of this work is to identify typical patterns of the curves of the indicator number of monthly attendance of the procedure “Reception with Risk Classification” in a sample of 50 EDs. For this, a quantitative and exploratory research was carried out that adopted the Machine Learning technique known as Cluster Analysis not supervised by the AGNES “AGlomerative NESting” method. 10 profiles (groups) of curves for the Reception with Risk Classification were identified in the selected EDs. Group 1 characterizes the standard profile of these EDs (76% of the total) and the other groups characterize the atypical patterns. The results were obtained using the free software R.

The aim of this study was to analyze the contributions of the Lean Healthcare approach through the use of methods and tools to reduce Healthcare-Associated Infections (HAI). The guiding question of the study was to investigate how can the Lean Healthcare approach contribute to the implementation of actions to mitigate the risk of HealthCare-Associated Infections? Articles were collected from 2010 to February 2021, in the Web of Science, Scopus, Emerald databases. An analysis form was created. Of the 21 articles collected, 09 were selected, according to the PRISMA®. As for Lean Healthcare methods and tools, the use of standardized work, CTQ and SIPOC was identified as the most used to mitigate the risk of HAI. The high LOS of patients in the hospital increases the risk of HAI, with Lean Six Sigma results in the reduction of hospital costs and lower rates of LOS, with the reduction of the number of complications and infections. Standardized work has reduced the rates of HAI, which reinforces the idea of the need to implement the Lean Healthcare approach, with a focus on respect for people and consequently on the valorization of teamwork, which leads to change in the organizational culture.

The objective of this study is to analyze the adherence of the Karakuri (low-cost automation) technology for eliminating Lean wastes in healthcare units. To carry out this study reports of continuous improvement practices were analyzed from three Units located in the State of São Paulo (Brazil). These practices of Lean Healthcare approach implementation were applied during the period from June to October 2020. The present literature on the term Karakuri was also analyzed in four data research bases and it was made a study around correlate cases. In conclusion, it is noted that Karakuri seems capable of eliminating the main wastes reported in the analyzed health units in question, which are: defects, motion, waiting, and not using human talent.

Home healthcare routing and scheduling problems provide a better condition for elderly and disabled people. In the presence of the worldwide pandemic of coronavirus disease 2019 (COVID-19 pandemic), these problems have shown their efficiency and necessity to notice this new group of patients suffering from COVID-19. For this purpose, a new mathematical model is developed for a home healthcare routing and scheduling problem (HHCRSP) regarding the impact of the COVID-19 outbreak. The satisfaction and cost of assigning staff to patients with suspected or confirmed COVID-19 are considered. This problem considers travel time between patients and aims to minimize it. The proposed model is solved using GAMS optimization software. Computational experiments are considered for several test problems and a sensitivity analysis is conducted to validate the model performance.

Evidence from current publications mainly US, UK, Canada and recently Europe has highlighted the relevancy of Lean healthcare in improving patient pathway efficiency. The aim of this case study is to demonstrate how the French public hospital succeeded at using Lean thinking, and how, using the Lean tools with the team’s involvement, it improved the quality of health services delivered to the patient. Emergency complaints dropped by 50%, length of stay reduced by 30%. This paper highlights how Lean was implemented to revolve around the patient, creating valuable results which improved the global performance, whilst maintaining employees’ involvement and satisfaction. Two Lean experiences in hospital units are described showing the reduction of wastes and the possible monitoring of KPI to sustain Lean pillars Jidoka and JIT. These two first Lean experiences are not alone, more hospital services have been experiencing Lean approaches and more results will be communicated soon in wider publication.

Home health care (HHC) services represent a set of medical services given to patients at their homes. The patients require a set of care that must be coordinated and treated by skilled caregivers corresponding to their needs. This study proposes an HHC routing and assignment approach based on a mixed-integer linear programming model that aims to minimize total route cost. The HHC approach takes into account a set of HHC specific constraints and criteria. Secondly, we propose a new robust counterpart HHC model under uncertainty based on the well-known budgeted uncertainty set. The robust counterpart HHC model deals with travel and service times uncertainty. The computational results compare the deterministic model with its robust counterpart model. The small and medium instances have been solved using TSP benchmarks with specific data concerning HHC problems. The models have been implemented using ILOG CPLEX Optimization Studio. The computational results of small and medium instances indicated the efficiency of the proposed approach. Robustness analysis of the obtained results was conducted using a Monte Carlo simulation and indicated the price of robustness. The increase of route cost in comparison with the risk of infeasibility shows the importance of the designed robust routes for HHC routing and scheduling problems.

The objective of this paper is to propose a systematic application of Lean Healthcare in the activity of fetching the next patient in the X-ray examination process. The methodology used is based on two pillars: the first, called a survey, aims to map the process in which the focus activity is inserted and, thus, obtain the associated waste, identifying the root cause of what will be treated. Then, the proposed Lean pillar will propose a tool to remedy the waste in question, applying the necessary actions within a schedule and, finally, collecting the results and comparing them with the initial measurement. As a result, there is an improvement in the hospital processes from the Lean perspective, specifically in the activity of fetching the next patient, in the X-Ray examination, reducing the non-added value identified in this activity, that is, in the waiting time of the patient. As conclusions, it was possible to obtain concrete information about the implementation of this proposed systematic application, so that it can be replicated in other wastes, generating a systematic proposal, filling a gap in the literature, which covers the lack of fully structured studies with practical results of Lean Healthcare systematics in the medical clinic wing of hospitals.

Lean Manufacturing has been used in many types of organizations, including healthcare. In the healthcare area, lean healthcare is a management philosophy to develop a hospital culture characterized by increased patient, and other stakeholder, satisfaction through continuous improvements. The starting point for improving activities in the healthcare is identification of the problems and wastes in the processes. In this context VSM is a helpful tool. That why the purpose of this paper is to present the possibility of application the VSM for improving the selected process in the healthcare. Thanks to the application of this method, the necessary information on the sources of losses in the analyzed process was obtained.

The hospital environment influences the chain of transmission of pathogenic microorganisms linked to the incidence of infections, making cleaning and disinfection (L&D) management measures necessary in order to contribute to patient safety. This study aimed to propose the viability of using the Lean Six Sigma approach in the management and improvement of the hospital terminal hygiene process. Method: Exploratory descriptive research, through a theoretical survey of tools used in the Lean Six Sigma approach. The DMAIC method, was used as a guide for this project hypothesis of management of the process of terminal hygienization of beds of a University Hospital in the city of Rio de Janeiro. Results: In the definition and organization phase of the project, the terminal sanitization process can be mapped through the VSM diagram and by an employee interview instrument during GEMBA. L&D quality assessment tools like fluorescent markers, ATP test and microbiological cultures can serve as pre and post indicators for possible improvement interventions. Tools such as: Current Reality Tree; GUT matrix; Prioritization matrix and 5W2H plan have proven to be good choices for the analysis and improvement phases. Finally, audits, visual and participative management, indicator reports for maintenance of actions can be done. Conclusion: The application of the DMAIC method of the Lean Six Sigma approach in hospital cleaning processes proved to be objective and feasible according to the proposed method, presenting itself as an alternative basis for future projects in the area.

This paper’s aim is to propose a new methodology for organizing and identifying the assembly operations that ought to be automated in an automotive assembly line. A state of the art in the matters of automation methods is presented to situate the research work and to analyze the different methods presented in the literature. As a result, there are some lacks in terms of methods that sought to help improve the level of automation. In the last part, the different requirements of the methodology are defined, which led to a proposal for a method that respects all the requirements and that allows not only the grouping of operations, but also the analysis of the automation and the line balancing. Finally, and to verify the proposal, three activities of the automation methodology have been applied on a Stellantis assembly line. The result of the study showed that it is possible to group several screwing operations.

The reconfigurable manufacturing system (RMS) is one of the newest manufacturing paradigms. In this paradigm, machine components, machine software, or handling units can be inserted, removed, modified, or interchanged as needed and, where appropriate, imposed by the need to adapt and adjust quickly and cost-effectively to changing requirements. RMS is considered to be a convenient processing paradigm for the manufacture of varieties as well as a scalable enabler for this variety. Considered as two of the six main RMS characteristics, in this paper, we review the most used models and solving approaches dedicated to scalability and convertibility in reconfigurable manufacturing environments. Moreover, we highlight the most critical research gaps.

Today’s global manufacturing environment is characterized by intense competition in dynamic and uncertain markets. Consequently, manufacturers are required to accommodate a higher variety of products with frequent new introductions and shorter life-cycles in a rapid and cost-efficient way, to sustain competitiveness. In light of these requirements, changeable manufacturing systems appear promising. However, empirically founded research is limited in regard to how different requirements lead to different applications and resulting potentials in various industrial settings. Therefore, this paper presents a comparative study of requirements, enablers, and potentials of changeability in two industrial cases (i) a Danish manufacturer of capital goods for the energy sector, (ii) a Danish manufacturer of sporting goods for the maritime sector. The objective of the paper, is to generate insights which can support various industrial settings in the transition towards changeable manufacturing. Findings include: (i) in high-volume contexts, reconfigurability is suitable to accommodate a production mix with increasing dimensions of parts, with potential to improve equipment utilization to reduce capital expenses (ii) in global manufacturing contexts, reconfigurability is suitable to accommodate frequent changes of production location, with potential to improve demand proximity to gain a competitive advantage.

The implementation of changeable and reconfigurable manufacturing systems and realization of benefits connected to rapid, efficient, and dynamic change of functionality and capacity is key to achieve manufacturing competitiveness. Therefore, this paper proposes a systematic methodology for the design and development of changeable and reconfigurable manufacturing systems, derived from design theory, reconfigurability theory, as well as practical experience. The methodology consists of a concrete course of actions that connects design phases and working steps based on the content of the design task. Furthermore, the paper addresses project-related and contextual aspects of reconfigurability development, which indicates how the proposed methodology should be adapted to the specific company and task at hand. Thus, the proposed methodology is intended for further validation in different types of manufacturing companies that are transitioning towards reconfigurability.

Manufacturing companies need changeability in order to adapt to change drivers, such as unpredictable market demand and increasingly relevant sustainability requirements. Specific change drivers determine different changeability requirements, thus leading to the need for different changeability enablers. Therefore, before starting the identification and design of changeability enablers, companies should effectively identify their changeability requirements. In this study, an industry-applicable screening tool for the clarification of changeability requirements is proposed. The tool allows companies to discern whether they need flexibility or reconfigurability enablers. The tool has been validated with industry experts and is ready to be disseminated in industry.

The need for frequently adapting manufacturing systems to dynamic market demand, short product lifecycles, technology evolution, and sustainability requirements is increasingly challenging manufacturing companies. To this end, Reconfigurable Manufacturing Systems (RMSs) are relevant, however, still far from their wide implementation in industry. In this regard, a main barrier to the introduction of reconfigurability is the justification of investments in developing and purchasing reconfigurable systems and equipment. Often such reconfigurable systems have high initial investments that provide returns in the mid/long-term, while at the same time being subject to and dependent on high uncertainty in product and market evolutions. Thus, uncertainty in different aspects of demand and the time-horizon of evaluations are main aspects in evaluating the financial benefits of reconfigurability. Therefore, this paper investigates how different choices of financial evaluation parameters affect the financial feasibility of reconfigurable manufacturing compared to more traditional manufacturing concepts. The findings of the paper provide valuable insights on how practitioners should proceed in adequately capturing the value of reconfigurability during investment decisions.

Remanufacturing is a practice that postpones the product ‘end-of-life,’ returning the properties or features of a new product to a used product. This type of process represents an efficient circular economy strategy to extend product life, reducing its footprint. However, remanufacturing systems must overcome different challenges related to uncertainty and its impact on efficiency. In this sense, this study exposes a hybrid remanufacturing-manufacturing architecture with embedded features from cellular and reconfigurable manufacturing systems. The architecture was synthesized in a Mixed Integer Non-Linear optimization model, which defines the remanufacturing system configuring the cells and product families, balancing the workloads, establishing the scheduling sequence and quantifying the reconfigurability cost. The result is a self-adaptative system that maintains a continuous production rate by managing its capacity.

In this paper, we present a simple assembly line balancing problem for two different sets of workers: trainer workers who are more experienced and likely older, and inexperienced workers who are usually younger and require more time to perform some tasks. Therefore, the main characteristic of this problem is that trainer workers are involved in helping and supporting inexperienced ones in executing some tasks which are more complicated to be carried out. Moreover, task times vary according to the stations where they can be performed due to different sets of equipment we can find in each of them. The problem is modelled as a linear program and solved optimally by applying it to a real-case application. The developed model can be successfully applied in order to help companies to manage a high level of turnover.

In today’s manufacturing and supply chain environments, many companies face challenge in responding to customers’ requirements quickly and providing customized products quickly at low cost. Mass customization can help companies in providing customized products and services quickly and at a low price. Integrated decision-making has been found effective in many situations. This paper reviews the scheduling research of flexible manufacturing systems (FMSs). The FMS scheduling problem is part of the FMS production and operation management problem. Because the production management of FMSs is very difficult, the FMS scheduling problem is very complicated. Many researchers have investigated the FMS scheduling problem. The paper summarizes the FMS scheduling research with recent development. In addition, a framework of FMS scheduling integration for mass customization is developed based on the literature survey. A control flow of FMS part processing is designed as part of the framework. Further development of the FMS scheduling integration is suggested.

Due to increasing product variety and uncertain demand for highly individualized products, a rising need for flexibility of manufacturing systems can be observed. In this context, the concept of matrix-structured manufacturing systems (MMS) has attracted increasing consideration. MMS aim to achieve high operational flexibility by implementing a flexible product flow between stations with automated guided vehicles and by providing redundant resources for each operation, thus eliminating constant cycle times and the serial arrangement of stations. This paper investigates the design of MMS pursuing an economic objective. We formulate a mixed-integer program for the design of MMS. Introducing a numerical example, we illustrate the effectiveness of our approach and derive future research opportunities.

This paper deals with aggregate planning of Reconfigurable Assembly Lines (RAL). The assembly line considered in this paper consists of hexagonal cells. These have multiple slots where processing modules can be inserted to perform certain operations. In addition, each cell has a single central slot where a central module can be inserted for inter-cellular and intra-cellular transportation of parts. Multiple products with different assembly sequences must be handled over multiple planning periods. An Integer Quadratic Programming (IQP) model is proposed to solve the following problems simultaneously: (i) assigning processing modules and a central module to the cells; (ii) installation of the cells and conveyors between the cells; and (iii) routing products, ensuring that availability of the resources is not exceeded. The assembly line should be reconfigured over time to adapt to possible product functionality and demand changes at minimum reconfiguration, operational and material handling costs while ensuring the demand is met within each period. The IQP model is implemented and solved for an illustrative problem and its extensions using Gurobi.

The reconfigurable manufacturing system (RMS) has been acknowledged as an effective manufacturing paradigm to tackle high volatility in demand types and amounts. However, the reconfiguration needs an amount of time and leads to some level of resource wastage. Accordingly, a high frequency in the system’s reconfiguration may have a negative impact on its performance. In this regard, this paper investigates the advantage of using cloud manufacturing (CMfg) resources in enhancing the performance of an RMS system. A novel mathematical model is developed for the integrated workforce allocation and production scheduling problem utilizing the CMfg under a non-permutation flow shop setting. This model simultaneously makes decisions on the utilization of the CMfg capacity for performing some jobs, and for the remaining jobs, determination of machines’ configurations for each job, scheduling of the jobs on the machines, and allocation of operators to machines as well. This model aims to minimize the sum of job processing costs, overtime costs, and the cost of utilizing the CMfg resources. Finally, a computational experiment is conducted, which shows a promising improvement in the total cost of the production system by utilizing the CMfg capacity.

The reconfigurable manufacturing systems aim to efficiently respond to demand changes. One of the key characteristics of these systems is the scalability, i.e. the ability to modify the volume of the throughput in order to fit to the demand variability. The design of the RMS has a high impact on its scalability. In the literature, there are only few indicators to evaluate the scalability of a system and most of them are a posteriori measures. In this article, we propose a new measure to assess the scalability since the design phase of the RMS. We present experimental results on state-of-the-art instances to validate our approach. They show that the proposed measure evaluates accurately the scalability.

Due to the rapid development of new generation information technologies (such as IoT, Big Data analytics, Cyber-Physical Systems, cloud computing and artificial intelligence), Digital twins have become intensively used in smart manufacturing. Despite the fact that their use in industry has attracted the attention of many practitioners and researchers, there is still a need for an integrated and detailed Digital Twin framework for Reconfigurable Manufacturing Systems. To investigate related works, this manuscript reviews the existing Reconfigurable Manufacturing Systems Digital Twin frameworks. It also presents a classification of several studies based on the Digital Twin framework features and properties, the used decision-making tools and techniques as well as on the manufacturing system characteristics. The paper ends with a discussion and future challenges to put forward a structured and an integrated Reconfigurable Manufacturing Systems - Digital Twin framework.

In this paper, we propose a new mathematical model for the combinatorial optimization problem of batch machining at multi-positional machines with turrets where the parts are sequentially machined on m working positions. Sequential activation is realized by the use of turrets. Constraints related to the design of machining of turrets and working positions, as well as precedence constraints related to operations are given. The objective of the optimization is to minimize the total cost. The paper provides the problem definition, all aspects of the mathematical modelling and the model has been validated by presenting the case of an industrial example.

Brazil and the UK have been strategic partners throughout the years. While the former has traded minerals and food products – mainly agricultural commodities – the latter has been critical to infrastructure development in the South American country. However, the Crisis of COVID-19 Pandemic altered the scenario of international food production and distribution. This article aims to analyse Brazilian food exports to the UK in 2019 and 2020 to identify the impact of COVID-19 on the trade flows. To do so, we collected data from the Brazilian Ministry of Economy regarding the exports between the two nations and performed an exploratory investigation using graphical and quantitative analysis. The results suggest that the Pandemic crisis rose Brazilian exports of cereal and grains to the UK by around 50%, and the shortage of these items in the internal market has increased consumer prices by more than 60% during 2020.

The study objective was to evaluate the performance of SAGAC in optimizing a linear mathematical model in whole variables to determine the most cost-effective solution in transporting cattle for slaughter. The model determines the choice of refrigerator truck, road (route), and an open-truck in a scripting process. The tests performed with the SAGAC algorithm for optimizing the proposed model were compared with the results obtained, under similar conditions, by the branch-and-bound method for solving entire problems and solving a problem optimally. After the first twenty-two experimental trials, for comparison between the two methods, nine more experimental trials were carried out, with an increase in the degree of complexity, only with the SAGAC algorithm. The results obtained in the first twenty-two experimental trials demonstrate an equivalent performance between the two methods, showing that the SAGAC algorithm, even though it is not a technique that guarantees optimal results, in this case, was also able to find them. The nine final experiments performed only by SAGAC showed satisfactory results, with an evolutionary curve of exponential behavior.

We address a problem from a meat company, in which orders are produced in two stages, consisting of preparing meats on benches and allocating them to conveyors to be packed in disposable trays. In an environment where machines are unrelated, the company has to take daily decisions on the number and start time of working periods, the number of workers and their allocation to machines, and the scheduling of activities to satisfy the required orders. The objective of the problem is to minimize, in a lexicographic way, the number of unscheduled activities, the weighted tardiness, and the total production cost. To solve the problem, we propose a multi-start random constructive heuristic, which tests different combinations of number of workers in the machines and for each combination produces many different schedules of the orders. The results of our computational experiments over realistic instances show that the heuristic is effective and can support the company on its daily decisions.

The COVID-19 pandemic is changing consumer behavior and accelerating the interest for online grocery purchases. Hence, traditional brick-and-mortar retailers are developing omnichannel solutions enabling online purchases in parallel to normal activities. Buy-Online-Pick-up-in-Store concepts are flourishing in this context, and they are the topic of this work.In this paper we propose a novel application of the sequential ordering problem to model products picking throughout the store shelves. The result is an optimized picking sequence that however takes also into account the characteristics of the goods (fragility, weight, etc.). The aim is to preserve goods integrity while allowing the pickers to optimize their route through the shop. The approach is exemplified on historical online orders of a real German shop.

In this paper, the development of a Digital Twin of a beverages pasteurization system for temperature monitoring, using NI Lab-VIEW control system toolkit, is described. A cyber-physical production system, composed of a real-time simulation tool and a controller, has been set up and tested on a pilot plant set up in a university laboratory. The paper shows how the software platform, together with the hardware, has been implemented in a traditional system, not (yet) ready for Industry 4.0 technologies, and therefore underlines the main issues occurred during its development. The Digital Twin includes a CompactRIO controller and a set of probes, connected with the controller. The aim of the Digital Twin is to monitor the machine status, with a particular attention to the temperature reached by the service water and the required steam flow. To demonstrate the effectiveness of this system, a set of experimental tests has also been carried out.

The role of institutional frameworks in curbing and preventing food wastage cannot be overstated. Recently, policy makers have increased their interest in helping supply chain actors to reduce food waste by initiating policies at national and local level, but also opened questions on how such initiatives are affecting supply chain actors’ practices towards sustainable food supply chains. Based on this concern, this study set out to investigate the role institutional frameworks have to play in tackling the problem of food wastage at the retailer-supplier interface within the European Union (EU). To this end, the study mainly focuses on the issue of food wastage in France by taking a look at the impact of the French food waste law on the retail-supplier activities and relationship.This qualitative study underlines that the French food waste law has led to initiatives that are promoting circular economy and closed loop food supply chain, though the policies are being limited by behavioural aspects of actors along the food supply chains. Therefore, this research calls for a better understanding of behaviour of actors and collaboration along the supply chain through the use of information technology to have a full positive impact of the policy interventions towards sustainable production and distribution of food.

Trade is essential for countries development. In Brazil, coffee has been one of the most important export items and by large is commercialized as a green or roasted bean. The aim of this article is to establish a prediction mode for coffee exports using time series and trend lines. To do so, we collected the exportation volume from the two main export countries in each segment: Brazil and Germany. A five-year forecasting was produced using regression curves provided by Microsoft Excel. Our results indicated that polynomial fits best and this function is consistent with agricultural production that is conditioned to edaphoclimatic factors.

In the present work, we consider a real-world scheduling problem arising in the color printing industry. The problem consists in assigning print jobs to a heterogeneous set of flexographic printer machines, as well as in finding a processing sequence for the sets of jobs assigned to each printer. The aim is to minimize a weighted sum of total weighted tardiness and total setup times. The machines are characterized by a limited sequence of color groups and can equip additional components (e.g., embossing rollers and perforating rolls) to process jobs that require specific treatments. The process to equip a machine with an additional component or to clean a color group takes a long time, with the effect of significantly raising the setup costs. Furthermore, the time required to clean a color group between two different jobs depends directly on the involved colors. To tackle the problem, we propose a constructive heuristic followed by some local search procedures that are used one after the other in an iterative way. Extensive tests on real-world instances prove that the proposed algorithm can obtain very good-quality solutions within a limited computing time.

Traditional Logistic Systems related to the modules of routes and vehicle tracking were observed, which use classical logic. In this study, whose main objective is a proposal for digital transformation with the implementation of the use of Paraconsistent Logic Evidential Annotated Eτ for optimization and reduction exposure to the risk of the cargo transport process in these types of systems. The methodology used para-analyzer algorithm, with selection of specialists in decision-making issues to improve the safety of the routes and minimize the risks exposed in the cargo’s path from shipment to the destination. An exploratory research was used surveys through applied logical questionnaires. The main results were compared to the Case Study of the Volkswagen Modular Consortium - Trucks and buses, from a logistical point of view, in which the three main factors resulting from this study can prove an alignment regarding the implementation of this innovation. In addition, data on incidents occurred at the Agrobusiness Company regarding the Routes and Tracking Systems were presented, proving the risk of the loads and the need for digital transformation in these types of systems.

The present study investigates the various arrays of the sectors involved in the logistic of fresh food transportation and distribution, considering the sustainability of the overall process, applying the AHP technique. The focus was on Brazil’s fresh food distribution centers and how it is distributed in large cities. The criteria applied in two levels were selected from the literature, and the judgment was made by three experts using an online AHP platform. The final computation was considered the best array with a very high (90.4%) degree of agreement between the participants. The choice of Local food represented 72.1% in the concept of high sustainability. Choosing local foods must not be feasible in large countries or in countries that depend on food imports. However, for fresh food production, the local food production benefits go beyond economic costs, as it helps reduce greenhouse gas emissions, improve the carbon footprint of consumers, encourage sustainable agriculture, and have the shortest traceability.

We seek to optimize the production planning of a three-echelon remanufacturing system under uncertain input data. We consider a multi-stage stochastic integer programming approach and use scenario trees to represent the uncertain information structure. We introduce a new dynamic programming formulation that relies on a partial nested decomposition of the scenario tree. We then propose a new extension of the recently published stochastic dual dynamic integer programming algorithm based on this partial decomposition. Our numerical results show that the proposed solution approach is able to provide near-optimal solutions for large-size instances with a reasonable computational effort.

The transition towards Circular Economy is a crucial issue of European environmental policies. It requires a complete overhaul of production systems, in order to improve product lifecycles and to reduce the ecological footprint. In this context, maintenance is key to extend the products durability. This study addresses maintenance planning optimization within the Circular Economy framework.An original lot-sizing model for tactical maintenance planning on a single-machine with multiple components is presented. The main features of this model are the consideration of the component health index and the global budget on environmental impact. The computational limits of the model and the impact of the budget constraint are assessed through experimentations.

We consider a two-stage supply chain in which two production plants are collaborating in an industrial symbiosis to satisfy their respective stochastic demands. We formulate the production planning problems of these two plants as an integrated capacitated lot-sizing problem, in which the second production plant uses as an alternative raw material a by-product obtained as a residue from the production of the first plant. The goal is to minimize the overall total cost in the supply chain, including production and inventory of the final product and by-product transfer costs, while meeting the stochastic demands. First, a natural formulation of the problem is proposed, and is solved using the Sample Average Approximation (SAA) method. The analysis of the gaps exhibits however quite large optimality gaps. To improve these optimality gaps, a plant location like reformulation for this integrated lot-sizing problem is developed. The analysis has been carried out again to evaluate both formulations’ performances in terms of the optimality gaps and computational times, both when items demands follow Gamma and Normal distributions. The analysis indicates that despite having a computational time of on average 1.7 times higher than the main formulation, the plant location reformulation provides better optimality gaps on average 22% improved and better ranges for upper and lower bounds under stochastic demands.

This paper focuses on an industrial lot-sizing and scheduling problem that arises in the food industry and includes lost sales, overtimes and sequence-dependent setups on parallel machines. We propose a preliminary version of a three-phase iterative approach to optimize separately the affectation, the sequencing and the production of items. Our first numerical results suggest that with some additional improvements, this approach could be use in real-life by planners to reduce their costs.

In a manufacturing context, the lot-sizing problems (LSP) determine the quantity to produce over a planning horizon. Often, the parameters used in the LSP models are unknown when the decisions are made, and this uncertainty has a critical impact on the quality of the decisions. However, the large amount of data that can nowadays be collected from the shop floor allows inferring information on the LSP parameters and their variability. Therefore, a recent research trend is to properly account for the uncertainty in the LSP optimization models. This work presents a survey on data-driven optimization approaches for the LSPs. We also provide a comparison of some promising optimization methodologies in the context of data-driven modeling of LSPs.