Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action

Continuous industrial processes will play a key role for the sustainable transition worldwide. Different flows of matter and energy must be recovered through these systems and integrated in a Circular Economy fashion. To foster in such a virtuous trend the involvement of companies, mostly SMEs (often lacking critical assets, funds, technologies or knowledge), the continuous processes should be packaged, servitized and marketed as plants-as-a-service. Model-based design (MBD) tools can provide test before invest and decision support in the feasibility and procurement phases, as well as optimization and self-diagnosing during operation, in a cyber-physical system (CPS) setting. To ease their provision, a cloud-based collaboration platform, enabling providers to deploy tools in a sandbox, has been developed by the HUBCAP project. The purpose of this paper is to introduce the web application tool built for the evaporation process simulation, validated against real-world performance data for the reference evaporation plant, and deployed to the HUBCAP platform. To structure it, data collection, filtration, processing, and reporting have been performed on the full-scale pilot plant (the EVAPOSIM experiment), a triple-effect evaporator operating in counterflow and vacuum condition. To explore the sustainability of their plant, companies can use this MBD tool through the sandbox of the HUBCAP platform under a servitized (use- or result-oriented) business model (software as a service).

The digital twin technology offers a tight coupling between the simulated process and the actual world events. In the field of tool wear monitoring (TWM), such system characteristic is essential to maintain the accuracy of the wear prediction by taking into account the actual condition data during the machining process. This paper presents the implementation of the digital twin technology where the micro-milling machining process is simulated by using the spindle controller, spindle motor, and cutting torque model. The wear monitoring was performed by comparing the spindle motor’s simulated and real-time electric current. A virtual environment was developed using FreeCAD - an open-source CAD system, to represent the processes and objects involved in the machining. The digital twin framework ensures that the simulation and real-time data were synchronized in the virtual environment. This paper focuses on the building blocks and technical implementation in realizing a digital twin application in the domain of micro-tool wear monitoring.

The design of a circular PSS solution goes beyond the traditional perspective of circular product design encompassing multiple complexity dimensions that need to be considered and addressed in the early stages of design. The paper provides a literature-based outlook on the levels of complexity to be faced when making decisions in early PSS design by positioning the concept of changeability inherited from the systems engineering literature into the context of the design of circular PSS. The paper ultimately stresses the need to consider the changeability of PSS as a relevant dimension in the assessment of its circularity potential. It does so by proposing a framework for the design of circular PSS solutions, summarizing the main design strategies and approaches currently described in the literature to mitigate the uncertainties generated by PSS complexity.

In the transition towards a more sustainable future, circular economy is a key concept. Manufacturers play a key role in this transition, and different circular manufacturing strategies rely on digital technologies and information systems to be fulfilled. These systems and technologies need master data, basic data a firm’s business activities are based on, to work. However, manufacturing companies still have little knowledge on which master data are needed, and how these data can support decisions in circular manufacturing strategies. In this paper, we bridge different circular manufacturing strategies with important master data elements in a framework for master data management. The basis for the framework is scientific literature within the domains of circular manufacturing strategies and information systems. The framework can be used by researchers to explore master data requirements for different strategies, and by practitioners to get an overview of data requirements for different circular manufacturing strategies.

The Industry 4.0 has boosted technological advancements leading to the development of predictive maintenance solutions in the manufacturing sector. In this scenario, companies are dealing with complex decision-making problems involving investments in technological solutions and data analytics modelling implementation. Therefore, there is a need for strategic guidance for defining the best investments options through a technical-economic approach based on system modelling and lifecycle perspective. This paper presents the implementation within a relevant Italian food company of a methodology developed to evaluate predictive maintenance implementation scenarios based on alternative condition monitoring solutions, under the lenses of Total Cost of Ownership. Technical systemic performances are evaluated through Monte Carlo simulation based on the Reliability Block Diagram (RBD) model of the system. The results provide concrete evidence of effective applicability of the methodology guiding decision-makers toward a solution for improving technical system performances and reducing lifecycle costs.

The present study aims to analyze the impact of Precision Agriculture (PA) on food production in Brazil. We applied the multicriteria group decision using the Analytical Hierarchy Process (AHP) to weigh several selected criteria. The group consensus was very high (91.9%). The most critical criteria in level 1was the machinery input (48.9%), followed by software (44.4%) and human resources (6.7%). In level 2, within the machinery criterion, the sub-criteria soil was the most critical (31.9%), followed by pest control (28.5%). In level 2, within the software criterion, the sub-criteria input management was the most critical one (48.4%), followed by product management (42.3%). The four most significant global priorities are the sub-criteria soil (31.9%), Input management (21.5%), Product management (18.8%), and Pest control (13.9%). Results indicated that the use of PA in grain crops has a high impact on food production (71.1%).

To ensure the farmer follows up on the new consumer demand for pork traceability and welfare during production, we developed a concept of applying the good practices during on-farm pig production. The productive meat sector has undergone technological transformations in recent years by searching for products with better quality, social responsibility, and sustainability. The worldwide good-practice norms are already known, and further information is already published by breeders, technical manuals, and scientific papers. New advancements in information and communication tools (ITC) can allow farmers to access real-time farm data and check their compliance with the established good-practices norms. The present study aims to conceptualize a digital solution suitable for evaluating the compliance of the pig farming management conditions. A mobile application will be made based on the standards of good practices as a digital transformation. The producer will be able to continuously check in real-time through the mobile application, assuring that his herd complies with the worldwide standards during production.

Currently, DPO (Data Protection Officer) professionals are working in sectors of the economy in the adaptation of the LGPD (Brazilian General Data Protection Law) in Brazil with the use of best management practices. Companies with consolidated corporate governance admit the incorporation of the LGPD (Brazil’s General Data Protection Law) into their strategy, with highly trained professionals to consolidate leadership and monitor results. The commitment to enhance the structures that serve logistics companies is preferably used only by highly trained professionals, including internationally. In this sense, it was sought to list points that the sector should develop throughout the adaptation and especially in a continuous way to help achieve the objective within the institution. Data provided and collected in 2022 by the ANPPD (National Association of Data Privacy Professionals) from the almost 4 thousand (associates) trained professionals working in the Brazilian market show us that service sectors (50%) are using professionals trained in the LGPD, as well as in commerce (17%), in the industry (15%), in other segments (14%), and even showed in particular growth in agribusiness (4%) of professionals both in awareness and adaptation of the LGPD. And as strengths that must be followed in both awareness and adequacy, are the continuous improvement in acculturation processes, creation of orientation guides, training, until its implementation by outsourcing (DPO-as-a-Service), and professional responsibilities trained in LGPD and recognized by the CBO (Brazilian Classification of Occupations).

The world’s population is estimated to exceed 9 billion people by 2050. A rapid and safe increase in food production associated with the use of agro-industrial technologies is necessary. The aim of this article is to investigate the use of technologies in the coffee sector. Recognizing the need for a concise and accessible source of literature to disseminate the findings about the coffee sector regarding new technologies. It collects abstracts of articles from 2017 up to 2022 systematically raising important issues to be studied. The coffee sector brings proposals on the use of different technologies and how to use them. A gap is evident for studies. The quality of the grains is emphasized, but no studies which corroborate to the competitiveness of the segment are presented. The results suggest investments in the agro-industrial sector to promote competitiveness and offer products with higher quality and competitive prices.

Projections by the Food and Agriculture Organization of the United Nations (FAO) of a significant increase in protein production by 2050 to feed an estimated 9 billion people raise concerns about healthiness and sustainability, as this growth is linked to the challenges non-increase in land use, decrease in energy and water consumption and reduction of CO2 emissions. This scenario makes the food and food ingredients industry look for innovative ways of producing proteins. The production of edible insects is part of this quest. The objective of this article is to analyze the evolution of the scientific field of the role of insects in human food in a sustainable way, considering the state-of-the-art study (reference to the current state of knowledge about a particular topic being study) as a tool to analyze the value chain of edible insects. The bibliometric results show the evolution of publications referring to the term “edible insects”, which were qualitatively categorized into three main topics: 15 articles about insects as food and feed, 34 articles about food science, and 1 article about veterinary humanities and social sciences. This article may be useful for researchers interested in the topic, especially those who wish to respond to the challenges imposed to meet the global demand for sustainable protein.

The use of digital technologies has spread to areas other than industry and has given rise to new areas such as smart cities, smart health, and education 4.0. The milk production chain is also benefiting from these digital innovations, but due to their characteristics, in a gradual and focused way. The objective of this research is to verify the use of digital technologies through digital transformation to improve the dairy production chain in the last 14 years. The methodology consisted of a longitudinal survey between 2008 and 2022. In 2008, we analyzed the digital technologies used in 3 large Brazilian dairy farms. In 2022, we look at the digital technologies that the XPTO company offers to dairy farms. We were able to verify that the use of digital technologies through digital transformation improved the production of the dairy chain. We can highlight that the main advances were in the automation of production with intelligent sensors such as IOT and digital platforms for online data processing with the use of AI, the evolution of computer network technologies and farm management systems.

Governments are facing the challenge of feeding students in schools in a city environment in a process that revolves around the coordination of multiple producers, distributors, logistics operators and traders of perishable foods. This paper aims to analyse food collection and distribution to assess the potential of urban food systems regarding school feeding. To do so, we compared school feeding distribution systems in Brazil and France to identify the main issues and investigate the role of local food systems. Our results showed that all cities are concerned about the involvement of short food supply chains to provide vegetables and fruits for schools to promote small local farming but there are cultural characteristics that require the use of different approaches.

School meals made in partnership with family farmers tend to foster local economies, driving productive diversification and increasing the income of family farmers. Through the Brazilian School Meals Program, purchases of family farming products are encouraged, under special conditions of at least 30% of the resources transferred by the Brazilian Fund for Education Development. This incentive allows better marketing alternatives and valorization of the local farmer. The municipality has a significant return on its economy when these resources are added to those spent by the municipality itself. Numerous benefits would be generated for family farmers, shop- keepers, industrialists, and local service providers, as well as the return of revenue in the form of consumption taxes. This paper describes financial and social gains in the presence of municipal policies that induce the food acquisition process.

The one-third rule, which sets tight wholesale and retail limits for food products, is arguably a primary cause of huge food waste in Japan. However, the effect of relaxing the limits was found to vary depending on some conditions. Thus, to further understand how these limits affect food wastage, we take a game-theoretical analysis approach. In this approach, we formulate a normal-form game played by a manufacturer and a retailer in a milk supply chain, develop a simulation model of the chain, and use the simulator to obtain sample values of the players’ payoffs earned under every pair of their strategies. We then apply a statistical multiple comparison test to the data to identify the statistical best responses of a player to each opponent strategy, derive statistical Nash equilibriums, and compare the equilibriums obtained under different limits and consumers’ preferences. Consequently, it is confirmed that relaxing the limits may undesirably impact food waste.

The agri-food sector accounts for 26% of the EU energy consumption, where 28% of this energy belongs to natural resource processing. As multiple products are produced on the same line and numerous process steps are interrelated, the energy use in this industry is complex in general. Moreover, bottom-up data analysis of how much energy processes are used, and furthermore, why and where opportunities for improvements exist, are less prevalent. This paper proposes a methodology for estimating the energy consumption of the agri-food processing. The collection of real data from several industrial enterprises in Germany as part of research programs enabled an accurate assessment of energy consumption and savings.

With the use of technology in the livestock sector, it has been allowed an increase in animal production and reduction of waste with more precision. The use of these technological resources in the sector gave rise to Precision Cattle Raising, which makes use of information and communication technology to extract the best from animal production with more precision. Studies show the use of te-lemetry to measure the state of animal welfare. However, during transportation from the farm to the slaughterhouse there is no specific way to check animal wel-fare, this can affect meat quality and the livestock economic sector and animal production. This study aims to identify the variables that can be read through sen-sors or biomarkers for monitoring animal welfare.

We seek to enrich the literature by investigating the digitalization journey of a high-tech, manufacturing small and medium-sized enterprise (SME) to shed light on the topic. We undertook an interpretive longitudinal study between 2009 and 2020, capturing the transformation journey of an award-winning high-tech SME that is designing and manufacturing high-end home entertainment systems including digital streaming products, music players, and speakers. This study offers important contributions to theory and practice. We conceptualize and define the link between technological obsolescence and the digital transformation process. We offer a conceptual framework to explain the interplay of the adaptive capabilities namely empirical sensitivities and habitus in the context of digital transformation in SMEs. In addition, our study has important implications for practice. SME managers should pay attention to developing non-cognitive dynamic capabilities to effectively respond to digitalization trends by orienting their employees toward careful management of technology obsolescence in a manner unique to the firm’s history and experiences.

Numerous approaches for assisting Smart Factory implementations in production companies have been published over the last few years. However, guidelines for calculating the profitability of these efforts have barely been addressed by scientific approaches so far. This paper aims to close this research gap using the Smart Factory application Condition Monitoring as an example. Therefore, a framework of the expense structure, as well as a framework of accompanying effects on business processes and resources, are presented. Based on these frameworks a procedure to support the financial assessment prior to the actual implementation is proposed. This procedure enables decision-makers to follow a deductive approach when identifying the economic relevant factors of smart factory applications. The authors argue that the shift towards a descriptive character of effect assessment simplifies and precises the profitability calculation. The construct validity of the frameworks and the usability of the proposed approach are confirmed in two case studies in separate production plants of ZF Friedrichshafen AG.

Industry 4.0 is significantly transforming the traditional way of managing supply chains. However, Industry 4.0 tools can be expensive and not affordable and can be implemented in a variety of ways. Therefore, the benefits of implementing these tools should be clarified before investing in digitizing the Procurement process. The objective of the work is to present the dimensions (Competencies, Management, Partnerships, Processes, Systems/Technologies, and Sustainability) and the tools of Ind4.0 motivating the trends of evolution in the procurement area in the face of these changes in technologies and digital transformation. Despite the importance of this issue, few studies have attempted to address the effects of Ind4.0, technologies, and intelligent systems in procurement. To fill this gap, in the applications of Ind4.0 tools a conceptual model was developed to classify different value propositions provided by the different applications of Ind4.0 tools in the internal and external processes of the area. Finally, the results conclude that the six dimensions proposed in the conceptual model can provide a better understanding of the Procurement area, demonstrating the trends of the implementation of Ind4.0 tools related to different activities, presented by the literature authors.

In today’s highly dynamic and volatile market, the need for change increases steadily in companies induced by megatrends like globalisation or digitalisation. To keep up with current developments and to meet ever higher customer demands ensuring their satisfaction many companies initiate a digital transformation process. Digital Transformation (DT) processes might change products, processes or entire business models in an organisation, to ensure staying competitive. For realising constant adaptation and change, companies are forced to initiate projects in their factories on a regular basis. Multi-project management (MPM) models are used to plan and control projects efficiently. In this article, digital transformation projects are described, an approach for multi-project planning and control (MPPC) in the factory is presented and a way of categorising projects is shown. By combining knowledge from those segments, a fast lane for MPPC is introduced that enables companies to standardise certain tasks in project management to focus on disruptive digital transformation projects.

In this paper, we introduce a software-based heuristic approach for scoring the grade of digitalization for business processes modeled in BPMN: The DigiTrack tool. Our tool enables the measurement of the digitalization maturity of a business process by calculating scores for each task. This new method is shown though a case study with a simplified check in process at airport.

Due to the new remote working conditions driven by the consequences of the Covid-19 pandemic, we extend our previous work on sentiment-enabled business process modeling by including crowdsourcing capabilities with a web interface: SentiProMoWeb. These capabilities enable us to perform sentiment-driven business process re-design method with remote stakeholders from different locations. SentiProMoWeb implements an enterprise social information system to capture the feedback from stakeholders in a crowdsourced manner. We demonstrate the crowdsourcing capabilities of our approach with an illustrative scenario by using our SentiProMoWeb software.

Industry 4.0 has led to the emergence of various digital technologies. To keep pace with the digital transformation, manufacturing companies must adapt to the new conditions imposed by the business market and increasingly strive to implement so-called smart factories. This paper points out the most common digital technologies related to the management of manufacturing systems. In addition to identification, a systematic literature review was conducted to investigate their impact on increasing productivity. Results indicate that a significant increase in the number of research began in 2015 and is experiencing its greatest expansion in 2018. Important methodologies observed during the literature analysis are a systematic review of the literature for the largest number of theoretically oriented papers, and case studies, practical models for the minority that make up the practice-oriented papers. Also, 68 percent of papers focus more on the theoretical than on the practical implications, and 72 papers indicate a direct link between productivity and digital technologies used in the digital transformation and implementation of smart factories.

A major portion of industrial maintenance data is in unstructured form, which makes its organization, search, and reuse very challenging. For this reason, the knowledge embedded in historical maintenance data is seldom analyzed or reused for purposes such as root cause analysis, failure prevention, and maintenance diagnostics. If the valuable knowledge patterns nested in maintenance data are identified, liberated, and formalized, they can significantly improve the intelligence of maintenance management systems by providing actionable insights. The objective of this research is to help advance the progression from data to information and knowledge through data-driven creation of a public and open-source knowledge graphs built from the textual data available in maintenance workorders. A SKOS-based thesaurus is used to support automated entity extraction from the text. A formal OWL-based ontology provides the semantic schema of the knowledge graph. A software tool (KnoWo) is developed to streamline the text-to-graph translation process. It was observed that the proposed text-to-graph tool chain improves knowledge discovery by analyzing maintenance logs.

In recent years, digital transformation has gained increasing interest from researchers and practitioners, putting efforts towards developing a better understanding, resulting in emerging independent research areas. Consequently, the research under the digital transformation, even though becoming a hotspot, remains very fragmented. This study has tried to provide a quick overview of the scientific output in this field, recognizing prolific authors, most productive countries and most relevant and influential journals in the DT field by using bibliometric analysis on 946 publications published during the timespan 2001:2022, retrieved from Thompson Reuters Web of Science. Also, this study presented the thematic analysis and provided insights to researchers and scholars in the field of DT regarding the current research landscape and prospects. The resulting knowledge will help scholars and researchers detect research opportunities and gaps for future works and contribute to continuing research in this area.

Material flow simulation is a powerful tool to realize efficient operation in complicated production systems such as high-mix and low-volume production. However, it takes great efforts and expertise to construct accurate simulation models. On the other hand, in recent years, IoT and machine learning techniques that collect and utilize field data are advancing rapidly. In this research, we propose a data-driven and multi-scale modeling approach which constructs accurate simulation models semi-automatically. The proposed approach aims to optimize the configuration of simulation model by combining deductive models such as queue model and inductive model such as machine learning model to maximize accuracy. In this article, we introduce the concept of the proposed method and experimental results on a simple production system.

With recent liberalization and enlarging of trade among companies, it is necessary to generate an optimal supply chain planning by cooperation and coordination of supply chain planning for multiple companies without sharing sensitive information such as costs and profit among competitive companies. A distributed optimization can solve the optimization problems with limited information. A distributed optimization method using subgradient and consensus control methods has been proposed to solve continuous optimization problems. However, conventional distributed optimization methods using subgradient and consensus control methods cannot be applied to the supply chain planning for multiple companies including 0–1 decision variables. In this paper, we propose a new distributed optimization method for solving the supply chain planning problem for multiple companies by subgradient method and consensus control. By branching the cases 0–1 variables, an optimal solution can be obtained by the enumeration. A method to reduce the computational effort has been developed in the proposed method. From numerical experiments, it is confirmed that we can obtain an optimal solution by the reduction of the computation.

The robust aircraft maintenance routing problem (RAMRP) is adopted by airlines to determine aircraft routes with better withstanding for possible disruptions. This can be achieved using a common approach called the buffer time insertion approach (BT). From the literature, it was observed that this approach has a pitfall of reducing the fleet productivity while inserting long buffer times. Besides, it cannot accommodate flight delays while inserting short buffer times. These disadvantages were the motivation to conduct this study to propose a RAMRP solution that incorporates a novel robustness approach, called turn-around-time reduction (TR), in which all the previous drawbacks are avoided. An ant colony-based algorithm (AC) was developed to solve the proposed RAMRP. To demonstrate the viability and effectiveness of the proposed approach, experiments are conducted based on real data obtained from a major airline company located in the Middle East. The results show that the proposed TR outperforms the existing BT in terms of fleet productivity and delay accommodation.

Supply Chain & logistics as a subject lends itself readily to game-based learning. SCM subject learning is primarily about decision making, logistics and strategic management of resources. Most of the serious games designed for SCM are used in a workshop setting, and much of the learning outcome is achieved through the debriefing part of the workshop, i.e., not as an integrated part of the game. However, many such serious games expose their internal mechanics too easily. This side effect coupled with high development costs and limited and often constrained assessment schemas are reasons for low uptake. Another aspect is that games age and thus can often not be used for a long period. The usage of commercial off the shelf games might be a solution, but it requires that the game can be modded to fit the intended learning outcomes in the course it should be used. This article reports on the work carried out to identify if such a game, not specifically designed for the specific curriculum of SCM, can be used or not.

This paper describes and analyses the application of Demings PDCA circle in combination with action learning methods to support the commercialization of digitally-enabled products and services. It does this through a single use case where one of the authors was embedded. The paper considers the challenges for a traditional firm selling solutions based on digital technology and how this is then converted into a value proposition. PDCA provided a change management framework that supported the action learning that was taking place by providing iterations with refection phases. This allowed the firm to proactively identify the barriers that it had to overcome and then to understand how it overcame these barriers. In doing so, it built new knowledge within the firm that could be standardized. This is an initial study, and additional studies should be made of alternative cases to allow for a deeper comparison.

Smart service ecosystems (SSEs) struggle a lot with interoperability. Interoperability consists of many types but two are of interest in this paper: - (1) syntactic and semantic interoperability, and (2) organizational interoperability. While both have received a fair amount of attention in the literature, there’s little discussion on the alignment between (1) and (2), which we argue, is a key enabler of dynamic service integration in SSEs.This paper explores the significance of (mis)alignment between (1) and (2), and the barriers to organizational interoperability in smart service ecosystems. The empirical data for the paper comes from an ongoing innovation project in Norway that aims to develop a smart, secure, and cost-effective home access solution for senior care homes in the municipality of Lillehammer. The empirical findings emphasize the significance of organizational contexts, and demonstrate in part, the theoretical limitations of the socio-technical systems approach when applied to the SSE perspective.

This paper focuses on the proposal of a framework for a servitization model applied on an asset centered environment – including production machines as the physical assets – and populated by micro-services as the means to deliver the asset-related services. The asset centered servitization is, for many production machines manufacturers, a core value proposition. As a matter of fact, servitization represents a business-model change, with companies moving from selling goods to selling an integrated combination of goods and services. Competitive advantage is an outcome of this shift. This research proposes an evolution from the traditional, monolithic approach of servitization, often materialized in the concept of “platform/catalogue of services” to choose from, towards a modern view of “environment of micro-services” in which the physical assets are immersed. In the proposed framework, micro-services are summoned and function depending on the operating conditions and the actual usage, so to achieve a flexible and dynamic environment by design.

Manufacturing companies (MFRs) are increasingly extending their portfolios with services and data-driven services (DDS) to differentiate themselves from competitors, tap new revenue potential, and gain competitive advantages through digitization and the subsequently generated data. Nonetheless, DDS fail more often than traditional industrial services and products within the first year on the market. Particularly, companies are failing to sell DDS successfully and efficiently with their existing (multi-level) distribution structures. Surprisingly, there is a lack of scientific research addressing this issue. Since there are currently no holistic models for an end-to-end description of distribution-tasks for DDS in the manufacturing industry, this paper contributes to a task-oriented reference model for mapping interactions in the multi-level distribution management. Therefore, a case study research approach is used, to identify and describe the interactions in the multi-level distribution management of DDS, as well as to develop a regulatory framework for MFRs and their multi-level distribution management. This research uses the established theoretical framework of Service-Dominant-Logic to address the co-creation in multi-level distribution management of DDS. As a result, this paper identifies different interaction variants as well as the need for a new management function with 4 main and 14 basic tasks.

More and more manufacturing companies are starting to transform the transaction-based business model into a customer value-based subscription business to monetize the potential of digitization in times of saturated markets. However, historically evolved, linear acquisition processes, focusing the transaction-oriented product sales, prevent this development substantially. Elemental features of the subscription business such as recurring payments, short-term release cycles, data-driven learning, and a focus on customer success are not considered in this approach. Since existing transactional-driven acquisition approaches are not successfully applicable to the subscription business, a systematic approach to an acquisition cycle of the subscription business in the manufacturing industry is presented, aiming at a long-term participative business. Applying a grounded theory approach, a task-oriented model for the manufacturing industry was developed. The model consisting of five main tasks and 14 basis tasks serves as best practice to support manufacturing companies in adapting or redesigning acquisition activities for their subscription business models.

A highly competitive global market and rapid technological changes have induced a transformation in the manufacturing industry. In order to stay competitive, companies are intensifying the collection of life cycle data from their products in order to add customized digital services. The resulting digitally-enabled Product-Service Systems (PSS) can boost differentiation, but concrete business opportunities and their implementation often remain vague. An example is the data-driven assessment of machine components health status. While such information could be used to generate services like predictive maintenance or remanufacturing, the necessary data and algorithms to predict the remaining useful life and ways to convey the value to the customer are often unclear. This paper illustrates the engineering of a predictive maintenance service base on operational machine data. Furthermore, possible PSS offerings and the related business models are analysed. The results are tested in a use case from the manufacturing industry and finally implications for digitally-enabled PSS are discussed.

Ramp-up often requires the implementation of a new production system and some adaptations for the entire supply chain. It is also a major issue for service companies in both secondary and tertiary sectors. Although some of these companies are not confronted with the management of a new production system, they also have to face many difficulties during ramp-up. These difficulties are amplified by the growing uncertainties on the markets and the volatility of the customer demand. The current paper sheds light on common and diverging aspects of product and service ramp-ups as well as on solution approaches for service ramp-up management. As such, current research provides foundation for future research dealing specifically with service ramp-up.

In the contention of the current industrial landscape, an increasing number of manufacturing firms are experimenting with the transition from product-centric offerings to service-based value concepts and product-service bundles as high-value integrated customer solutions to increase their revenues and build sustainable competitive advantages; a phenomenon known as the “servitization” of manufacturing. Nowadays, consistently with the Industry 4.0 paradigm, these companies have therefore started a process of integrating their traditional value offerings with digital services. This recent strategy is known as “Digital Servitization” and consists of developing new services and/or improving existing ones through digital technologies. However, this transformation is challenging, and companies often struggle to achieve their expectations. Thus, this study aims to shed light on the current state of Digital Servitization strategies in the manufacturing sector based on a survey addressed to the top and middle management. The results obtained by the analysis of the data collected from the survey show an increasing trend towards the adoption of digital technologies for enabling innovation and differentiation in service delivery processes.

Delivery date setting (DDS) is a challenging and competitively critical tactical decision in engineer-to-order (ETO) environments, which requires integrated planning for effective decision-making. Despite the variety of industrial contexts in DDS literature, the maritime equipment industry has been an unexplored context vis-à-vis planning needs for effective DDS. This study uses a sales and operations planning (S&OP) framework to investigate the current state of the DDS process of two maritime equipment suppliers. Findings indicate that the low market demand over the last few years has influenced the DDS process design in the companies, suggesting that the process should be reconfigured to remain effective under periods of high demand. More cases from the maritime equipment industry are needed to assess if the findings are valid across the industry.

The characteristics of ETO production call for further analysis to investigate the implications of traditional (deterministic) systems of planning i.e., centralized, and hierarchal, compared with decentralized systems. Accordingly, this study delineates the potential implications of centralized and decentralized planning approaches in the context of ETO. Hence, the contradictory pressure for either decentralized or centralized approaches promote one-sided solutions accentuating the crucial significance of a theoretical discussion. Our analysis suggests that implementing decentralized systems should engender flexibility, transparency and responsive, which in turn can strengthen the impact of production planning on project delivery. In contrast, implementing centralized systems is likely to stifle the impact of production planning due to the rigidity, sequential interdependence, and the top-down nature of this approach. As such, our study provides opportunities for extending extant theory on centralized and decentralized production planning within ETO contexts, while providing a tentative framework for ETO practitioners that can be applicable when decisions concerning an (re)evaluation of production planning systems are to be made.

The concepts of Industry 4.0 trigger the transformation of manufacturing firms. Digital technologies upgrade traditional products and services to increase the satisfaction of customers. In this paper, the authors investigate digital products and services in manufacturing firms. Additionally, the authors challenge relations between digital products and services and their share in the gross annual turnover of manufacturing firms. The data for this research are obtained through the Digital Servitization Survey coordinated by the IFIP WG5.7 Special Interest group on Service Systems Design, Engineering, and Management. We used the Serbian dataset from 136 manufacturing firms. The results show that 68% and 42% of manufacturing firms use digital technologies for product creation and digital services, respectively. Moreover, results demonstrate products have the 90% of the share in gross annual turnover in manufacturing firms. However, the prediction of the production managers for the next two years shows that services will reach a 30% share in gross annual turnover of firms.

At the heart of industry 4.0, industrials are developing integrated offers of “Smart Product-Service Systems”. Many industrial firms are moving toward product-service systems (PSS) due to their capacity to involve value systems, business models, boundary spanning, dynamic capabilities, and other factors leading to reduced environmental impacts. In this paper, we introduce an easy-to-implement method to evaluate Smart PSSs applied to residential heating systems. Our approach is based on existing environmental assessment methods (focus on Life Cycle Assessment) and accounts for resource consumption, toxic and greenhouse gas emissions as well as waste generation. On the other hand, it also considers different features of the circular economy including the upgradability of Smart PSS offerings and end-of-life heating systems.

Manufacturing companies operate in global environments where competition is increasingly aggressive. To remain competitive, they need to differentiate themselves by updating and expanding their offerings to customers, for instance through the digitalization and servitization phenomena, which allow companies to innovate business models in this direction. This paper deals with an analysis of the subscription business model, which has recently attracted the attention of manufacturing companies for the possibility to establish long-term partnerships with customers by providing services on a continuous basis in return of recurring payments. After a first analysis of the literature on this topic, the effective implementation of the subscription model in the manufacturing environment is analyzed through a case study. The analysis shows that the development of subscription models is strengthened by the utilization of digital tools since they enable processing customers’ data for new service offering generation, leading companies to differentiate their business towards customer-centric solutions. In conjunction, the case study shows how barriers to the implementation of subscription models in the manufacturing sector are still present. Despite this, the Covid-19 pandemic has highlighted the potential of this offer, allowing companies to stay in touch with their customers, and to maintain, or even increase, the revenue streams.

Digitalization, sustainability, and servitization are transforming economy and society globally. Companies are increasingly changing their business model toward providing a data-driven Product Service System (PSS), namely bundles of products and services integrated with some digital technology. Different methods and tools have been proposed to design PSS and, more recently, smart PSS, but they still mainly focus on value propositions and do not address which kind of data can be collected from the operational stage. To overcome this gap, this paper proposes the Data-driven Service Engineering Methodology (D-SEEM) for the design and engineering of data-driven PSS, considering the tradeoff between customer satisfaction and internal efficiency and focusing on data and information. A case study in the professional appliances industry is then proposed to show the application of a part of the methodology in a real context.

Global sustainability challenges are increasingly constraining and driving industrial development. Eco-efficiency and circular economy are powerful concepts providing guiding principles to achieve superior environmental performance. However, they are not systematically integrated into the design, planning, development, management and improvement of industrial systems, potentially resulting in increased environmental impacts and other unintended consequences. This paper presents a thematic research framework based on workshops with manufacturers and researchers in the field of production engineering and management. The framework aims to establish a stronger foundation to advance research and technological development for eco-efficient and circular industrial systems, embracing environmental sustainability as core operating principles.

The purpose of this paper is to increase the knowledge about the implementation of circularity and other sustainability approaches in value chains. The objective is to develop roadmaps for the implementation of digital Circular manufacturing (CMA) and Social-life cycle (S-LCA) assessments in Textile and Clothing (TC) value chains. Implementing these digital assessments in TC value chains can increase their transparency, by validating that product manufacturing safeguards worker wellbeing and the environment. TC is one of the sectors with most critical social and environmental impacts. The roadmaps were developed through a Design Science methodology, combining: i) case studies to understand the practical problem, ii) literature study on CMA and S-LCA to develop the roadmaps, and iii) action research to iteratively apply the roadmaps to the cases and refine them with participants in an EU project, representing the entire TC value chain. The EU project is developing digital sustainability assessments with Blockchain functionality for increased data trustworthiness. This study aims to contribute to theory, practice, and public policies by providing a validated overview of the status, barriers, goals, and systematic activities for the implementation of CMA and S-LCA in TC value chains and for increased sustainability.

As organizations are moving towards a circular economy to enable a transition to more sustainable business practices, there is a need for knowledge on how companies can leverage the capabilities of the entire organization to reach this goal.In this paper, we present some preliminary but promising results from a single company that has adapted the use of Hoshin Kanri—a strategic management method often associated with lean which seeks to engage the whole organization in breakthrough improvements in Safety, Quality, Delivery, and Cost. The case company has over the last year experimented with including Sustainability (the term the company uses internally) targets in their Hoshin, to develop circular capabilities within the organization. We present a literature study on Circular Economy, Sustainability, Kaizen and Hoshin Kanri, which formed the basis for Action Learning Research interventions. We then compare the results from these interventions with the findings from the review. Finally, we discuss the implications of the results and point to further research.

Recently, the increasing lack of raw materials is forcing the manufacturing sector in revising the internal operating and strategic activities to embrace Circular Economy (CE) principles thus, moving towards Circular Manufacturing (CM). CE principles are pursued during product design, product realisation, as well as product end-of-life. As an enabler of end-of-life CM strategies, disassembling represents the cornerstone to facilitate other ones to take place, as remanufacturing and recycling. Indeed, nowadays, empowering companies in the disassembling process by maintaining high their environmental sustainability performances is essential. Indeed, identifying the best disassembly sequence that is also energy-effective is an open challenge to guarantee a 360° application of CM strategies. Therefore, the objective of this contribution is to develop a framework able to automatically reconstruct the disassembly sequence while minimising the energy consumption. The solution is based on process mining technique, which aims at representing the original process, and genetic algorithm, which is instead in charge of identifying the solution with minimal energy consumption. Once the framework has been developed, its feasibility has been tested first at laboratory scale and then through a simulated case. The proposed framework represents a Proof of Concept that aims at promoting the pursue of CM strategies in the product end-of-life by facilitating the identification of the disassembly sequence which is also energy-effective.

Manufacturing and service companies need to increase service level to ensure their survival. However, in recent years this is not the only problem with production systems, the environmental impact became a major concern for manufacturing and service companies alike. In this article, we jointly consider time, cost, and environmental impact for production planning. To achieve this goal, collaborative decision-making with three decision-makers (DMs) is assumed to adjust sustainability performance through choosing the most suitable production type and appropriate production day. Financial managers, industrial managers, and environmental managers are three decision-makers who collaborate to improve responsiveness, and to reduce total production cost, and CO2 emissions sequentially. To this end, a mixed-integer multi-objective mathematical model is suggested; Ɛ-constraint is used to solve the model. With the proposed model, DMs can make decisions on which products are produced on which day in a way to have trade-off among indicators.

Emerging ubiquity of smart sensing in production environments provide opportunities to make use of fine-grained, real-time data to support decision-making. One, currently untapped opportunity is the prediction of order remaining completion time (ORCT) which can be used to improve production scheduling. Recent research has focused on the development of ORCT prediction models however, their integration into scheduling algorithms is an understudied area, especially in job shop environments where processing times can be highly variable. In this paper, an artificial neural network was developed to predict ORCT based on real-time job shop status data which is then integrated with classical heuristic rules for facilitating dynamic scheduling. A simulation study with four scenarios was developed to test the performance of our approach. The results demonstrated improved completion time, however tardiness was not reduced under all scenarios. In moving this research forward, we discuss the need for further research into combining static and dynamic characteristics and priority rule design for satisfying multiple objectives.

Cognitive intelligence-enabled manufacturing (CoIM) uses machines to utilize technologies that mimic human cognitive abilities to solve complex problems in manufacturing. With the support of a cognitive intelligence-enabled manufacturing system (CoIMS) architecture, information flow is organized and coordinated appropriately, starting from the machine sensory system, central system to the motor system. Machine perceptive abilities monitor, sense and capture equipment performance, aggregate data, and help gain valuable insights into the production process. It uses the industrial internet of things, data analytics, artificial intelligence and related techniques and cognitive computing and related technologies to address production issues in an autonomous manner. As such, CoIMS solves complex production problems. It also transforms manufacturing by improving product quality, productivity, and safety, reducing costs and downtimes, identifying knowledge gaps, and enhancing customer experience. Even so, a CoIMS is not responsible for making the final decision. Instead, it supplements information on the fly for engineers to take necessary actions.

The rising demand for customization and increasing convergence of the physical and digital worlds have led manufacturing companies to seek solutions to maintain competitiveness in the global business landscape. Distributed manufacturing (DM) enables small volume customized production in geographically dispersed locat and drives the supply chain (SC) to become more agile, flexible, and sustainable. This review paper aims to present future research opportunities and challenges for the facilitation of DM for digital supply chain (DSC) by emerging digital technologies and artificial intelligence (AI). After a review of DM, we identify three distinct types of DM platforms that may facilitate DSC based on transaction mechanisms. These are then explored from a technological perspective, in terms of enabling technologies and data analysis methods that support DM for DSC. We conclude by highlighting the need for empirical studies to investigate the motivations for DM platform adoption and identify a key challenge to their adoption, that is the lack of privacy-preserving AI algorithms in facilitating DM.

Gamification is the application of game elements in non-game contexts, and in manufacturing this can be applied on the shopfloor, with intralogistics, and for training. The term “game elements” is commonly used when describing an instance of gamification. In the manufacturing context, “game elements” describe the pieces of an implementation that allow the scenario to be considered gamified. In research, various publications have used differing terms to describe these “game elements,” including mechanisms, components, technology, and various others. Since these terms are not used universally across the field, it is important to develop a framework which describes how these terms relate to one another and how they are defined in relation to gamification. This research aims to review currently used game elements from gamification for manufacturing and to classify the identified game elements more discretely for the gamification community. The resulting framework of this research will serve the gamification community in i) developing a foundation of well-established language and diction used within the field to allow for clear communication and research and ii) providing context for implementing gamification across different scenarios in the manufacturing context.

Although the benefits and advantages are emerging more clearly, the transition of traditional manufacturing and logistics systems to Industry and Logistics 4.0 paradigms is still challenging for companies. In particular, the change management strategies suggested and employed so far lack consideration of the role of human factors to support a successful transition. Starting from the analysis of a case study conducted in a medium-sized Italian manufacturing company, this article aims to show how the critical consideration of the human factors involved in a change of operational processes in a logistics 4.0 perspective is crucial to achieve the objectives set. The article discusses the main strategies to consider from the beginning and the overall impacts on all the job profiles, tasks, and human factors involved to prevent potential resistance and inefficiencies in the implementation phases.

The demand for Additive Manufacturing (AM) is continuously increasing, so new challenges must be overcome. For instance, in the event of a pandemic, location-independent communication must be ensured so that the production and the training of workers can continue. As a result, digital learning and teaching techniques have gained traction, especially in location-independent interventions. Those techniques enable synchronous and asynchronous, location-independent transmission of information and data, however, they are mostly designed for theoretical contents. In order to ensure physical learning as well, more immersive technologies must be resorted to. Thus, this study investigates the extent to which Extended Reality (XR) technologies can be used in AM. Two workshops were held to gather expert opinions on how Virtual Reality (VR) and Augmented Reality (AR) can be utilized in AM. It turns out that the technologies can be used in a variety of ways, especially in areas such as training, visualization, information, simulation, checking, and communication. The greatest added value with minimal effort could be achieved in the location-independent training and assistance of employees.

To stay competitive, manufacturing companies are developing towards Smart Production which requires the use of digital technologies. However, there is a lack of guidance supporting manufacturing companies in selecting and integrating a combination of suitable digital technologies, which is required for Smart Production. To address this gap, the purpose of this paper is twofold: (i) to identify the main challenges of selecting and integrating digital technologies for Smart Production, and (ii) to propose a holistic concept to support manufacturing companies in mitigating identified challenges in order to select and integrate a combination of digital technologies for Smart Production. This is accomplished by using a qualitative-based multiple case study design. This paper identifies current challenges related to selection and integration of digital technologies. To overcome these challenges and achieve Smart production, the concept of data value chain was proposed, i.e., a holistic approach to systematically map and improve data flows within the production system.

Digital transformation is a process encompassing all organizations, requiring a proactive attitude and willingness to change. The Covid-19 pandemic highlighted the relevance of digitization through an increased awareness and implementation of digital tools for working life. The next wave of successful innovation in industry demands high-pitched adoption of technologies for production and workplace learning systems. Organizations are trying to understand which technologies to invest in, based on usability measures, cost effectiveness, and sustainability. It can be hard to predict which technology is best suited for specific tasks. This implies a growing risk regarding investments in technology. This paper describes the spontaneous use of technology for augmented reality (AR, Microsoft HoloLens 2) in a Norwegian manufacturing company during Covid-19. The case illustrates how AR technology can be used in assembling, installation and acceptance testing of machinery for selective soldering in the production of circuit boards. Data were collected through case study research and a qualitative research design, through observation and interviews with the participants. The results show that Microsoft HoloLens 2 is easy to adopt and could contribute to immediate and real value creation in industrial production companies. We believe that the spontaneous usage of AR technology in such extraordinary circumstances as a pandemic could motivate and guide other businesses facing important decisions related to technology implementation. The original value of this article is a contribution to the discussion on the Technology Acceptance Model, which is chosen as a theoretical framework for the paper.

Industry 4.0 and 5.0 have been posing new challenges to industries. From a focus on supporting resilience at a corporate level, there is a need to do it at a more operational level, enabling people to act with resilience as well. The resilient operator 5.0 is a new concept emerged from this need. It has the aim of providing more intuitive, symbiotic, human-centered, and cognitive working computing environments to enhance human adaptation capabilities, productivity, and mental health. In this direction, this paper presents an approach that combines softbots and augmented reality, called ‘augmented softbot’. Looking at a specific company, a software prototype has been implemented to evaluate how this approach can be useful and feasible for preventive maintenance. Three scenarios have been devised for that, and they are summarized in the paper. The achieved results are discussed, showing the high potential of the approach.

Industry 5.0 complements the Industry 4.0 paradigm by highlighting research and innovation as drivers for a transition to a sustainable, human-centric and resilient industry. In this context, new types of interactions between operators and machines are facilitated, that can be realized through artificial intelligence (AI) based and voice-enabled Digital Intelligent Assistants (DIA). Apart from the existing technological challenges, this direction requires new methodologies for the evaluation of such technological solutions that will be able to treat AI in manufacturing as a socio-technical system. In this paper, we propose a framework for the evaluation of voice-enabled AI solutions in Industry 5.0, which consists of four dimensions: the trustworthiness of the AI system; the usability of the DIA; the cognitive workload of individual users; and the overall business benefits for the corporation.

The shortage of skilled workers is a barrier to applying data analytics. Augmented analytics is an approach to lower it by using machine learning to automate related activities and natural language applications to assist less-skilled employees. Public information about augmented analytics case studies in manufacturing is hardly available. Therefore, this article presents a related case study from the white goods industry. It focuses on a quality test lab in a production line where workers use a digital assistant prototype to interact with descriptive and predictive data analytics. This article derives a framework from this case study to organize how an assistant could augment analytics. The framework has five areas: training data modification, model training, starting an analysis, retrieval of results, and decision support. The latter is relevant to the other four areas and includes, for instance, suggesting options to customize analytics. Four scenarios of different complexity concretize the framework’s areas. Finally, this article outlines four questions for future research.

Developing digital dashboards (DD) that support staff in monitoring, identifying anomalies, and facilitating corrective actions are decisive for achieving the benefits of Smart Production Logistics (SPL). However, existing literature about SPL has not sufficiently investigated the characteristics of DD allowing staff to enhance operational performance. This conceptual study identifies the characteristics of DD in SPL for enhancing operational performance of material handling. The study presents preliminary findings from an ongoing laboratory development, and identifies six characteristics of DD. These include monitoring, analysis, prediction, identification, recommendation, and control. The study discusses the implications of these characteristics when applied to energy consumption, makespan, on-time delivery, and status for material handling. The study proposes the prototype of a DD in a laboratory environment involving Autonomous Mobile Robots.

The value-driven Industry 5.0 has brought a shift in the approach towards worker well-being. However, the understanding of the effects on workers due to technological advancements of Industry 4.0, based on a human-centric approach, is limited. The reason for this limitation is that the tools are scarce, which is quantitatively evaluating and analyzing various factors in the workplace. To solve this problem, we propose a human digital twin system supporting decision-making regarding safety management and work management of workers. The human digital twin system consists of a digital twin module, an analysis module, and a visualization module. The proposed system connects a physical human and a virtual digital human model; analyzes the location, posture, and motion-time of workers; and delivers information about safety and work management. This information enables workers and managers to improve the work environment by making them resilient to workplace factors.

In rechargeable battery production—a component of mass customization—high quality, low cost, efficient delivery, and flexibility must be ensured. Efficient production operation can be achieved by solving the performance degradation problem, which is a limitation of mass customization. This degradation can be prevented through resilience, which can be achieved by satisfying four core functional requirements, which are as follows: (1) selecting robust actions; (2) measuring performance indicators; (3) notifying impermissible fluctuations; and (4) extracting the adjusted reactions. A digital twin (DT) is an advanced virtual asset that represents configuration, reflects functional units, and synchronizes information objects. This article describes DT application to satisfy the four core functional requirements and reflect the operational characteristics of three heterogeneous stations in rechargeable battery production. Analyses of the measures taken to achieve the resilience and operational characteristics of stations in rechargeable battery production are provided to present an appropriate design of the description. The asset description is designed with P4R classes based on this analysis. The designed asset description is applied to stations in rechargeable battery production, and the proposed method is verified with the implemented DT application. The proposed asset description presents an efficient method of satisfying the core activities and technical functionalities corresponding to the DT. The proposed method is an early case of DT usage in rechargeable battery production and can be considered as a reference for smart manufacturing technologies in the manufacturing domain in the future.

The modern manufacturing industry ought to solve various problems amid increasingly fierce competition. Particularly, the supply chain network of the manufacturing industry is expanding globally. Therefore, there is an increasing necessity for smart manufacturing. Smart manufacturing is an integrated manufacturing system that applies information and communication technologies to manufacturing, rendering the entire manufacturing process smart, for obtaining real-time responses to internal and external variations in factories, supply networks, and customer requirements. Although there are several technologies that promote smart manufacturing, the cyber-physical system (CPS), introduced into the manufacturing environment is the primary technology. Global manufacturing enterprises have limitations in that it is difficult to collect information generated at distributed manufacturing sites with independent applications, and it is impossible to make quick decisions. To build smart manufacturing at such global manufacturing scales, a platform that integrates information and provides various applications in a distributed environment using the CPS ought to be built. This paper proposes an integrated platform for smart manufacturing implementation. The primary components and functions of the platform are defined. Finally, the effectiveness of the proposed platform is verified through a case study.

Smart production logistics has introduced in manufacturing industries with emerging technologies such as digital twin, industrial internet of things, and cyber-physical system. This technological innovation initiates the new way of working, working environment, and decision-making process. Especially the decision-making process has changed from experience and intuition to knowledge and data driven. In this paper, digital twin-based services, and data visualization of material handling equipment in smart production logistics environment are presented. There are several applications of digital twin in manufacturing industries already, however feedback from the virtual environment to physical environment and interactions between them which are the essential features of digital twin are very weak in many applications. Therefore, we have developed digital twin-based services in the laboratory scale including feedback and interaction. In addition, data visualization application of material handling equipment in automotive industry is presented to provide insights to the users. Both applications have developed based on the same framework including database and middleware, so it has possibilities to develop further in the future.

The aim of this paper is to provide insights about the operational performance improvements that may arise from the combination of Industry 4.0 technologies with the tools of Lean Production. Indeed, companies and their decision makers are looking for actionable knowledge around the usefulness of Industry 4.0 technologies and their inclusion in existing operational excellence programs. Lean is a tried and tested means of promoting better thinking in organizations, contributing to an increase in customer satisfaction and business performance. The emergent technologies of industry 4.0 are also influencing performance improvement in both the development and delivery of products and services. Yet actionable knowledge of the combination of Lean Production and Industry 4.0 is relatively immature and requires deeper analysis. This paper presents insights into the possible integration of Lean Production and Industry 4.0 technologies by analyzing multiple case studies in France and Norway. We suggest an approach that depicts the way in which such integration can reduce and ultimately eliminate waste.

The importance of Visual Management (or “Mieruka” as it is called in Japanese) has been largely demonstrated over the last few years, especially when it comes to the creation and management of data-rich environments for effective and efficient data-driven decision-making, such as digital lean smart factories. Although the different functions of visual systems are already known by the scientific community, further analysis of the capabilities and benefits provided by these tools, especially when enhanced with modern digital technologies, has yet to be provided. Therefore, this paper aims to frame a list of capabilities of the current physical visual systems, and their cyber/digital equivalents, according to a reference framework called the “7Is”, which was extracted from a review of the current literature available. This may serve as a valid common reference for future research on this topic.

For years, manufacturing companies have been working with developing and implementing lean thinking to continuously improve the management of their operations. Since lean thinking provides tools and approaches to solve problems enterprise-wide, there is an ambition among lean companies to use the lessons learned while applying lean, to develop and implement a more circular economy approach to their operations. However, extant research combining lean and circular economy concern mostly the business model level and there is a lack of research on how to bring circular economy thinking to the operations. Even though both lean thinking and circular economy emphasize the importance of designing products that can be manufactured in an efficient way, using as few resources as possible, and without waste, the extant literature combining these concepts refers mainly to the processes concerning the product’s end of life. This paper deploys the ‘by design’ aspect of circularity through the lens of lean product development, a key element within the lean thinking concept.

Poka-Yoke devices have always been regarded by lean manufacturing companies as essential quality control and assurance tools to support efficient and effective manufacturing processes and procedures. Thanks to their ease of use and low cost, these devices help maintain high-quality standards and also encourage organisations to undertake Kaizen continuous improvement activities. With the advent of new digital and analytical technologies, these devices have undergone significant transformations. Based on a study of the scientific literature and the results of brainstorming sessions conducted with factory managers and lean experts, this paper analyzes how and to what extent digitalization changes the definitions, functions, approaches, and perspectives of traditional Poka-Yokes. Furthermore, it examines how the change in data collection, sharing, analysis, processing, and feedback (interpretation) approaches brought by the digitalization and smartification of Poka-Yoke devices affects the operational performance of modern Digital Lean Cyber-Physical Production Systems.

Every Lean optimization path typically starts with the mapping of a process, to identify criticalities and wastes embedded in manufacturing processes. Value Stream Mapping (VSM) represents the main and most suitable Lean technique to this purpose, thanks to its simplicity and immediacy residing in its visual effectiveness. Originally designed as a “draw by hand” mapping technique, VSM is moving on digital platforms; different software has in fact been developed over the last few years, each with its own functionalities and characteristics, reason why a software may not be always valid, but useful only within certain contexts. According to a classification of software based on mapping, analytical and collaborative functionalities, crucial aspects for a successful VSM application, the paper aims to depict, through a benchmark analysis, the current situation regarding VSM software and, therefore, serve as a reference model to orient manufacturing companies towards best suited VSM applications.