Advances in Production Management Systems. Towards Smart and Digital Manufacturing

Continuous improvement (CI) is a fundamental part of lean thinking and practice and will remain critical for the success of manufacturing firms during the fourth industrial revolution. The realization of CI is based on the active participation and involvement of the firm’s entire workforce – with everybody making incremental improvements, every day. This of course includes shop floor operators. In this paper, we explore various aspects that influence the successful involvement of shop floor operators in CI activities, by adopting a case study approach. The case company has achieved only partial success with CI on the shop floor, despite repeated efforts. As a result of the study, several organizational factors emerge as critical success factors for securing involvement and engagement of operators in CI, with positive results. Raising awareness of these critical factors may help manufacturing firms adopt an alternative approach to CI which promises to increase the level of operator participation in CI.

The application of didactic setups is one of the commonly used teaching methods at schools and universities nowadays. By using the didactic setups, students are introduced to real components and real processes that take place in the industry. However, the students often need to use the didactic setups outside of regular teaching activities and at times when teaching staff is unavailable. This paper presents a solution for improving the safety of using the didactic setups by applying an augmented reality solution. The presented solution was implemented on a didactic setup for control of a three-phase asynchronous motor. In addition to improving the safety, the proposed solution allows the students to autonomously use the didactic setups beyond regular teaching hours.

Production management involves many activities. In order to deal with Industry 4.0 requirements, many systems have developed solutions to gather real-time information from the shopfloor for more reliable decision-making. Empirical studies have been showing that this has created a tremendous overload of information to be handled by managers, causing stress, incorrect analyses and sometimes guessing-based decision-making, especially in SMEs. Using data analytics and maturity models concepts, this work shows Livia, a softbot with chatting capabilities. Deployed in a cloud and working on companies’ shopfloor information got via a MES system, Livia helps managers to identify their main problems, suggests corrective actions, and proactively performs many supporting actions. Results are presented and discussed in the end.

This paper aims at presenting an overview of how the manufacturing industry formulates business transformation and knowledge strategies, to find gaps in Industry 4.0 concepts’ impacts on the workforce. The results indicate that the industry is still focusing on the digital transformation era that was adopted at the end of the 20th century, and how to adopt computing technologies to work more efficiently in existing business processes. The approach of this article is to adopt a methodology of three areas; (a) Human resource processes, (b) Industry 4.0 pillars and (c) Process knowledge, where links between these three generate opportunities to address for further research.

Just as human operators are important production enablers in the factories of today, they are expected to stay key enablers also in future manufacturing. In today’s factories, operators often meet challenges related to poor information and communication design, which affects their possibilities to perform with higher efficiency levels. Therefore, they need to be provided with better cognitive support tools that are relevant to the challenges to be met. To ensure efficient and effective operator work in the factories of the future, operator support needs to be adequate for the new tasks arising from the evolving operators’ roles and work. Within this paper, the results of current operators’ work and challenges, based on six case studies, are combined with an outlook of the future of work of operators, based on the Operator 4.0 vision. The challenges categorized in this paper can be used to identify opportunities for improvement in the operators’ cognitive support in present factories as well as for researchers and developers of Operator 3.0 support solutions.

In this research work, we take an interoperability point of view on “production systems-of-systems”. Interoperability of production processes requires a stepwise planning of resources. An approach supporting the orchestration and coordination of human and artificial agents (i.e. collaborative robots) is developed. First tasks are assigned to production resources/agents, followed by an interaction and task execution design. Another step taken into account is the technology used as a human-robot interface.

Rapid technological advances present new opportunities to use industrial Big Data to monitor and improve performance more systematically and more holistically. The on-going fourth industrial revolution, aka Industrie 4.0, holds the promise to support the implementation of sustainability principles in manufacturing. However, much of these opportunities are missed as social and environmental performance are still largely considered as an afterthought or add-on to business as usual. This paper reviews existing data life cycle models and discusses their usefulness for sustainable manufacturing performance management. Finally, we suggest possible directions for further research to promote more sustainable cyber-physical production systems.

This paper describes an example of an explainable AI (Artificial Intelligence) (XAI) in a form of Predictive Maintenance (PdM) scenario for manufacturing. Predictive maintenance has the potential of saving a lot of money by reducing and predicting machine breakdown. In this case study we work with generalized data to show how this scenario could look like with real production data. For this purpose, we created and evaluated a machine learning model based on a highly efficient gradient boosting decision tree in order to predict machine errors or tool failures. Although the case study is strictly experimental, we can conclude that explainable AI in form of focused analytic and reliable prediction model can reasonably contribute to prediction of maintenance tasks.

In production, an intelligent analysis of the data provided along the production line bears huge potential for increasing process efficiency and reducing production costs. The (continuous) collection of relevant data is a crucial precondition for every Industry 4.0 or smart technology. While new machines have internal controllers and sensors to meet those requirements, this is usually not the case for machines being already in use. Especially in the textile industry, it is often standard to keep old machines since the manufacturing methods themselves haven’t changed significantly over the past decades. Hence, the successful exploitation of digitalization advantages requires these companies to first develop and implement a digitization strategy. In this paper we present a concept which allows to develop modular, scalable, flexible solutions considering the whole digitization process from data acquisition to storage. The concept is complemented by a guideline for its application in industry. Experience from a prototypical application in a textile company is described. The concept enables companies to determine the various conditions and requirements for digitization, to analyze different possibilities and to deduce scenarios that lead to a solution for the effective utilization of Industry 4.0 technologies.

Norway has a long cultural tradition for organizing its working life. A dialogue-based and cooperation-oriented model constitutes the way work life functions locally in companies as well as at the national level. This article addresses how the basic values of this practice promote processes of industrial digitalization in Norway. The digitalization of industry and society has many faces, but the focus here is on organization, forms of collaboration and management that create the foundation for successful digital implementation in the industry. Within the framework of a working life that requires ever-increasing competence at all levels, this article shows that intra-corporate collaboration between management and employees generates common goal understanding and process commitment. This paper highlights seven organizational enablers for digitalization in a Norwegian industrial context. Our findings from a cross-case analysis of data collected through 33 case studies of successful digitalization processes at Norwegian companies in a diverse set of industries suggest that the digitalization of industry requires cross-functional and inter-organizational collaboration to develop more specialized expertise from all employees, and this serves as a key element for success within industrial digitalization.

Nowadays, many industrial companies, including small and medium-sized ones, are adopting virtual manufacturing concepts to face global competition and major manufacturing challenges. The aim is to improve quality, shorten delivery time and reduce costs. However, most virtual manufacturing methodologies, tools and software are not integrated well enough to perform the required activities efficiently. Attention is usually focused on local and specific proficiency, thus jeopardising information exchange between departments, parallelism of work and communication along the product lifecycle. In these circumstances, the use of virtual production and digital representation of the production system and its processes becomes even more important to optimise production activities. Manufacturing industries are evolving towards digitisation, networking and globalisation. In the process of rapidly evolving information technology, digital tools and systems are being used in all industries, managing a variety of tasks throughout the product lifecycle with the use of PLM (Product Lifecycle Management) applications. PLM class systems integrate a set of applications supporting product development.The paper presents the concept of the integration of PLM application with VR (Virtual Reality) and AR (Augmented Reality) techniques based on the example of the proprietary system integrating advanced PLM app with a mobile application in which AR technology has been implemented.

In the last years, the manufacturing sector of developed economies is going through extensive changes to adopt Industry 4.0 principles. Prior studies investigated key enabling technologies for Industry 4.0 and their applications focusing on developed economies. However, there is a lack of studies covering emerging economies (e.g., Serbia). This research provides an overview of the use of technologies for automatic storing of operational data and the exchange of operational data between different entities from the manufacturing sector. For this purpose, the Serbian dataset of 240 companies from the European Manufacturing Survey gathered in 2018 is used. The empirical results indicate that 43% of manufacturing companies are utilizing the systems that automatically record operational data, 88.3% of manufacturing companies are creating an immense amount of data through ERP systems, and 78.6% of companies are using a digital exchange with suppliers or customers. The results reveal the big potential for the Big Data in the manufacturing sector in emerging economies.

This paper proposes a conceptual implementation model for small and medium enterprises (SMEs) to follow as part of their digitalization implementation. It can later be translated into a practical step by step guide for SMEs to practice during their digital transformation. The model is based on gradually developing industrial capabilities that can influence production processes performance. The model development was based on a critical literature review and a real case industry application. The case data served as direct feedback to the model to assess both the model validity and the actual SMEs needs. The capabilities included in the model are proved to directly influence the performance positively. In comparison with existing models and frameworks, this model envisions the company a full digital shift by proposing an achievable sequence which SMEs in a resource-efficient way could start deploying in compliance with their business needs. SMEs can utilize the capabilities as a foundation for a system that supports continuous improvement in the whole factory.

In this paper, we present the first steps towards realizing a digital twin with integrated virtual laboratory possibilities, for a newly established Energy Analytics and Solution lab, using the Unity3D game development platform. The presented example is a case study that shows the possibilities of such development environment for creating a fully connectable digital twin of an energy analytics lab and other more complex industrial environments.

Information and Communications Technology (ICT) integration in the entire process of manufacturing management is necessary and obliging from the perspective of efficient resource allocation, time-saving, broadening the variety of products, reducing waste, and increasing the productivity and economy of production. Delivering competitive advantage needed for being successful in the new digital era is of crucial importance for the Serbian manufacturing industry. This research, in the first instance, seeks to examine the empirical link between the ICT software solutions importance and the actual software application, and in the second instance, to explore the empirical link between ICT software solutions application and the company’s competitive position, as perceived by the respondents from 74 Serbian manufacturing companies included in the study. Research results have shown that if the managers in manufacturing companies believe that the usage of the specific software solution is vital for their business, the usage of that software will be empowered and, therefore, will positively impact the company’s competitive position.

Industry 4.0 is changing companies’ business by introducing new manufacturing techniques. Personalization is one key goal of Industry 4.0 that is transforming companies’ business models by bringing the customer’s preference in the production process. Companies require to adapt their business model to these disrupting manufacturing approach. In this paper, we present a management tool for guiding the business model innovation towards personalized products: The personalized product business model radar template. This template implements a pattern that guides the business model design process by introducing predetermined values that are common in the personalized product business model in Industry 4.0. We apply this management tool in a business case scenario in a workshop setting with professionals from smart manufacturing.

Digital manufacturing (DM) processes such as additive manufacturing (AM) technology, allow a high degree of integrability and functionality of printed parts. In this work, we present a proof of the DM concept focused on the integration approach where a product is developed and embedded with sensors. We also take this example one step further and introduce a method that allows 3D printing of heating elements into the specimens. The thermal characteristics of the developed heaters are investigated, and the results detailed. The novelty relates to a heater prototype injected and solidified into a curved 3D printed channel, which can produce a temperature between 23–46 °C on the printed surface of the sample both in a dry and wet environment. This research demonstrates that it is possible to construct parts with embedded electrical structures using the described method.

Digitalization has penetrated and transformed entire business models irrespective of industry belonging. Thus, digital transformation launches both possibilities and challenges for firms and their managers in how to execute (intra) organizational operations and strategies. This paper provides findings derived from an explorative interview study targeting required skillsets for future Engineer-to-Order (ETO) operations within the maritime industry. It has an introspective perspective, focusing on managerial perception of current and future needs to enhance efforts to digitalize ETO work further. Hence, as ETO firms tends to be conservative in their underlying dynamics of work it is important to gain insights on important skills and mindsets for successful leadership of digital transformation. The study heightens the effects of previous and future implications of digital transformation, while accentuating that new required skills are not only restricted to operators and engineers, instead it is equally relevant for managers to embrace such changing needs, as digitalization has resulted in blurred mechanisms for how to conduct management.

Manufacturing is highly distributed and involves a multitude of heterogeneous information sources. In addition, Production systems are increasingly interconnected, hence leading to an increase in heterogeneous data sources. At present, data available from these new type of systems are growing faster than the ability to productively integrate them into engineering and production value chains of companies. Known applications such as predictive maintenance and manufacturing equipment management are currently being continuously optimized. While these applications are designed to help companies manage their manufacturing and engineering data, they only use a fraction of the total potential that can be realized by linking manufacturing and engineering data with other enterprise data. In the future, the context in which the data can be set will play an essential role. A meaningful added value in manufacturing can be achieved only with context specific data. Against this background, this paper presents three main areas of application for contextualizing data (semantics, sensitivity and visualization) and explains these applications with the help of a contextualization architecture. The concept is also evaluated using an industrial example. Furthermore, the paper describes the theoretical background of contextualization and its application in industry. The major challenges of the ability of engineers to adapt their activities and the integration of process knowledge for semantic linking are addressed as well.

Companies are becoming aware of the potential risks arising from sustainability aspects in supply chains. These risks can affect ecological, economic or social aspects. One important element in managing those risks is improved transparency in supply chains by means of digital transformation. Innovative technologies like blockchain technology can be used to enforce transparency. In this paper, we present a smart contract-based Supply Chain Control Solution to reduce risks. Technological capabilities of the solution will be compared to a similar technology approach and evaluated regarding their benefits and challenges within the framework of supply chain models. As a result, the proposed solution is suitable for the dynamic administration of complex supply chains.

Moving towards more sustainable operations is a challenging goal for industries. The growth of interest in corporate sustainability performance has brought attention to the importance of accounting and transparency across economic, environmental and social dimensions. This paper provides an explorative study addressing sustainability from the perspective of industrial firms. The paper presents case studies on the application of the Triple Layered Business Model Canvas, which allows for relevant insight into how firms account for economic, environmental and social values. The findings show that while accounting for economic values is well taken care of, accounting for environmental values are at an initial stage, and accounting for social values are virtually lacking. Thus, the ability for the industrial firms to conduct a sustainability assessment is limited. The opportunities lie in the adaptation of digital technologies providing cost efficient feedback mechanisms for environmental and social values. This can support environmental and social accounting, giving industrial managers a decision management tool to guide their transition towards more sustainable operations, and aligning company goals with the UN Sustainable Development.

Smart Factories are complex manufacturing ecosystems where the converging of ICT and operational technologies and competences drive the digital transformation. Smart manufacturing operations planning and control program, as defined by NIST, implement advances in measurement science that enable performance, quality, interoperability, wireless and cybersecurity standards for real-time prognostics and health monitoring, control, and optimization of smart manufacturing systems.The traditional production processes and plants are evolving following this digitalization combining the long experience and the AI-driven methods to improve the production efficiency, to accelerate the fine-tuning and real-time adjustment of the process parameters oriented to the zero defect quality. The digitalization of multi-stages production processes (e.g. foundry) plays a key role in competitiveness introducing new integrated platform to monitor the process through an intelligent sensors network and predict quality and cost of castings in real-time.The application presented in this paper is the main outcome of EU FP7-MUSIC project giving a new age to the traditional multi-stages production. The actual regional project PreMANI (POR FESR 2014–2020) is a new extended application of AI-driven digital twin in manufacturing process and quality control. This paper demonstrates the applicability of data-driven digital twins to small and medium-sized enterprises (SME) and to complex manufacturing sectors integrating the process monitoring with advance data mining and cognitive approach to predict the quality, the efficiency vs cost and react in real-time with the support of decision support system.

Cloud manufacturing (CMfg) is considered to be a facilitator for mass manufacturing resources. It is a paradigm of intelligent systems, which makes the manufacturing procedures easier. In this regard, the most important issues of the manufacturing environment are discussed in the literature. Supplier selection and order allocation have been great concerns for researchers in all manufacturing systems, even in a cyber-physical environment allocating orders to the best supplier is of great importance. Hence, this research highlights one of the most challenging issues in a cloud environment which is related to supplier selection in CMfg. A hybrid multi-criteria decision-making framework (i.e., fuzzy DEMATEL-VIKOR) considering sustainable criteria is proposed to help the decision-makers for better dealing with supplier selection in a cloud environment. Selecting the best supplier is not the only issue discussed in this paper. Coordinating the suppliers is also taken into account because a better partnership supplier and the client need to work in a coordinated structure. In the second stage of this paper, the best coordination contract is proposed based on the client’s given score on some predetermined criteria. The results indicate that a revenue-sharing contract is an ideal coordination framework, which will satisfy the client and supplier and help them to work in a coordinated environment.

One of the most important operational issues in an assembly line is to allocate workers to tasks (or workstations) so that their workloads can be well-balanced. We address a new workforce assignment problem in an assembly line where workers can perform all tasks but have a different skill level, which affects not only the throughput but also the number of defective products. We derive two mathematical programming models that can be used in various production environments. One is with the objective of the throughput maximization while keeping the defect rate under a given criteria and the other is for the minimization of the total rework time while achieving a given throughput. Several experiments are performed with the proposed two models by changing the number of workers and a proportion of skilled workers. We also compare the two models and provide managerial insights.

Production logistics systems in the context of manufacturing, especially in automotive sectors today, are challenged by the lack of real-time data of logistics resources, optimal configuration and management strategies of materials, and optimisation approaches of logistics operations. This turns out to be the bottleneck in achieving flexible and adaptive logistics operations. To address these challenges, this paper presents a framework of real-time data-driven dynamic optimisation schemes for production logistics systems using the combined strength of advanced technologies and decision-making algorithms. Within the context, a real-time data sensing model is developed for the timely acquisition, storage, distribution, and utilisation of equipment and process data in which sensing devices are deployed on physical shop floors. The value-added data enable production logistics processes to be digitally visible and are shared among logistics resources. A multi-agent-based optimisation scheme for production logistics systems based on real-time data is developed to obtain the optimal configuration of logistics resources. Finally, a prototype-based simulation within an automotive manufacturing shop floor is used to demonstrate the proposed conceptual framework.

The wide application of Internet of Things (IoT) has fostered the development of Industry 4.0. In manufacturing domain, Industrial IoT (IIoT) are key components of the Factories of the Future (FoF). The big IIoT data are the foundation of implementing data-driven strategies. In current industrial practice, most of these IIoT data are wasted or fragmented in data silos due to security and privacy concerns. Novel data management approaches are required to replace traditional centralized data management systems. The rapid development of blockchain technologies provides a novel solution for this challenge leveraging its unique characteristics such as decentralization, immutability and traceability. However, blockchain is inefficient for exchanging big data due to transaction throughput limits. The peer-to-peer InterPlanetary File System (IPFS) provides a suitable complement for blockchain. Therefore, this paper aims to propose a decentralized IIoT data management approach based on blockchain and IPFS technology. The architecture and enabling technologies of the proposed system are introduced. A proof-of-concept implementation is realized and relevant experiments are conducted. The results demonstrated the feasibility of the proposed approach.

For global automakers that manufacture products through a globally distributed supply chain, it is essential for this supply chain to be managed efficiently to enhance the efficiency and responsiveness to uncertain changes in the market. These manufacturers face limitations associated with independent applications, such as difficulties in collecting information from distributed sites and the inability to make quick decisions. To solve this problem, researchers have been actively investigating the application of smart manufacturing technology to improve productivity as well as the optimal use of manufacturing resources in dynamic environments. Furthermore, different countries have different cultures, regulations, and policies. Hence, a universal integrated platform is required to address these difficulties. Accordingly, this study proposes an integrated cyber-physical system-based platform that reflects international standards and has versatile applications. This platform can be used to utilize information from various distributed manufacturing sites in real time. In addition, the proposed system was verified through a field application case study.

Digital Twins (DTs) are described as the next wave in simulation, a critical component of Cyber Physical Systems (CPS), and a key enabler for da-ta-driven decision-making. Yet, the literature presents limited understanding about the characteristics of DTs and their relation to current simulation capabilities. Addressing this problem, the purpose of this study is to analyze the characteristics of DTs and simulation models for CPS in production logistics. This study reviews extant literature on DTs and presents findings from a single case study at a South Korean manufacturing company developing a DT including Discrete Event Simulation (DES). The findings of this study highlight the importance of DES in DTs focusing on increased production logistics performance. The results of this study indicate that the use of DES may promote the development of DTs, but be insufficient in the characterization of DTs for CPS in production logistics.

The recent advancements in Internet of Things (IoT) technology and the increasing amount of sensing devices that collect and/or generate massive sensor data streams enhances the use of streaming analytics for providing timely and meaningful insights. The current paper proposes a framework for supporting streaming analytics in edge-cloud computational environment for logistics operations in order to maximize the potential value of IoT technology. The proposed framework is demonstrated in a real-life scenario of a large transportation asset in the aviation sector.

In this study, a new mathematical model is presented for the master surgical scheduling (MSS) problem at the tactical level. The capacity of the operating room for each specialty is determined in the previous level and used as an input for the tactical level. In MSS, elective surgeries are often performed in a cycle for a cycle. However, this problem considers both elective and emergency patients. The model of this problem is specifically designed to achieve this tactical plan to provide emergency care, as it provides the possibility of reserving some capacity for emergency patients. The current study, forecast emergency patients by applying an artificial neural network, and reserve capacity for them are based on the demand. Fuzzy chance-constraint programming is employed to handle the uncertainty in the model. The data of a private hospital in Iran is used to solve the problem using GAMS software. The results show that the performance of the proposed method against the solution in the hospital performed better.

Knowledge graphs are being increasingly adopted in industry in order to add meaning to data and improve the intelligence of data analytics methods. Simple Knowledge Management System (SKOS) is a W3C standard for representation of knowledge graphs in a web-native and machine-understandable format. This paper introduces SKOS Tool; a web-based application developed at the Engineering Informatics Lab at Texas State University. It can be used for creating knowledge graphs and concept schemes based on the SKOS standard. The main feature and functions of SKOS Tool are described in this paper. Beyond creating knowledge graphs, SKOS Tool has additional features that can be used to support semantic document classification based on the Bag of Concepts technique. To demonstrate the utilities of SKOS Tool, a use case related to classifications of manufacturing suppliers with Medical Grade Polymer Tubing capabilities is presented.

This paper describes the design, development and commercialization of a digital twin in an industrial product service system (industrial PSS). The twin was one of a number developed as part of a Smart Twin project in Switzerland with multiple industrial partners. The case here follows a commercialization process for digital twin-enabled services and will highlight some of the critical success factors. The service provided with the support of the digital twin was based on the reliable and predictable operation of a server room. Equipment in the room comes from several original equipment manufacturers (OEMs), and a system integrator manages the system. The approach is to apply action research as the methodology on a single case and then to identify and assess the aspects where Service Dominant (S-D) logic supported or hindered the development of the new digital technology-based service that was being co-created between the supplier and the customer. The analysis of the case confirmed that S-D logic supported the development of Smart Service offerings and showed that value co-creation can be developed.

The digital transformation of industrial firms is providing opportunities to improve efficiency, design new value propositions and re-engineer their business models. With the adoption of digital technologies in combination with the use of data, firms can develop new smart services for their internal or external customers, enabling new value co-creation opportunities and, possibly, leading to a competitive advantage. Nevertheless, assessment of the impact of smart services in financial and organizational terms, and the way to reach such a competitive advantage, is problematic for firms. The challenge firms are facing is that the value created through smart services consists of many small improvements which add up rather than a single point of value. This paper introduces the background literature research, then presents and discusses the results from a use-case where the application of enabled smart services was developed. The outcomes show that digitization can support both internal value creation and the development of new customer value propositions based on servitization and allow non-manufacturing firms to develop new value propositions through smart services.

Service systems in the smart living domain integrate a multitude of heterogenous data sources and affect the most private area of human lives. Therefore, particular challenges for service systems engineering arise in terms of interoperability of Internet-of-Things (IoT)-devices, privacy concerns and creating truly smart value propositions. By applying a promising approach, this paper examines smart service systems engineering and reveals the potential for extensions and adaptations of existing methods. A need for the integration of data science and software engineering approaches as well as a focus on acceptance, usability and the business perspective within a holistic smart service systems engineering method is discussed. This enables smart service systems to reconcile their human-centered and data-driven qualities.

The use of service in the manufacturing sector is growing in the last decade. Moreover, there are developed and implemented many service business models across the world. Prior studies investigated the role of service business models in the manufacturing sector of developed countries; however, the role of service business models in transition economies (e.g., Serbia) is neglected. This research provides an overview of the use of service business models in the manufacturing sector of the Republic of Serbia from the dataset of 240 manufacturing firms from the European Manufacturing Survey conducted in 2018. Furthermore, results show a positive impact of service business models on the manufacturing firm’s performance.

In recent years, systems have become large scale and complex due to the development of Internet of Things (IoT). Therefore, it becomes difficult to understand the influence of component specification changes that occur during the system development stage. Thus we focus on Model-Based Systems Engineering (MBSE), which is capable of expressing hierarchical structure and overviewing information. In this paper, we propose a method for analyzing the influence on the smart interactive service with digital health modeling caused by changes in system elements using index of network theory. As a conclusion, we clarified the degree of influence on the whole system caused by changing the specification of each component with using eigenvector centrality, which is one of the network indices.

The design of Product-Service Systems (PSS) has been approached from several perspectives like the process, innovation, engineering and operational ones providing, for each one of those, a specific view on the problem. This paper proposes a Data-Driven Product-Service System Design and Delivery (4DPSS) methodology focusing on the collection and exploitation of delivery data to feed the design phase. The logic of the methodology relies on aggregating operational data (collected in the delivery phase) to build a consistent body of knowledge to be exploited in iterative PSS design activities, thanks to better identification of customer needs, and product and service process design issues. This paper presents the 4DPSS methodology at a theoretical level, the implementations of the different methods constituting the methodology are referred to in the text, while its implementation and test as a whole are demanded to future work.

Many manufacturing companies are now facing the transition towards the development of a structured service offering in the servitization fashion. Especially in the case of a service like maintenance, the definition of a coherent process, able to collect and exploit in the right way the data from the field for decision-making scopes constitutes the base to run an economically sustainable offering. The authors proposed a structured framework that, considering a dual perspective (asset and service), aims to address this problem and to improve the maintenance decision-making. The paper, using as a case study an Italian manufacturing company willing to accelerate its servitization process, addresses the testing and improvement of the framework. Company A service department’s employees were interviewed in the scope of validating the framework and identify improvements for its structure and the related decision-making instruments.

This paper contributes to an assessment framework for valuing data as an asset. Particularly industrial manufacturers developing and delivering Smart Product Service Systems (Smart PSS) are comprehensively depended on the business value derived by processing data. However, there is a lack in a framework for capturing and comparing the Smart PSS data value with the purpose of increasing the accountability of data initiatives. Therefore a qualitative data value assessment approach was developed and specified on Smart PSS, based on an industrial case study research.

With the digital revolution, the IoT (Internet of Things) platform approach emerges as a supportive way for the implementation of IoT platform-based Product-Service System (PSS) offerings. To exploit the full potential of the platform economy and considering the complexity of the multitude of different actors in all the lifecycle stages, value creation architectures for digital and service platforms are one of the critical points to develop. In this work, the concept of Platform-based PSS is presented, and for representation purposes, an extension of the RAMI 4.0 reference model has been proposed. Specifically, the ecosystem perspective has been added to RAMI 4.0, to approach Platform-based PSS with a multi-actor lens. Moreover, the different contributions and benefits of the main participants in Platform-based PSS are illustrated. Therefore, three different scenarios have been exemplified with the proposed RAMI 4.0 extension relating the actors’ possibilities to contribute to Platform-based PSS to the level of openness of a platform.

The advancements and adoption of digital technologies enable manufacturers to approach intelligent production and reach a higher level of automation in the wave of Industry 4.0. Even though manufacturing is at the center of this industrial revolution, the impact of digital technologies is more far-reaching: indeed, one of the biggest growth potentials is recognized in the paradigm shift from a traditional product orientation to the provision of bundled solutions. Particularly the possibility to gather and analyze data has been recognized as a key enabler for the advancement of the product-services offering. In this view, the research presented a systematic literature review exploring the state of the art considering on one side the newest technologies related to data and digitalization and on the other side servitization, aiming at understanding the point of contact between them. Four different perspectives have been identified and discussed and possible research directions have been proposed.

The design of innovative offers involves deep collaboration between the provider and the customer to create higher value than in traditional offers. Product-Service Systems (PSS) are bundles of products and services and constitute innovative offers designed to fit complex customer needs. The academic literature on PSS development has a strong focus on the provider perspective, and only a few works address customer involvement in the process of value co-creation. This paper proposes a methodological framework composed of a customer view and a provider view, highlighting the interface between them, in which value co-creation takes place. The proposed framework aims at supporting the collaboration process between the customer and the provider during the entire PSS development. The framework has been built within a R&D project in a French company called Vibratec.

Digital transformation (DT) is gaining interest and changing citizens’ expectations of service organizations’ ability to deliver high-value, real-time digital services. However, from an organizational perspective, DT entails a continuum of transitions that emphasize cultural, organizational, processual and relational changes. Over the past few decades, Lean thinking has been a dominant part of many organizational philosophies and proven to be an important enabler, to cater for the aforementioned changes. With its focus on reducing organizational complexity and increasing value for the end-user, it can support DT through its systematized utilization of methods and tools for improvement. The purpose of this paper is to demonstrate the use of Lean thinking to develop and enhance service processes and its contributing effect to enable DT in service organizations. Accordingly, a conceptual process for Lean digital transformation (LDT) is developed and discussed. In order to test the developed LDT process, action research was conducted in a sales and service organization in Norway, where an after-sales process was selected for current-state analysis. The conducted study resulted in the development and commercialization of a software system, which has been licensed and implemented by approximately 100 users within a year. The findings of this study reveal a great improvement and innovation potential in utilizing Lean thinking to enable and drive DT.

Optimization of resource utilization plays a significant role in the continuous improvement initiatives of an organization providing services. Lean thinking and systematic approaches, such as multicriteria analysis (MCA), are necessary to optimize the utilization (or allocation) of human resources (HR) in a public service organization, especially to assure that functional performance satisfies organizational and public needs and objectives. This manuscript demonstrates the use of functional priority assessment (FPA) and functional failure risk (FFR) assessment to support and optimize human resource allocation (HRA) management in a public sector organization. Action research has been carried out in one Norwegian police district, to investigate the appropriateness of FPA and FFR assessment for HRA. First, functional priorities have been assessed, based on their impact relative to nine central organizational criteria. Further, based on a tailor-made risk matrix composed of six criteria, consequence of failure (CoF) and probability of failure (PoF) have been qualitatively assessed, resulting in a quantitative representation of FFR levels. The suggested Lean and MCA-based methodology provides significant support to strategic management and Lean practitioners who are involved in implementing or locating improvement initiatives in service organizations, especially in optimizing resource utilization.

Firms struggle to estimate the expected benefits of improvement suggestions. As a result, pointless or even damaging suggestions are sometimes implemented at the expense of potentially valuable improvement suggestions. This paper reviews, discusses, and advises on the use of available value assessment methods. Thereby, the paper contributes to the production improvement literature and practice with an overview and classification of common value assessment methods.

Within the field of management, knowledge management (KM) enables the generation, sharing, utilization and management of knowledge and information. In order to optimize the processes of KM, Lean thinking and tools can contribute to identifying and eliminating or reducing waste or activities and/or actions that disrupt or do not add value to the information and knowledge generation that ultimately returns value to the end user. Projects, such as product development projects (PDPs), are in general knowledge work (KW) dominated. This manuscript presents an exploratory case study, conducted at a company providing oil & gas operations’ services in Norway. The study examines the initial phases of their PDPs, where KM and KW play a more significant role, as information and knowledge are often limited, and uncertainty is high. It is hypothesized that waste in the early phases causes subsequent underperformance in the overall project. This manuscript first elaborates the notion of waste in knowledge work dominated projects from a theoretical perspective. Furthermore, a mixed-method approach was applied, while a survey and semi-structured interviews were conducted. Finally, the Gioia methodology is utilized to process the findings and present a conceptual framework to support KM as a means to prevent or minimize waste in KW-dominated projects.

In many areas, there is a multitude of terms/designations and definitions for the same concept, leading thus to misunderstanding. This also occurs with the designated Lean Production, which started to be known as a “thing” from the shop floor. However, it was quickly realized that it is much more than that (and should be understood as much more), otherwise the transformation of the operations will not be possible, as each company has its own organizational culture that could enable or inhibit the Lean implementation. Lean Production is underneath Lean Thinking, otherwise designated as philosophy, organizational culture, organizational model, production paradigm and others. This paper intends to present terms/designations and definitions that had been associated with Lean Thinking. The objective is to clarify that Lean Thinking is, in fact, all of that. Companies need to understand this in order to improve their operations, by recognizing value for the customer and eliminate wastes.

Since it was popularized in the 1990s, the adoption of lean production has been a primary driver for continuous improvement efforts worldwide. The next wave of industrial improvement is widely considered to be driven by industry 4.0 and digitalization. This has more recently led to the emerging concept of digital lean manufacturing. In this paper, we address a shortcoming in the extant literature, which presents an abundance of roadmaps for digitalization but very few addressing this in combination with lean. As such, we present a learning roadmap for digital lean manufacturing, with a core focus on cybersecurity. The roadmap has been developed and tested by combining theory with practical insights at the Norwegian Catapult Lean4zero Lab, Norway’s first and only full-scale digital lean simulator.

Digital Lean Manufacturing has emerged as a new approach to Lean Production, combining lean thinking and practice with the new opportunities presented by innovative digital technologies and Industry 4.0 concepts. However, this combined approach also raises certain challenges for the manufacturing industry. In this paper, we explore both the challenges and opportunities presented to manufacturers in light of digitalization contra Lean Manufacturing, with a specific focus on Production Planning and Control. Drawing on insights from four diverse explorative industrial cases studies, we identify the challenges and opportunities experienced by manufacturers embarking on a journey towards Digital Lean Manufacturing and highlight important avenues for further research.

Gemba Walks are an important mean of vertical integration in Lean Manufacturing environments. They ensure that all levels of the company stay connected with the front-line, “the Gemba”, where the actual value is created. However, traditionally Gemba Walks have been restricted to one location. This is a shortcoming in production environments characterized by interconnected and often globally dispersed problems where information from several locations is needed simultaneously. In response, this paper explores the emergence of new forms of Gemba Walks enabled by the adoption of new digital technologies. We intend to identify the advantages and disadvantages of using digital technology to support the execution of these new forms of Gemba Walks in more complex, globalized environments and to get a grasp of the extent to which digitalization changes communication characteristics between the parties involved.

Decreasing defects, waste time, meeting customer demand and being adaptable are the goals of a Zero Defect Manufacturing (ZDM) strategy. Scheduling is an important tool to perform that. It should take in account buffer size allocation. In this study, a method to solve the Buffer Sizing Problem (BSP), which is NP-hard problem. The current research work focuses on finding the optimal buffer allocation using Tabu-search (TS) algorithm. The goal is to minimize buffers’ sizes while maintaining a certain productivity. The evaluation of the alternative buffer solutions were performed using the following performance indicators; Makespan, Tardiness and the Buffers Cost. In the developed method the following are considered: multitasking machines subjected to non-deterministic failure, non-homogeneous buffer sizing, and non-sequential production line. The propose approach was tested via a real life industrial use case from a leading Swiss company in high precision sensors. The simulation results showed that the proposed methodology can effectively design the buffer strategy for complex production lines.

Considering the striking achievement of additive manufacturing (AM) as a revolutionary technology, it has increasingly attracted the attention of academia and industrial communities in recent years. Scheduling and production planning in AM play an essential role in the efficient and economical manufacturing of customized products through the saving of time and cost. In this paper, an AM scheduling problem is taken into account with different order specifications, especially the material type and due date on non-identical parallel machines. To formulate the problem, a bi-objective mixed-integer linear programming (MILP) model is proposed to minimize the makespan and the total tardiness penalty. Assuming parts with different material types necessitates the consideration of sequence-dependent setup time that depends on the material type of the current and previous jobs on an AM machine. Finally, an augmented $$ \varepsilon $$ –constraint method is applied for the problem to achieve a Pareto-optimal front in an illustrative instance.

Keeping pace with rapidly changing customer requirements forces companies to increase the capability of adaptation of their production systems. To fulfill the market requirements in a reasonable time and cost, distributed manufacturing has been emerged as one of the efficient approaches. Moreover, the ability of reconfigurability makes manufacturing systems and tools to be more adaptable. This research deals with a dynamic production scheduling problem simultaneously in several different shop-floors consisting of reconfigurable machine tools (RMTs) by utilizing the real-time data extracted from a cyber-physical system (CPS). First, a mathematical programming model is presented for the static state. Thereafter, by utilizing the CPS capabilities, a dynamic model is extended to schedule new jobs, in which there have already been some other jobs in each facility. A numerical example is solved to illustrate the validation of the model. Finally, some potential solving approaches are proposed to make the model implementable in real-world applications.

One of the main challenges of Industry 4.0 is the adaptation of existing production lines. Robots are substituting human workers in modern smart factories, as they are much more suitable for repetitive tasks. In contrast to that, Industry 4.0 predicts a high rise in product customization. The total disruption of the current factories, although the easiest solution, is not welcomed by the traditional industry stakeholders. To offer adaptation rather than disruption, and to promote man-machine collaboration rather than complete substitution of the human workforce, we present a Digital Factory solution capable of orchestrating different types of resources —humans, machines, robots— according to their capabilities. The core component of the solution is a real-time Orchestrator that orchestrates factory resources in order to produce the desired product. Orchestrator is a complex, modular, highly scalable, and pluggable software, responsible for dynamical matching, scheduling, and executing of production steps, allowing high customization and lot-size-one production.

This paper aims at studying the combination of different collaboration modes between operator and collaborative robot in order to optimize an assembly process for both economic and ergonomic objectives. Based on a real case study, and using a energy expenditure ergonomic model, the authors have determined by experiment the different ergonomic and economic variables under each possible collaboration mode. They propose a set of indicators to evaluate the quality of assignment solutions, as well as a multi-objective cost function to determine optimal trade-offs between the different collaboration modes. An initial set of trials has indicated that combining several modes of collaboration may deliver benefits for both economic and ergonomic performance.

Recent developments of computer technology and information and communication technology are realizing highly-distributed manufacturing systems (HDMSs) in which each machine is computerized and can communicate with other machines. For this type of manufacturing system, a distributed method of discrete event simulation and a distributed method of job shop production scheduling were proposed, respectively. Because these methods use a common distributed sequencing algorithm for HDMSs, they can be integrated and an integrated method of distributed simulation and scheduling was also proposed for job shops. This paper describes an extension of the method to flexible job shops, in which an operation is processed by one of multiple machines. A distributed algorithm for selecting the machine which processes the next operation of an intermediate job based on a given dispatching rule was proposed. By incorporating this algorithm, the conventional method can be applied to flexible job shops. In addition, a method of optimizing the machine selection by adjusting the ratio of each of multiple dispatching rules was proposed. The feasibility of the proposed method was shown by computer experiments.

The emergence of Industry 4.0 and its related technologies transformed modern manufacturing environment by making them more intelligent. This is associated with the fast evolution of data acquisition technologies and the enormous amount of generated data. Among these modern manufacturing environment, Reconfigurable Manufacturing System (RMS) is a concept able to cope with the current market conditions, characterized by an increasingly personalized and volatile demand. At the same time, Digital Twin (DT) emerged as a new concept. DT represents a new data-driven vision that combines real time data analytics, optimization and simulation. When managing modern and complex manufacturing systems, DT provides new insights and potentials in decision-making process support. In this context, this paper is an attempt to present an integrated RMS digital twin (RMS-DT) modular framework. RMS-DT is a model that can represent the system state at any moment in time while allowing a holistic system visibility to improve its performances and enable flexible decision-making. The paper is concluded with a discussion, future challenges and perspectives in order to enhance the proposed RMS digital twin framework.

Reconfigurable manufacturing systems (RMS) emerged in literature during the last two decades with the aim to respond to the rapid increase in product demand and variations. The implementation of such solutions in the industry is very recent and remains difficult. In this article, an analysis of the industrial requirements and challenges involving four key aspects of RDSS (reconfigurability, digitalization, servitization and sustainability) is based on semi-structured interviews conducted with representatives from the industry. Further, the identified requirements and challenges are compared to those extracted from an extensive literature review. The findings of the comparison are divided into technology and organization oriented issues and show a strong interconnection of the four key aspects: Digitalization offers possibilities for the implementation of sustainable systems, servitization creates the possibility for companies to achieve more flexibility through reconfigurable systems and the further development of RMS offers more possibilities for digitalization and thus a better adaptation to current requirements.

With the rise of mass customization, manufacturing companies are increasingly adopting mixed-model assembly lines. These lines can produce multiple products instead of a single one in a dedicated manufacturing system. Consequently, mixed-model assembly lines can benefit from reconfigurations of the workforce and equipment to adjust the line to the production requirements. This study investigates the impact of dynamic task assignment on the design of a mixed-model assembly line with walking workers. In the dynamic task assignment strategy, the assignment of tasks to stations changes depending on the item sequence. In this work, we propose a scenario-based integer linear program to design such an assembly line. The numerical results show that the dynamic task assignment strategy significantly reduces the number of required workers when compared to the fixed task assignment strategy, but it slightly increases the total equipment costs.

In this paper, we investigate the use of the scalability property of RMS to reduce the energy cost during the production. The corresponding optimization problem is a new Bilevel Optimization problem which combines a line balancing problem with a planning problem. A heuristic based on a simulated annealing algorithm and a linear program is proposed. An illustrative example is presented to highlight the potential of this new approach compared to the cost obtained with a classic production line.

Shipyards around the world have several differences that affect the logistics processes at each yard. The purpose of this paper is to develop a framework for mapping the key factors affecting shipyard logistics. We test and validate the framework by applying it to three case shipyards—one Norwegian and two South Korean. To develop the framework, we first identify key factors affecting shipyard logistics, based on a review of the existing literature. The framework is then applied using data from the three cases. Through a comparative analysis of the collected data, we identify and outline the main logistics differences and the key factors’ main implications for the shipyards. The findings from the analysis indicate that there are important differences between the shipyards, and these have implications for their scope of planning and execution of shipyard activities, their primary focus of coordination, and their primary flows, among others. Through the framework development and comparative analysis, the paper contributes to an enhanced understanding of shipyard logistics, as well as how it is affected by internal and external yard characteristics.

Industrial Augmented Reality (IAR) has proven its potential in ETO industry by improving productivity and acting as a key technology for consistent digital information flows. In order to leverage these potentials on a significant scale, broad distribution of IAR inside a company should be aimed for. However, most applications have not yet overcome a prototype state and failed to make the step into production. Primary limiting factors are inadequate device availability and acceptance of current IAR solutions, which focus on smart glasses and tablet devices. The aim of this paper is to examine whether smartphones have the potential to serve as a platform for IAR in ETO industry. First the user interface of an existing tablet IAR prototype is adapted to a smartphone in order to later evaluate user experience. Hence a survey to assess worker preferences between tablet and smartphone devices for common IAR supported tasks is developed. The survey design is tested on a group of workers from ETO industry.

Construction 4.0 is an engineering and construction paradigm derived from Industry 4.0 that describes the fourth industrial revolution. Construction 4.0 promises to revolutionize the way buildings are constructed and managed; however, there are several major hurdles to be overcome, such as the human skilling and degree of automation, the information systems aspect, and especially the heterogeneous nature of the projects and the supply chains involved. This paper elaborates on these challenges in achieving the Construction 4.0 paradigm and describes possible solutions using various concepts based on Collaborative Networks and Enterprise Architecture disciplines to enable the full potential of the Construction 4.0 paradigm.

Inclusion of ‘circular principles’ in the activities of the purchasing process from the initial stages to the end of product life can help all actors in the value chain to deliver sustainability goals through an active, cost-effective and accountable approach. Yet, research on this linkage has been virtually nonexistent. This study extends perspectives and theories on purchasing and circular economy business models (CEBMs) for engineer-to-order (ETO) production. Based on a case study, a framework that identifies critical purchasing activities relevant for enhancing the implementation of CEBMs is developed. The framework advocates that engaging in the proposed activities can compel the purchasing function to increase its strategic focus by being proactive and relentless in embracing circularity in its agenda. In addition to accentuating the relevance of purchasing function in ETO production, the framework shows how harnessing it can benefit circular strategies.

The complexity of ETO projects reflects also in the challenges of planning and controlling them. Most ETO companies apply planning procedures based on elements from the traditional project management literature with a linear approach that cannot deal with the challenges of such an environment. Moreover, most of these procedures focus on planning the production activities with little focus on planning the design-, engineering-, and procurement activities. This research looks into how ETO characteristics affect the planning of procurement activities since their outcome have a significant effect on the total cost of the project. The studied literature reveals a need for more knowledge on how to actually plan procurement activities since delayed materials and components contribute to major costs overruns and delays in ETO projects.

Companies are more and more required nowadays to monitor and to improve their cost structure. Hereby, ETO enterprises experience particular difficulties, due to the high complexity and dynamics as well as less opportunities for standardization which are an intrinsic part of their products and processes. Therefore, a systematic and holistic approach for cost management is needed, considering hard factors as well as soft factors, like organizational and behavioral aspects. In this paper, we introduce a product cost maturity model, which integrates a multitude of success factors for cost transformation from different categories in an ETO environment. Hereby companies will be enabled to determine and evaluate their current state as well as a future state to derive a roadmap for necessary actions. The maturity model is based on a thorough literature research and on practical experiences in ETO companies. It is complemented with a systematic procedure for cost transformation projects. We describe the maturity model and its application as far as possible in detail, hereby also justifying every design decision we made.

Today’s productions systems become more and more complex, comprising a multitude of different resources interacting with each other. The Theory of Constraints (TOC) describes the dilemma that the overall output of such a system is constrained by one or more resources called “bottlenecks”. Identifying those bottlenecks has been subject of many research papers. Classical approaches focus typically solely on the bottleneck utilization and neglect accumulated deviations against the planned utilization. This so-called backlog is critical as it has a direct effect on the on-time-delivery. This paper therefore provides a practical approach that can be used by the operative management in order to identify, prioritize and manage bottlenecks in a backlog situation efficiently and effectively.

Cross-functional coordination among engineering, sales and production departments is known to be beneficial for improving order fulfillment processes. In Engineer-to-Order (ETO) companies, sales, design and production activities are strongly interrelated and sometimes they overlap, thus requiring cross-functional coordination. In these companies, design and production activities can be both partially performed before the customer order arrival. ETO companies pursue different objectives and implement different managerial approaches before and after the customer order decoupling point (CODP). However, despite its relevance for company performance, how ETO companies manage cross-functional coordination and how departments are coordinated before and after the CODP is still understudied. This paper sheds light on this topic by investigating 12 case studies in the Italian machinery industry. Results suggest that the coordination mechanisms used before and after CODP are different, and vary depending on the CODP configuration chosen.

Many communities are interested in acquiring healthy food without pesticides and chemical preservatives. At the same time, they want to protect local production and cultural characteristics. In this sense, the academy starts to discuss a change in food supply chains and called of Short Agri-Food Chains. These networks have proven to be a reliable substitute for conventional supply chains creating empowerment from family farmers. The present study investigated the role of food banks’ on the structural basis of short food supply chains. The results pointed out that food banks may be an important instrument for consolidating these supply chains contributing to the development of Family Farm and Small producer.

Brazil is one of the main country growers in soybean production. With the purpose to maintain this position, the production is going to the new areas in the country seeking the low cost of land. In this sense, the Piaui state appears as a new frontier of growth. However, it causes a direct impact on infrastructure in the corridor of exportation. The study intends to analyze the production of soybean in Piauí state, Brazil, and the main logistics barriers. The work was carried out through qualitative research that allowed to characterize the producers regarding the size, productivity, costs, the origin of the input, transport, and issues in logistics infrastructure. The results showed the competitive advantages of soybean production in Piaui, as well as the main challenges pointed out by producers.

Ensuring the delivery of temperature-controlled products in transportation is an increasing challenge, especially in countries with continental extension and tropical climate such as Brazil. Products with this type of specificity generally have a higher added value and involve specialized equipment and labor. Thus, route mapping is necessary for the logistics of the cold chain. The study aimed to predict the transport conditions in the cold chain. The data set analyzed includes the temperature of the loads and the route information (Southeast to Northeast and South of Brazil). The classification of temperature excursions considered data below 15 °C or above 30 °C. The Naïve Bayes and Multilayer Perceptron algorithms are used to predict the optimal temperature excursion model. The Multilayer Perceptron algorithm proved to be the most suitable for a thermal route mapping model. With this identified standard, logistics decision making can be improved to reducer o waste and ensure product integrity with less recourse.

The study presents a model for classifying the environmental impact caused by the transport of vegetables from the production centers of several Brazilian states to a distribution center in Teresina, Brazil, using data mining. The distances from production regions to the distribution center were calculated. CO2-eq emissions and Global Warming Potential (GWP) were estimated. The GWP indicates the potential for the environmental impact that gas causes in each period (usually 100 years). We applied the data mining approach using the Rapid Miner Studio® software to build up the models. The target was the environmental impact indexed as “low”, “average”, and “high”. Results indicated that considering the on-road modal transport presented in the trees, the “product,” “distance,” and “quantity” classification for high environmental impact depends on the amount of product transported as well as the distance traveled. The found trees classify the impact and can be used as guidance for the decision-maker, as it can be used when planning and purchasing fruit and vegetables for public consumption.

The concern to meet food needs of the world population and preserve the environment is frequent today. In this context, Brazil has a significant participation in world agriculture. For instance, the country is the largest producer and exporter of green coffee grain. Coffee, besides being part of cultural habits in many countries, still corresponds significantly in the Brazilian trade balance, in the generation of jobs and income in the food industry chain worldwide, having its consumption associated with meetings and socialization and also as food base of children in a situation of economic and social vulnerability. The aim of this work is to discuss the economic and environmental performance, through the analysis of different combinations of cargo vehicles in different paving and non-paving conditions for the flow of coffee grain production from Guaxupé/MG to the Port of Santos/SP. It was observed that the best combination for load is “rodotrem” because it presents the best economic performance due to lower costs with diesel per ton of coffee grain transported and the best environmental performance which is maintained in all load combinations. It is clear that the Brazilian road network needs investments related to paving, technologies to improve fuel use and tire performance.

Order-sizing in replenishment planning and control for perishable products is studied in grocery retail context. There is a need for age-based policies that consider multiple products, the impact from price reduction (due to close-to-expiration), and product substitution in order to reduce waste, increase availability and improve freshness. This study develops a theoretical extension to known EWA-models considering positive and/or negative interdependence in substitution between products, impact from price reduction and expired products, as well as the inventory impact from other products safety stocks.

Food production chains have to respond to disrupted global markets and dynamic customer demands. They are coming under pressure to move from a supply to a demand-driven business model. The inherent difficulties in the lifecycle management of food products, their perishable nature, the volatility in global and regional supplier and customer markets, and the mix of objective and subjective drivers of customer demand and satisfaction, compose a challenging food production landscape. Businesses need to navigate through dynamically evolving operational risks and ensure targeted performance in terms of supply chain resilience and agility, as well as transparency and product assurance. While the industrial transition to digitalised and automated food production chains is seen as a response to such challenges, the contribution of industry 4.0 technology enablers towards this aim is not sufficiently well understood. This paper outlines the key features of high performing food production chains and performs a mapping between them and enabling technologies. As digitalisation initiatives gain priority, such mapping can help with the prioritisation of technology enablers on delivering key aspects of high performing food production chains.

This study introduces service robots to improve restaurant industry labor productivity because restaurant productivity is the lowest among service industries. Furthermore, this study represents an attempt to improve robot productivity because low robot productivity is the main hindrance to robot introduction into service industries. Service robot systems developed based on AGV robot systems were incorporated into operations of an actual restaurant. Staff operations changed. The AGV replaced conveyance operations to reduce staff work loads. Moreover, the AGV systems are refined to increase AGV work loads: the number of AGV battery chargers was increased to avoid electric battery power shortages, AGV boards were changed from fixed type to removable type. Moving patterns increased from 1 to 4 to adopt AGV operation for restaurant operation situation changes. Results indicate that the system redesign improves labor productivity. It reduces working time and increases service quality. Furthermore, the AGV system refinement enhances robot productivity: in fact, the total AGV daily moving distance was doubled.

In this paper, the number of customers visiting restaurants is forecasted using machine learning and statistical analysis. There are some researches on forecasting the number of customers visiting restaurants using past data on the number of visitors. In this research, in addition to the above data, external data such as weather data and events existing in ubiquitous was used for forecasting. Bayesian Linear Regression, Boosted Decision Tree Regression, Decision Forest Regression and Random Forest Regression are used for machine learning, Stepwise is used for statistical analysis. Among above five methods, the forecasting accuracy using Bayesian Linear Regression was the highest. The forecasting accuracy did not tend to improve even if the training data period was extended. Based on these forecasting results, the characteristics of days with low forecasting accuracy are analyzed. It was found that the human psychology around the payday and the reservation customers affected the number of visitors. On the other hand, the weather data such as temperature, precipitation and wind speed did not affect the accuracy.

Japan’s aging is rapidly progressing, and it is important to extend healthy life expectancy considering the increase in social security costs such as medical expenses and long-term care benefits and the decline of local cities. Nutritional management is considered to play a major role in extending healthy life expectancy. This study focuses on the menu plan for managed meals provided by hospitals or nursing homes. This study has proposed a menu planning method using Genetic Algorithm for 30 days (90 meals) in consideration of the order of provision so as not to get tired of a meal. This paper proposes a menu planning method that takes into account not only the variety of meals but also the cost of ordering ingredients.

In this paper, we perform a basic analysis on employee satisfaction and production planning by introducing a service robot which delivers dishes in restaurant service. As an example of a service robot introduced in a Japanese restaurant, we focus on the pantry staff, kitchen staff, and customer service staff, as well as production planning that individual employees implicitly plan and update in their heads. The service robots are used to deliver the dishes prepared at the kitchen to the customer service floor, where the worker specifies the destination and transports the dishes while patroling the restaurant. By analyzing the productivity and employee satisfaction before and after the introduction of the service robots into the categories of serving, cooking area, and customer service, how employees can identify and coordinate work between humans and machines and change process design. From December 2019 to January 2020, an analysis was conducted based on the results of employee questionnaires and interviews conducted at a restaurant.

Circular economy (CE) has been recently considered one of the most promising sustainable strategies for industrial companies, aiming at reducing resources consumption, extending resources life cycle and making recirculate resources within the life cycle stages. The transition from a linear economy towards a circular one requires the internal reorganization of companies without limiting the focus on product life cycle management, also considering how to appropriately manage internal assets, both physical, e.g. machines, and social, e.g. workforce. Indeed, the objective of the present work is to investigate the adoption of CE in industrial asset management (AM), thus focusing on the physical assets. A systematic literature review has been performed with a two-fold goal: firstly, to envisage the synergies between CE and AM and, secondly, to identify existing research gaps. Through this review, it was possible to notice the shared life cycle orientation of the two theories but the still embryonic adoption of it in the AM for a circular aim. Indeed, the major focus in the AM theory, from a CE perspective, is on the role of maintenance activities to extend asset life cycle during its Middle of Life stage while CE adoption at the Beginning of Life of industrial assets is still lagging. This limits a life cycle orientation which would boost industrial companies’ sustainability. In order to encounter policymakers’ expectations these two theories should be furtherly integrated.

Circular economy (CE) is considered one of the drivers pushing towards sustainable development. Indeed, this economy is defined as an “industrial economy that is regenerative and restorative by intention and design” and thus, it boosts responsible consumption and production which is one the sustainable goals promoted by policymakers. In particular, from the extant literature emerged that to pursue the transition towards CE, manufacturers adopt different CE strategies. Their implementation implies to have a clear vision about stakeholders involved along product life cycle and about the implications that are encountered during the decision process of the producer thus, by its companies’ functions. Indeed, all the actors involved should operate concurrently for a single and common direction. For this reason, the objective of the present work is to investigate the vertical information flow within a manufacturing company at strategic, tactical and operational levels while adopting CE strategies to appropriately manage product lifecycle. The paper objective is pursued through a literature review on Scopus together with practitioners’ interviews. The outcome is the development of a conceptual framework to support the decision process. The framework structures the information flow for adopting CE across the three levels above mentioned. Indeed, the main finding of this research is the key role of information as a facilitator for the adoption of CE strategies in manufacturing.

This research proposes a new conceptual model of the IT ecosystem required in the scope of global asset management. To accomplish this aim, the functionalities required by maintenance management are integrated with those required by Asset Management needs, thus extending the current scope of work of extant IT systems to a lifecycle management perspective. The allocation of the functionalities to three asset control levels (operational, tactical, strategic) is propaedeutic to derive the IT ecosystem structure based on three main software families. The model has been built along a collaborative project with a world leading company in the food sector. Lessons learnt on the proposed IT ecosystem for a centralized AM over geographically dispersed production plants are reported.

The purpose of this study is to provide an overview of a methodology, which will enable industrial end-users to identify potential components to be manufactured by Freeform Injection Molding (FIM). The difference between the technical and economic criteria needed for part selection for Additive Manufacturing (AM) and FIM will be discussed, which will lead us towards proposing a new methodology for part selection for FIM. Our proposed approach starts by identifying the most similar components (from end-user part libraries) to some reference parts, which can be produced by FIM. Identification will be followed by cluster analysis based on important factors for FIM part selection. As there are some interdependency between the factors involved in the clusters, some decision rules using Fuzzy Interference System (FIS) will be applied to rank the parts within each cluster using user-defined technical and economic criteria. Once the first set of potential FIMable parts have been identified, Design of Experiment (DOE) will be conducted to investigate which factors are most important and how they interact with each other to generate the desirable quality of the FIM parts. The DOE results will be validated in order to finetune the ranges of the parameters, which gives the best results. Finally, a predictive model will be developed based on the optimum feasible range of FIM parameters. This will help the end-users to analytically find the new FIMable parts without repeating the algorithm for the new parts.

Production ramp-up is a critical step in product life cycle as it could lead to either success or failure of product introduction into the market. The criticality of this step is owed to several factors including the uncertainty underlying this step regarding both expected costs and benefits, and thus to the complexity of decision-making. In order to enlighten decision makers particularly in multi-variant production contexts, this paper elaborates on an analytical model supporting cost-benefit analysis of production ramp-up strategies. The model takes into account capacity planning decisions and learning curves in determining cost-benefit estimates. The model is illustrated and discussed through a keyrings manufacturing process.

Production system lifecycle includes phases ranging from concept pre-study to ramp-up and operations. Manufacturing companies often face challenges to reach operational performance targets during ramp-up time and operation phase. The design phase is considered crucial as major decisions related to the future production system are taken during this phase. There is an opportunity to utilize the production system design phase to improve the operational performance during both the ramp-up and operation phase. This research aims to identify the critical factors of the design process that affect the performance in the ramp-up and operational phase. A case study was conducted in a pharmaceutical company where a completed project of launching a new production line for a new product was followed in retrospect. Data were collected by conducting interviews with different members involved in the project and the production team on the shop floor. By qualitative data analysis, critical factors affecting the project´s operational performance were identified; such as level of internal technical competency; involvement level of future line manager, operator and project sponsor within the project team; project team´s competency; pre-study of the business case; time pressure to complete the project; expertise of product and process; organization’s continuous improvement culture; and relationship with the supplier.

Fully dynamic cross-company production networks that adapt to individual customer orders are a core vision in Industry 4.0. For different reasons, like failure of machines of a supplier or a sudden increase of demand, additional production capacities might be required at short notice. However, there are barriers to finding and integrating suppliers with free capacities into existing ordering and logistics processes. A Dynamic Production Network Broker (DPNB), which is an online marketplace that actively connects suppliers and consumers of production resources to dynamic, cross-company production networks, might bridge this gap. New generic service-based business models are required for operation and usage of the DPNB platform. In this paper, such a business model is drafted.

Product take-back programs are becoming increasingly popular and widespread driven by continuous focus on sustainability and circular economy. As a result, manufacturing systems need to be designed to handle not only disassembly, but also reprocessing of materials, re-assembly, and remanufacturing in a cost-efficient way. Compared to traditional manufacturing, this involves higher need for changeability due to higher uncertainty e.g. in terms of timing, quantity, and quality of received items to handle, and in particular due to significant variety in returned items. Therefore, the aim of this paper is to provide empirical insight on how changeability and reconfigurability can be applied to meet challenges in development of closed-loop manufacturing systems for product take-back.