Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing

Manufacturing firms are constantly evolving to accommodate new customer requirements as well as emerging technologies, materials, processes and equipment. As a consequence, a broad range of production innovation opportunities arise for manufacturing firms to produce their products in smarter, more flexible, agile and sustainable ways. This paper proposes a strategic planning framework for “production innovation” and discusses its implications for the evolution of companies-specific production systems and competitive advantages.

“The most profound technologies are those that disappear… They weave themselves into the fabric of everyday life until they are indistinguishable from it” wrote computer scientist and visionary Mark Weiser nearly 25 years ago in his essay “The Computer for the 21st Century.” It turns out he was right: in the age of “Industry 4.0”, digital technologies are the core driver for the manufacturing transformation. In fact, the introduction of such technologies allows companies to find solutions capable to turn increasing complexity into opportunities for ensuring sustainable competitiveness and profitable growth. Nonetheless, the effective implementation in manufacturing still depends on the state of practice: it may slow down, or even worst, may prevent from implementation. Indeed, we assume that a minimum level of capabilities is required before implementing the digital technologies in a company. Based on this concern, our research question is “are manufacturing companies ready to go digital?”. This paper wants to illustrate a “tool” to answer this question by building a maturity assessment method to measure the digital readiness of manufacturing firms. Based on the inspiring principles of the CMMI (Capability Maturity Model Integration) framework, we propose a model to set the ground for the investigation of company digital maturity. Different dimensions are used to assess 5 areas in which manufacturing key processes can be grouped: (1) design and engineering, (2) production management, (3) quality management, (4) maintenance management and (5) logistics management. Thus, the maturity model provides a normative description of practices in each area and dimension, building a ranked order of practices (i.e. from low to high maturity). A scoring method for maturity assessment is subsequently defined, in order to identify the criticalities in implementing the digital transformation and to subsequently drive the improvement of the whole system. The method should be useful both to manufacturing companies and researchers interested in understanding the digital readiness level in the state of practice.

Recently, the concept of smart manufacturing has emerged as a new paradigm, with which manufacturers can enhance their competitiveness in the market. Smart manufacturing paradigm can be viewed as the convergence of Information & Communication Technologies with human capabilities and manufacturing technologies. The new paradigm is expected to bring a new wave of performance improvements to manufacturing industries. However, manufacturing enterprises need to expend significant effort when preparing to adopt new technologies and realize their full benefits. MESA (Manufacturing Enterprise Systems Association) created the Manufacturing Operations Management/Capability Maturity Model (MOM/CMM) to help evaluate the maturity and readiness of manufacturing enterprises from the factory operations perspective. However, the model, in its raw form, can be time- and resource-consuming. It also lacks improvement strategies that use results of evaluation. The objective of this work is to restructure the questionnaire to reduce its completion time and to outline strategies through which a manufacturing enterprise can derive its improvement plans.

In order to meet the ever-changing customer demands, smart manufacturing needs to intelligently integrate and coordinate the entire manufacturing supply chain. Event-driven architecture has been considered as a promising enabler for smart manufacturing. However, in the primitive event-driven architecture all components work in parallel, and there are few frameworks or standards that deal with organizing and managing the components. This paper proposes an ontology based holonic event-driven architecture for smart manufacturing systems. Powered by the developed ontology model and Semantic Web technology, the ontology based architecture enables the distributed components in a smart manufacturing system to be configured autonomously and collaborate with each other even when they don’t know much about their counterpart.

The research on Industry 4.0 is increasing in importance over the years due to the expectation that it represents a new industrial paradigm, increasing competitiveness to the industries that can adopt it. The objective of this paper is to study the main points of research on Industry 4.0, featured universities and research centers. Using methodology based on bibliographic review, we analyzed a total of 546 papers, which composed the proceedings of the International Conference Advances in Production Management Systems (APMS), in 2014 held in Ajaccio (France), 2015 in Tokyo (Japan) and 2016 in Iguassu Falls (Brazil) and selected 39 papers to make this research. The results revealed that Industry 4.0 is increasing in importance, broadening the field of research; some suggestions for future research are presented.

Production Internet as an eco-systemic Web-based infrastructure goes beyond the traditional setups of industrial cooperation, as well as existing peer-to-peer services for economic exchange, like e-tailing, e-sharing or crowd-funding. This paper investigates the feasibly functional arrangements of Production Internet. Throughout reflection on the state-of-art, supplemented by a foresight research, the needs, requirements and benefits are identified then composed into functional setting, which was validated using a prototype implementation. The discussed conceptualization is expected to improve operational effectiveness, efficiency of resources, and reduce transaction costs.

Human beings have suffered from disasters continuously, therefore, the post-disaster efforts to reduce additional damages have been widely conducted. In this study, we focused on the repair crew scheduling to set a plan for repairing destroyed roads. Rural areas were considered because of limited link members of supply chain networks, and rural isolation caused by road destruction is the main concern of this study. To reflect the intrinsic nature of a disaster in the short-term, additional damages and variable damage rates were considered. The repair crew scheduling problem considering variable disaster aspects was proposed to minimize total damages caused by isolation. A small-scale experiment was conducted and the result showed that our model can be used to effectively reduce further damages compared to previous research.

Innovation projects in manufacturing domain often include several end users with different use cases that require a special approach for converging to one architecture solution, which addresses the needs of all end users. The communication between end users and developers in different research and software development projects should be supported correspondingly. This paper describes an approach to development of software intensive system architecture in large collaborative projects that extends traditional approaches with different architecture viewpoints and additional iterative steps aiming to design a main platform integrating project solutions. The approach is applied and validated in a large collaborative EU-funded H2020 research project entitled Z-Factor, i.e. Zero-defect manufacturing strategies towards on-line production management for European factories. Based on the standard ISO/IEC/IEEE 42010 that implies a process based on a set of relevant architecture viewpoints and following the architecture development approach introduced in this study, Z-Factor platform is defined by the following viewpoints: conceptual, functional, information, and deployment.

Many manufacturing companies have limited insight into the complete life cycle of their products. Insight that is necessary to manage the environmental impacts associated with the company’s operation. One possible solution to this challenge is to apply emerging sensor technology for communicating with products. Despite the current trend of digitalization and use of sensors in the manufacturing industry, there is little attention given to how product communication may improve environmental sustainability.We propose a framework on how product communication can facilitate improved life cycle management, by contributing with needed information for improved value chain insight, design for environment and end-of-life management.

The paper has the objective of investigating the effect of the arrangement of the maintenance interventions along the planning horizon on the reliability of a mechanical system. In fact, the specific arrangement of the maintenance interventions during a specified time horizon influences the system reliability, and this is an aspect that has not received much attention in the literature. The aim of this paper is to propose an orchestration model that finds the best arrangement of maintenance interventions based on a deep analysis of the system reliability using the cross-correlation mathematical operator. The research demonstrates that, by means of a proper arrangement of the preventive maintenance interventions, higher minimum system reliability can be achieved.

The integration among the organizational functions managing assets along its lifecycle is a crucial aspect for production companies to implement Asset Management. In this paper, an iterative four-step methodology is presented to support the joint maintenance and production planning considering the system configuration optimization as well. The objective is to overcome the limitations of most of the approaches that can be found in the literature regarding joint optimization models, by integrating it with a system-oriented and reliability-based approach. Reliability, Maintainability and Availability analysis at system level is used to support the traditional joint optimization models. The methodology is applied in an industrial case in the mining sector.

Operation and Maintenance (O&M) is a key value driver for offshore wind farms. Consequently, reducing O&M costs improves their profitability. This paper introduces different typologies of dispositioning maintenance tasks in offshore wind farms, in order to help design the strategies and organization of maintenance. Based on the special requirements of offshore wind farms regarding planning and controlling the O&M activities, a morphological analysis was developed. With this different disposition strategies for offshore wind farms could be generated. The consequences of choosing different characteristics are allegorized in an exemplary fashion. The work presented in the following is the foundation for designing a software-based dispositioning tool for usage in offshore wind farms, which will help to increase the effectiveness of the disposition in offshore wind farms by maximizing the number of accomplished tasks per day and minimizing the time technicians stay on the wind turbine and the ships.

As a promising technology with increased adoption in recent years, Internet of Things (IoT) realizes ubiquitous interconnection of physical devices through internet, opening doors for building powerful industrial applications by leveraging the advances in sensor technology and wireless networks. IoT technologies can be viewed as enablers for smart manufacturing and Industry 4.0. This review paper focuses on applications of IoT in manufacturing, which is also known as Industrial Internet of Things IIoT. To that end, technologies relevant to the application of IoT in manufacturing, such as wireless sensor networks (WSNs), smart sensors, big data analytics, and cloud computing are discussed. A service oriented architecture (SOA) based four-layer model for realizing IoT applications in manufacturing is proposed. Finally, a review of the state of art of IoT applications in manufacturing including shop floor automation, predictive maintenance, energy aware manufacturing, and smart workers is presented with relevant industry use cases.

Industry 4.0 is today one of the major opportunities for companies competing in the market. In last few years, more and more companies have started to define the path to move from their traditional production systems towards the Industry 4.0 paradigm; accordingly, different models have been proposed in literature to support the business transformation. This paper reviews the technological improvements proposed by Industry 4.0 to understand what are the main processes involved in the transformation and what are the suitable strategies to face the business and operational changes that are required. In particular, we identify and discuss two main perspectives: the suppliers and the customers. For both of them, different business opportunities are presented, and the expected performance improvements discussed.

Lean Production has proven to be a valuable methodology to improve productivity while reducing costs. Notwithstanding the countless successful lean implementations in the extant literature, others highlight its limitations, especially in production environments characterized by demand volatility, high product mix and reduced lot sizes. Technology is seen by many as a potential solution to such limitations, especially in the last years, with Industry 4.0 becoming an emerging frontier for the smart factories of the future. However, studies about the relationship between lean and smart manufacturing are scarce and often anecdotal. Therefore, the proposed work aims to fill this gap by developing a comprehensive model that links these two perspectives and serves practitioners to achieve lean’s core goals in smart factories.

The fourth industrial revolution (“Industry 4.0”) promises a multifaceted paradigm shift in manufacturing. This study aims to gain an in-depth understanding of what the transition to Industry 4.0 may involve. We do so by looking for, and learning from, experiences of similar shifts in the past. Specifically, we conduct a structured literature review of the rich operations management literature on Advanced Manufacturing Technologies (AMTs). AMTs were arguably central in the shift from the second to the third revolution in the second half of the 20th century. A review of the existing AMT literature allows us to infer relevant observations, theories, and findings for the emerging shift into Industry 4.0. We employ the review process defined by Tranfield et al. (2003).

The accelerated use of technologies has led to what is termed the fourth industrial revolution, or Industry 4.0. It is based on machinery, robots, production lines, items and operators connected via the Internet to each other and to back-end systems, as a part of the Internet of Things (IoT). In this paper, we propose a new IoT Technological Maturity Assessment Scorecard that can assist manufacturers in adopting IoT-technologies. To demonstrate the Scorecard, we present a case study applying the scorecard in four Norwegian manufacturing companies.

Promising developments and further improvements of cyber physical logistics systems (CPLS) and automated guided vehicles (AGV) lead to broader application of such systems in production environments and smart factories. In this study a new mixed integer linear program (MILP) is presented for the scheduling of AGVs in a flexible reentrant job shop with blocking. Optimal solutions to small instances of the complex scheduling problem in a make-to-order production, minimizing the make span, are calculated. Different numbers of jobs are considered. Feasible schedules for the machines and the AGVs are generated from different sized instances to evaluate the limits of the mathematical model.

Energy and resource efficient manufacturing has become one of the most relevant research topics, for the increasing attention to sustainable development at planetary level. This work focuses on deployment of a Cyber Physical Production System in a laboratory setting in Technical University of Braunschweig, Institut für Werkzeugmaschinen und Fertigungstechnik (TUBs IWF), with the aim of improving production systems operation in terms of efficiency in resource usage, taking inspiration by the work developed in Politecnico di Milano by some of the authors of this article, focusing on a production system energy aware control, explored so far by means of simulation experiments.The objective of this article is studying alternative ICT architectures for the CPS-ization of a production line, namely, a serial line, which matches the main requirements needed from Cyber Physical Production Systems (CPPS), machine to machine communication and local processing, in preparation to deployment, with support of state of the art technologies, such as OPC-UA communication. The proposed solution is interesting for industry, as it shows a practical solution for application on the shop-floor of the Cyber Physical Production systems approach in the vision of Industry 4.0.

The use of sensor data as well as the combination of different data from diverse systems in production and logistics lead to new opportunities for monitoring, controlling and optimization of processes within the scope of Industry 4.0. New developments of camera-based systems support this trend, which is particularly relevant for the control of cyber physical systems (CPS). This paper discusses a new approach for camera-based dynamic identification and localization, including speed and orientation determination, in combination with joined data form different sources and data analysis for CPS. In order to assess the potential of the approach, various possibilities and methods for camera-based optimizing CPS are discussed. A production-oriented logistics application shows the technical feasibility of the approach.

In this paper, we present an approach to how to store production plans directly on an RFID tag in the form of an automaton. Based on a modular manufacturing system, this enables manufacturing systems to become more flexible and changeable and, in addition, reduces the engineering effort for adaptation in an existing plant. The connection between different production modules is implemented via carriers and a Mealy machine that is stored on an RFID tag. This machine’s states represent the production steps of the product on the carrier.

This paper focuses on the internal network of multinational companies (MNC) and aims to investigate the role of information systems (IS) based on modern information and communication technologies (ICT) in transferring knowledge between different plants of the MNC, a subject still debated in the literature. To shed more light on this relationship, we propose that in the context of the MNC, the plant’s role in the knowledge network has to be taken into consideration.The analysis is based on a case study approach with interviews conducted at thirteen manufacturing plants. Data analysis shows that plants can have two basic roles in the knowledge network: knowledge senders or knowledge receivers. Knowledge sending plants see IS less supportive in transferring knowledge, while most knowledge receivers rely heavily on some form of IS. Furthermore, IS proved unhelpful if the quality of data entered in the system was low, or when strategic support to allocate resources to use IS was missing.

In the Industry 4.0 era, numerous manufacturing enterprises have tried to obtain a smart manufacturing system. A key component of a smart manufacturing system is data analytics to support optimal decision-making in production systems. Consequently, many IT service providers have developed data analytics services. However, many small- and medium-sized enterprises (SMEs) have very low penetration of data analytics services compared to large enterprises because of the low profitability for IT service providers. Mass-customizable data analytics (McDA) services, which can be applied to various manufacturing SMEs, can give IT service providers the opportunity to increase their sales and thus their profits by applying services to more companies at little extra cost. This paper proposes a conceptual development process of McDA services for manufacturing SMEs and suggests future research issues. We believe that this paper can contribute to the dissemination of a smart manufacturing system.

Manufacturing companies advertise their manufacturing capabilities and services on their company website using unstructured natural language text. The unstructured capability data published on the web is a rich source of formal and informal manufacturing terms and knowledge patterns. Through systematic mining of a large collection of capability text, new semantic models and knowledge graphs can be extracted that can be used as the stepping stone of more formal ontologies. The objective of this research is to develop a framework for better understanding, analyzing, and summarizing manufacturing capability data that is available on the websites of manufacturing companies. The findings can support supply chain decisions and may result in the discovery of new trends and associativity patterns in the data. The focus of this paper is on demonstrating how visual analytics (VA) tools can be used for gaining insights into manufacturing capability and the associativity pattern among the capability entities labeled by various terms. A visual analytics system, named Jigsaw, is used for exploring the connections between various entities such as process, material, industry, and equipment across the documents in the experimental dataset.

The market share of renewable energy is rising all over the world and leads to a more and more volatile energy supply. The challenge of keeping supply and demand constantly balanced is getting more complex and dynamic. Large scale energy consumers like industrial facilities need to take on an active role in the energy system and adapt their energy consumption to the energy availability. Denoted as energy flexibility this approach controls the energy consumption by changing e.g. the production plan. Storage technologies decouple offer and demand of energy, that end-users are enabled to adapt their energy consumption. Testing new applications for storages can be technologically demanding and is associated with high costs. This paper proposes a hardware in the loop test environment, with which hardware integrations and control strategies of electric storage systems can be tested on a small scale.

Information is a crucial factor for companies in all lines of business. Within the years the requirements to do business have changed and got more complex. Due to recent developments and trends such as Industry 4.0, Cyber-Physical-System, data and/or information is omnipresent. In this context Information logistics is a relevant discipline to deliver the right information element. To attain this goal, it is essential to manage and supply efficient data and information. Therefore, this paper deals with trends and models behind Big Data and the influence on production and logistics.

As companies struggle with the various challenges of their PSS, identification and elimination of possible errors and potential challenges early in the design phase is crucial. This paper aims to identify the universal key aspects of success for a PSS; thus, providing a general foundation for many companies within the industry to focus their initial efforts. Utilizing historical and contemporary literature within the PSS field, one central aspect was identified with three additional aspects serving to support the core aspect that all PSS are centered around – creating customer value. Standardization, product usage information (PUI), and environmental sustainability all support the goal of ultimately adding customer value. While there is a potentially unlimited number of additional individual supporting aspects that are important and contribute to this objective, these do not apply to all PSS universally. As a combinational effort, the three supporting aspects help to ensure that value is added to the customer in different, but related routes. These aspects involved may be utilized to allow companies to examine and analyze their current PSS and implement these aspects into their PSS operations and/or, ideally, reflecting on them during the PSS design phase. A limitation of this study is, that the universal nature of the 4 identified key aspects reflects only the PSS cases within the literature reviewed.

The importance of social networks and, in particular, enterprise social networks in business contexts is increasing significantly. Regarding the prerequisites for a successful implementation of an enterprise social network, exclusively providing the technical infrastructure is insufficient. A holistic view that considers and integrates different perspectives is crucial for success. This includes technological, organisational and human aspects as equally important parts of the network. This paper identifies prerequisites for a successful launch of enterprise social networks and groups them along these three dimensions.

Companies are currently preparing for the fourth industrial revolution, which is envisioned to radically change manufacturing processes, logistics and business models in global manufacturing networks. Previous research have emphasized the need to respond to the changing landscape of the digital economy in dynamic and innovative ways. This study aims at exploring how small and medium-sized companies are prepared to meet this opportunity and challenge. In order to do this we have applied insights from innovation theories and empirical findings from eight companies that are part of two industrial clusters. The findings in this study indicate that even though most of the case companies have ambitions to position themselves in a new digital landscape, they prepare themselves differently. We see that organizations that has progressed furthest in implementing Industry 4.0 related concepts are the ones that make actively use of their external network in cooperation and sharing knowledge. These companies also have managed the balance between exploration and exploitation internally, where employees are both engaged in efficient manufacturing of existing products and product development. Consequently, we claim that both openness and organizational ambidexterity is vital for successful implementation of Industry 4.0.

With customers demanding more and more holistic answers to their problems, solution providers respond with complex systems, integrating product, service and ICT elements into their offer. These solutions need to be aligned to a high number of requirements, coming not only from the individual customer but also from an environment of network partners, technology providers and other stakeholders. Especially for Product-Service Systems, where the solution provider takes responsibility in the operational phase, this environment is dynamic over the system life cycle. Stakeholders may enter or leave, as well as changing needs and technological capabilities. This makes the requirements towards the solution volatile, demanding a suitable Requirements Engineering approach. In this paper, it is discussed how environmental dynamicity can be monitored for its effect on requirements, with a special focus on organizational issues. Through a literature review and industrial case studies it is analysed, how it can be ensured that environmental changes can be taken into account in Requirements Engineering, leading to an optimal system configuration to address the customer problem.

The prevailing industrial digitalisation flagship initiative, Industrie 4.0, gathers a substantial part of its functionality from the human in the system. This will drive a need for focus on both human and social dimensions of technology. The paper explores the roles of the Social Operator 4.0 in smart and social factory environments, where humans, machines and software systems will cooperate (socialise) in real-time to support manufacturing and services operations. A Social Factory Architecture based on adaptive, collaborative and intelligent multi-agent system is proposed for enabling such cooperation. Further, production scenarios are proposed, to show how social operators, social machines, and social software systems will communicate and cooperate via enterprise social networking services to accomplish production goals in the Social Internet of Things, Services and People.

With rapid advancements in the application of Industry 4.0 technologies throughout industries, a collection of different views on its potential implications for workers are emerging. Various authors agree that these technologies and their application in manufacturing systems is structurally different compared to current methods of production. Consequently, it is expected that the impact on manufacturing jobs, specifically on the tasks, is profoundly different from what we already know from literature. However, authors often borrow from existing literature to describe changes in work, and are not explicit on how and why Industry 4.0 and the implications is conceptually different. Until now, little research has focused on defining the technical functionalities that give rise to new job designs. This paper therefore focuses on synthesizing the diverging views on the effect of Industry 4.0 on employees’ jobs and specifically aims to understand how the technical changes of the transformation towards a Cyber Physical System in production relate to changes in job design. The central question this paper addresses is: How do the technical changes of the transformation towards a Cyber Physical System impact job design in industrial production? The contribution is an overview of the technical functionalities of Cyber-Physical Systems that are conjectured to change direct and indirect value-adding jobs in industrial production. This model will be used as a basis for further empirical inquiries. Moreover, it provides central points of interests for organizations involved with the design and implementation of Industry 4.0, focusing on job design.

Industry 4.0 is at the center of the current debate among manufacturing leaders, industrial practitioners, policy makers and researchers. Despite the increasing attention paid to changes in jobs and skills generated by Industry 4.0, research in this domain is still scarce. Our study focuses on the evolution of technical skills in the context of Industry 4.0 and it provides qualitative insights gained from an on-going collaborative research project involving a variety of manufacturing stakeholders in Northern Italy (e.g., manufacturing companies, industrial associations, academic and education experts, recruiting companies, IT providers, consultants, etc.). Our findings contributes to shed light on manufacturing skill needs linked to Industry 4.0, setting the stage for future research on the topic and providing companies, policy makers and education stakeholders with first indications to detect skill gaps and initiate competence development.

The recent advances in digital technologies and in additive manufacturing (AM) in particular are revolutionising our industrial landscape. These changes require new engineering and management skills to exploit fully and sustainably the benefits offered by these advanced technologies. The current talent shortage calls for new education programmes to deliver a skilled, capable and adaptable workforce. Existing courses on design, engineering and management related to production and manufacturing do not systematically deliver the necessary skills and knowledge for an effective deployment of AM technologies. Based on a literature review and evidence collected from multi-stakeholder workshops, this paper presents the key themes for education programmes to address the current skill gap and barriers to AM adoption and exploitation.

With the application of smart technology concepts, the fourth stage of industrialization, referred to as Industry 4.0, is believed to be approaching. This paper analyzes the extent to which smart factory concepts and e-learning have already deeply affected manufacturing industries in terms of performances in transitional economy. Empirical results indicate that manufacturing companies that have introduce both e-learning and selected smart factory technology concepts differ significantly. E-learning is mainly applied on graduates in production. Results reveal that two smart factory concepts are significantly and positively related to the firm performance when e-learning is applied.

Human resources are one of the most important assets of an organization. The setup of a proper Human Resource Management system undoubtedly represents one of the pillars upon which any organization should be built. Many effective standards and solutions have been proposed in the past decades. However, the ever changing environment and the emerging technologies, such as ontologies and linked data, lead to adapt them and consider new approaches. The solution proposed in this document aims to combine existing standards for manufacturing information and ontology modelling. As a result, the development of an ontology model enhancing the HR information flow with semantics, on the one hand, enables the use of common data formats and exchange protocols promoted by the world Wide Web Consortium (W3C) and exploitable on the Sematic Web. On the other hand, it lays the foundations for an automated decision making process based on inference rules and smart data management. A study has been performed in a real-life industry revealing highly notable results.

The recent advancements of manufacturing towards the Industry 4.0 paradigm should be supported by the effective training of industrial workers in order to align their skills to the new requirements of companies. Therefore, the evaluation of the training is becoming in this context increasingly important, given also the possibility of exploiting a huge amount of data from the shop floor about the workers’ activities. These data – indeed – can be properly collected and analysed so as to provide real-time indications about the workers’ performances and an evolving classification of their skills. In order to pursue this objective, a solution can be represented by the integration of semantic technologies with training evaluation models. For this reason, the paper aims at presenting a Training Data Evaluation Tool (TDET), which is based on the integration of a Training Evaluation Ontology (TEO) with a Training Analytics Model (TAM) for the definition of the skill levels of the workers. The main components and features of the TDET are provided in order to show its suitability towards the collection of data from the shop floor and their subsequent elaboration in summary indicators to be used by the management of the company. Finally, the implications and next steps of the research are discussed.

Currently, much attention is given to the technological opportunities and challenges that “Industry 4.0” entails. However, though the change towards Industry 4.0 is driven by technology, this industrial revolution is not strictly technological. The human aspect of Industry 4.0 is still an emerging field, and must be further researched if modern manufacturers are to reach their full potential. While manufacturers have a high focus on modernizing production processes, the accelerating automation and consequent increasing complexity of tasks is not accompanied by the necessary support for the operator. This results in inefficiency and non-optimal use of workers’ capabilities and potential. We argue that operators need technical support systems for increased situation awareness, to be able to efficiently handle an increased pace and complexity of tasks. Our empirical evidence shows that this is not only valid for high-tech manufacturing, but can also be seen in “traditional” manufacturing. We use case studies from three Norwegian manufacturers to illustrate how digitization is yet to reach the operator.

The time for maintenance operations is often restricted due to external circumstances. When conducted with inexperienced operators, additional uncertainties can arise and in case of a delay, high follow-up costs emerge. By using Virtual Reality (VR), operators may practice their skills in advance. This paper analyzes the training content and describes which parts of a generic operating cycle have the potential to be supported by VR as training technology. Furthermore, a training procedure and a resulting system architecture that allows to work efficiently with VR as a training technology are presented.

High productivity is essential for companies in order to survive in international competition, especially for those located in high-wage countries. Recent developments of digitalization open up new opportunities to manage and increase productivity. The development of company-specific strategies for the management of productivity, which are increasingly embossed by digitalization, is therefore an elementary task. This paper presents a framework for systematic design of productivity strategies for industrial production and explains conceptual potentials for the design of strategies. A detailed description of terms provides the necessary understanding. The framework encompasses three axes, the goal of productivity, the application of digital information management in industrial production and the different direct production areas as well as indirect supporting areas. The application of this framework is described for different corporate levels considering task- and goal-setting for various time horizons as well as an integrative view on technological, organizational and personnel aspects.

This paper presents the results of a study that was conducted in the Demonstration Factory Aachen in order to analyze the potential benefits of digital assembly instructions compared to paper-based ones. The aim of this study is to validate three hypotheses regarding the benefits in terms of productivity, quality and learning rate. The results will be used to assess the benefits of a potential rollout of digital assembly instructions in a German mid-size company that assembles multi-variant products in the machining equipment sector.

Maintenance tasks are a frequent part of shop floor machines’ schedule, varying in complexity, and as a result in required time and effort, from simple cutting tool replacement to time consuming procedures. Nowadays, these procedures are usually called by the machine operator or shop floor technicians, based on their expertise or machine failures, commonly without flagging the shop floor scheduling. Newer approaches promote mobile devices and wearables as a mean of communication among the shop floor operators and other departments, to quickly notify for similar incidents. Shop floor scheduling is frequently highly influenced by maintenance tasks, thus the need to include them into the machine schedule has arisen. Moreover, production is highly disturbed by unexpected failures. As a result, the last few years through the industry 4.0 paradigm, production line machinery is more and more equipped with monitoring software, so as to flag the technicians before a maintenance task is required. Towards that end, an integrated system is developed, under the Industry 4.0 concept, consisted of a machine tool monitoring tool and an augmented reality mobile application, which are interfaced with a shop-floor scheduling tool. The mobile application allows the operator to monitor the status of the machine based on the data from the monitoring tool and decide on immediately calling AR remote maintenance or scheduling maintenance tasks for later. The application retrieves the machine schedule, providing the available windows for maintenance planning and also notifies the schedule for the added task. The application is tested on a CNC milling machine.

Nowadays, manufacturers must be increasingly flexible to quickly produce a high mix of on-demand, customer-specific, low volume product types. This requires flexible assembly lines with operators that are well-supported in their constantly changing assembly task, while producing high-quality, first-time-right, zero-defect products. Information coming from various supporting systems, such as ERP, MES and operator support systems, needs to be combined by the operator that configures the assembly line with materials, instructions and machine initialization settings. In this paper, we present a knowledge model that captures the main concepts and their relations in flexible manufacturing to deal with these challenges. This model is constructed by integrating existing manufacturing ontologies and can be used as the basis for information collection, exchange and analysis in information systems used in flexible manufacturing. The model supports (1) easy definition of recipe-based manufacturing instructions for engineers and operators, and (2) flexible, modular and adaptive support for human/cobot instructions. We also describe a demonstration set-up with an existing operator support system (OPS) in which the recipe concept is used in the engineering process to easily reuse existing modular components for assembling different product types.

The paper presents a holistic evaluation model of an innovating enterprise that considers the social dimension. The design and functioning of the model is consistent with the paradigm that assumes a balance between economy, natural environment and the society. The evaluation is conducted with the use of a three-step approach (size – module – evaluation factor), and it also takes into account relations between particular structural elements.

The purpose of this work is to envision the future of maintenance without forgetting the past and present of maintenance practices. Indeed, there is a big potential for maintenance, fostered by the promises of the Fourth Industrial Revolution. At the same time, there is still a widespread evidence of the state of practices leading to assert that maintenance is not yet advanced as it would be expected. Thus, comparing the vision supported by advanced maintenance systems, through the concepts of E-maintenance, Internet of Things and Cyber Physical Systems, with the evidences on the state of practices collected based on a sample of more than 300 industrial plants, we come up with a reflection on the advancement of maintenance management towards Smart Maintenance.

The digitization of manufacturing through the introduction of Industrie 4.0 technologies creates additional business opportunities and technical challenges. The integration of such technologies on legacy production machinery can upgrade them to become part of the digital and smart manufacturing environment. A typical example is that of industrial monitoring and maintenance, which can benefit from internet of things (IoT) solutions. This paper presents the development of an-IoT-enabled monitoring solution for machine tools as part of a remote maintenance approach. While the technical challenges pertaining to the development and integration of such solutions in a manufacturing environment have been the subject of relevant research in the literature, the corresponding new security challenges arising from the introduction of such technologies have not received equal attention. Failure to adequately handle such issues is a key barrier to the adoption of such solutions by industry. This paper aims to assess and classify the security aspects of integrating IoT technology with monitoring systems in manufacturing environments and propose a systematic view of relevant vulnerabilities and threats by taking an IoT architecture point of view. Our analysis has led to proposing a novel modular approach for secure IoT-enabled monitoring for legacy production machinery. The introduced approach is implemented on a case study of machine tool monitoring, highlighting key findings and issues for further research.

The emergence of Industry 4.0 leads to the optimization of all the industrial operations management. Maintenance is a key operation function, since it contributes significantly to the business performance. However, the definition and conceptualization of Condition-based Predictive Maintenance (CPM) in the frame of Industry 4.0 is not clear yet. In the current paper, we: (i) explicitly define CPM in the frame of Industry 4.0 (alternatively referred as Proactive Maintenance); (ii) develop a unified approach for its implementation; and, (iii) provide a conceptual architecture for associated information systems.

Artificial intelligence applications are increasing due to advances in data collection systems, algorithms, and affordability of computing power. Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a review of recent fault diagnosis applications in manufacturing that are based on several prominent machine learning algorithms. Papers published from 2007 to 2017 were reviewed and keywords were used to identify 20 articles spanning the most prominent machine learning algorithms. Most articles reviewed consisted of training data obtained from sensors attached to the equipment. The training of the machine learning algorithm consisted of designed experiments to simulate different faulty and normal processing conditions. The areas of application varied from wear of cutting tool in computer numeric control (CNC) machine, surface roughness fault, to wafer etching process in semiconductor manufacturing. In all cases, high fault classification rates were obtained. As the interest in smart manufacturing increases, this review serves to address one of the cornerstones of emerging production systems.

The increasing use of sensors in manufacturing enterprises has led to the need for real-time data-driven information systems capable of processing huge amounts of data in order to provide meaningful insights about the actual and the predicted business performance. We propose a framework for real-time, event-driven proactive supplier selection driven by Condition Based Maintenance (CBM). The proposed framework was tested in a real in automotive lighting equipment scenario.

To facilitate root cause analysis in the manufacturing industry, maintenance technicians often fill out “maintenance tickets” to track issues and corresponding corrective actions. A database of these maintenance-logs can provide problem descriptions, causes, and treatments for the facility at large. However, when similar issues occur, different technicians rarely describe the same problem in an identical manner. This leads to description inconsistencies within the database, which makes it difficult to categorize issues or learn from similar cause-effect relationships. If such relationships could be identified, there is the potential to discover more insight into system performance. One way to address this opportunity is via the application of natural language processing (NLP) techniques to tag similar ticket descriptions, allowing for more formalized statistical learning of patterns in the maintenance data as a special type of short-text data. This paper showcases a proof-of-concept pipeline for merging multiple machine learning (ML) and NLP techniques to cluster and tag maintenance data, as part of a broader research thrust to extract insight from largely unstructured natural-language maintenance logs. The accuracy of the proposed method is tested on real data from a small manufacturer.

Predictive maintenance is a maintenance strategy of diagnosing and prognosing a machine based on its condition. Compared with other maintenance strategies, the predictive maintenance strategy has the advantage of lowering the maintenance cost and time. Thus, many studies have been conducted to develop a predictive maintenance model based on a growth of prediction methodology. However, these studies tend to focus on building the predictive model and measuring its performance, rather than selecting the appropriate components for predictive maintenance. Nevertheless, selecting the predictive maintenance policy and target component are as important as model selection and performance measurement. In this paper, a selection method is proposed to improve component selection by referencing current literature and industry expert knowledge. The results of this research can serve as a foundation for further studies in this area.

We discuss alarm monitoring in process control supported by best practice used and standards implemented into recently developed engineering tools. Our paper describes aspects of a joint project development in the specific engineering company and VSB-Technical University of Ostrava. Our work focuses on monitoring assets and viewing status of conditions with help of data files acquired during commissioning process implementing the technology into practice. Alarm and event data received from technology process were analyzed according to standardized approaches with the aim to point out further limitations of alarm reports and develop an engineering tool for configuration of event and alarm limits of monitored variables in a control system during the commissioning phase under operation conditions.

With big data-technologies on the rise, new fields of application appear in terms of analyzing data to find new relationships for improving process understanding and stability. Manufacturing companies oftentimes cope with a high number of deviations but struggle to solve them with less effort. The research project BigPro aims to develop a methodology for implementing counter measures to disturbances and deviations derived from big data. This paper proposes a methodology for practitioners to assess predefined counter measures. It consists of a morphology with several criterions that can have a certain characteristic. Those are then combined with a weighting factor to assess the feasibility of the counter measure for prioritization.

In any manufacturing systems, planning and scheduling are not intuitive. Some dedicate tools may exist to help some specific companies to daily plan and assign their activities. Our purpose is to develop a generic decision support tool to solve any planning or scheduling problems. To do so, we use a hybridization between a metaheuristic and a list algorithm. The metaheuristic is generic to any studied problems. The list algorithm needs to be specific to the considered problem. The use of our tool needs a minimum work development. In this paper, our proposal is illustrated by the case study of a textile company which intends to schedule its production, by assigning it resources and dates. This problem can be seen as a Discrete Lot Sizing and scheduling Problem (DLSP).

Allocation of jobs to machines and subsequent sequencing each machine is known as job scheduling problem. Classically, both operations are done in a centralized and static/offline structure, considering some assumptions about the jobs and machining environment. Today, with the advent of Industry 4.0, the need to incorporate real-time data in the scheduling decision process is clear and facilitated. Recently, several studies have been conducted on the collection and application of distributed data in real-time of operations, e.g., job scheduling and control. In practice, pure distribution and decentralization is not yet fully realizable because of e.g., transformation complexity and classical resistance to change. This paper studies a combination of decentralized sequencing and central optimum allocation in a lithography job-shop problem. It compares the level of applicability of two decentralized algorithms against the central scheduling. The results show better relative performance of sequencing in stochastic cases.

Industry 4.0 technology is based on the concepts of flexibility and dynamic assembly system design. This enables new production strategies and creates new challenges for job shop scheduling. In particular, manufacturing processes for different customer orders may have individual machine structures whereas the flexible stations are able to execute different functions subject to individual sets of operations within the jobs. This study develops a control approach to job shop scheduling in a customized manufacturing process and job sequencing of operations within the jobs. The developed approach presents a contribution to flexible distributed scheduling in the emerging field of Industry 4.0-based innovative production systems.

In this paper, a genetic algorithm (GA) and GA with diversification generator (DG) for solving scheduling problems with crane conveyance are proposed. It becomes very difficult to obtain an optimum or near optimum schedule under consideration of restrictions to avoid crane interference in addition to many restrictions on operation of each machine. GA-based algorithms are applied to obtain high quality crane assignment which successfully leads to few working hour delays caused by crane interference. Effectiveness of this algorithm is confirmed by numerical experiments.

The main goal of this study is to make a production schedule considering users’ demand about due date and price in mass customization. For minimizing the total tardiness, a proposed method using Combinatorial Auction is applied to develop an optimal plan in planning phase. In operational phase, a technique for inserting additional orders at idle time is proposed using Single Auction approach. To evaluate effectiveness of the proposed approaches, computer experiments are conducted.

Industries such as aviation tend to hold large amounts of capital tied to spare parts inventories to insure a high availability [1]. One effective approach to increase the efficiency in inventory management has been resource pooling [2]. However, the emergence of additive manufacturing (AM) enables the new paradigm of production capacity pooling, which varies from current ones. AM’s inherent characteristics may realize capacity sharing among distinct industries, alleviate the need for high safety stock levels and enable better customer service through the reduction of transshipments for spare parts. The advantages can be extended to the broader fulfillment reach of the firm in other geographical areas without expanding its existing production capacity or inventory (and other benefits from a distributed production setting). However, issues with inter-organizational agreements, testing and production reliability may slow down the pooling process while the required facilities are in place. This paper aims to extend the existing literature on implications of this growing phenomenon on inventory management practices. Study methodology is conceptual analysis.

Sequence-dependent setup times force companies to bundle similar products to avoid setup efforts. While this increases the output rate the schedule reliability tends to decrease due to the sequence deviations enfaced by this sequencing policy. Our paper presents a model to predict the impact of different strategies for setup-optimized sequencing and their actuating variables on the sequence deviation. Through this it enables a positioning in the trade-off between a high output rate and a high schedule reliability.

The work presented in this paper is part of a project aimed at streamlining and improving the process flow at a leather furniture manufacturing company. The manufacturing throughput time is highly variable, and this makes planning difficult for the assembly of components at the downstream stages. Throughput time predictability at the upstream stages where the components are manufactured would facilitate the planning of their assembly according to their expected arrival times for specific product models. Research conducted in a previous phase of the project showed that the application of the CONstant Work In Progress (CONWIP) control mechanism to regulate inventory yielded significant improvements in the throughput time’s mean and variation. However, as it is the case with tighter control of inventory in manufacturing, previously unrealised problems were exposed in relation to the selection of the product model to release into the CONWIP loop. This has significant impact on the balance of the distribution of workload across the system’s workstations and among the multi-skilled teams at one of the workstations. This research implements a nested configuration of the Paired-cell Overlapping Loop Of Cards with Authorisation (POLCA) and the Generic Kanban control mechanisms to achieve a balance of the workloads. This ensures a synchronised flow of the different product mix through the entire manufacturing system.

Portuguese footwear industry has improved dramatically to become one of the main world players. This work is part of a project in cooperation with a large footwear company, operating a new automated assembly equipment, integrating various lines. Balancing such lines implies going from an almost manual preparation executed by experienced operators, to a planning supported by optimisation systems. These complex mixed-model lines have distinctive characteristics, which make balancing a unique problem. The paper proposes the ASBsm – Assembly System Balancing Solution Method, a new method that integrates a constructive heuristic and an improvement heuristic, which takes inspiration from Tabu Search. The solutions obtained, based on real instances, are quite encouraging when compared with other effected factory solutions. Consequently, the balances obtained by ASBsm are now being implemented and articulated with sequencing methods.

Influenced by the high dynamic of the markets and the steadily increasing demand for short delivery times the importance of supply chain optimization is growing. In particular, the order process plays a central role in achieving short delivery times and constantly needs to evaluate the trade-off between high inventory and the risk of stock-outs. However, analyzing different order strategies and the influence of various production parameters is difficult to achieve in industrial practice. Therefore, simulations of supply chains are used in order to improve processes in the whole value chain. The objective of this research is to evaluate two different order strategies (t, q, t, S) in a four-stage supply chain. In order to measure the performance of the supply chain the quantity of the backlog will be considered. A Design of Experiments approach is supposed to enhance the significance of the simulation results.

Passenger transport is a key player in urban mobility. However, it imposes some disadvantages, such as: time wasting, cost of fares and other costs, insecurity, discomfort and damage to the environment. These disadvantages herein called disutilities affect passenger choices and therefore it is necessary to consider them encompassing all modes of transportation. In this study, an analysis of these disutilities was conducted with the purpose of measuring its drawbacks. To this end, a case study in the Greater Tokyo Area (Japan) was carried out and assessed using the Analytic Hierarchy Process. The results showed that from 0,000 to 1,000 automobiles produce the highest level of disutility (0.182) compared with seven other modes of transportation.

Several earlier studies have viewed organizational culture (OC) as a key factor for performance measurement systems (PMS), yet its role is not well understood and the reviewed literature indicates a gap in articles describing the relationship between various types of OC and PMS. Thus, the following study sets out to explore how OC type impacts PMS design, implementation and use. The investigation is carried out using the case study methodology in two Moroccan manufacturing SMEs. Our findings suggest that there is a relationship between OC type and PMS life cycle.