Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems

This opening paper of the CARV/MCPC 2021 book of proceedings presents a study of papers published within the series of Changeable, Agile, Reconfigurable and Virtual Conferences (CARV) and Mass Customization & Personalization Conference (MCPC). In total, 398 papers are included from the three most recent MCPC conferences and the four most recent CARV conferences. In addition, 119 papers from the CARV/MCPC 2021 conference are included as well. Bibliometric analyses are presented, highlighting the most cited papers and authors, the most productive authors, and recurrence of authors across conference years. In addition, a sentiment analysis highlights trends in research, applying text mining techniques on paper titles, keywords, and abstracts. Finally, past trends are compared to trends found in papers published in the joint CARV/MCPC 2021 conference proceedings, which highlights future prominent research areas and new emerging topics relevant to the CARV and MCPC communities and future conferences.

Mass customization has become more attractive but requires a transformation towards more flexible solutions in contrast to dedicated manufacturing systems. Flexibility includes complex tasks such as the introduction of new products or new manufacturing processes as well as to efficiently handle daily balancing. The main challenge when it comes to flexibility in manufacturing is to be able to handle the technical aspects and still be competitive. In this article we consider the cost for flexibility to include two main things; (1) setup time, e.g., time for planning, design, programming and configuration, installation, ramp-up, scrapping of old equipment, preparation of facility, hardware installation, and (2) need of competence, inhouse knowledge, external competence, or external expert competence.This article presents an overview of available solutions and the level of flexibility and the level of competence that is needed for a reconfiguration one can expect out of a specific solution. Further, most of the existing solutions found do not consider or address the full problem of flexibility. However, we describe a possible future of industrial concept: Plug & Produce, which can address flexibility within manufacturing more completely and sustainably over time. Methods for configuration instead of programming are developed by University West.

Increasingly shortening product life cycles, regional market challenges and unforeseeable global events require highly iterative product and production adaptions. For faster adaptation, it is necessary to have a systematic understanding of the relationships between product design and production planning. A unified model and data structure are fundamental. Basic data must be extracted from both domains and integrated for consistent product-production co-design. For this purpose, we use a biological analogy, the genome-proteome phenomenon, to model the interdependencies of product (customer needs, functional requirements, design parameters) and production (technologies capabilities, machine information, process chain alternatives). From the genome, which represents the totality of available data of product and production, we contextualize the proteome, which represents an instance of a concrete product design and the corresponding production configuration. Thereby, one gene represents one incremental information set consisting of all above mentioned product and production information for a specific product function. For each of the mentioned information domains (e.g. product requirements) within a gene, a methodology exists (e.g. NLP) to model the interlinkage to the adjacent information domain (e.g. product function). Utilizing the interdependencies and heredity of product design and production planning enables quick analysis of adaptation-induced impact which will provide enhanced competitiveness in a volatile world.

A new Reconfigurable Machine Tools (RMT) based on an innovative modular and scalable axis driver is presented. The elements and the characteristics of the RMT are analysed and discussed. A comparison between the conventional and the new RMT by using the Entity-Relationship model is reported. The features of the new RMT enable a new manufacturing organization based on manufacturing capacity sharing that increases environmental sustainability.

New automation solutions need to be developed for flexible materials such as fabrics. Compliance in grippers should be incorporated to prevent product damage while firmly securing an item. The solution needs to be low cost, customizable, and sustainable. Using origami-inspired lamina emergent mechanisms and a material extrusion based, additive manufacturing (AM) process, two compliant clamping style gripper designs with living hinges are fabricated and tested. The AM process allows designs to be readily realized; however, the AM build strategies introduce process related sensitivities. Additional research will be conducted in both design and manufacturing aspects. This solution has potential for many domains, including agriculture and fabric handling applications.

Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), was proposed to solve the convertibility problems of traditional RMS. The core philosophy of D-RMS is to maintain partial production capability through postponing reconfiguration to latter stages of manufacturing system. Configuration design is necessary to implement D-RMS with the consideration of postponing reconfiguration. Therefore, a configuration design method of D-RMS is proposed in this paper. To cater for the demand of intelligent manufacturing, the industrial robot is considered during configuration design as well. Two illustrate examples are provided to show the effectiveness of the proposed configuration design method.

Nowadays, supply chain disruptions caused by COVID-19 pandemic, demand variation, raw material shortage, etc., made the supply chains unable to deal with emerging market problems. Responding to the new requirements has underscored the need to ensure a reconfigurable supply chain in order to survive in this uncertain economic environment. Indeed, the objective of this study is to identify the quantitative factors of each reconfigurability characteristic representing the reconfigurability assessment indicators (modularity, integrability, convertibility, diagnosability, scalability and customization). Based on the literature review, quantitative factors used to assess the degree of reconfigurability in supply chain are determined. These factors allow classifying reconfigurability characteristics according to the degree of their influence on the supply chain structure or supply chain functions. Through this research work, we try to facilitate the assessment of reconfigurability based on its characteristics in order to determine the ability of the supply chain to cope with new emerging disruptions.

In the dynamic environment of today's manufacturing industry, companies need to be changeable, i.e. capable of adapting to changes quickly and cost-effectively. In this context, the diagnosability characteristic, allowing fast and economic ramp-ups of new manufacturing settings, becomes particularly relevant. Depending on their diagnosability requirements, companies can exploit different technologies and applications. In this study, five diagnosability requirements have been identified. Through a literature review, the five requirements have been further investigated; thus, the extent to which these five requirements can be fulfilled, and their enabling technologies and applications has been specified. Finally, a case study has been conducted to show how diagnosability requirements are fulfilled differently in three manufacturing contexts.

Implementing reconfigurability is essential to manufacturing companies that aim to make the transition from conventional to reconfigurable manufacturing systems (RMS). The novel technologies promoted by the industry 4.0 paradigm are key factors to make the implementation of reconfigurability possible. However, previous research highlights that there is a small number of studies that explore this idea of integrating reconfigurability and industry 4.0 technologies. In practice, companies recognize five core characteristics that enables reconfigurability: modularity, integrability, customisation, adaptability and diagnosability. This work conducts a literature review to identify the barriers in the process of implementing reconfigurability. After that, it classifies the barriers identified in three contexts: technology, organisation and environment. Finally, the paper discusses whether some of the industry 4.0 technologies can contribute to overcome these barriers. The findings show that technology and organisation barriers can be exceeded with the acquisition and use of some novel technologies promoted by the industry 4.0.

Since current battery cell production lines are not flexible regarding format and material, it is necessary to develop new production systems. It is also required to develop this production line as an agile system in order to be able to flexibly counteract unit-specific capacity fluctuations. In addition, only low scrap rates are allowed when integrating new material systems which requires a holistic in-process or in-line control and the associated quality assurance. Agile production systems open up new possibilities for developing the battery cell product. Therefore, this article will present a novel product-production co-design that can be specifically adapted to customer requirements.

In this paper, we propose a new production paradigm called Swarm Production. A swarm production system employs both dynamic part routing and movable workstations. This allows it to adjust its topology during operation, in contrast to existing flexible production paradigms such as matrix production and reconfigurable manufacturing systems where the topology is defined during setup. Run-time optimization of the topology yields an unprecedented ability to quickly adapt to fluctuating demands or new production constraints. We present a formal definition of the swarm production paradigm and outline a preliminary roadmap by highlighting key research challenges imposed by swarm production. Finally, we present an exemplifications of a swarm production system in terms of key technologies and systems, based on an existing customisable product.

A well-performing product realisation process in order to introduce new products with high frequency to a low cost, is becoming more of a pre-requisite for manufacturing companies. In a multiple case study, this paper investigates applied industrial practices in production development to support the production realisation process and reports on the current ways of working and challenges therein. The areas of current production development practices, production platforms, standardised work, and knowledge development are explored. Identified challenges towards long-term production development based on the explored areas are presented. The inclusion of future need of production system adaptions from future products is argued for to increase its efficiency. Through including future need of the production system, the notion of considering one product at the time during industrialisation is challenged and a more proactive perspective can be taken. The production platform approach is considered as one enabler for such an improved production development.

Reconfigurable manufacturing systems (RMSs) emerged as a strategy to achieve more responsive systems, capable of adjust the functionality and capacity when required. This topic is a current issue to manufacturing companies because the feasibility of RMSs was achieved recently due to the novel technologies promoted by the Industry 4.0. In RMSs, the reconfigurability is the ability that allows changes from one product to another, the addition or removal of resources, with minimal effort and without delay. For this reason, the level assessment of the reconfigurability is of utmost importance for the industry. The objective of this paper is to describe the development a reconfigurability index that can be used by companies to define how reconfigurable their manufacturing systems are. To build the index, a questionnaire survey was used to select the variables and a principal component analysis (PCA) was applied to the survey results to determine the contributions of the core characteristics. The findings show that each core characteristic contributes with a different amount to the composition of reconfigurability. Adaptability and diagnosability contribute the most, with 25% each.

Dealing with uncertain market conditions is a growing challenge for manufacturers. Changeable manufacturing systems have been suggested as a means to overcome the challenges resulting from uncertain markets. However, for changeability to acquire wide acknowledgement and implementation within industry, a clear economic justification considering the higher initial investment of such a system is needed. This paper attempts to develop an investment model comprehending the benefits of changeability, while focusing on applicability in industry. The proposed model complements a traditional Net Present Value method by evaluating the added value for changeability at either system or machine level, using a combination of Real Options approach. The model is applied in an industrial case and demonstrates an added value for changeability in both a flexible and reconfigurable manufacturing system.

Reconfigurable Manufacturing Systems (RMS) have been proposed as a means to accommodate today’s dynamic requirements in a rapid and cost-efficient way. However, the industrial transition towards RMS is limited as it is perceived to be uneconomic due to an increased initial investment, yet desirable for the ability to respond to uncertainty and co-evolve with life-time requirements. This ability makes economic evaluation of RMS concepts inherently complex, which is not supported by traditional approaches. Nevertheless, concept evaluation remains critical during development as the majority of the life-time cost is determined by initial design decisions. Therefore, the objective of this paper is to review state-of-the-art literature to provide an overview of methods and models to evaluate the investment of RMS. Papers were retrieved from a structured subject search and screening, then classified according to seven characteristics, and mapped in a decision tree to aid practitioners in the selection of suitable evaluation approaches. Based on the review, there is a lack of quantitative models for comparative investment evaluation which is validated in industry and consider network implications, uncertainty and life-time requirements. To mitigate this gap, aforementioned is proposed as viable directions for further research.

Since more and more production tasks are enabled by Industry 4.0 techniques, the number of knowledge-intensive production tasks increases as trivial tasks can be automated and only non-trivial tasks demand human-machine interactions. With this, challenges regarding the competence of production workers, the complexity of tasks and stickiness of required knowledge occur [1]. Furthermore, workers experience time pressure which can lead to a decrease in output quality. Cyber-Physical Systems (CPS) have the potential to assist workers in knowledge-intensive work grounded on quantitative insights about knowledge transfer activities [2]. By providing contextual and situational awareness as well as complex classification and selection algorithms, CPS are able to ease knowledge transfer in a way that production time and quality is improved significantly. CPS have only been used for direct production and process optimization, knowledge transfers have only been regarded in assistance systems with little contextual awareness. Embedding production and knowledge transfer optimization thus show potential for further improvements. This contribution outlines the requirements and a framework to design these systems. It accounts for the relevant factors.

Today, increased demands for personalized products are making human-robot collaborative tasks a focus of research mainly for improving production cycle time, precision, and accuracy. It is also required to simplify how human-robot tasks and motions are generated. A graphical flow control-based programming can be one of such methods. This work investigates whether the graphical approaches (e.g., using RAFCON) yield a better real-time simulation or not compared to agent approaches (e.g., using MOSIM-AJAN). This work may support the agility of the digital manufacturing process by enhancing the efficiency of human-robot collaboration.

Populating printed circuit boards (PCB) with through-hole-technology (THT) components for small-sized batch productions remains a manual task. Placement of delicate components is inherently difficult, and complexity is added when components are delivered to the system in bulk. Part feeding remains an obstacle in automation, and economically feasible flexible part feeding for electronics components even more so. This paper presents an assembly platform for placement of THT components. The platform handles feeding of components from bulk, tubes, and trays, and features two robotic manipulators for feeding and placement. To simplify the setup task and increase flexibility each component of the system is programmed and designed from digital models and simulation and uncertainties are corrected for during execution.

In the past decades, the programming of welding-robots has been given significant attention in the literature and commercially. However, in an engineer-to-order context, it still involves hard-coded parameters and requires a high amount of time to complete in an error-free manner. Welding-robot programming is a time-consuming process that requires critical human input to plan and execute the welding task. A direct consequence is low utilisation of welding-robots which renders the ownership of such devices unprofitable. This paper presents a novel approach for automatic programming of welding-robots based on product models. Instead of relying on hard-coding, the proposed system draws the necessary parameters from a product model, which contain most of the data needed to obtain a robot welding program. The aim is to integrate welding-robot programming in a seamless one-piece information flow across the production chain. The conceptual approach is demonstrated in practice and validated by experimenting with an elementary product. Results show that human input can be eliminated when it comes to the programming of robots, as long as specific criteria are met.

Over the past decades, automation industry has seen a major shift from traditional, hard-tooled lines to reconfigurable and reprogrammable robotic cells. Robots have added increasing value to the industry with special focus on robotic arms which enable many repetitive tasks to be carried out with high repeatability, reliability, flexibility, and speed. Grasping, carrying and placement of objects are the basic capabilities of robotic arms. However, with the integration of new technologies the capabilities, of robotic arms can be extended. As such, grippers being an essential component of robots play an important role in many handling tasks since they serve as end-of-arm tools. The aim of this paper is to propose a new end effector design, which is integrated with a sensing system, for improving the adaptivity and flexibility of a robotic cell in comparison with the State-of-the-Market end effector solutions. The proposed design is used to extend this research work and to develop an intelligent end effector based on the implementation of a Machine Vision algorithm, for the recognition of the part, the gripper, and 3D scanning the produced part. The recognition of the part is essential in order for the robot to grasp the object appropriately and facilitate the machining process. The 3D scanning of the part geometry will be utilized for CAD comparison versus the original drawings. Finally, based on the Finite Element Analysis (FEA) and the topology optimization, a reduction of the material used for the 3D printing of the gripper has been reduced by 19.57%.

Due to commercial pressure and changing consumer needs, organizations continuously strive to automate their manufacturing processes. Robotisation options, such as collaborative robots, are attractive to maximize the benefits of higher throughput for highly cognitive tasks. Nevertheless, automation and robotization efforts continue to be limited by highly cognitive processes that are either complex to automate or do not make sense from a business perspective. This challenge is further compounded by an absence of clear guidelines and structured frameworks for guiding automation decisions. This paper aims to bridge this gap by reviewing automation decision-making, including task design and psycho-social influences such as cognitive decisions. This includes consideration of cognitive decisions made by operators, further influenced by the task design, workplace design, and task safety. The proposed approach is demonstrated for real-world use cases, of which feasible automation solutions are proposed following structured decision-making steps.

Human capabilities to interact, interfere, support, supervise and take over different tasks in a production environment are often not considered in the context of automated and self-organizing factories (smart factories). Adaptive Task Sharing (ATS) is a method to combine the strengths of automation and human skills to provide flexible and resilient factories. ATS offers huge potential for improving and enhancing ergonomics and human factors in production. The assembly worker plays a vital role in such hybrid manufacturing as a partner and coordinator of production services. In recent years, techniques have been developed to efficiently compute production execution graphs optimized for various technical criteria, like minimum makespan or energy consumption. This paper shows how to use these techniques for a flexible production planning that incorporates human workers and investigates different scenarios of task allocation between humans and machines and their impact on production workflows. Such socio-technical interactions between humans and machines need to be at the core of resilient production systems to deal with unforeseen circumstances, provide the flexibility required in high-variability, low-volume production scenarios, and increase productivity and workplace quality of human workers.

This paper analyses the synergetic potential of Augmented Reality (AR) and offline programming (OLP) in the commission of industrial robots. We discuss that AR could utilise existing programming paradigms present in the OLP to simplify the online commission in manufacturing plants with its ability to freely combine digital and real content. Focussing on the complementary use of AR and OLP a theoretically promising use case is derived. The use case is implemented in a simplified mock-up and a user study is conducted to practicably evaluate the feasibility. Ten participants commissioned an offline created program using the developed AR application. As nine out of ten participants could achieve a valid commission by using the developed AR application, the study indicates the feasibility of the presented use case.

As product specifications change, manufacturing processes have to adapt. In manual production tasks, the human worker is forced to adapt at the same pace. Fast-changing work tasks lead to high stress and therefore increase failures. Digital assistance systems aim to support the human workforce by providing assembly instructions at the right time and in the right place to reduce the cognitive load. The latest digital assistance systems provide multimodal human-machine interfaces, such as augmented reality, haptic feedback, and voice control to provide information or react to the user's input. However, those digital assistance systems require the manufacturing information themselves, which are mostly provided through text-based or graphical programming. Both manufacturing experts and programmers are needed to create a digital assistance system workflow or adapt it to changes. This process is costly, time-consuming, and inflexible. This work presents a gesture recognition based approach for a self-learning digital assistance system. Therefore, assembly gestures are classified based on anatomical grip descriptions. Assembly sequences are recognized and learned by the digital assistance system using machine learning techniques. The learned procedures are used to automatically generate work instructions and guide the worker through the assembly task.

Detecting faults in manufacturing applications can be difficult, especially if each fault model is to be engineered by hand. Data-driven approaches, using Machine Learning (ML) for detecting faults have recently gained increasing interest, where a ML model can be trained on a set of data from a manufacturing process. In this paper, we present a use case of using ML models for detecting faults during automated screwdriving operations, and introduce a new dataset containing fully monitored and registered data from a Universal Robot and OnRobot screwdriver during both normal and anomalous operations. We illustrate, with the use of two time-series ML models, how to detect faults in an automated screwdriving application.

This paper describes a framework for using 3D simulation as a safety assessment tool based on ISO/TS 15066:2016 guidelines for a cobot work cell. A human-robot collaboration-based work cell has been developed. The digital counterpart of this work cell is developed beforehand to perform several safety assessments based on ISO/TS 15066:2016. It is observed that the 3D simulation model can be used as a safety assessment tool to ensure the safety of a cobot work cell even before its creation. The study also signifies the simulation software for safety and human factors in the design of a cobot cell..

In the paradigm of smart factories, flexible and scalable manufacturing resources are essential. The human worker offers great flexibility; however, the operators are often a sparse resource in high-wage countries. Consequently, they are often responsible for several tasks at once and must prioritise the most critical ones. Consequently, the productivity on less critical tasks will suffer in the absence of the operator. In this paper, we present a case study on the effect on productivity when deploying a collaborative robot assistant in a plug and produce fashion to substitute a human worker at manual workstations on a production line. Realistic cycle and changeover times are derived from physical experiments and used in discrete event simulation to analyse two scenarios. The results show that if an operator must abandon his/her workstation, deploying a robot assistant as a substitute reduces the loss of productivity.

This paper develops and describes a model for the forward kinematic chain between a Collaborative Robot (COBOT), human worker, and a manufacturing task. This paper aims to solve the forward kinematic equations of the COBOT, human, manufacturing task kinematic chain in order to open new possibilities in COBOT manufacturing work cell design and optimization. Results compare the simulated and predicted position and orientation matrices for the COBOT, human and task object. The simulated results were consistent with the predicted values with small differences in a small number of instances.

Repairing defective parts offers the potential to provide spare parts more cost-effectively, faster and with less use of resources. High process reliability and reproducibility in the repair of metallic parts can be achieved by using additive manufacturing. However, additive repair has only been used in a few cases for the maintenance of parts. For a broader use, users lack concrete guidance regarding the technical feasibility of additive repair and the design of the repair process. For this reason, the paper presents a decision support tool for the evaluation of a part’s repairability by additive processes. Therefore, a knowledge-based assistance system was developed containing manufacturing restrictions and application examples of additive repair. The system additionally configures a suitable repair process chain if additive repair can be used. The applicability of the system is evaluated using a specific part as an example.

Soft robots provide the opportunity to handle a diverse range of products, contributing to mass customization in modern production environments. Both, their manufacturing and behavioral modelling are crucial challenges, due to their unique, bio-inspired design, as well as with respect to the elastic materials, which are applied. Commonly, the actuators and grippers of these robots are manufactured in a traditional casting approach, which is both elaborate and requires molding clearances. In this paper, the additive manufacture (AM) of thermoplastic polyurethane (TPU) is investigated in the context of its application as soft robotic components. Compared to other elastic AM materials, TPU reveals superior mechanical properties with regard to strength and strain. By selective laser sintering, pneumatic bending actuators (pneu-nets) are 3D printed as soft robotic case study and experimentally evaluated with respect to deflection over internal pressure. Leakage due to air tightness is observed as a function of minimum wall thickness of the actuators. In an automated production environment, soft robotics can complement the transformation of rigid production systems towards agile and smart manufacturing.

Snap joints provide the opportunity of joining two components in a very simple, economical and rapid way. Therefore, snap joints are a feasible option for assembly of prototypes. Snap joint design guidelines currently focus on injection-molded parts, which may not be suitable for rapid prototyping. In contrast to injection molding, additive manufacturing provides a higher degree of design freedom. Applicability of design guidelines for injection-molded snap joints to additive manufacturing technologies has not been comprehensively investigated yet. In this work, we present a study comparing mechanical properties of snap joint specimen that are manufactured from three different materials with the two manufacturing processes FDM and SLS. Results show significant impact of both material and manufacturing technology. The presented results may lead to improved design guidelines for additively manufactured snap joints.

Laser Powder Bed Fusion (LPBF) is a promising additive manufacturing technique used for realizing complex and bespoke designed metal parts. Despite its good performance, its quality assurance is still hampered by the absence of in-process optimization and control. In this sense, real-time thermal analysis can facilitate fault predictions and rectifications. High-fidelity three-dimensional thermal modelling with the Finite Element Method (FEM) is generally time-consuming since the heat transfer equation is nonlinear and high-dimensional. The challenge is thus to compute fast, reliable and accurate thermal predictions that capture the nonlinearity triggered by the phase changes of the part during printing. Gaussian Process (GP) with Isomap dimension reduction is investigated to find and predict the low-dimensional representations of the high-dimensional thermal profiles in FEM without intricate processing. Based on these representations, the high-dimensional predictions are then approximated using localized radial basis functions. To validate the performance of this reduced GP heat emulator, a heat simulation during fabricating an Aluminum object is performed to compare FEM-based temperature calculations against reduced GP emulations. Retaining 0.06% of the original model dimension the execution time per temperature profile is 0.70s on average achieving a 95.07% reduction, while maintaining at least 85% accuracy (with respect to the FEM simulation) for 96.80% of the thermal profile queries and at least $$80\%$$ 80 % for 89.38% of the thermal history queries. With this encouraging performance, the reduced GP heat emulator can be a step forward in online defect prediction, process optimization and closed-loop control in LPBF.

Virtual prototyping (VP) technologies promise a viable solution to handle challenges in shorter product and production lifecycles and higher complexity. In this paper, we present the demonstration, and preliminary evaluation of the previously introduced digital twin (DT) based virtual factory (VF) concept for VP in the context of new product introduction (NPI) processes. The concept is demonstrated in two cases: blade manufacturing and nacelle assembly operations Vestas Wind Systems A/S. The preliminary evaluation results show that DT based integrated VF simulations provide immersive virtual environments, which allow users to manage complex product and production systems and significant cost savings. Finally, we present and discuss the evaluation of the concept demonstration by industry experts for the proposed solution.

This work presents a Digital Twin (DT) architecture for smart production cells tested on a case of automated drone assembly. In this Industry 4.0 setting each step of the robotic assembly sequence is carefully monitored through feedback from each hardware component being relayed to the overall smart factory, where it is located through an IoT messaging protocol. Through this case study, we illustrate how robotics-centered DTs are programmed through simple visual programming blocks, and assisted by detailed simulation, can be a powerful tool for facilitating production in smart factories.

As AI technology is increasingly used in production systems, different approaches have emerged from highly decentralized small-scale AI at the edge level to centralized, cloud-based services used for higher-order optimizations. Each direction has disadvantages ranging from the lack of computational power at the edge level to the reliance on stable network connections with the centralized approach. Thus, a hybrid approach with centralized and decentralized components that possess specific abilities and interact is preferred. However, the distribution of AI capabilities leads to problems in self-adapting learning systems, as knowledgebases can diverge when no central coordination is present. Edge components will specialize in distinctive patterns (overlearn), which hampers their adaptability for different cases. Therefore, this paper aims to present a concept for a distributed interchangeable knowledge base in CPPS. The approach is based on various AI components and concepts for each participating node. A service-oriented infrastructure allows a decentralized, loosely coupled architecture of the CPPS. By exchanging knowledge bases between nodes, the overall system should become more adaptive, as each node can “forget” their present specialization.

Smart products, Social Media and innovative market research lead to an abundance of customer data, yet due to their heterogeneous sources and structures, they are scattered throughout the company. Joining these different types of data can lead to a large gain in customer insights that would not have been possible by analyzing the data individually. It is a necessary step for the transition of the current mostly hypothesis-based product design process towards a data-driven one and enables accelerated product development with truly innovative products tailored to the customer. This paper explains the holistic approach to identifying customer needs and requirements: the digital shadow of the customer. It is a concept transferred from the Internet of Production and its digital shadows of products and processes. The paper first gives an overview of customer data that form the customer data lake and reviews current data analysis methods using an explorative literature review. We then explain the concepts of the digital shadow and data lake, their main principles and benefits of using digital shadows for product development.

In the industry, connecting machines and tools - also known as the industrial Internet of things (IIoT) - is an essential part of the digital transformation of a company. The aim is to increase the efficiency and predictability of complex processes. In manual and semi-automatic processes, imaging sensors can help to monitor conditions, gives automated feedbacks to a central system, and e.g. provide current information for a digital twin. However, when imaging sensors are integrated into established IIoT platforms, they quickly reach their system limits due to the multidimensionality and high update and data rates. This paper presents a software platform that enables decoupled automated image processing through the abstraction and contextualization of the sensor technology and its data as well as a plugin architecture. Analogous to edge computing, partial processing can already be performed close to the sensor node to condensate data and reduce network loads and latencies. Thereby, all these approaches increase the longevity, flexibility and scalability of multi-sensor systems and associated processing algorithms. Based on the generic structure of the sensor network, the user is provided with an intuitive user interface that is based on IIoT platforms and enables the integration of their processing pipelines even for non-experts, despite the high complexity of the data.

Current trends, such as globalization, demographic change, and individualization are increasing the complexity of production. In many production applications, holistic decision-making presents a challenge due to the lack of transparency, inadequate databases, and the unknown effects of decision alternatives [1]. Digital twins as high potential decision support tools are widely recognized as addressing these challenges in production. The design process, however, still requires research and methodologies. This paper presents a requirements-oriented procedure for designing digital twins in production. The successive specification of requirements results in validated digital twin concepts for various applications.

Global, competitive markets which are characterised by mass customisation and rapidly changing customer requirements force major changes in production styles and the configuration of manufacturing systems. As a result, factories may need to be regularly adapted and optimised to meet short-term requirements. One way to optimise the production process is the adaptation of the plant layout to the current or expected order situation. To determine whether a layout change is reasonable, a model of the current layout is needed. It is used to perform simulations and in the case of a layout change it serves as a basis for the reconfiguration process. To aid the selection of possible measurement systems, a requirements analysis was done to identify the important parameters for the creation of a digital shadow of a plant layout. Based on these parameters, a method is proposed for defining limit values and specifying exclusion criteria. The paper thus contributes to the development and application of systems that enable an automatic synchronisation of the real layout with the digital layout.

The advent of the industrial digital transformation and the related technologies of the Industry 4.0 agenda has uncovered new concepts and terminology in the manufacturing domain. Clear definitions represent a solid foundation for supporting the manufacturing research community in addressing this field consistently. This paper addresses this need focusing on the “smart factory”. Starting from a review of the extant literature and integrating it with the outcome of a Delphi study, we propose a new definition of a “smart factory” and discuss its key characteristics. These are related to interconnectivity capabilities and adaptability to the surrounding environment in order to generate and appropriate value. Eventually, such characteristics are exemplified in an empirical context. The aim of this paper is to provide the research community with an updated definition of a smart factory taking both industrial and societal values into account. Furthermore, it may represent a reference for practitioners engaged in the digital transformation of their factories.

As digitalization in factories continues, companies increasingly want to establish their production as a cohesive digital representation. One method to achieve this over the lifecycle of an asset is the digital twin (DT). It contains model descriptions of products and processes and incorporates asset-specific information. Due to the variety of data generated, efficient handling poses a challenge. Previously used databases often follow a relational approach, which is not suitable for storing extensive, heterogeneous, unstructured data. NoSQL, which can basically handle this data, however, only supports one data model (e.g., graphs), resulting in multiple databases for each data model. We propose the use of multi-model databases (MMD) for digital twins which can store the whole range of data models needed within one single system. Since MMDs found only little application in this context, advantages over the aforementioned approaches for their use in digital twins are shown. The MMD allows to connect all generated data consistently which thereby enables an efficient cross-data model query. The development and application of a digital twin in an MMD is demonstrated by an industrial quality inspection process. In accordance with the DT, all necessary data, such as inspection equipment, plans and processes, are represented in a suitable data model inside the MMD. Finally, the results of the application of MMD in DTs are discussed and aspects for future work are pointed out.

The modular design of load-bearing structures out of precast concrete components offers considerable potential for more efficient and sustainable construction. A new concept of individual assembly can be used by compensating dimensional deviations of the individual modules through selective positioning of the components within the structure. The quality of the resulting structure depends on the combination of all component dimensions. In this paper, an adaptive process control for production is presented which aims at an optimal result over all components of the structure. Such a cross-component process control has not been considered in literature yet, since components of a product are only considered individually so far. The paper furthermore considers that the component dimensions depend in an unknown and stochastic way on certain setting parameters of the production process. By using active sampling to vary the setting parameters adaptively during production, knowledge about the transfer function can be gained efficiently. In particular, the heat treatment process for ultra-high performance concrete components is considered as use case. A cross-component process control is presented, which iteratively approximates the transfer function of the manufacturing process during operation by means of regression. Different strategies for the selection of parameter settings are developed, optimized and compared by simulation. In comparison with a benchmark strategy based on complete information, it can be shown that good results can be achieved by selected strategies depending on the overall number of components considered.

Industries rely on a flexible manufacturing process to face high competitive market and customization constraints. It relies on an efficient production system that allows it easier to reconfigure and interact with other devices. Currently, Cyber-physical systems (CPS) are proposing an answer to the fourth industrial revolution. The CPS technology and application for production are named Cyber-physical production system (CPPS) for the last few years. It is an autonomous system and has cooperative elements connecting across all production levels, from machines to operators. This paper address how the new customization strategies affect/requires the system to be modified and develop a new drone system. Using the system Engineering approach, the elicitation of the system requirements from the different concepts of operation for customization operations in CPPS is realized. This work is instantiated on an experimental CPPS platform. It illustrates the structured requirement framework for the new customization strategy and elaborates by presenting a requirement model of the system. It is the required step to bring effective customization to the CPPS Platform.

Designing, building, and testing new adaptable automation equipment for the discrete manufacturing industry is becoming a still more complex discipline where static Technical Product Specifications are no longer the situation in a dynamic and changing environment. Therefore, computer simulation tools at different abstraction levels become very important in designing and building automation solutions. This paper investigates how to develop and test machines effectively, taking offset in a practical industry case. The paper proposes a methodology from the first simulation models to a digital twin environment. The result is a practical-based framework that helps machine builders and end-customers identify the appropriate simulation methods in advance, ensuring faster and simpler development of automation equipment that will lower the overall costs.

Freeform Injection Molding (FIM) is a novel technology based on sacrificial 3D-printed injection mold tooling. Key FIM benefits include shorter lead-times, lower start-up costs and increased design freedom, compared with existing injection mold tooling technologies. In this paper, it is discussed how FIM as an example of a novel manufacturing technology can be adopted considering internal and external factors. Internal factors include manufacturing technology strategy, organizational contexts, manufacturing system, and supply chain factors. External factors include FIM technology, Social Readiness Level (SRL), market and environmental considerations. Although FIM has reached technical maturity, it has not been widely adopted by manufacturing companies and consequently, the economic impact of FIM has not been fully realized yet. In this paper, a high-level FIM adoption process outlines a series of actions supporting the implementation of FIM as an emerging manufacturing technology. The adoption process starts with introducing the technology and comparing the benefits and limitations of FIM with conventional technologies through awareness building workshops. These workshops will be followed by introducing methodologies that enable the companies to identify components suitable for manufacturing by FIM. Once the suitable components are identified and key requirements defined, the components may be manufactured with FIM and their performance need to be validated to make sure that the criteria/requirements defined by the company are fulfilled. Finally, a procedure is developed to determine the impact and potential route to implementation.

Reliable electricity transmission in battery cells and modules is indispensable for energy storages. However, common joining technologies for such devices such as bolting or soldering suffer from several drawbacks, including force-dependent resistance or low dynamic strength. Laser beam welding shows potential to overcome these disadvantages. Besides excellent joint properties, it is applicable to small assembly spaces and has potential for the implementation of lightweight construction. In addition, laser beam welding allows users to precisely adjust the weld seam’s electrical conductivity and mechanical strength by an adaption of the weld seam trajectory. For industrial purposes, low costs and short development cycles are crucial. These short development cycles require a fast and easy design-to-production process. Therefore, an adapted Machine Learning method (Generative Adversarial Networks) is presented to simplify and accelerate the weld seam trajectory planning for laser beam welding. The algorithm predicts a suitable weld seam trajectory to achieve the desired electrical conductivity and tensile strength. For the algorithm used, feasibility was demonstrated using a dataset of the Modified National Institute of Standards and Technology (MNIST) database.

As technology advances and modern Industry 4.0 solutions are becoming more widespread, the need for better-suited datasets is rising. The commonly used datasets for training machine learning focus on simple data of often publicly available information. Within the industry, there is only a handful of datasets publicly available to use. In this paper, we present a new authentic industrial cloud data (AICD) dataset collected from an actual operating pick-and-place machine handling items with variations in shape, size, and weight. The AICD dataset contains various analogue sensor values and states of the machine, collected from an existing cloud solution. Within the data, an error is present when the machine fails. Therefore, this dataset is suited for testing and developing predictive maintenance and anomaly detection algorithms to be used in the industry. Moreover, the paper also presents a baseline implementation as a performance indicator for future models.

Assembly systems are required to be more flexible due to increasing product variety. This is accomplished by breaking up the prevailing rigid linking of assembly stations in classic line configurations to line-less assembly systems (LAS). For a quantified potential analysis, it is necessary to assess the dependency of performance indicators on input parameters in a large number of production scenarios. Existing methods are either experience-based or computationally expensive due to full-factorial experiment plans. Therefore, the contribution of this paper is threefold. First, a seamlessly automated scenario analysis tool is developed, simulating a large set of assembly scenarios using a discrete-event simulation. Second, an artificial neural network (ANN) based approximation pipeline of the scenario analysis is implemented. An integrated AutoML pipeline for hyperparameter optimization (HPO) and Neural Architecture Search (NAS) allows for a faster potential approximation with sufficiently accurate prediction. Last, an integrated decision support system including the mentioned components is defined. It includes a priori planning of the overall system and allows the assessment of adaptions in reaction to the current system status.

Throughout their useful life, plastic injection moulds operate in rapidly varying cyclic environments, and are prone to continual degradation. Quantifying the remaining useful life of moulds is a necessary step for minimizing unplanned downtime and part scrap, as well as scheduling preventive mould maintenance tasks such as cleaning and refurbishment. This paper presents a data-driven approach for identifying degradation progression and remaining useful life of moulds, using real-world production data. An industrial data set containing metrology measurements of a solidified plastic part, along with corresponding life-cycle data of 13 high production volume injection moulds, was analyzed. Multivariate Statistical Process Control techniques and XGBoost classification models were used for constructing data-driven models of mould degradation progression, and classifying mould state (early run-in, production, worn-out). Results show the XGBoost model developed using element metrology & relevant mould lifecycle data classifies worn-out moulds with an in-class accuracy of 88%. Lower in-class accuracy of 73% and 61% were achieved for the compared to mould-worn out less critical early run-in and production states respectively.

The future of production is smart and autonomous and so should the technologies that allow it. The application of machine learning (ML) in the manufacturing sector has been designated as one of the key players in the digital transformation. In general, the tendency of most ML application fields goes into more complex algorithms and deeper architectures. However, it is not the only available approach and often times not the most suitable for all use cases in the manufacturing industry where environments and products are highly standard. ML has the potential to solve complex problems by finding and learning relationships that humans cannot identify. Our hypothesis is that these relationships are invariant and exist inside specific tasks but also between a wide catalogue of similar, although not yet known, problems. An algorithm that finds and solves these invariances at a general and abstract level creates a general solution that can be directly deployed into new use cases leading to an increase in efficiency by reducing the required effort, time, and knowledge and allowing to leverage ML. Through three preliminary use cases, we demonstrate the potential of applying ML at a general and abstract level to increase the flexibility and reduce the model, task, and dataset dependency of ML solutions. This approach facilitates the conversion of automated environments into autonomous environments.

Large enterprises in the world are making substantial investments to adopt smart manufacturing technologies (Industry 4.0). Machine learning is one of the main driving forces behind this industrial revolution. On the other hand, small and medium-sized enterprises (SMEs) in the manufacturing sector are falling behind. Thus, for SMEs, there is an urgent need for adopting a machine learning based approach to gain competitive advantage. Machine learning helps SMEs to improve efficiency, catch manufacturing defects, predict machine failures, reduce unplanned downtime and increase productivity. This paper elaborates on machine learning project development life cycle for manufacturing SMEs. Furthermore, the paper presents a real-life case study of a medium-sized manufacturing company. Finally, the paper offers new insights and suggestions for other SMEs to adopt machine learning successfully.

Due to the variety and interaction of volatile influencing factors as well as the increasing requirements resulting from individualization, the prediction of future demand development is becoming increasingly difficult and complex. In manufacturing companies, this leads to a need for shorter and faster production planning cycles. In addition, the production network must be secured against uncertainty. This is possible by scenario analysis integrated into automated planning. In this paper, an automated scenario analysis in combination with deterministic modeling for integrated product allocation and global network configuration is developed to tackle demand uncertainty in a medium-term planning horizon. When creating scenarios, a trade-off arises concerning the completeness of possible developments and the manageability of the set. The objective is to achieve a representative coverage of possible future states by a small number of reasonable scenarios. Therefore, change drivers are defined that can lead to modifications of customer orders. This is followed by an automated simulation of the occurrence of the change drivers using a Monte Carlo simulation with a high number of samples for statistical validation. A cluster analysis with upstream principal component analysis is used to reduce the number of scenarios while maintaining representativeness. Finally, the scenarios are optimized in a production planning tool. The approach is applied to a real use case. The results are used to validate the representativeness of the scenarios, as well as to conclude robust decisions.

A growing number of manufacturing companies organize their production in global production networks, which tend to show a high degree of complexity. Improving operational performance or managing risks like production breakdowns and delayed transportation between production plants is challenging in these complex systems. Literature proposes discrete event simulation as an adequate tool to support the management of network operations. Companies are currently not able to exploit the potential of the required large-scale simulations of production networks as the development process of these simulations is lengthy, challenging and cost-intensive. In this paper, we introduce a concept to use highly granular data originating from production and information systems to automate simulation model development and parametrization.

The growing demand for individualized products, shorter product life cycles and rapid technological change are challenging the manufacturing industry. In order to cope with these challenges and satisfy individual customer demands, alternative production systems like the Matrix-Structured Manufacturing System or Fluid Manufacturing System have to be considered. However, to enable the alternative production system to have the necessary flexibility and to ensure an efficient material supply, adaptable and flexible logistics concepts are necessary. In order to develop innovative logistics concepts, the exploration of present and future requirements and challenges for logistics concepts have to be discovered. Therefore, this paper analyzes requirements and challenges for production logistics by conducting qualitative research with a sample of various German companies from five industry sectors (automotive supply, automotive production, mechanical engineering as well as special mechanical engineering and component manufacturing). It concludes with specific approaches to developing new logistics concepts.

The technological evolution that enterprises are facing is increasingly fast and dynamic, demanding more and more flexibility and agility in the application of new technologies in an increasingly globalized and competitive business environment. This work has a broad view of the influence of industry 4.0 in the global supply chain companies, as it is essential for long-term business sustainability and one of the main drivers of profitability and growth. The model proposed in this research effort considers the application in the processes of a global company in the supply chain, which includes from the beginning of the demand request, also the industrial activities, to the analysis of the post-sale; finally, an unprecedented indicator was developed to measure the level of application of industry 4.0 concepts in organizations according to the state of the art in the evaluated processes. Then, the model was applied to the real case of a global high-tech supply chain company. The first findings demonstrate the ease of understanding and applicability of the model for companies to analyze the digital transformation in their processes and identify gaps to convert them into real opportunities to leverage their business.

Intelligent Transport Systems (ITS) relying on Information and Communication Technologies (ICTs) have been recognised as important enablers of efficient intermodal logistics processes and are therefore receiving attention from EU policy makers. ITS can indeed boost performances and facilitate the development of the intermodal logistics industry itself. ITS could be particularly important for countries with a good potential for the implementation of intermodal logistics. Italy is among these, being the second-largest market in Europe for domestic rail-road transport. Moreover, it is accessible via sea due to its strategic positioning in the Mediterranean area and is closely connected with Central and Northern Europe markets. Still, there are many barriers to a broader spread of ITS in Italy, such as the high fragmentation of the freight transport chain, the limited vertical integration among the players and the reluctance to embrace change when outcomes are uncertain and difficult to assess. This paper provides an overview of the barriers to ITS adoption in intermodal logistics, maps the operative needs of Italian companies in this regard, and presents some of the results of a funded project aiming at fostering ITS adoption in intermodal logistics in Italy.

The blockchain technology represents a decentralized database that stores information securely in immutable data blocks. Regarding supply chain management, these characteristics offer potentials in increasing supply chain transparency, visibility, automation, and efficiency. In this context, first token-based mapping approaches exist to transfer certain manufacturing processes to the blockchain, such as the creation or assembly of parts as well as their transfer of ownership. However, the decentralized and immutable structure of blockchain technology also creates challenges when applying these token-based approaches to dynamic manufacturing processes. As a first step, this paper investigates existing mapping approaches and exemplifies weaknesses regarding their suitability for products with changeable configurations. Secondly, a concept is proposed to overcome these weaknesses by introducing logically coupled tokens embedded into a flexible smart contract structure. Finally, a concept for a token-based architecture is introduced to map manufacturing processes of products with changeable configurations.

Blockchain technology enables new paths for supply chain management by serving as a medium of transparent information sharing across organizational bounds. This paper highlights a developing supply chain case in which the goal is to improve the service and maintenance of wind turbines by introducing blockchain-enabled traceability. The increased transparency is intended to facilitate better opportunities for proactive maintenance of the turbines by giving commodity components a unique identification code (QR) and sharing information on said components throughout their lifecycle by appending it to a blockchain solution thereby creating an immutable, tamperproof history. The paper focuses on identifying and analyzing the benefits these technologies enable in terms of economic, social and environmental value (the triple bottom line). The case will first present the current situation in the supply chain service and proceed to evaluate the blockchain-enabled solution.

In virtual production planning, simulating human motions helps to improve process planning and interaction efficiency. However, simulating multiple humans sharing tasks in a shared workplace requires understanding how human workers interact and share autonomy. In this regard, an Inertial Measurement Unit based motion capture is employed for understanding shifting roles and learning effects. Parameters such as total time, distance, and acceleration variances in repetition are considered for modeling collaborative motion interactions. The results distinguish motion patterns versus the undertaken interactions. This work may serve as an initial input to model interaction schemes and recognize human actions behavior during team assembly. Furthermore, the concept can be extended toward a human-robot shared autonomy.

Flexibility and changeability are key enablers for assembly systems to meet the challenges of progressing from mass to individualized production. The new paradigm of Line-less Mobile Assembly Systems (LMAS) provides the necessary changeability, by mobilizing multipurpose resources and realizing adaptive system behavior. A holistic allocation of tasks to resources is introduced by extending the allocation through a study of its spatial feasibility.To evaluate the state of the art and to assess the transferability of theoretical task allocation approaches to industrial assembly, key criteria are identified. The presented approach for dynamic task allocation for cooperating, heterogeneous assembly resources expands existing concepts by the consideration of spatial motion constraints and cooperative behavior. Through the identification of cooperatively solvable tasks, the solution space for allocation is expanded, and a reduction of non-value-adding waiting times is expected. The potential of reconfigurable resources is leveraged through incremental re-auctioning of tasks, enabling dynamic reaction to changes, such as process delays, altered task prioritization or downtimes of resources. Interdependencies between task allocation, trajectory and formation planning are included through verification of spatial feasibility. The verification contains the confirmation of an executable formation of allocated resources as well as collision-free trajectories to reach the planned formation.

Despite progressively agile factory planning processes, target values of factory planning projects are frequently exceeded, and, consequently, enterprises are exposed to risks. In this context, the authors conducted an online questionnaire-based empirical study with 45 management-level participants from international manufacturing companies and identified the risk associated with different factory planning cases. This paper presents the design, results, and implications of the study.The questionnaire revealed that factory planning is particularly risky due to many planning participants, uncertain information, and informational interdependencies. However, risk assessments differ depending on the factory planning case.In the future, the authors intend to investigate the reasons for these differences and obtain a risk-based prioritization of factory planning tasks. Finally, a risk management model for factory planning, considering the impact of uncertain information, could be developed. Hence, the findings contribute to better risk management decisions and higher achievement levels of project target values in industrial practice.

Greenfield factory planning projects have to deal with uncertainties in the initial planning stages resulting in a poor economic and ecological target attainment. Due to the lack of time-dependent data, initial planning stages are usually based on static values. This primarily affects energy and technical building system planning since the system components size is primarily based on peak load demands. As a result, oversized system components are planned with higher capital costs and inefficient operating states, resulting in higher operational costs and environmental impact. Synthetic load profiles represent a possible solution to deal with the unavailability of measured data in greenfield factory planning. This paper evaluates the state of the art concerning the generation of synthetic load profiles for manufacturing cells. On the one hand, it was found that estimating the time-dependent energy demand in industry is usually based on measured load profile data for load forecasting or prediction. On the other hand, approaches for residential systems achieve results without measured load profile data. Nevertheless, residential systems are not within the scope of industrial systems. An approach for generating industrial load profiles in greenfield energy system planning is missing.

Industry faces the market demand towards individualized and functionalized products, which challenges traditional linear linked manufacturing. To overcome limitations of current production concepts the authors presented in previous works the manufacturing concept called Incremental Manufacturing (IM). IM is a hybrid-manufacturing concept where standardized base parts are produced first, then assembled and finalized by additive and subtractive manufacturing steps. This tool free concept can help to reduce machine and equipment investment costs and enables a scalable production concerning batch size, material, process and product spectrum of multi-material parts. Furthermore, this concept affects a design method with minor restrictions with regard to production and a process planning with flexible manufacturing sequences. These new possibilities result in a high degree of freedom in both disciplines, which must be used purposefully and should be restricted in a manageable way. For this purpose, part design and process planning must be interlinked in an early manufacturing planning stage. This paper presents an approach for evaluating different production routes of an IM-designed part based on Inter-process interactions and manufacturing key figures.

Flexible and varied manual assembly processes in the automotive industry are often based on manual labor. While simulation can be used to improve planning to maximize efficiency while minimizing ergonomic issues for workers, common simulation tools require extensive modeling time. In such simulations, the users are often process engineers who want to easily create complex human motion simulations. This paper presents a concept developed to create complex human motions for interacting with objects in a production environment with little effort. The concept separates between geometric constraints and the semantic meaning of the respective geometry. With a set of data types developed for this purpose based on a unified ontology, a range of geometric and semantic information can be specified for arbitrary objects. In this way, an action-specific motion generator can be used to define the appropriate motion for the interaction with an object depending on the action without defining case-specific constraints. For a first proof, the concept is tested and demonstrated in the assembly of a pedal car and sitting on a chair at a manual workstation. Based on the use case, the effect of effort reduction is shown.

Information exchange is a fundamental process for manufacturing enterprises, especially when the product data needs to be exchanged between different domains, areas, or external suppliers during the product lifecycle. Lack of standardized information management processes, undefined information requirements from both parties, and incompatible formats to exchange information lead to delays in product development and therefore, economic losses. An alternative that has demonstrated benefits when carrying product manufacturing information is the Model-based definition (MBD) approach. A preparatory step to structure an MBD application for a specific domain is to define its data content, which can be built upon a categorization of product requirements for the target domain. The presented study proposes a method that starts by studying the interactions between involved stakeholders and the related information exchanged at each stage of the development of a component, and results in a categorization of requirements to support assembly planning, enhancing product realization. Enhanced product realization can lead to shorter development time, better supplier compliance to the requirements, and fewer errors in the physical interfaces. Future work is to use the categorized requirements as a base to build an MBD structure for exchanging assembly-related information.

In the last decades, Seru Production Systems (SPSs) are introduced to face short product life cycle and high demand volatility, rising as a new cellular production pattern and a relevant alternative to lean systems. SPS is a particular class of cellular system in which the manufacturing cells are reconfigurable rather than fixed and they can be efficiently used for assembly, packaging and testing operations rather than fabrication alone. A particular class of seru is the divisional, staffed with several partially cross-trained workers. This paper proposes a quantitative model and simulation to balance workers in divisional serus. Results highlight that the working time of the operators is well balanced among them. In addition, a multi-scenario analysis, varying the number of operators, shows that the productivity of the system increases by decreasing the number of operators. Such a result follows the working mode of SPSs, in which the number of operators is progressively reduced, i.e. the most experienced (faster) operators are removed as long as just one operator is in the seru. This philosophy is justified by the assumption that the operators, as they work into the system, acquire competence and become faster.

An increasing trend towards lightweight construction can be observed in both the aviation and automotive industries. This is accompanied by a rising demand for (carbon) fiber-reinforced plastics (FRP). Since the production of FRP is cost intensive, possibilities for cost reduction in the manufacture of components are being sought. Tailored textiles (TT) are a promising solution due to their single-stage production method and low waste percentage. Although TT have great potential, they are not yet in industrial use due to a lack of research into material and quality properties. Appropriate and automated quality controls must be identified and tested. In this work a machine learning approach is used to detect defects in composite preforms. A total of 1.800 samples were divided evenly in six classes. In order to prevent fluctuations in results, a cross validation was carried out and the mean values of the four validation sets evaluated. The results suggest that, despite a rather small sample size, training of the machine learning algorithm is possible and learning success can be measured.

Contract manufacturing companies have to calculate offer prices for individualized products in cost-driven markets with experience-based estimations on limited data. Especially, the estimation of processing and set-up times is time-consuming and challenging. Algorithms providing suitable and transparent estimations with low configuration effort and licence costs for several machining processes are currently not available. Therefore, a method is developed to provide automated estimates for machining and set-up times from production data based on similarity metrics. This method is evaluated using feedback data from turned, milled and whirled workpieces. Experiments show that, compared to experience-based methods, an accuracy increase of 7.8% is achievable for process time estimation of turned parts and 21.2% for set-up operations.

Numerous methods for bottleneck detection, along novel approaches for bottleneck prediction, are available in literature. To facilitate the development and application of such methods, this paper proposes a holistic methodology for Bottleneck Analysis in dynamic value streams. Analogous to established data analytics levels, namely descriptive, diagnostic, predictive, and prescriptive analytics, the methodology specifies objectives for data-driven Bottleneck Analysis. Based on state-of-the-art bottleneck detection methods, the methodology provides measures for the diagnosis of bottleneck severity and frequency. Additionally, it considers prediction methods to anticipate emerging bottlenecks, depending on available databases. Finally, the methodology provides a context for the yet unexplored field of bottleneck prescription, which aims to mitigate bottleneck effects by data-driven control recommendations. Further practical application of the methodology has to confirm its suitability as a holistic framework for analyzing bottlenecks in dynamic value streams.

Customer satisfaction is a key factor to ensure long-term business success. Therefore, automotive manufacturers offer various options to individualize a car. Furthermore, customers and dealers are allowed to change their configuration until the vehicles are scheduled for production. This point is called order freeze. While vehicle specifications remain nearly unchanged in a build-to-order fulfillment strategy, this is not the case in build-to-stock. Mass-customized products with billions of possible car configurations, changing customer demands, or a dynamic environment are some of the challenges that confront the manufacturers in the planning and ordering processes. In this paper, the concept of multiple option-specific order freeze points is developed, which allows customers and dealers to change the configuration specifications at an even later point. For this purpose, the planning process, customer preferences, feasibility rules as well as technical and sales-operated option dependencies are evaluated. Furthermore, independent option-specific order freeze points are detected based on data-driven methods to handle the requirements for agile production systems by using current analytical technologies. The concept of multiple option-specific order freeze points has a high potential to be applied in a practical usage and is validated by a real-world use case of the Dr. Ing. h.c. F. Porsche AG.

Global markets are moving towards increased product variety and shorter product lifespan, and this also applies to bakery products from large industrial bakeries. Application of the business strategy mass customization could potentially counter this, as this strategy aims for offering customized products at near mass production cost. However, today the production is characterised by experienced-based adjustments of the processes and long product development time, which is partly caused by lack of knowledge of the dough properties and analysis of the dough at industrial settings. To address this, a protocol for analysis of large deformation properties for yeasted wheat dough samples from industrial bakeries has been established. Test of the new protocol revealed that it was able to differentiate between some variables such as different dough types, while some other variables were not possible to detect with this protocol. The perspectives for the analysis protocol may be to develop a range of industrial applicable analysis methods, which can be used for predictable, reliable and objective measurements of the dough. However, before the industrial dough analyses can be fully utilised, it is necessary to make a model of the correlations between all the variable relevant for the final product, including ingredient properties and process parameters. This is a prerequisite before mass customization can be implemented.

The engineer-to-order (ETO) industry’s business environment constantly changes, which results in challenges related to project management, on-time delivery, quality, and market competition. Companies face pressure to optimize production while demand for personalized products, and accordingly the complexity level increases. To address these challenges, companies require to identify the most important complexity drivers to improve planning, get a better overview of the resource allocation, and improve internal processes. This study proposes a design-time estimation model based on the most important complexity drivers: 1) Functional requirement, 2) Number of technologies, 3) Level of connectivity, 4) Regulation and standards. This study presents key complexity drivers for assessing the expected hours to design a product in an ETO industry. Complexity drivers are explored qualitatively and quantitatively from (i) literature review; (ii) internal regular meetings and; (iii) data analysis. The gathered complexity drivers are weighted and combined in order to develop the mathematical design-time model. Finally, an IT-tool is prototyped to test the mathematical model at the case company. The application of the developed IT-tool is also tested at the case company to prove the usability.

Roth’s design catalogue was developed particularly as repository for engineering knowledge also for the early phases of the design process and as a methodological support for the designer. Design catalogues are not only a simple collection of known and proven solutions for design problems but allow for structuring and prioritizing solutions according to requirements and development goals. In the current article, a knowledge-based engineering environment is presented that combines a computer aided design catalogue and a case based reasoning system with geometric and analysis models. At the example of dust separators it is shown how Roth’s design catalogue is used to determine the most suitable separation technique and to access component configurators, e.g. for a cyclone separator.

During product development, many decisions have to be made which affect the entire product life cycle. Especially in early phases in product development these decisions are subject to uncertainties when involving customer specific requirements. To support the designer in developing robust products that are insensitive to uncertainty, this paper compares two opposing approaches for modeling and assessing uncertainties: an explicit method in form of a Bayesian decision network with utility functions and an implicit method in form of a robust numerical optimization with sampling of a model-based product representation. Hence, a quantitative and qualitative comparison is provided regarding the modeling expense and the usability in early design stages. Regarding the integration into the product development process we propose a combination of the two approaches as complementary tools.

Increased product variety, shorter product life cycles and smaller production batches are market conditions faced by manufacturers in both discrete and process industries. Consequently, attention has been given to complexity management techniques, such as product platforms and product modularity. Nevertheless, literature examples of product platforms are almost exclusively provided for discrete products, indicating a lacking body of knowledge on the subject for process industrial products. This study, therefore, applies existing methods for platform-based development in the process industry, which have previously only been demonstrated for discrete products. The methodology used in this paper is Modular Function Deployment developed by Erixon in the 1990s and is applied on a product group in a Danish company in the process industry. The evidence presented suggests that there may be value in investigating the applicability of existing methods for designing product platforms in the process industry, and that these methods may prove a starting ground for further development of methods tailored to this industry.

In the field of medical technology is a continuous trend towards product individualization. This trend depends on the realization that the patient's characteristics, as well as the influences of the individual lifestyle and the environment, have a significant influence on the respective disease and its possible therapy. For this purpose, this paper presents a partially automated process chain using a visual programming language that performs a computer-aided, parametric topology synthesis of a short-shaft hip endoprosthesis based on computed tomographic data sets of the patients. The parametric design of the implant topology, which is modeled in the reverse engineering process, respects the patient-specific osteology of the femur bone and can be adapted by the user to any pathological cause of the indication for artificial joint replacement. This method demonstrates an approach to the realization of mass customization in arthroplasty.

Since the invention of the modular function deployment (MFD) method in the 1990s, the amount and volume of available data in manufacturing companies has increased substantially. This paper explores the possibility of augmenting the analysis of module drivers using data commonly available in manufacturing companies to provide decision support to managers and analysts during design or re-design of modular product platforms. This is achieved by analyzing the 12 module drivers of the MFD method against available data in a case company to propose novel metrics for evaluation. It is found that while some module drivers lend themselves favorably towards data-based analysis, others are inherently more complicated to quantify through available data. Utilizing a data-driven approach to augment the module driver analysis provides a less subjective estimate of the module drivers and presents analysts with an improved decision foundation for modular product platform design.

Product personalization respectively product individualization promises the exact fulfillment of individual requirements for each customer. In contrast to the customer benefit, a company has to deal with numerous internal product and process modifications due to repeated, customer-specific product adaptions. In this context, product adaptions can affect one or more product characteristics whose attributes need to be adapted according to individual needs. At this point, it is essential to understand and evaluate the personalization workload and the resulting costs caused by a specific adaption of a product characteristics’ attribute.So far, the literature offers little support in the cost-based evaluation of potentially personalization-relevant product characteristics. What is missing is the evaluation of the cost increase, if, instead of variant and customer-anonymously developed attributes, now individualized, customer-specific attributes would be realized. Therefore, a framework for a characteristic-oriented complexity cost analysis for the evaluation of customer-specific attributes is developed in this paper to support the decision-making process for or against the personalization of a product characteristic.

Modular product architectures are for many manufacturing companies seen as one of the solutions to an ever-increasing demand for more customized products. However, the transition from a non-modular to a modular product portfolio can be difficult and hard to realize. Previous research has focused on creating ontologies and data models in order to create new knowledge to be used during this transformation process. However, these models require extensive information and data and are therefore difficult to incorporate in today’s industry. Therefore, the purpose of this research is to present an approach to create a data model in which it is possible to use data mining technics to generate valuable knowledge for the transition process and the continuous management of a portfolio of modular product architectures. The approach and techniques are presented through a company case and a discussion is made on the usability of the result.

Industry 4.0 has gained momentum due to its potential to transform economies. Research on this subject has proliferated presenting numerous maturity models. However, SMEs, major pillars of the economy, are facing obstacles in their implementation. We introduce a methodology framework to assist SMEs. Based on existing models and tools, we provide an ‘end to end’ approach: from vision definition, through initial diagnosis, to immediate action plan. This was implemented in a pilot project carried out with four companies of the Oil & Gas Industry, later replicated nationally as part of the ‘SMEs Digital Transformation Program (PTD 4.0)’. Results prove its feasibility and (i) suggest that the sectoral approach presents advantages that boost the adoption of Industry 4.0, (ii) show SMEs need specific diagnostic models and iii) provide insights on key practical aspects for the successful application of Industry 4.0 frameworks. The main attributes of our methodology are: assistance vs. self-assessment, sectoral perspective, thorough yet concise, easily replicable and transferable. The main results are a tailored roadmap and the upgrade of the digital capabilities of employees. Our work also reveals valuable experience in the implementation of tools that are already being replicated in other business sectors as part of the PTD4.0.

In the past few years a considerable number of maturity models were introduced to guide companies towards Industry 4.0. However, a disconnection between them and self-assessment tools is observed as well as the need of tailored instruments for SMEs, specially addressing their actual starting point. We introduce a tool to assess the readiness of SMEs, focusing on the definition of a ‘Level 0’. This is essential to identify the first actions that could bridge the gap between their reality and first stages of I4.0 extended frameworks. Our tool proposes a holistic analysis and is precise and clear, not requiring external guidance. It is the result of an extensive literature survey and insights gained after the development of a Pilot Project carried out with four SMEs to help them start their Digital Transformation Process. RAISE 4.0 was tried in a field study involving 103 companies and experts from 7 Universities in ‘Awareness Workshops’ held countrywide during the ‘SMEs Digital Transformation Program’. It facilitated the selection of those companies better fit to enter the program. Our research serves the double purpose of providing useful information to SMEs regarding their maturity status and next steps and of providing valuable data for scientific analysis.

Despite the potential of Industry 4.0 and the increasing interest from the manufacturing industry, the adoption of Industry 4.0 is still lacking behind in SMEs in the manufacturing industry. In this paper, we explore why this is happening. The research is based on a multiple case study of 24 small and medium sized Danish manufacturing companies, which have all started their Industry 4.0 journey. We analyze the case data from the perspective of dynamic capabilities. Our findings show that the companies experience multiple barriers related to the sensing and seizing capabilities, which hinder their engagement with Industry 4.0. The lack of capabilities to sense and seize opportunities in relation to Industry 4.0 leads us to question whether manufacturers understand Industry 4.0 as a strategic asset or a set of disconnected technology improvements which may bring benefits to the operations, but do not utilize the systemic potential of Industry 4.0.

Manufacturing companies are increasingly engaging in digital transformation initiatives with the intent to improve the performance of their production operations. Nevertheless, a common challenge lies in understanding where to focus to obtain a significant production improvement, as well as in quantifying such potential. This research, structured according to the Design Science Research framework, proposes and demonstrates a novel tool – the Digital Factory Mapping – to address these issues. This analytic tool has been built on top of previous research efforts that shared the same intention, linking the continuous improvement philosophy with the digital maturity concept. The tool has been applied to four small- and medium-sized Danish manufacturers, validating its capability in facilitating the identification of company-specific production improvement opportunities, the formulation of digital innovation initiatives to capture them and the quantification of their potential. This paper contributes to the operations management body of knowledge by answering the need for frameworks to tangibly support the digital transformation process. From a managerial perspective, the feedback obtained by the four test cases highlighted how the tool generates an internal awareness concerning production improvement needs and digital innovation opportunities, catalyzing the execution of the suggested digital innovation initiatives by providing a quantification of their potential impact.

Existing implementation strategies for Industry 4.0 and Digital Shopfloor Management often focus on technology. This is accompanied by a lack of transparency regarding production processes and information structures, often preventing decentralised decision-making by employees. Thus, the implementation of I4.0 requires a socio-technical implementation approach that takes human, technology and organization into account.This work presents a model to implement Industry 4.0 combining the dimensions of people, technology and organization. The approach supports companies in adapting their socio-technical work system to include digitalisation. Taking the example of Digital Shopfloor Management, a socio-technical implementation strategy is developed and associated acceptance methods are derived. This pro-cedure ensures that the potential of Industry 4.0 can be achieved and implemented with the help of a socio-technical approach.

To stay competitive in a “winner takes it all” market, adopting digital technologies on the shop floor level in manufacturing seems inevitable. However, accomplishing a digital transformation does not seem to be an easy task to overcome. Without the right approach and mindset, practitioners will not be able to succeed. The conventional belief suggesting that a higher level of automation and digitalization result in less human interaction is misdirecting practitioners in having an increased focus on the technical factors, leaving the social factors out. In light of this situation, this paper tends to study the preconditions for implementing a digital transformation strategy considering both the social and technical factors at the shop floor management level.

Industry 4.0 is widely acknowledged in industry, and many companies have already started their digital transformation journey. However, companies approach the digital transformation differently, and research has demonstrated that companies have different strategies and approaches to digital transformation. In the light of the different strategies and approaches, this research paper investigates how the strategic approach chosen by SMEs in the manufacturing industry is affected by their level of digital maturity. The study is based on a multiple case study of thirteen SMEs in the Danish manufacturing industry. The paper draws the conclusion that there is a significant relation between the companies’ digital maturity level and their strategic approach. Three distinct patterns of the relationship between strategy and digital maturity are identified and discussed.

A virtual prototype which enables the configuration and validation of a customized product is a vital component when handling complexity in mass customization. Developing virtual prototypes for the configuration, validation and production of customized products is, however, often resource consuming and therefore challenging. It can be especially resource-consuming for SMEs as they are smaller than traditional companies that perform mass customization. This study focuses on SMEs and aims to identify and investigate the barriers to implementing virtual prototypes in the design process. It is assumed that a deeper understanding of these barriers will help companies overcome these challenges. On the basis of empirical data from 16 Danish SMEs, this study starts by identifying the barriers to implementing virtual prototypes in the design process. Next, some of these barriers are investigated in detail by means of a case study of a successful implementation of virtual prototypes at a Danish SME. As the case study focuses on a single, successful implementation and investigates only some of the identified barriers in detail, a broader perspective remains a topic for future work.

In the age of digitization and data-driven technologies, business models around physical products, regardless of whether smartphone or industrial printing machine, are not limited to sale or the offering of accompanying services like maintenance contracts. Rather, new offerings regard the product itself already as a platform for value creation, thus making it an integral part of overarching data ecosystems. Customers thus begin to value the utility of service rather than possessing a product. This study focusses the liaison between product and service from the rather unexplored perspective of networked functionality. We introduce the functions-oriented offerings model as an operatable abstraction that determines data-driven offerings as an individual configuration that is drawn from a solution space between product functions and services and creates individual value. Even though practice preaches dominant business types for data-driven ecosystems such as platform providers, our successive permutative analysis suggests multiple viable business types for future data ecosystems.

Marketing, production and operation of product-service systems require many different competencies and open up different directions for optimization. However, as in traditional product businesses, not all services from the extraction of raw materials and their refinement through to delivery and maintenance will be provided by a single supplier, but by a value creation network. Design approaches for product-service systems may be thus characterized by the degree of design and control sovereignty for the solution system and the development organization. Understanding this as spectrum, the extrema can be characterized as centralized with focus on the overall system, or decentralized, where multiple participants of the value creation network contribute components to an evolving system. This contribution proposes an according classification for fundamental development strategies with total, local and integral approaches and exemplarily illustrates development methodologies.

In 2020, the digital world experienced ten years of eCommerce growth in 90 days. With Covid-19 modifying consumer habits, 100,000 stores are expecting to close within five years. Transiting toward online experiences, the way human beings consume made immediate headway with higher shopping and personalization expectations. Generation Z members are even enjoying a virtual life with 80% of their friends never met in the physical world. These drastic consumer behavior changes also bring momentum for a new range of virtual experiences. Considering 3% is the average Retail eCommerce sale conversion rate, understanding the remaining part of the traffic, the 97% of visitors not buying on eCommerce platforms, is mandatory to ensure a sustainable digital future. It is critical for brand manufacturers and retailers to understand if eCommerce reached its full potential and how consumers respond to Extented Reality (XR) in this context. The purpose of this explanatory study is to discuss prospects of consumers about the current state of eCommerce and to examine their attitudes toward XR Commerce. This is confirmed by this study which focused on the requirements of consumers when adopting digital ecosystems: consumers find eCommerce experiences sterile and demand more online individualization of their brand experiences. Customization of the brand experience should not be seen as a mass goal, but from individual perspective: one-to-one and virtually augmented. At the end of this exploratory study, appropriate measures are discussed to satisfy the right consumers with the right digital experiences. A total of 335 subjects participated in the study.

Megatrends such as globalization and technological advances increase the pressure on manufacturing companies. A possible way out is to provide customized combinations of products and services. These so-called Product-Service Systems (PSS) enable companies to access new markets and can be used to differentiate themselves from competitors. However, providing PSS also creates challenges for companies, for example, an increase in complexity within the company, resulting in higher costs. In the first part of this paper, a literature review is used to identify drivers of complexity in the context of PSS. One of the identified main drivers is a rising variety of components and processes within the company. In order to reduce this internal variety and thus also the variety-induced complexity, new tools for the design of variety-oriented PSS are presented in the second part of this paper. The objective of the tools is to reveal the variety and help to reduce the internal variety of product and service components and processes without restricting the external variety of offerings. The newly developed tools are presented schematically and then the application of the tools on an industrial example is discussed. Finally, an outlook on further research topics is given.

A coherent customer experience across touchpoints requires all participating firms to share data between those touchpoints. Traditionally, this has been achieved by means of horizontal integration, extending the firm’s boundary across touchpoints through M&A or formal partnerships. In many cases this is not possible nor attractive from a financial or operational perspective. The question therefore remains: How do we create coherent experiences across touchpoints when extending the firm boundary is not an option?Through new platform business models and proliferation of available data, the opportunity for mass-customized experiences moves from intra-firm next best experience to inter-firm coherent next best experience (CNBX). For services and products with life cycles that span time and firm boundaries it will become increasingly important for the end-to-end experience to be coherent - moving from same-experience-for-all in each touchpoint towards mass-customized individual experiences across touchpoints between firms.Considering these changes, firms supplying touchpoints in user journeys with low levels of capability reuse and little integration between touchpoints are suitable cases for creating coherent experiences via data sharing. Moreover, information sharing, incentives to collaborate and orchestration between touchpoints are proposed as crucial factors that enable coherent experiences across firm boundaries. Building upon insights from the automotive industry, the conceptual framework is applied to the cases of OKQ8 and Polestar and draws attention to central aspects, such as open innovation and new business models for data.

The demand for sustainability information of products is steadily increasing. Therefore, companies must evaluate the sustainability of their products appropriately and make the results available to consumers. For Mass Customization (MC), this represents a challenge due to the wide variety of product variants. Product configurators have the potential to support the sustainability assessment of MC products. The work focuses on the ecological dimension of sustainability and is oriented towards the ISO 14040 standard for Life Cycle Assessment (LCA). The LCA of products is integrated into the configurator by including information from ERP systems and sustainability databases. Products are evaluated based on their modules, and generic sustainability data is applied. Both measures facilitate the complex procedure. Consequently, individual configurations of MC products can be assessed and results provided.

The implementation of enhanced clinical decision support systems promises a significant improvement in healthcare quality. Research, encompassing both literature review and health professionals’ assessments, has underpinned the urgency of an improved prescription process. A configuration system could support a novel proactive, standardized, and efficient decision-making process of the physician during the drugs prescription process and, consequently, reduce the associated mortality, morbidity, and health cost rates of medication errors. The practicality of using configurators to support the drugs’ prescription to prevent adverse drug events is evaluated through a feasibility study and validated by successfully developing a configurator prototype for a Danish hospital department. The medicines’ attributes and patient conditions are mapped through an adapted version of the so-called product variant master and detailed on the named class-responsibility-collaboration cards, generally used for modeling mechanical products.

Co-Innovation Processes demand the right setup to allow individuals to participate in a purpose-driven way that creates sustainable results. Design Thinking and other concepts favor physical team approaches to interact, share and learn. But different challenges from cross-industry influenced innovation processes to lead user integration methods demand agile scenarios that are customizable to tap into the realm of maximal individual contribution results. In describing a configurable workplace vision this paper researches the parameters of future proof human centered co-innovation spaces that blend the physical with the digital interaction world and use modular tools and elements to allow customizable collaboration experiences.

With a growing research interest in the significance of user experience for mass customization, the Configurator Database Research Project intends to provide more data related to user experience of online product configurators. This paper aims to introduce some new ideas for data collection for the Configurator Database. More specifically, a special focus on user experience related data leads to the question of how to measure it from an external perspective without access to underlying business data and with as little room for bias as possible. This new form of data should allow for deeper analyses, might help discover patterns throughout certain industry clusters as well as enable the identification of smart user experience enhancements, solutions for personalized marketing and interdisciplinary trends.

People are co-designs, unique masterpieces of art and science. Each is tangible, real, and being, as well as intangible, abstract, and feeling. The mass customization (MC) co-design toolkit enables the consumer to convey uniqueness and achieve her ideal offering – be it a good, service, experience or, ultimately, transformation [1]. MC toolkits increase functional, transactional benefits, rendering experiential, relational value, generating tangible and intangible worth for the individual, and leading to loyalty [2]. Ever-evolving contexts of URL and IRL converge, compressing the continuum between virtual and real life, and presenting the MC field with prospects to complement extant work. Via comparative analysis, this paper aims to determine if discernible patterns exist between tangible and intangible MC toolkits. If so, are these patterns palpable, or nuanced structural distinctions? Do they reveal other dimensions of consumer value allowing deeper understanding of key benefits identified by extant MC literature? Findings contribute to theory and practice regarding MC co-design experience value for individual consumers and providers.

Product configuration systems (PCS) are widely adopted because of their advantages in terms of time reduction, quality improvement and sales increase. However, the creation of a PCS often proves to be challenging, especially the acquisition of relevant knowledge. In this paper, we propose a shift from knowledge acquisition to knowledge articulation by the domain expert. A supporting toolset is developed. Our method reduces the overhead of translation and interpretation by the knowledge engineer. We demonstrate the method with a case study of an international manufacturer.

With the advent of industry 4.0 and smart manufacturing, the panorama for factories and businesses is changing at high speed at all operation levels. Hence, it is imperative that human capital keeps up and adapts to meet present and future needs. Professionals should possess breadth and depth in knowledge along with meta-skills to thrive in the rest of the 21st century. This paper discusses how technologies have impacted and shifted the roles of people in the factory and their interactions with smart machines and systems, and the challenges that professionals will face in the future. Initiatives that governments and organizations are rolling out to bridge the skills gap are examined. The desired adaptation of education necessary to produce professionals ready for the fast-changing manufacturing environment is discussed. Recommendations for successful human development, through reskilling and upskilling, in the smart manufacturing context are provided.

Many companies are managing the complexity of customized products, such as product variants, by implementing product modularity strategy. The modular product structure supports in realizing the potential benefits if it is exploited in relation to customer needs (Market Dimension), configuring to process capabilities and thereby cost and quality (Process Dimension), and configuring to supply chain architecture and thereby speed and predictability in supply and delivery (Supply Chain Dimension). This paper introduces interactive training and learning methods that can reveal and exemplify the complex relationships between efforts and benefits when implementing modular strategies. This approach is comprised of a combination of two parts: a serious play and simulation. The resulting setup based on a real but simple industrial product, the LEGO Collectable Minifigure Series, has demonstrated that the potential qualitative benefits of a modular strategy can be hold up against a synchronized quantitative financial impact assessment. It has been possible to demonstrate the complexity of realizing potential benefits from a modular strategy. In particular, it has been visualized how short-term benefits can differ significant from long-term benefits.

In the advent of Industry 4.0 and the continuous digitalization of manufacturing, the role of human labor changes from working alongside with technology, to working together with it. Therefore, educational institutions also need to accommodate the demands for new skills and competences. Educational institutions must strive to produce graduates who fit working in an intelligent production environment. This paper aims to examine the immediate and future industrial needs across European regions, and to compare these needs to current educational activities at European educational institutions. The findings of the paper present a gap in terms of capabilities, where current educational activities are missing in a range of areas.

Learning factories constitute a promising approach for the acquisition of specific competencies, especially in terms of a digital transformation of the economy. Respectively, a variety of such factories differing in technology, learning concept, and potential audience have evolved. A precise and recent overview of those does not exist. However, such an overview is required for the implementation of concrete political measures, a future-oriented development of the individual learning factories, and an adequate selection by the audience. For this purpose, the authors investigate the current state of the art of European learning factories in the context of digitization. Thus, the terminology and definition of learning factories are provided. Moreover, using a structured literature review, the factories and their operation mode are outlined. Subsequently, the authors evaluate whether the different factories can build the required competencies among the audience and thus, support a successful digital transformation. Additionally, expert interviews with learning factory operators are performed to obtain profound information on the performance of learning factories. The findings help to assess the pertinency of European learning factories and provide a trace for their future development.

Industries face major challenges in designing manufacturing systems that are able to respond to changing customer requirements quickly, while keeping product quality and process technologies up to date. Higher education in the field of Engineering must recognize the needs of industries to face the current challenges, enabling graduates to assist companies in establishing manufacturing systems based on industry 4.0 technologies. This paper presents an approach proposal for teaching the transition from conventional to industry 4.0 based systems, which will be applied to the Engineering graduation courses at the Federal University of Itajubá. The approach is based on the learning system MPS®200 of Festo Didactic acquired by the university to compose the Production Systems Lab. The problem-based learning (PBL) methodology will be used to prepare students for real world problems.

Manufacturing innovation management is a core process within the continuous improvement of manufacturing systems. Innovation managers are constantly trying to improve manufacturing systems with regard to the target criteria reliability, variability and productivity. However, manufacturing innovation management is significantly different to innovation management in product development, as manufacturing requires more structured processes and a more hierarchical organization due to its operational objectives. In order to improve the understanding of innovation management in manufacturing, relevant scientific and practical approaches have been studied and adapted to the requirements of manufacturing. This approach presents and describes the identified core elements of a holistic framework for manufacturing innovation management, which are innovation culture, strategy, organization, management, methods and soft- and hardware. Furthermore, it is described how the use of learning factories in the context of manufacturing can successfully support these core elements by improving the employee’s motivation and ability to innovate by creating open space for innovation in manufacturing.

Organizations have been witnessing several transformations due to the integration of new technologies and concepts in the Industry 4.0 era. With these transformations come several challenges such as financial investments for organizations desiring to adopt Industry 4.0 related methods and tools and most importantly finding engineering and project managers who are well equipped with the right competencies to be able to pilot industry transformation projects. The current paper studies learning requirements in the field of project and engineering management within Industry 4.0 Era. A combined literature analysis and interview method is adopted in order to overcome the lack of state of the art. The results underline the key role of project and engineering manager as integrators.

Increasing the efficiency of designers indirectly affects the price of offered goods. Particularly in the case of variant designs for small series production, it is advantageous to automate repetitive activities as completely as possible during the respective development cycles. As known from Mass Customization, knowledge-based configurators are an ideal co-design tool for customers, which helps in defining all necessary requirements and the resulting product characteristics. At single universities, courses for design automation and configuration are available, like at the University of Hannover. This contribution puts a student team’s results of a semester project in this lecture in the focus. After a few introductory words about the teaching concept and the implementation of the lecture on knowledge-based CAD, the process of creation and detailed results of the project “motor hoisting crane”, which the co-authors performed, are described. This is complemented by a reflection of the learning process within the student team and possible future learning set-ups.

In the digital era, individualized educational services, e.g. distributed by intelligent tutoring systems, are becoming increasingly popular and important for life-long learning but also during the COVID pandemic as many universities had to switch to distance learning immediately. Intelligent tutoring systems simulate behavior and expertise of physical teachers and support learners individually. In addition to closed questions, which can be modeled simply as if-then statements or decision trees, the use of artificial intelligence enables more and more the implementation of open questions and poorly structured problems, which is of particular importance for e.g. engineering education. In order to make the learning experience authentic, it is important to understand the learners as individuals and to confront them with learning content and in-depth knowledge tailored to their needs and skills. If, e.g., a formative assessment shows that a certain content has not yet been internalized, the tutoring system must detect this and react accordingly. Since this largely corresponds to mass customizing the teaching process, the following article frames digital education with focus on intelligent tutoring systems in context with mass customization. For cracking the code of mass customizing digital education, the three mass customization key competences solution space development, robust process design as well as choice navigation are taken as reference to set up digital educational content.

Industrialized house builders need to integrate knowledge between product development and production to ensure producibility in the factory as well as at the build site. The traditional manufacturing industry has previously received attention in literature on the topic of knowledge integration. However, industrialized house building remains seemingly unexplored. Therefore, this paper presents a current state on knowledge integration between product and production and highlights the identified challenges. The paper also elaborates on knowledge integration differences and similarities with the traditional manufacturing industry. Identified practices involved the use of stage gate models, cross-functional teams, meetings, and partial/sub-assembly prototypes. Challenges distinguished were related to market/technology uncertainty, product/production complexity and geographical/organizational dispersion. The results are based on a case study that used semi-structured interviews within two industrialized house building companies. The paper contributes to the area of knowledge integration between product and production within industrialized house building and presents potential areas for future research.

This research paper presents Turing: an innovative tool that approaches mass-housing design and demonstrates how cloud computing and generative design can be conjointly used via an accessible web-based application to achieve a high level of user input and co-design integration. The technologies implemented are a Grasshopper cloud application with Rhino.Compute, linked to a web server and using Three.js as a visualisation engine on a website.This work explores the design, industrial and commercial opportunities of co-designed, platform-based processes for customisable collective residential developments through generative design and cloud computing from a human-centred perspective.The findings of this research explore an user integration approach in mass customisation using web tools. This paper also investigates the potential of generative design and cloud computing by examining how residential models can be co-designed by architects, developers, manufacturers and users through a novel workflow.This study addresses the following conference themes: smart products, services and product-service systems, open innovation, user co-creation, and data-driven approaches for mass customisation offering a novel approach developed in collaboration with an industry partner.

In light of demographic change as a driver of innovation, products need to align with requirements of an aging society. Based on evidence from organizational psychology, which has identified key benefits of high age diversity in teams, and the benefits of co-creation, we expected positive outcomes in terms of innovative ideas, when product designers integrate elderly users on eye-level in co-creation processes. Therefore, we developed a design thinking field-experiment with six groups of product designers and six co-creation groups (mixed teams consisting of users aged 50–65 and design experts). With each of the 12 groups (n = 75), a two-hour formalized design thinking workshop was conducted. The workshop was designed to meet requirements of both designers and non-designers (users). As a result, 75 ideas as provided by both groups, were prepared and presented to two juries (n = 10, n = 13), one jury of interdisciplinary experts and one of non-experts. They rated all ideas in the categories “creativity”, “impact on life-long autonomy”, “contribution to social coherence” and “market potential”. Our results show that ideas of age-divers co-creation groups were rated significantly more creative and had higher impact on life-long autonomy as compared to the age-homogeneous product design groups. Thus, co-creation with elderly people facilitates innovation processes, which results in products allowing to face the demographic change. Finally, the statistics show that all 75 ideas made an above-average contribution to “social coherence”. We therefore discuss our workshop as a contribution to social innovation.

During the last decade, more healthcare organizations have been turning to Lean and data driven principles to improve the efficiency and generate a positive impact on throughout, patient satisfaction and quality care. Given the high competition in private healthcare market, the need for achieving higher quality medical care to attract new patients and retain existing patients is getting even more critical for private healthcare organizations such as infertility treatment clinics. This paper builds, tests, and reports the results from the application of value stream mapping during the pre-treatment stage in an infertility treatment clinic. In the case study presented here, we demonstrate how analyzing healthcare processes early in the patient journey and mapping the pre-treatment stage enables the organization to identify the bottlenecks, eliminate waste and deliver more efficient customized patient-focused care. After running a multitude of simulations, this study results in recommending two scenarios as the optimal future states that increase the capacity of the clinic, lower waiting times for patients, improve their experience, and lower the stress on the staff.

Today, retailers are looking for new ways to build relationships with their customers. In this context, retailers are shifting to a multichannel retailing strategy, but how exactly will they do so? In this study, we interviewed Onward Personal Style Co, Ltd. (OPS) to analyze how customization is being used in their multichannel retailing strategy. Specifically, OPS strategically integrates both real stores and online operations to build long-term relationships with customers by providing customized suits within a week at a price comparable to ready-to-wear and high-quality suits that fit each customer's size. In particular, the company has developed a directly to Consumer (D2C) strategy using a smartphone with QR codes to create a system that delivers high-quality customized products to customers.

The sustainability of factories is becoming more and more important for manufacturing companies. Resource efficiency is one of the most important fields of action when implementing sustainable production systems. Additionally, industry increasingly focuses on the revitalisation of urban areas as innovative and important places for value-creation. Thus, the resource space is getting more important as a typical characteristic of sustainable factories and the increase in space efficiency is one of the most urgent tasks. Hence, a measurement approach for space efficiency of production systems is necessary. However, in existing approaches there is no method for identifying and measuring space efficiency and its drivers within manufacturing companies. The goal of this paper is to describe and evaluate space efficiency indicators by investigating the value-creation of manufacturing companies in combination with the limited space availability at urban factories. Thereof, this paper presents a descriptive and evaluative model for space efficiency. It is applicable regardless of type and size of the production system and summarizes space efficiency as one key metric. This key metric can be used to compare different production systems with each other. Both theoretical and practical implications of the model as well as limitations and future research directions are discussed.

The modern manufacturing challenge of meeting increasing demands while reducing environmental impact requires a shift to a circular manufacturing business model. Industry 4.0 technologies can be used to streamline the transition from linear economy to circular economy manufacturing systems through the collection, analysis and coordination of product and process data at various stages in the system. A framework for Industry 4.0 implementation in manufacturing-remanufacturing closed loop system is proposed. The framework assists decision makers in identifying Industry 4.0 technologies and their interactions across the entire supply chain. It also provides a basis for system-wide idea generation and evaluation as opposed to isolated and uncoordinated case-by-case implementation approach. Furthermore, the implementation plans can be coordinated in the long term to maximize the efficiency and eliminate conflicts.

Due to increased customization of products, manufacturing companies are subjected to assembly challenges arising from unique product configurations and higher number of variants to be able to produce in low volumes. This translates to an increased production flexibility requirement, while maintaining a high production efficiency. A higher variation among products would potentially disrupt the assembly line and create discrepancies in sequence, tools, and flow of operations. This paper explores how discrete event simulation (DES) can be used as a tool for assembly cell planning by applying key performance indicators (KPIs). A literature review is conducted to identify relevant KPIs that can be applied to compare different assembly scenarios. The KPIs are grouped under the three pillars of sustainability and analyzed as to how DES can be applied to assess them, thereby helping in evaluation and planning of an automation-assisted assembly cell. Finally, an interplay between Automation, KPI and DES, is presented.

The unpredictable occurrences of a pandemic and trade conflicts have recently demonstrated the fragility of global, industrial value chains. Local value creation structures have the potential to mitigate these issues by increasing resilience and meeting present ecological, economic and social challenges. However, the idea of localizing manufacturing encompasses various concepts of value creation that are often used without clearly differentiating them. This paper presents a meta-synthesis which evaluates study results on the topic of local manufacturing aiming to outline Distributed Manufacturing (DM), Re-Distributed Manufacturing (RDM) and Urban Manufacturing (UM). Key attributes are identified and used to characterize the concepts, also highlighting overlaps and differences between them. This allows for a better understanding of local manufacturing and consolidates multiple descriptions of these concepts, thus enabling a more universal and unambiguous communication when referring to DM, RDM or UM.

Socio-political and environmental factors are increasingly pushing companies worldwide towards more sustainable production. When considering whether to invest in new production systems, the question arises as to the expected increase in sustainability, i.e. the ecological, economic, and social properties of the system. It is, therefore, necessary to have appropriate methods for assessing the sustainability of manufacturing systems. Based on a comprehensive literature review, this publication presents and categorises current approaches for production-related sustainability assessment. The authors systematised existing approaches in three steps: First, the underlying methodological concept was analysed, differentiating material flow-based methods, indicator-based methods, and multicriteria-based methods. Second, the normative scope was assessed, i.e. the sustainability dimensions considered and the objective of the respective approaches. Lastly, the operational setting was evaluated, i.e. the company level at which the approach can be applied. Analysing the systematisation, an unequal distribution of assessment methods across the categories became noticeable. Few methods for system and cell level assessment were identified; in particular, a lack of social aspects is evident. Concluding, the authors propose future research in the field of manufacturing-related sustainability assessment.

Manufacturing companies that seek to improve circularity performance across the supply chain, face many challenges in the transition of traditional linear approaches into more circular supply chain models. Reverse logistics is a key area for reuse, recycling and refurbishment of products and materials, where collection and material handling are often critical barriers. This research identifies strategic aspects of reverse logistics in circular supply chains, with focus on mass customization. A literature review on reverse logistics and reverse supply chain management is carried out and used as a basis for a case study of a mass customization furniture manufacturer. Key aspects of a reverse logistics strategy in mass customization settings are discussed, considering supply chain, product and customer-related factors. The large variety of products often complicates collection, material handling and recovery processes after end-of-life. This study presents further insights to strategic reverse logistics aspects for improved circularity performance of mass customization manufacturers, for instance how modular product architectures across the product portfolio may be beneficial for increasing circularity.

To link the two research fields of Sustainable Development and Mass Customization (MC), areas like enablers and impact factors, business models for sustainable MC and analyzing or nudging consumer purchase decisions are often themed. Robust process design as MC key competence allows a different view on this, putting process-oriented and resource-based approaches to the foreground. Since a resource-based view is also followed partly in the discussion about circular economy and the sharing economy, we would like to motivate new research at this intersection. We thus discuss the scenarios of MC for circular maintenance and a waste bin manufacturer who turns from MC supplier to a sharing economy supplier taking part in urban freight cycles. Following this we develop further related research questions.