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Part VI - Meta-Heuristics and Optimization Techniques for Energy-Oriented Manufacturing Systems; International audience; This work presents an Order Acceptance Scheduling (OAS) problem with the introduction of energy consumption limits at each period of the planning horizon. The objective is to maximize total profit of accepted orders while considering energy usage over time, in accordance with imposed deadlines. A time-indexed formulation is presented to tackle the investigated problem.

Published in Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 630 , pp. 73-80, Springer, Cham, 2021Part II - Hybrid Approaches for Production Planning and Scheduling; International audience; This paper proposes an approach for robust scheduling on parallel machines. This approach is based on a combination of robust mathematical and discrete event systems models which are iteratively called in order to converge towards a schedule with the required robustness level defined by the decision maker. Experimentations on a small instance (10 jobs and 2 unrelated machines) and a more complex one (30 jobs and 6 uniform machines) show that this approach permits to converge quickly to a robust schedule even if the probability distribution associated to the uncertainties are not symmetrical. The approach achieves a better rate of convergence than those of the literature’s methods.

Part 1: Artificial Intelligence Based Optimization Techniques for Demand-Driven Manufacturing; International audience; The new consumption behaviors and environmental awareness are pushing the textile apparel industry to profound transformations. Co-design and production on demand are two success factors for this paradigm shift but require efficient and practical tools for companies. In this context, the European H2020 FBD_BModel project provides to practitioner a suitable digital platform. More specifically for the supply chain management, efficient and well-known algorithms are implemented in an user-friendly web-based application composed of two modules. The first module aims at selecting suppliers with multi criteria decision making techniques. This is particularly important to deal with a huge variety of products and a large number of small factories with their own features. The second modules proposed a genetic algorithm-based optimization for order scheduling and supplier allocation. In a demand driven strategy, this task is crucial to reach the requirements in terms of cost and lead time. This paper provides a brief description of the methods implemented in the web-based application. A numerical example, based on real data of the industrial partners of the FBD_Bmodel project, shows the results obtained with the proposed solution.

Part I - Artificial Intelligence Based Optimization Techniques for Demand-Driven Manufacturing; International audience; End-consumers satisfaction with the higher efficiency and reliability of the products and services provided by the enterprises is a highly important factor in their competitiveness. However, providing efficient tracking and tracing of shipped products enhance customer loy-alty and the enterprise image. Satisfied customers are one of the enter-prise’s greatest assets. In doing so, we are mainly interested in detection of fraudulent transactions and late delivery of orders, as well as track-ing commodities and related supply chain costs over different countries. Two datasetes are used for model training and validation: DataCo Sup-ply Chain Dataset and SCMS Delivery History Dataset. A case study is worked out, and the finding results are compared to some related works in the literature. The obtained results show the added value of our pro-posed models.

Part 5: New Trends and Challenges in Reconfigurable, Flexible or Agile Production System; International audience; In this paper, we propose a new mathematical model for the combinatorial optimization problem of batch machining at multi-positional machines with turrets where the parts are sequentially machined on m working positions. Sequential activation is realized by the use of turrets. Constraints related to the design of machining of turrets and working positions, as well as precedence constraints related to operations are given. The objective of the optimization is to minimize the total cost. The paper provides the problem definition, all aspects of the mathematical modelling and the model has been validated by presenting the case of an industrial example.

Part VIII - Modern Analytics and New AI-Based Smart Techniques for Replenishment and Production Planning Under Uncertainty; International audience; Production ramp-up is a key stage in the product life cycle since it can determine whether a product’s launch into the market or the increase of production capacity succeeds or fails. Ramp-up as a phase of value creation, begins with the completion of a product’s design and ends with the reach of maximum production capacity. In today’s world, there is a significant advance in technology but simultaneously there is an increasing uncertainty as we have experienced with the Covid-19 crisis. Within this context, the importance of ramp-up management become more than ever evident. Whilst some products like face masks saw their demand increase drastically, some other companies had to shut off their production or switch to manufacture new products like hand sanitizers. Hence, it is critical today to have a successful ramp-up management in order to predict and meet clients’ demand in terms of quality and quantity. This paper aims to provide a set of guidelines for ramp-up management considering crisis context. The paper relies on an exploratory research coupling literature analysis and interviews among practitioners. The insights drawn from the literature and from the interviews are expected to provide decision makers with valuable guidance with regard to ramp-up management.

International audience; This paper deals with the assembly line balancing issue. The considered objective is to minimize the weighted sum of products’ cycle times. The originality of this objective is that it is the generalization of the cycle time minimization used in single-model lines (SALBP) to the multi-model case (MALBP). An optimization algorithm made of a heuristic and a tabu-search method is presented and evaluated through an experimental study carried out on several and various randomly generated instances for both the single and multiproduct cases. The returned solutions are compared to optimal solutions given by a mathematical model from the literature and to a proposed lower bound inspired from the classical SALBP bound. The results show that the algorithm is highperforming as the average relative gap between them is quite low for both problems.

Part VIII - Modern Analytics and New AI-Based Smart Techniques for Replenishment and Production Planning Under Uncertainty; International audience; A stochastic inventory system with multiple products controlled by a periodic review joint replenishment policy P(s, Si)is considered. This system places a joint replenishment order to bring the inventory position of each item i to its order-up-to level Si when the aggregate reorder point of all items drops below s at each review moment. By imposing service levels on the system, we propose an algorithm for optimizing the policy to minimize the total cost of the system. The performance of this algorithm is evaluated by numerical experiments on randomly generated instances.

Part II - Hybrid Approaches for Production Planning and Scheduling; International audience; This study tackles an unrelated parallel machines rescheduling problem. Sequence and machine dependent setup times and limited resources are taken into consideration. The study focuses on the objective of proposing an efficient and stable rescheduling solution. The resolution approach is explained and illustrated. Different indicators to optimize the rescheduling planning are tested and results are analyzed. The problem is inspired from a concrete case of textile industry.

Part I; International audience; Demand-Driven Manufacturing (DDM) is the solution that most companies are heading to in our days. Although this strategy consists of producing goods based on what consumers demand, companies should also rely on accurate forecasting systems to prepare their production chain for such an operation by supplying enough raw material, increasing production capacity to fit the desired demand, etc.… However, due to the fact that most companies have been relying on massive production, most sales forecasting systems usually used rely on sales data of previous years that, not only contain the actual demand, but takes into consideration the marketing strategy effects like massive promotions. Hence, the resulting forecasts do not mainly reflect consumers' demand. For this reason, a switch to demand forecasting, instead of sales forecasting, is essential to ensure a good transition to DDM. This paper proposes an artificial intelligence based demand forecasting system that aims to determine "potential sales", mainly reflecting consumers' demand, by correcting historical sales data from external variables' effects. A comparison with other sales forecasting models is performed and validated on real data of a French fashion retailer. Results show that the proposed system is both robust and accurate, and it outperforms all the other models in terms of forecasting errors.

Part VIII - Modern Analytics and New AI-Based Smart Techniques for Replenishment and Production Planning Under Uncertainty; International audience; This work studies the problem of multi-period multi-sourcing supply planning with stochastic lead-times, quantity-dependent pricing, and delivery flexibility costs. We present a problem formulation that takes also into account holding and backlog costs and finite capacities of suppliers. The objective is to minimize the expected total cost while respecting suppliers’ capacity constraints and satisfying customer demand. In this paper, the proposed stochastic integer linear program is detailed and the first results of experiments are presented and discussed.

Part 1: Artificial Intelligence Based Optimization Techniques for Demand-Driven Manufacturing; International audience; With the development of e-commerce, payment by credit card has become an essential means for the purchases of goods and services online. Especially, the Manufacturing Sector faces a high risk of fraud online payment. Its high turnover is the reason making this sector is lucrative with fraud. This gave rise to fraudulent activity on the accounts of private users, banks, and other services. For this reason, in recent years, many studies have been carried out using machine learning techniques to detect and block fraudulent transactions. This article aims to present a new approach based on real-time data combining two methods for the detection of credit card fraud. We first use the variational autoencoder(VAE) to obtain representations of normal transactions, and then we train a support vector data description (SVDD) model with these representations. The advantage of the representation learned automatically by the variational autoencoder is that it makes the data smoother, which makes it possible to increase the detection performance of one-class classification methods. The performance evaluation of the proposed model is done on real data from European credit cardholders. Our experiments show that our approach has obtained good results with a very high fraud detection rate.

Part 5: New Trends and Challenges in Reconfigurable, Flexible or Agile Production System; International audience; The reconfigurable manufacturing system (RMS) has been acknowledged as an effective manufacturing paradigm to tackle high volatility in demand types and amounts. However, the reconfiguration needs an amount of time and leads to some level of resource wastage. Accordingly, a high frequency in the system’s reconfiguration may have a negative impact on its performance. In this regard, this paper investigates the advantage of using cloud manufacturing (CMfg) resources in enhancing the performance of an RMS system. A novel mathematical model is developed for the integrated workforce allocation and production scheduling problem utilizing the CMfg under a non-permutation flow shop setting. This model simultaneously makes decisions on the utilization of the CMfg capacity for performing some jobs, and for the remaining jobs, determination of machines’ configurations for each job, scheduling of the jobs on the machines, and allocation of operators to machines as well. This model aims to minimize the sum of job processing costs, overtime costs, and the cost of utilizing the CMfg resources. Finally, a computational experiment is conducted, which shows a promising improvement in the total cost of the production system by utilizing the CMfg capacity.

Part I : Artificial Intelligence Based Optimization Techniques for Demand-Driven Manufacturing; International audience; This paper deals with the problem of planned disassembly lead time calculation in a Reverse Material Requirement Planning (RMRP) environment under uncertainty. A multi-level disassembly system with one type of end-of-life product and several types of components at each level is considered for the first time in the disassembly planning problem under uncertainty. The paper presents a mathematical model with corresponding proofs. The objective of the proposed model is to minimise the total expected cost which is composed of holding and backlogging costs. Some advantages of the proposed model and perspectives of this research are discussed.

Part 1: Advanced Modelling, Simulation and Data Analytics in Production and Supply Networks; International audience; After disasters occurred, many refugees have to suffer a lot. To relieve this detrimental situation, various commodities are distributed to the pre-determined warehouses. However, the initial multi-commodity distribution may be imperfect, which results in some warehouses having surplus commodities compared to other unmet warehouses. Hence, it is necessary to rebalance commodities among those warehouses. Because of the uncertain environment after a disaster, the demand is usually uncertain. To plan this multi-commodity rebalancing process appropriately, it is usually assumed that the collected data are uncontaminated. However, this assumption can be easily violated due to the uncertain environment or human error, which results in the biased estimation of the solution. In this study, we propose a strategy for remedying the difficulties associated with data contamination so that a set of robust decisions are obtained. Through a case study, we show that the proposed strategy facilitates effective decision-making in the multi-commodity rebalancing when the data contamination is involved.

Part 5: Industry 4.0 Implementations; International audience; The 4th industrial revolution generates a high potential for smart production systems. Many manufacturing companies considering therefore the application of cyber-physical systems in the sector of intralogistics. The aim is to achieve better logistics performance or lower costs. However, small and medium sized enterprises (SME) are hesitant about introducing Industry 4.0 technologies. They fear high implementation costs, low benefits and the lack of know-how increases the reluctance of the companies.This paper presents a procedure which enables SME’s to assess the benefits of Industry 4.0 technologies by themselves. The model follows the recognized principle: First improve your processes, then automate them:Methodical basis is a process model intralogistics, which also considers self-controlling cyber-physical systems. In addition, the benefit aspects are assigned to the individual process steps.In the specific application, the company first determines the digitization potential of the individual activities and then the associated benefits of Industry 4.0 technologies.The procedure reduces on the one hand the uncertainty regarding of wrong decisions and creates on the other hand the possibility for companies to select Industry 4.0 technologies goal-oriented. The described procedure was validated with SMEs.

Part 1: Smart Supply Networks; International audience; This paper presents a methodology combining a flow optimization and a cost models in order to, simultaneously, realize a tactical planning of a productive system, and evaluate the financial performance of the proposed plans. The system addressed is a multi-site, multi-product supply chain structure with finite capacities of production, storage and transport. In order to model physical flow, we propose an optimization model taking into consideration all the physical system’s constraints. It calculates production and transport plans while maximizing demand satisfaction rate. Then, in order to financially evaluate the solution found by the optimization model, we propose a cost model using Activity Based Costing (ABC) as a valuation method using cost drivers mechanism. Finally, in order to couple both optimization and cost models in a global integrated model, we use an approach called PREVA for PRocess EVAluation, generally used to set up a supply chain’s management control system using financial and physical metrics.

Part 3: Production Management Theory and Methodology; International audience; Most researches of facility layout planning (FLP) have aimed at finding a layout with which evaluation indices based on distance are minimized. Because temporal efficiency has not been considered in this stage but in post stages, the resultant temporal efficiency may not be optimal enough. The authors have developed an FLP method considering temporal efficiency, in which facility layout is optimized using genetic algorithm (GA), and have enhanced it so that robustness against changes in production environment can be taken into consideration. However, the enhanced method involves a large computational load, since numerous production scenarios need to be considered. This paper provides a method for reducing computational load in the robust FLP based on the sampling approach where each layout plan is evaluated with only a limited number of production scenarios in the optimization process by GA. Numerical experiments showed the potential of the proposed method to efficient robust FLP considering temporal efficiency.

Part 1: Lean Production; International audience; Lean management remains the most popular approach to operational excellence within industry. The next wave of industrial improvement is widely considered to be driven by the application of digital technologies. Organizations are seeking to understand how the two approaches can be utilized synergistically. This paper aims to sketch the landscape for current theories and managerial challenges facing Lean and digital transformation based on Lean principles with respect to organizational design, supply chain planning, decision systems and supplier relationships. We raise fundamental questions that should be addressed by future research in the field of digital lean manufacturing and lean digital transformations.

Part 11: Intelligent Diagnostics and Maintenance Solutions for Smart Manufacturing; International audience; This paper demonstrates a detailed performance diagnosis of a production process. With limited investment power for new technologies, managers want to diagnose the reason for system underperformance, i.e. diagnosing performance gaps. This paper found detailed performance measures for specific production orders by using event log data, i.e. a set of timestamps that denote the occurrence of an atomic event in production. Sequential time registrations for each production order give detailed insights in how the production process is behaving. The reported case study gave managers a web application that lets them zoom in and out of different characteristics to get an understanding how their production process results in a certain performance. Based on the background and case, a framework and way forward are proposed on how to perform detailed diagnosis to explain performance gaps in production.

Part 3: Sustainability and Reconfigurability of Manufacturing Systems; International audience; This paper introduces a Unity3D-based simulation of reconfigurable assembly cells. A systematic approach defining flexibility ranges and comparing product requirements and cell capabilities allows the automated proposal of reconfigurations in the assembly cell. With this approach, the suitability of an existing cell can be examined for different variants of products while taking reconfiguration aspects into account. The simulation simplifies the process of introducing a new product to an assembly line. Through the virtual approach, designing of the product and line planning can be executed simultaneously, thereby decreasing cost and time-to-market for new variants in an existing production system.

Part 3: Production Management Theory and Methodology; International audience; The paper studies an engineer-to-order (ETO) manufacturing firm. A novel approach is used to assess the fit of capacity planning methods in the planning environment of the firm, and towards delivery date setting, which is of strategic importance for ETO firms.

Part 1: Lean Production; International audience; Many manufacturers are in the process of evolving from mass production to mass customization to cope with the increasing diversity of customer requirements. This induces an increasing complexity resulting from the high variety offered to customers. This problem is heightened by integrating product and service within the same offering. Modularity is considered as a driver for reducing complexity while increasing offered variety of products and services. This paper addresses the question of how to modularize products and/or services considering different criteria from the designers and domain experts. To this end, a Design Structure Matrix (DSM) based method is introduced. The method relies on a set of modularization criteria and on clustering to form product and/or service modules. The applicability of the method is illustrated through a test case in the manufacturing sector.

Part 5: Variety and Complexity Management in the Era of Industry 4.0; International audience; Advanced analytics has the potential to redefine manufacturing. However, practical implementation is in its infancy. One reason is a lack of management tools that enable decision-makers to choose suitable techniques from advanced analytics for domain-specific problems in manufacturing. This paper uses a systematic mapping review in order to identify seven application areas to which analytics can add substantial value. Each area is then matched with suitable techniques from the field of advanced analytics. The resulting systematic map provides a novel management tool for the purpose of identifying promising analytics projects in manufacturing and thus facilitates decision-making.

Part 9: Operations Management in Engineer-to-Order Manufacturing; International audience; This paper discus the feasibility of using advanced planning and scheduling (APS) in an engineer to order manufacturing company. The starting point is a company producing customised variants of standard products involving engineering, machining, sub-assembly, and final assembly operations. There is limited degree of parts commonality as the product variety is high with respect to material and size. APS is normally used in repetitive manufacturing companies but we argue that some ETO companies with a somewhat fixed planning setup could benefit of implementing an APS engine on top of their ERP system. One of the critical elements is, however, whether the company is able to formalise their tacit knowledge in the complicated planning processes to rules and options in an APS environment.

Part 10: The Operator 4.0 and the Internet of Things, Services and People; International audience; To accomplish a flexible and highly productive production system, collaborative robot applications, such as co-bots, can be one solution. Hence, last year: 2018, less than 4% of the industrial robot investments had “collaborative roots”. In order to increase this number, clear strategies for the implementation of co-bots are vital. This paper will present the results from the study of 40 SMEs, and six OEMs, regarding where and when to implement co-bots in production. Furthermore, which KPIs to consider when implementing these collaborative robot applications. The Lotus Blossom technique has been used to create the first steps towards strategies for implementing collaborative robot applications for the Operator 4.0. Seven areas of application have been pre-selected, and one area has been left free of choice for the companies. The results show that the areas with greater strategic interest are ‘pick-n’-place’ tasks and ‘load-unload’ tasks.

Part 1: Lean Production; International audience; Based on an analysis of research and industrial trends, the paper introduces the basis of a conceptual research framework for an innovative methodology dedicated to design, implement and manage Reconfigurable Manufacturing Systems (RMS). The authors present key challenges extracted from the literature and key industrial needs for RMS, drawn from expert interviews via an industry study. A conceptual framework for reconfigurability management is proposed, which opens several avenues for future research.

Part 13: Collaborative Technology; International audience; Nowadays, manufacturing companies collaborate with universities in innovation projects to sustain or achieve competitive advantage. However, fundamental differences between the industrial and academic worlds hamper the utilization full innovation potential of such collaboration. As a countermeasure, industry stresses the need for the development of knowledge management tools that can increase the value of collaborative innovation projects. This paper covers a qualitative study of research-based innovation projects owned by manufacturing companies and partly funded by government, where the academia has the role as research provider. We seek to answer two research questions: (1) how can the strategies and objectives for collaboration to meet both partners’ expectations be defined? (2) how to facilitate the projects to enhance the creation and exploiting of knowledge? The study identifies that a modified version of Nonaka’s so-called five-phase model of organizational knowledge creation is applicable for the given context. Based on this, we propose a conceptual knowledge management model of university-industry collaboration in innovation projects. The proposed model provides (1) management initiatives that intensify knowledge creation and exploiting processes (2) ensures partners’ commitment to collaboration along with the continuing improvement of university-industry collaborative concepts. It is proposes that the model will support knowledge managers of industry and university in conducting innovation projects more effectively and efficiently, as well as deliver even more innovation values to partners and society. The model can also assist national and federal research/innovation councils in decision-making when assessing industrial research project applications.

Part 3: Production Management Theory and Methodology; International audience; In order to match financial sustainability with the delivery of high-quality healthcare hospitals need to seek efficient ways of managing inventories with a large variety of medical supplies. To find a balance in the trade-off between cost and service levels that ensures on time and high-quality patient treatment is a challenge. It is especially crucial in the hospital setting, where the consequence of a stock-out can be much more severe than lost revenue. The process of ensuring that the required supplies are available at the right time is a particularly important supporting role within hospital logistics. The scope of this study is inventory control at the point-of-use inventories in hospitals. It concerns the short-term planning and control area, which focus on coping with actual demand and making necessary changes in order to match efficiently to plans. This study aims to model the inventory control process and discuss how technology can support high availability of medical supplies in hospitals.

Part 5: Variety and Complexity Management in the Era of Industry 4.0; International audience; Both external and internal influences affect the way production systems are planned and operated. Long-standing trends combined with topics such as digitization and artificial intelligence are increasing the complexity of tackling problems in production systems. Along with this, there is the ever increasing risk of planning errors and the loss of long-term competitiveness. This paper presents an innovative approach that supports problem-solving processes in the planning and operation of production systems. For this purpose, qualitative structural modeling and analysis of problem situations are applied. At its core, the article focuses on a framework, which forms the basis for the modeling of problem situations, their analysis and the organizational and technical integration of the method. Furthermore, with the concept of building block based domain-relation models, a possibility is presented with which the structure of problem situations can be modeled and analyzed.

Part 11: Collaborative Technology; International audience; The concept of collaborative networks has been encountered very frequently these days as the reply when trying to adapt and enhance enterprises in this tremendously competitive commercial environments. A lot of knowledge has been gathered for collaborative networks so far, from defining network kinds to levelizing partnerships and also proposing models for partnership developments. But most of these efforts didn’t tackle a very vital obstacle which is detecting and predicting collaboration possibilities between enterprises. In this paper, a new enterprise characteristics classification is proposed, which will be used as a profile for characterizing enterprises susceptible to take part in a collaborative network. The proposed detection approach is based on the enterprise characteristics concept as well as collaboration network types. Also a hypothesis to rank the potential partners using KPIs is shown along with the big picture of this approach accompanied by the future work that has to be done.

Part 4: Data-Driven Production Management; International audience; Due to a growing number of product variants, shorter lead times, and global supply chains, planning and launching production systems is becoming increasingly important. Therefore, managing the period of production ramp-up becomes a competitive advantage. To handle the increasing complexity and uncertainty in this special phase, data availability is necessary in terms of efficient decision making. However, in this early phase of the product and production system lifecycle, data quantity and quality are not guaranteed. This is due to the degree of novelty of processes, technologies and human behaviors in this special phase. In this paper it will be analyzed that selected data from the factory planning phase as well as the factory operation phase needs to be jointly processed by ramp-up involved personnel as value adding information. Finally, the presented use case, as well as the derived data management approach, will help companies to better manage production ramp-ups in the future.

Part 1: Lean Production; International audience; The increased environmental awareness of these days urge companies towards energy efficient production. This work focuses on the consideration of power consumption since the design stage of a production system. A new Assembly Line Balancing Problem is introduced in which each task is associated with a power consumption. The aim is to find an assignment of tasks to workstations that minimizes the overall power peak while complying with given maximum cycle time and number of workstations. An Integer Linear Programming formulation is proposed and tested on benchmark instances enhanced with power features. Numerical results are discussed.

Part 9: Operations Management in Engineer-to-Order Manufacturing; International audience; In this paper we will propose a set of considerations and guidelines we believe are critical to achieve efficient flow in non-repetitive production. The proposed factors are insights gained through application of lean principles to two cases of non-repetitive manufacturers.

Part 1: Lean Production; International audience; It’s one of the most popular and most misunderstood business concepts of our time. Since the term Lean Production was first popularized in 1990, lean has come to mean very different things to different people. In this paper, we go back to the origins of Lean Production - an alternative business approach pioneered by Toyota Motor Corporation - and present insights into how Toyota actually developed a learning system to gain competitive advantage through the continuous development of people, rather than simply designing and optimizing an efficient production system. We draw on our observations from Toyota plants around the world and our discussions with various Toyota sensei, and present a framework for understanding lean as a learning system, not a production system.

Part 5: Industry 4.0 Implementations; International audience; The concept and vision of Industry 4.0 has been around for almost a decade and gain a lot of momentum and attraction globally. Central to the vision of Industry 4.0 is the concept of a “Cyber-Physical system”, linking the IT elements of an enterprise (Cyber) with the physical system (man and machine) of an enterprise. This vision is well known and promoted as crucial in radically transforming todays manufacturing industry. While there is a plethora of papers and studies of the various “cyber” aspects, the concept, visions, benefits as well as the downsides of Industry 4.0, few papers have much to say about the actual implementation. Based on a digital maturity mapping of ten front line manufacturing enterprises in Norway this paper analyses implementation at shop floor level of both cyber and physical system and their interaction. From the survey data a clear picture emerges of the development of a cyber system, as well as worker usage and benefit of the system. However, the two systems don’t interact very well, worker interaction is limited to plain old keyboard usage, instead of employing more mobile, handsfree, voice based or similar interaction methods. Currently there is no cyber-physical system, rather a burgeoning cyber system poorly linked to the physical world. If the cyber-physical system is to be realized there is a need for a rethinking and upgrading of man-machine interaction.

Part 1: Lean Production; International audience; The capital goods industry supplies highly volatile marketplaces with products customized to increase product performance and reduce operating costs. Consequently, an order winning criteria for capital goods manufacturers is to quickly and effectively reconfigure supply and manufacturing systems to suit ever changing product and volume requirements within short time frames. In order to clarify such potentials of reconfigurability, this paper presents a case study on reconfigurability potentials in a large capital goods company. The framework applied for this relates reconfigurability drivers with different production levels and purposes. The findings suggest that local content and sub-contracting requirements are main drivers for the potential application of reconfigurability on network level, being reinforced by reconfigurability on system and equipment level. Thus, the paper extends previous research on reconfigurability and addresses potentials beyond shop floor level in a multi-dimensional approach.

Part 5: Industry 4.0 Implementations; International audience; Logistics systems have a key function to meet competition criteria like delivery time, punctuality or flexibility. Industry 4.0 technologies are considered as an important key to master increasing requirements like individualization, shorter product lifecycles or global competition. However, bringing the complex structures and processes of a logistics system to a higher level of maturity is not an easy endeavor. The actions to be planned and implemented need to be rooted in the overall digitalization strategy of the company. Furthermore, they need to be interlinked with the development of other corporate functions like production, quality or planning and they need to be based on current capabilities. To support such a systematic development process, maturity models seem to be the method of choice, and there is already a considerable amount of such models available. As those models are mainly focused on the company as a whole or specifically on production systems, we identified the need for a specific support for logistics. Therefore, in this paper we describe the relevant background as well as the components of a maturity model for an Intralogistics 4.0.

Part 10: ICT for Collaborative Manufacturing; International audience; Manufacturing Execution Systems (MES) have evolved to alleviate the drawbacks of Enterprise Resource Planning (ERP) systems by providing real-time information exploitation from the shop floor. In parallel with the increasing number of companies adopting MES, MES vendors have exponentially increased over the past two decades. While companies tend to focus merely on the technological aspects of the MES implementation, the adoption of MES implies an organizational transformation process that needs to be properly addressed by companies for implementation success. This is important because the new functions, services, and operability offered by the MES needs to be aligned with existing business processes and practices. Considering the human, technological, and organizational aspects holistically, this paper outlines critical success criteria and proposes an organizational readiness model for MES implementation.

Part 1: Lean Production; International audience; Lean Manufacturing and Industry 4.0 are at times portrait as conflicting paradigms. However, we take the stance that they are two sides of the same coin, and should be considered as mutually beneficial. Based on this understanding, this paper is part of a series where we discuss established Lean practices in the emerging Digital Lean Manufacturing World. In this paper, we specifically focus on the issue of “buffer waste”, and what that implies within a cyber-physical production system. We discuss the vicious cycle of Mura, Muri, and Muda, and provide observed examples in industry for “buffer waste” from four different, yet interdependent perspectives: (i) physical to physical, (ii) physical to digital, (iii) digital to physical, and (iv) digital to digital. The results of this study confirm that “buffer waste” is indeed an issue that deserves our attention as academics and practitioners in the emerging Digital Lean Manufacturing environment.

Part 3: Sustainability and Reconfigurability of Manufacturing Systems; International audience; The increasing demand for customer-specific, cost-effective products with shorter production times is shaping the industrial production environment. Therefore, both human workers and machines must react to these changes with greater flexibility and productivity. This paper shows a procedure to plan the employee ideally into the Reconfigurable Manufacturing Systems (RMS). The approach systematizes the competences of employees and enables an assessment of different expertise levels to finally realize a flexible integration of them into the constantly changing work environment. Therefore, the competencies of employees must be opposed with the needed skills so that the work processes can be executed adequate according to the product requirements. When this procedure is finally described digitally, companies will be able to realize a flexible workforce planning in the RMS, which is not possible so far.

Part 1: Lean Production; International audience; The continuous improvement process (CIP) enables companies to increase productivity constantly by sourcing ideas from their employees on the shop floor. However, shorter production cycles require manufacturing companies to also adapt their production processes in a faster manner and reduce resources for CIP activities. Traditional CIP approaches fall short in such a fast-paced environment characterized by uncertainty. This study proposes a novel approach for increasing the efficiency and speed of the CIP by using data of previous improvements and predict current potentials. This results in a prescriptive model supporting the employees how to improve their processes.

Part 1: Smart Supply Networks; International audience; This research improves understanding of the impact of specific types of truck driver behaviour and temporal scheduling on the effectiveness of a terminal appointment system. A discrete event simulation model of a bulk cargo marine terminal is developed to analyse parameters related to driver behaviour (punctuality and proportion of planned appointments) and temporal scheduling (appointments per time window and time window spacing) on truck flows and turnaround times at the terminal. The model is based on an Australian wood chip export marine terminal currently experiencing significant truck congestion. The terminal operator and stakeholders have expressed interest in the implementation of an appointment system to address this issue. The modelling presented in this research was used to inform their investigation into developing an appointment system solution.Simulation results indicate that the proportion of planned appointments, used as a proxy for the appointment system use, has a significant impact on truck turnaround times. Greater truck arrival punctuality only marginally improves truck turnaround times. Interestingly most optimization approaches continue to focus on improving punctuality through service rules or financial penalties in order to achieve optimal turnaround times. However, the additional cost in terms of complexity or assumptions for optimal solutions against non-optimal approaches are rarely weighed in terms of dividends of the marginal improvements generated. By involving terminal users (drivers and transporters) in the design of an appointment system and its scheduling parameters, terminal operators can significantly improve appointment system use and effectiveness by increasing the probability of positive users’ behaviours.

Part 1: Lean Production; International audience; Reconfigurable manufacturing systems (RMS) are designed to be able to be reconfigured to produce new items. Nevertheless, reconfigurations of a RMS may be time consuming and costly if they are not considered since early steps of new item design. This work describes a decision support system to auto-matically generate and test configurations for such RMSs based on a computer-aided design (CAD) model of a new product. The proposed methodology consists of two main steps. First, a matrix of possible assembly plans (taking into account resource/tool compatibility, geometric constraints, …) is generated with a skill-based comparison between the new item and the production resources. Second, the assembly plan with minimum reconfiguration cost is found through mathe-matical optimization. The solution is analyzed by a simulation model in the end. Experiments performed on small use case validate the proposed methodology.

Part 8: Knowledge Management in Design and Manufacturing; International audience; In manual assembly systems, there is often little transparency and great potential for optimization, especially in assembly systems with small batch sizes. In this paper, a model is developed that supports an approach to automated assembly optimization. For this optimization, actual data is collected in manual assemblies. Based on the data, the optimized assembly sequence is derived by developing a best practice. Best practice describes a combination of assembly processes performed by the workers during the data collection. The model shows the relationships and the dependencies in the assembly systems and allows to improve it.First, the considered assembly system is defined as a socio-technical system and general modeling principles are prepared. After presenting the benchmark approach to derive the best practice, the requirements for the model are identified. Then, the model is developed in four steps: The system boundary is defined, the features are described, and the model is formalized. Finally, the model is applied and tested in an example project and its purposefulness is shown.

Part 1: Smart Supply Networks; International audience; Reducing the volume of trucks carrying empty or below capacity loads on road networks are both socio-economic and environmental sustainability issues for the logistics industry. Planning concepts for a collaborative logistics exchange based on real-time data are described as well as the benefits in terms of optimizing load capacity utilization, minimization of empty running, reducing costs, traffic congestion, and truck emissions.

Part 5: Variety and Complexity Management in the Era of Industry 4.0; International audience; The diffusion of Industry 4.0 within companies depends on their size. The resources and processes of large companies are more structured, so they are likely to deploy 4.0 technologies more quickly than SMEs. In order to prevent these SMEs from becoming victims of this industrial revolution, it is essential to fully understand the organizational, human and technological challenges of Industry 4.0. This article presents a review of the barriers and problems that a 4.0 investment can create for small and medium-sized businesses depending on their sector. Our analysis proposes a synthesis matrix with the effects of industry 4.0 in the different sectors of SMEs. This matrix presents the impacts, opportunities and risks of each of the main technologies of industry 4.0. Based on process innovations such as training plans or customer proximity, SMEs seem to have adopted their own Industry 4.0 concepts according to their sectors of activity.

This is a post-peer-review, pre-copyedit version of an article published in Advances in Production Management Systems. Production Management for the Factory of the Future. APMS 2019. IFIP Advances in Information and Communication Technology, vol. 566. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-30000-5_71. The high complexity in Engineer-To-Order (ETO) operations causes major challenges for manufacturing logistics, especially in complex ETO, i.e. one-of-a-kind production. Increased digitalization of manufacturing logistics processes and activities can facilitate more efficient coordination of the material and information flows for manufacturing operations in general. However, it is not clear how to do this in the ETO environment, where products are highly customized and production is non-repetitive. This paper aims to investigate the challenges related to manufacturing logistics in ETO and how digital technologies can be applied to address them. Through a case study of a Norwegian shipyard, four main challenges related to manufacturing logistics are identified. Further, by reviewing recent literature on ETO and digitalization, the paper identifies specific applications of digital technologies in ETO manufacturing. Finally, by linking manufacturing logistics challenges to digitalization, the paper suggests four main features of digitalized manufacturing logistics in ETO: (i) seamless, digitalized information flow, (ii) identification and interconnectivity, (iii) digitalized operator support, and (iv) automated and autonomous material flow. Thus, the paper provides valuable insights into how ETO companies can move towards digitalized manufacturing logistics.

Part 1: Smart Supply Networks; International audience; This paper presents the use of advanced simulation modeling to optimize the operation of a fully automated modular conveyor system in a large-scale warehouse. At its peak capacity, the smooth flow of material in the system was greatly impaired due to the appearance of bottlenecks. A full-scale 3D discrete event simulation (DES) model of the system was built and time-dependent statistical models were carefully designed and implemented in the model in order to capture the randomness and complex dynamics of the operation. The model was verified and validated, and several scenarios have been analyzed. The paper demonstrates a practical example of how data-driven simulation modeling provided a cost-effective solution to enhance efficiency. The paper highlights the crucial aspects that must be taken into account in the modeling of the system in order to create a reliable standalone decision support system. Moreover, the paper highlights the identified key steps that are yet to be taken from dynamical modelling towards a Digital Twin.

Part 13: Collaborative Technology; International audience; Projects is the preferred model for one-of-a-kind production. Projects may be difficult to manage due to complexity and many involved stakeholders. Stakeholders are a major source of uncertainty. Uncertainty may be both positive and create opportunities and negative giving risks. Risks and opportunities are either operational, strategic or contextual.The traditional approach to managing risk comprise identification and analysis of risks as well as response planning and control. There is a need for a shift in mindset for managing risks. Rather than regarding risks as “evil”, they should be managed because uncertainties also create opportunities. The Bermuda Risk Triangle is the intersection between operational, strategic and contextual risks. Project risk navigation is about how project leaders can navigate in this triangle to reach their objectives.Opportunities are often more or less neglected in projects. At the most, just a few are identified.A framework for managing opportunities is suggested. It builds on the project control variables: time, cost and scope of work. It contains a classification of eight opportunity types. Using this classification in dedicated workshops has shown to produce far more opportunities than usual.The framework is verified in a case study. The case is the construction of the new National Museum in Oslo, Norway. Through the framework a total of 246 opportunities have been identified representing an estimated cost reduction of about 64.2 million USD.