The impact of data-driven technologies on supply chain design

Recent supply chain disruptions following Covid-19 and international crises have led to changing paradigms in supply chain design. Likewise, data-driven technologies housed under the term Industry 4.0 have an increasing impact on how supply chains are orchestrated and shaped. This paper gives an overview to several examples of recent and expectable trends in supply chain design. Advanced manufacturing technologies, data-driven technologies in logistics and supply chain management, electrification of vehicles, as well as microchips and semiconductor manufacturing are described as representative drivers of new forms of supply chain design. In this context, a special emphasis is devoted to European initiatives such as the European Chips Act or the European Battery Alliance. Examples such as manufacturing ecosystems or platform based manufacturing are given as well as locally independent supply chains that provide potentials for supply resilience and sustainability. The paper concludes with a research agenda that includes seven areas for future research, including changes in supply chain structure, changes in inter-firm interaction, integration of small and medium-sized enterprises, changing roles of humans and new forms of business models and collaboration. In this context, the interrelations between technologies (product and production level) as well as the research avenues must be emphasized

Supply Chain Disruptors and their Impact on the Future of Manufacturing, Logistics, and Distribution

In this paper, we outline some of the changes occurring in the world and how businesses are adapting to these changes. We also list disruptors – factors or new ideas that disrupt the status quo – and their impact on manufacturing, supply chain, logistics, and distribution

a tailored maintenance management system to control spare parts life cycle

Abstract The maintenance of complex production systems became increasingly crucial to ensure the competitiveness of companies and service level to their clients. Because of product customization the number of mechanical and electrical components and functional groups of manufacturing lines enhanced with their complexity. To face this concern, the physical and logical design of such systems is typically partitioned among several groups of engineers and designers. Consequently, a holistic awareness of the whole project is lacking and the maintenance of such systems becomes even more challenging. In view of this, new tailored support-decision tools able to manage and control the life cycle of spare parts from their design, throughout the run time, and to their failure and replacement are necessary. This paper illustrates an original maintenance management system (MMS) resulting by the combination of different computerized tools able to integrate the information flow behind the life cycle of a generic component. The proposed system supports coordination among groups of engineers and practitioners through graphic user interfaces (GUIs) and performance i.e. cost, reliability, dashboards, which lead decision-making from the design phase to the planning of maintenance tasks along the life of the manufacturing line. These tools are validated with a real-world instance from the tobacco industry which allows assessing how components belonging to the same functional group may differently behave over their life cycle. The results suggest that the holistic awareness on the whole manufacturing system provided by the proposed MMS can support task design and schedule of maintenance actions providing the reduction of more than 20% of the total cost and time for maintenance actions. The practical example shown contributes to shed light on the potentials of new paradigms for maintenance management in the industry 4.0

Maintenance optimization in industry 4.0

This work reviews maintenance optimization from different and complementary points of view. Specifically, we systematically analyze the knowledge, information and data that can be exploited for maintenance optimization within the Industry 4.0 paradigm. Then, the possible objectives of the optimization are critically discussed, together with the maintenance features to be optimized, such as maintenance periods and degradation thresholds. The main challenges and trends of maintenance optimization are, then, highlighted and the need is identified for methods that do not require a-priori selection of a predefined maintenance strategy, are able to deal with large amounts of heterogeneous data collected from different sources, can properly treat all the uncertainties affecting the behavior of the systems and the environment, and can jointly consider multiple optimization objectives, including the emerging ones related to sustainability and resilience

Designing Smart Services: A System Dynamics-Based Business Modeling Method for IoT-Enabled Maintenance Services

This paper reports on a design science research project aiming to develop a method to support business decision-making regarding IoT-enabled maintenance services for Original Equipment Manufacturers (OEMs). Often, these OEMs remain reluctant to make full use of recent advances in the Internet of Things (IoT), sensor technologies and data analytics for providing services on installed equipment with Asset Owners (AOs). These new developments allow them to advance on their servitization journeys from selling products to selling product-centered services. The method is based on System dynamics (SD), a powerful modeling methodology to capture all these complexities in an integral, coherent and visible manner with all stakeholders. It also allows for a quantitative analysis of the business case for “smart maintenance services”. The paper describes servitization, smart (i.e. digitally enabled) mainte-nance services and then the method itself. A case study illustrates the application of the method for an OEM in the semiconductor industry

Spare Parts Demand Forecasting in Maintenance, Repair & Overhaul

Despite a high degree of uncertainty about the scope of future orders and the corresponding capacity and material demands, Maintenance, Repair & Overhaul (MRO) service providers face high expectations regarding due date reliability by their customers. To meet these requirements while at the same time keeping delivery times short, the availability of the required spare parts or pool parts is an essential success factor. As these cannot be kept in stock in large quantities due to their high monetary value, reliable spare parts demand forecasts are of vital importance for the profitability of MRO service providers. As a result of a high degree of information uncertainty and the mostly lumpy demand patterns, conventional time-based and statistical methods do not show sufficient forecasting quality for application in the MRO industry. Data-based approaches incorporating machine learning methods offer promising capabilities to achieve improved predictive accuracy but still need to be adequately linked to production planning and control to realize their full potential. This paper first analyses potential approaches to spare parts demand forecasting in the MRO industry, focusing on forecast accuracy and potential for integration into material and production planning. Based on this, a classification of demand forecasting approaches is presented and an approach for order-based material demand forecasting with two-step feature selection is proposed. Finally, the presented approach is applied on a real dataset provided by a MRO service provider

DeSyRe: on-Demand System Reliability

The DeSyRe project builds on-demand adaptive and reliable Systems-on-Chips (SoCs). As fabrication technology scales down, chips are becoming less reliable, thereby incurring increased power and performance costs for fault tolerance. To make matters worse, power density is becoming a significant limiting factor in SoC design, in general. In the face of such changes in the technological landscape, current solutions for fault tolerance are expected to introduce excessive overheads in future systems. Moreover, attempting to design and manufacture a totally defect and fault-free system, would impact heavily, even prohibitively, the design, manufacturing, and testing costs, as well as the system performance and power consumption. In this context, DeSyRe delivers a new generation of systems that are reliable by design at well-balanced power, performance, and design costs. In our attempt to reduce the overheads of fault-tolerance, only a small fraction of the chip is built to be fault-free. This fault-free part is then employed to manage the remaining fault-prone resources of the SoC. The DeSyRe framework is applied to two medical systems with high safety requirements (measured using the IEC 61508 functional safety standard) and tight power and performance constraints