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Institut / FIR-Bereiche
Due to shorter product life cycles the number of production ramp-ups is increasing, while customers have a soaring demand for more variable and individualized products. In the future, optimizing the production ramp-up will become an important differentiation criterion for companies. Considering the whole supply chain in the ramp-up process becomes therefore indispensable. This is what the presented research in this paper concentrates on. The intention of the research project is to develop a model of a supply chain in the production ramp-up stage. Through this model, approaches for optimizing the production ramp-up in the whole supply chain will be derived.
Further the research project concentrates on measuring the production ramp-up performance in the supply chain, showing the impact on economic and financial measures. The result of this research is an approach to align the tasks and objectives of Supply Chain Management with the tasks and objectives of ramp-up management in order to optimize the whole supply chain in the ramp-up stage.
Rebound Logistics
(2009)
Today, the flow of product returns is becoming a significant concern for many manufacturing companies. In this research area, three fundamental aspects of product returns need to be taken into consideration: First, companies become increasingly aware of the fact that product returns may offer an opportunity for enormous profit generation and for improving the competitive advantage of a manufacturing company when taking into account the accretive value of the products and technology. Second, the impact of green laws, legislative provisions and the increasing impact of a sustainable production management due to marketing aspects force companies to design and manage the reverse supply chain actively. Third, the importance of managing the reverse supply chains effectively will be enforced by the currently volatile economic climate. This paper outlines first results of designing a methodological framework for implementing an integrative reverse supply chain for manufacturing companies based on a type-specific Reverse Supply Chain Reference Model.
Within each of the three design fields numerous design elements exist (e.g. degree of centralization, number of warehouses etc. in the field network design). Hence, the interdependencies of all design elements have to be analyzed to allow optimal decisions for the design of an efficient and effective spare parts logistics. Nevertheless, the complexity among all interdependencies can hardly be understood. Therefore it is necessary to reduce the complexity of design decisions by focusing on the most important design elements according to the logistical requirements of different spare part categories. In order to achieve this goal, a classification of spare parts in terms of their key characteristics has been developed. For different spare part categories only a smaller set of design elements and their interdependencies has to be taken into account. The reduced number of key design elements per spare part category can be analyzed and understood in depth. Thus a Systems Dynamics approach is used to allow a better configuration of network design, cooperation concepts and inventory management in spare parts Supply Chains on the basis of specific logistics requirements of different spare part categories.
In dynamic markets flexible and efficient production systems are the main success factor for companies. The production system in this context includes all five phases of the SCOR-Model: Source, Make, Deliver, Plan and Return. In a subproject of the cluster of excellence "Integrative Production Technology for High-Wage Countries" at RWTH Aachen University, a configuration logic is being developed that enables companies to configure their production system according to the dynamic requirements of the market. As a major intermediate result, a holistic description model for production systems has been defined. In combination with numerous attributes in the sub-models, a detailed characterization of the production system is possible.
The sub-model for the design of the Supply Chain (mainly Deliver) will be depicted in detail in this paper. Representative for the design of a Supply Chain, spare parts logistics - as one of the most challenging tasks in logistics planning - is analyzed in depth. For this purpose spare parts logistics is divided into three design fields: network design, cooperation concepts (e.g. with logistics providers, customers, suppliers) and inventory management. Decisions in the design fields are highly interdependent, any spare parts logistics configuration has to take these interdependencies into account.
With big data-technologies on the rise, new fields of application appear in terms of analyzing data to find new relationships for improving process under-standing and stability. Manufacturing companies oftentimes cope with a high number of deviations but struggle to solve them with less effort. The research project BigPro aims to develop a methodology for implementing counter measures to disturbances and deviations derived from big data. This paper proposes a methodology for practitioners to assess predefined counter measures. It consists of a morphology with several criterions that can have a certain characteristic. Those are then combined with a weighting factor to assess the feasibility of the counter measure for prioritization.
Manufacturing companies are facing an increasingly turbulent market – a market defined by products growing in complexity and shrinking product life cycles. This leads to a boost in planning complexity accompanied by higher error sensitivity. In practice, IT systems and sensors integrated into the shop floor in the context of Industry 4.0 are used to deal with these challenges. However, while existing research provides solutions in the field of pattern recognition or recommended actions, a combination of the two approaches is neglected. This leads to an overwhelming amount of data without contributing to an improvement of processes. To address this problem, this study presents a new platform-based concept to collect and analyze the high-resolution data with the use of self-learning algorithms. Herby, patterns can be identified and reproduced, allowing an exact prediction of the future system behavior. Artificial intelligence maximizes the automation of the reduction and compensation of disruptive factors.
In a subproject of the cluster of excellence “Integrative Production Technology for High-Wage Countries” at RWTH Aachen University a configuration logic is under development that enables companies to configure their production system according to the dynamic requirements of the market. As a result of this project, a holistic description model for production systems has been defined. With numerous attributes in the sub-models a detailed characterization of the production system is possible.
The sub-model for the design of the supply chain will be depicted in detail in this paper. Representative for the design of a supply chain, the spare parts logistics of the wind energy industry is analyzed in depth. Designing this supply chain is not only one of the most challenging tasks in logistics. Only a responsive but also cost efficient design of the spare parts supply chain guarantees high productivity, extended life spans of the wind turbines as well as the expected profit for all companies in the supply chain.
In the last decade, enterprises realized the high value of data and learned to successfully utilize it for internal processes and business models, and they are trying to find more ways to acquire relevant data. Since enterprises are part of complex networks, the data from their partners and customers can also be beneficial: from adjusting the demand and supply to planning production and aligning capacities. One such example is adaptive process control: detailed material data from a supplier can be used to adjust process parameters in their production. This approach may be especially beneficial for the steel industry, as there is a possibility to adjust the material properties by changing the speed, force, or temperature in their own production processes. However, such an approach requires tight collaboration, e.g., regarding improving IT infrastructure, ensuring data acquisition and transfer and most importantly, the utilization of such data.
Companies in the manufacturing sector are confronted with an increasingly dynamic environment. Thus, corporate processes and, consequently, the supporting IT landscape must change. This need is not yet fully met in the development of information systems. While best-of-breed approaches are available, monolithic systems that no longer meet the manufacturing industry's requirements are still prevalent in practical use. A modular structure of IT landscapes could combine the advantages of individual and standard information systems and meet the need for adaptability. At present, however, there is no established standard for the modular design of IT landscapes in the field of manufacturing companies' information systems. This paper presents different ways of the modular design of IT landscapes and information systems and analyzes their objects of modularization. For this purpose, a systematic literature research is carried out in the subject area of software and modularization. Starting from the V-model as a reference model, a framework for different levels of modularization was developed by identifying that most scientific approaches carry out modularization at the data structure-based and source code-based levels. Only a few sources address the consideration of modularization at the level of the software environment-based and software function-based level. In particular, no domain-specific application of these levels of modularization, e.g., for manufacturing, was identified. (Literature base: https://epub.fir.de/frontdoor/index/index/docId/2704)
Influenced by the high dynamic of the markets the optimization of supply chains gains more importance. However, analyzing different procurement strategies and the influence of various production parameters is difficult to achieve in industrial practice. Therefore, simulations of supply chains are used in order to improve the production process. The objective of this research is to evaluate different procurement strategies in a four-stage supply chain. Besides, this research aims to identify main influencing factors on the supply chain’s performance. The performance of the supply chain is measured by means of back orders (backlog). A scenario analysis of different customer demands and a Design of Experiments analysis enhance the significance of the simulation results.