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Towards the Generation of Setup Matrices from Route Sheets and Feedback Data with Data Analytics
(2018)
The function or department of production control in manufacturing companies deals with short-term scheduling of orders and the management of deviations during order execution. Depending on the equipment and characteristics of orders, sequence dependent setup times might occur. In these cases for companies that focus on high utilization of their assets due to long phases of ramp up and high energy costs, it might be optimal to choose sequences with minimal setup time times between orders. Identifying such sequences requires detailed and correct information regarding the specific setup times. With increasing product variety and shorter lot sizes, it becomes more difficult and rather time intense to determine these values manually. One approach is to analyse the relevant features of the orders described in the route sheets or recipes to find similarities in materials and required tools. This paper presents a methodology, which supports setup optimized sequencing for sequence dependent setup times through constructing the setup matrix from such route sheets with the use of data analytics.
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.
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.
Influenced by the high dynamic of the markets and the steadily increasing demand for short delivery times the importance of supply chain optimization is growing. In particular, the order process plays a central role in achieving short delivery times and constantly needs to evaluate the trade-off between high inventory and the risk of stock-outs. However, analyzing different order strategies and the influence of various production parameters is difficult to achieve in industrial practice. Therefore, simulations of supply chains are used in order to improve processes in the whole value chain. The objective of this research is to evaluate two different order strategies (t, q, t, S) in a four-stage supply chain. In order to measure the performance of the supply chain the quantity of the backlog will be considered. A Design of Experiments approach is supposed to enhance the significance of the simulation results.
This research area focuses on the management systems and principles of a production system. It aims at controlling the complex interplay of heterogeneous processes in a highly dynamic environment, with special focus on individualized products in high-wage countries. The project addresses the comprehensive application of self-optimizing principles on all levels of the value chain. This implies the integration of self-optimizing control loops on cell level, with those addressing the production planning and control as well as supply chain and quality management aspects. A specific focus is on the consideration of human decisions during the production process. To establish socio-technical control loops, it is necessary to understand how human decisions are made in diffuse working processes as well as how cognitive and affective abilities form the human factor within production processes.
One major problem of today’s producing companies is to reach a high adherence to delivery dates while considering the volatile market situation as well as economic aspects. This problem can only be solved by using a production control that is optimally adapted to the processes. A good working, process-oriented production control is essential for being able to control the production situation and to ensure a high adherence to delivery dates. Data generation and processing determine the success of production control. Current processes and IT systems have several shortcomings in meeting these challenges.
The solution for this problem is the so called “cyber physical production control” (CPPC). It optimally supports the production scheduler in his decision making process based on real-time high-resolution data. With the help of data analytics, the production controller receives decision support over various steps. Due to CPPC, the overall goal of a high adherence to delivery dates can be fundamentally increased.
Industrie 4.0 ist in Politik, Medien, Wissenschaft und Wirtschaft derzeit omnipräsent. Intelligenter, individueller, effizienter, schneller, vernetzter – so lauten nur einige Versprechen dieses neuen industriellen Zeitalters. Tatsächlich sind die Potenziale gerade für den deutschen Maschinen- und Anlagenbau gewaltig: Sowohl für Anbieter als auch für Anwender von Technologien rund um das Thema Industrie 4.0. Aber noch existieren viele ungelöste Fragen, Unsicherheiten und Aufgaben. Hier wollen wir mit unserer Readiness-Studie ansetzen und Hilfestellung leisten. Denn ein Selbstläufer wird Industrie 4.0 nicht. Mit der vorliegenden Studie soll die große Vision näher an die betriebliche Realität gebracht werden. Auch zeigen wir die anspruchsvollen Wegmarken auf, die für viele Unternehmen hinsichtlich ihrer Industrie 4.0-Fähigkeit noch zu passieren sind. Die Studie untersucht, an welcher Stelle der Maschinen- und Anlagenbau aktuell bei der Umsetzung steht. Motivation und Hemmnisse der Unternehmen werden ebenso in den Blick genommen wie die Unterschiede, die sich zwischen Mittelstand und großen Unternehmen ergeben. Im Ergebnis ist es erstmals möglich, die „Industrie 4.0-Readiness“ der Maschinenbau-Industrie detailliert und systematisch abzubilden.
Industrie 4.0 is all around us today: in politics, in the media, and on the agendas of researchers and entrepreneurs. Smarter, faster, more personalized, more efficient, more integrated – those are just some of the promises of this new industrial era. The potential, especially for Germany ́s mechanical
engineering industry and plant engineering sector, is indeed great, both for providers and for users of technologies across the spectrum of Industrie 4.0.
But there are still many unresolved questions, uncertainties, and challenges. Our readiness study seeks to address this need and offer insight. Because Industrie 4.0 will not happen on its own.
This study is intended to bring the grand vision closer to the business reality. We also highlight the challenging milestones that many companies must still pass on the road to Industrie 4.0 readiness.
The study examines where companies in the fields of mechanical and plant engineering currently stand, focusing on what motivates them and what holds them back, and on the differences that emerge between small and medium enterprises on the one hand and large enterprises on the other.
The results make it possible for the first time to develop a detailed, systematic picture of Industrie 4.0 readiness in the engineering sector.
The study concludes with recommendations for action in the business community, complementing the diverse suite of programs and activities offered by VDMA’s Forum Industrie 4.0. We would like to take this opportunity to thank the two sponsors of this project from the VDMA Forum, Dietmar Goericke and Dr. Christian Mosch, whose efforts played a critical role in making this study a success.
We are convinced that Industrie 4.0 can become a success story for Germany’s engineering sector. May our “Industrie 4.0 Readiness” study do its part in this effort.
Ziel des Forschungsbereichs "Selbstoptimierende Produktionssysteme" ist es, sowohl technische als auch soziotechnische Produktionssysteme zu entwickeln, die durch Selbstoptimierung eine bessere Performance erreichen, als bei der Auslegung geplant und erwartet werden kann. Im Fokus steht die Steigerung der Produktivität in der Produktion direkt vor Ort. Bedeutend ist die dezentrale Entscheidungsfähigkeit der Mitarbeiter auf dem Shopfloor und in unterstützenden Bereichen, sowie der kognitiven und adaptiven Systeme und Netzwerke in der Produktion.