Cyber Physical Production Control
Cyber Physical Production Control 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 produ
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1 Current Challenges of Production Control Today, manufacturing companies are increasingly confronted with the influences of a dynamic environment and the ensuing of continuously increasing planning complexity. Companies have to adapt themselves and their processes to dynamic environment conditions like movements in customer demand, reschedules in supply as well as turbulences in networks. Nevertheless, a successful production control is characterized by high process efficiency and a high availability of information. V. Stich ⋅ M. Blum ⋅ J. Reschke ⋅ D. Schiemann Institute for Industrial Management (FIR), RWTH Aachen University, Campus-Boulevard 55, 52074 Aachen, Germany e-mail: Volker.Stich@fir.rwth-aachen.de M. Blum e-mail: Matthias.Blum@fir.rwth-aachen.de J. Reschke e-mail: Jan.Reschke@fir.rwth-aachen.de D. Schiemann e-mail: Dennis.Schiemann@fir.rwth-aachen.de A.G. Schuh ⋅ C. Reuter ⋅ F. Brambring ⋅ T. Hempel (✉) Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Steinbachstr. 19, 52074 Aachen, Germany e-mail: [email protected] A.G. Schuh e-mail: [email protected] C. Reuter e-mail: [email protected] F. Brambring e-mail: [email protected] © Springer International Publishing Switzerland 2017 S. Jeschke et al. (eds.), Industrial Internet of Things, Springer Series in Wireless Technology, DOI 10.1007/978-3-319-42559-7_21
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Examples for these challenges are steadily decreasing process times, increasing product and process variations as well as the unclear market and production environment [36, 42, 52]. In this context, delivery times in the machine and plant manufacturing industry have been reduced by about 50 %, which has massive influence on the capacity flexibility and also on the entire order fulfillment process [48]. An example of the realignment of the order fulfillment process is found at the company Siemens. In the healthcare business unit the processing time of a computer tomography scanner has been reduced by 67 % and the delivery time by about 86 % through a consistent logistical orientation [38]. According to SCHUH, the product and process complexity has reached such a high level that it can only be controlled under very high monitoring and coordination efforts [28]. Other challenges for the production planner are the volatile economic situation, fluctuating customer demands, short-term rescheduling, an early release of orders or capacity overload [44]. Furthermore, incorrect or outdated data often leads to poor planning results. The production planner often has no appropriate evaluation variables for control decisions. With several production planners on duty, several locally optimal decisions can exist, which can have a negative effect on global corporate targets [23]. Today the granularity of different production data is often not sufficient. As numerous practical examples from projects with the industry have shown in the last years, the typical overall throughput time of a production process is composed
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