Production Planning

The large number of systems offered on the market makes a well-founded selection process from requirement collection to final selection necessary. A comprehensive model that systematically supports and simplifies this process is the subject of the following article. The methodo-logy goes beyond a questionnaire-based query and validates system capabilities using structured case studies. The first part of the article describes the process steps from the survey of the requirements of the system to their structuring in customer specifications. The second part of the system selection process is described in detail in the second part of the article.

New production technologies have inherent technical risks, but also established production technologies can not always be transferred to new products or components without problems. Accordingly, these technical risks must be taken into account when planning production systems. This paper presents an approach for the optimized allocation of development resources with regard to the technical risks of production technologies and processes. First, the production scenario is modeled and simulated using GRAMOSA. Then the simulation results are used for the optimized assignment of the development budget by means of mathematical optimization.

In the Industrial 4.0 era, much more data is entered into production planning and the selection of suitable production and storage locations than before. In view of the large number of decision-relevant data, it is best to use mathematical methods to calculate which product should be produced at which location and when. In this context, linear programming is of particular importance. The article describes its use by means of practical examples. On IBM Decision Optimization Center as technical platform, software partners can create applications for better decisions in all areas of production and location optimization, including the entire production and logistics network.

Production planning and control (PPC) software schedules the availability and optimal use of human and operation resources. The system must allocate the operation steps and operation resources to the machines in the right order under the control of qualified personnel. The scheduling takes place according to customer-specific objectives such as adherence to delivery dates. These objectives compete against the cost-specific objectives such as high workload of the machines. For the cost specific objectives, the topic of resource and energy efficiency become the focus of attention.

The research project Analytic Manufacturing (ANAMA) focuses on the better integration of the tactical and operational planning level, which is an important step to the final target of a decentralized production control. Technically, for the implementation of ANAMA a sophisticated data layer is necessary which guarantees the interoperability of different data sources. Based on this new data source the two fields of research, i.e. visual analytics and data sonification, are used to improve the collaboration of the tactical and operational planning level. The merge of the two research fields establishes the innovative methodology “DataViSon”.

Production companies are faced with an increasingly turbulent business environment, which demands very high production volumes and delivery date flexibility. If a decoupling by storage stages is not possible or undesirable from a logistical point of view, load scattering effects the production processes. This expresses itself in the form of heavy load scattering. What kinds of quantification of the load scattering exist and how these have been further developed are subject of the following article.

Both rural and urban regions have their specific advantages for companies. In practice, the central question is, how the structure of areas can be distinguished and under which conditions companies should settle in the city, in the country or in intermediate regions. In a study, companies were surveyed about their location in the metropolitan area Hannover-Braunschweig-Göttingen-Wolfsburg.

The efficient performance of service tasks on high-value capital goods like maintenance, repair and overhaul of aircrafts and railway vehicles is influenced by a turbulent environment. In this context, excellent production logistics in maintenance shops can be a way out to cope with this turbulence. This contribution describes a bottom-up approach which is the basis for improving the logistical processes by dimensioning material flow-oriented buffer stocks.

Despite increasing networking and electronic data interchange for many companies future demand curves are unstable. Resulting from demand fluctuations and deviations from planned processes, high requirements exist for production planning and control. The necessary flexibility to respond to such variations can be created by supply chain event management (SCEM). Based on the example of an automotive supplier process chain SCEM is defined and then implemented in an Advanced Planning and Scheduling System.

Volatility in market demand leads to temporary over- and under utilization of productive assets. Heijunka aims at de-coupling the production system from market volatility. The production program is spread as even as possible over time. This achieves high asset utilization, short lead times, and low inventories. There are validated Heijunka methods for the manufacturing industry, but for the process industry this remains a research gap. This article describes the development of a Heijunka model for the process industry in order to close that gap.