production planning and control

In a previous approach of Lager et al. [1], the development of knowledge, competence and role profiles of employee groups in the course of industry 4.0 was studied. Based on that, the role of the overlapping focus IT is to be examined more closely and the analysis is extended to the tactical level using the example of production planning. In addition, the effects of the need for increased IT competence in all areas of the manufacturing industry on the erosion of current role profile boundaries are presented.

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.

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.

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.

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.

Nowadays, a well-designed capacity supply is essential for a company’s competitiveness. In this situation, a flexible and efficient capacity control is among the most important measures. In this paper, we presented the potential of reconfigurable machine tools. Based on this, we described the concept of the throughput time harmonizing capacity control. Moreover, we evaluated the theoretical potential of this capacity control approach in a simulation experiment. In further steps, we will analyze the total potential in different simulation studies.

Nowadays, the production of many goods takes place in distributed facilities within supply chains. The production planning of single facilities is based on due dates for delivery of raw materials or intermediate products that were agreed with supply chain partners. Thus, for an efficient overall system it is of major importance that these due dates are fulfilled. However, production systems are usually subjected to disruptions that put the execution of a given schedule at risk. This paper presents a method for the signal-based detection of faults in production systems. In addition to the fault detection, a heuristic method is employed that mitigates the disruptions and leads to a reliable production.

In an increasingly turbulent environment, today enterprises are confronted with the challenge of aligning available capacities with the capacities required using targeted measures. An approach is presented to support the selection of measures quantitatively.