Increasing the Traceability Through Targeted Data Acquisition for Given Product Process Combinations

AbstractToday's manufacturing companies are faced with the challenge to achieve a high adherence to delivery dates under volatile market demands and to achieve a high efficiency of the order to delivery process. This challenging situation can only be handled with the help of an optimal alignment of the production, the production planning as well as the production controlling processes. Sufficient and high quality information from the production are the major basis for successfully mastering the tasks of production planning and control. With the help of the approach proposed in this paper, companies can start setting up a targeted data acquisition concept for their product process combination. It helps them, amongst other things, preventing production problems and responding rapidly to fluctuating customer needs

Improving data integrity in production control

AbstractStudies show that companies which focus on high adherence to promised delivery dates as their main logistic goal, regularly outperform their competitors. Only with a highly accurate production planning and control (PPC) companies can accomplish this goal. However, usually there is a gap between the planned forecast of the Advanced Planning and Scheduling System and the actual output of the production system. One of the reasons for this discrepancy is inconsistent data which is collected on the shop floor and builds the foundation for the planning process. In this paper, a methodology is presented to assure higher integrity in production control data

Achieving Higher Scheduling Accuracy in Production Control by Implementing Integrity Rules for Production Feedback Data

AbstractExcellent production planning and control (PPC) processes are a prerequisite for accomplishing a high adherence to promised delivery dates. Despite enormous efforts which are put into achieving high scheduling accuracy, manufacturing companies still regularly struggle in meeting their logistic targets. In consequence, these companies deal with high stocks, long lead times and ultimately only achieve a bad adherence to promised delivery dates. An important reason for this discrepancy are data inconsistencies, which occur in data collected on the shop floor, because these data are used to update the near- and middle-term scheduling of current production jobs. In this paper, the impact of several data inconsistencies in real-world production feedback data sets are investigated. Integrity rules for selected data inconsistencies are proposed and tested for their effects on a number of logistic targets in a simulation study