Development and Evaluation of Forecasting Methods for Production Planning and Control by Considering Prediction Models of Nonlinear Dynamics

• Applicant: Professor Dr.-Ing. Bernd Scholz-Reiter
• Subject Area: Production Systems, Operations Management, Quality Management and Factory Planning
• Term: from 2010 to 2015
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 165973614

In order to achieve a well-founded production planning and control, manufacturing enterprises need to predict future customer demands as precisely as possible. As a result, the company´s profitability can be influenced positively. For the purpose of a prediction with high accuracy, firstly, an appropriate forecasting method has to be chosen. In addition, this method has to be adjusted to the considered demand evolution. In literature, there exists an abundance of different prediction methods. Nevertheless, in practical applications, often relatively simple methods are used because more complex methods require a high amount of expert knowledge and an enormous amount of time for adaptation. Hence, potentials of complex prediction methods to improve forecasting accuracy are not tapped sufficiently. In response to this, the proposed research approach deals with a development of a technique to choose appropriate prediction methods. Based on a broadly conceived simulation study, an assignment matrix with recommendations shall be constructed. On the one hand, this matrix will contain information about suitability and accuracy of various prediction methods for different demand evolutions. On the other hand, it will include applicable parameter configurations for the adjustment to special evolutions. Hence, the matrix comprises expert knowledge for choice and adjustment of appropriate prediction methods in different application scenarios of production logistics. In order to develop this assignment matrix, firstly dynamic characteristics of different demand evolutions shall be studied. On that account, methods of linear and nonlinear time series analysis will be applied to identify characteristic patterns and classification indexes. Using these results, a classification of different demand evolutions shall be accomplished. In parallel, prediction methods will be applied to simulation based as well as to real case production data and evaluated with regard to prediction accuracy over different time horizons. Particularly, methods of nonlinear dynamics will be adapted, which can model complex dynamic time series evolutions and thus have great potential to predict customer demands. Concluding, the dynamic characteristics of the classified demand evolutions will be combined with the prediction accuracy of different forecasting methods. As the main result of the project, the developed assignment matrix will provide recommendations to choose suitable prediction methods as well as adjustments to different demand evolutions.

• DFG Programme: Research Grants