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

为了实现有根有据的生产计划和控制，制造企业需要尽可能准确地预测未来的客户需求。因此，公司的S盈利能力可以受到积极影响。为了获得高精度的预测，首先必须选择合适的预测方法。此外，该方法还必须根据所考虑的需求变化进行调整。在文献中，存在着丰富的不同预测方法。然而，在实际应用中，往往使用相对简单的方法，因为较复杂的方法需要大量的专业知识和大量的适应时间。因此，复杂预测方法提高预测精度的潜力没有得到充分挖掘。针对这一点，建议的研究方法涉及一种技术的发展，以选择适当的预测方法。在广泛设想的模拟研究的基础上，应构建一个带有建议的分配矩阵。一方面，这个矩阵将包含关于不同需求演变的各种预测方法的适用性和准确性的信息。另一方面，它将包括适用的参数配置，以适应特殊的演变。因此，该矩阵包含了在生产物流的不同应用场景中选择和调整合适的预测方法的专家知识。为了建立这个分配矩阵，首先要研究不同需求演化的动态特征。在这方面，将应用线性和非线性时间序列分析方法来识别特征模式和分类指标。使用这些结果，将完成对不同需求演变的分类。同时，预测方法将应用于基于模拟的以及实际生产数据，并就不同时间范围内的预测精度进行评估。特别是，将采用非线性动力学方法，可以对复杂的动态时间序列演化进行建模，从而具有预测客户需求的巨大潜力。最后，将分类需求演化的动态特征与不同预测方法的预测精度相结合。作为该项目的主要成果，开发的分配矩阵将为选择合适的预测方法以及调整不同的需求演变提供建议。

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.