Methodical decision support for mid-term order planning in global production networks for multi-variant series production under uncertainty of customer order configurations

在全球生产网络中，多品种系列生产的生产能力的利用是一个挑战，但这是定制产品的竞争性生产和成本效益供应的先决条件。此外，客户要求较短的交货时间，在收到订单之前，就客户需求与最终组装能力和供应商能力的平衡造成了不确定性。因此，对于中期的稳健规划，必须明确考虑订单配置的不确定性。目前的规划方法要么完全忽略中期订单规划，要么忽略订单配置的不确定性。为了解决这些缺点，手头的研究建议旨在开发一种方法，以支持在客户订单配置不确定的情况下，全球生产网络中多变体系列生产的中期订单计划决策。因此，必须根据客户订单的中期可用性来区分不同行业的不确定性类型——要么是可用的，但配置不确定，要么是不可用的。在客户订单没有特定订单配置的情况下，在生产网络中分配订单时必须考虑这种不确定性。客户订单的可能配置必须由场景来预测和表示。因此，客户订单可以根据基于场景的优化模型的结果分配到生产设备和周期。在中期无法获得客户订单的情况下，可以通过生成计划订单并应用确定性优化模型将其分配到生产网络中来控制不确定性。稍后，可以将传入的客户订单分配给生产网络中已经分配的计划订单。因此，优化模型可以支持根据配置和交付时间选择最优计划订单。为了启用分配，所选的订单将适应客户订单。总而言之，中期订单计划允许中期和短期计划范围之间的一致性。如果只有短期计划是基于订单的，这是无法保证的。此外，一致的信息基础可以使公司的功能单位，如销售、生产和采购，考虑到各自的需求，进行综合规划。

The utilization of production capacities in multi-variant series production in global production networks is a challenge, but is a prerequisite for the competitive production and cost-efficient supply of customized products. Additionally, customers requiring short delivery times cause uncertainties prior to the receipt of orders regarding the balancing of customer demand with capacities of final assembly and capacities of suppliers. Thus, the uncertainty of order configurations has to be explicitly considered for robust planning in the mid-term horizon. Current planning approaches neglect either mid-term order planning at all or the consideration of uncertainties of order configurations.In order to address these shortcomings, the research proposal at hand aims at the development of a methodology to support decisions regarding mid-term order planning in global production networks for multi-variant series production under uncertainty of customer order configurations. Therefore, the type of uncertainty has to be differentiated for several industries depending on the mid-term availability of customer orders - either they are available, but their configurations are uncertain, or they are unavailable.In the case customer orders are available without certain order configurations, this uncertainty has to be considered when assigning orders in the production network. Possible configurations of customer orders have to anticipated and represented by scenarios. Thus, the customer orders can be assigned to production facilities and periods based on the results of a scenario-based optimization model.In the case of customer orders being unavailable in the mid-term horizon, uncertainty can be controlled by generating planned orders and assigning them in the production network by applying a deterministic optimization model. Later on, incoming customer orders can be assigned to the planned orders already assigned in the production network. Therefore, an optimization model can support the selection of the optimal planned order regarding configuration and delivery time. In order to enable an assignment, the selected order is to be adapted to the customer order.All in all, mid-term order planning allows for consistency between mid-term and short-term planning horizons. This cannot be guaranteed if only short-term planning is order-based. Moreover, a consistent basis of information enables integrated planning of functional units of a company such as sales, production and procurement considering their respective requirements.