Quantification of central input factors for the determination of planning lead times in shop floor production

量化中心输入因素，以确定车间生产的计划提前期

• 批准号: 506650355
• 负责人: Professor Dr.-Ing. Matthias Schmidt
• 金额: --
• 依托单位: Fachgebiet Planung und Steuerung von Lager- und Transportsystemen
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/506650355?language=en
• 关键词: Quantification; central; input; factors; determination

The determination of planned order lead times is still an unsolved issue in companies with complex production structures. As a result, companies often have problems delivering to customers on time. In addition to insufficient adherence to schedules due to late work processes, premature work processes lead to avoidable inventory. Precise planned order lead times are required to confirm a feasible deadline to the customer, to plan production capacities and to better organize procurement.In practice, however, rigid methods for determining the planned order lead times are often used, which do not react sufficiently to changing environmental influences. Possible environmental influences are e.g. short term sick calls or fluctuations of the load of production. Classical approaches for the determination of planned order lead times include, in particular, methods based on estimates, historical values, logistic models and simulation. These methods take into account different information and data for the determination. The limitations of most of the classical methods are especially in too simplified assumptions, which makes a high planning quality difficult. Approaches such as simulation counteract these limitations, but lead to a very high application effort. Especially in complex production environments like shop floor production (e.g. tool and special machine manufacturing), precise planning is not implemented in practice due to scattering work content and a fluctuating number of operations per job. In addition, for contract manufacturers, insufficient determination of the planned lead times has a direct impact on the adherence to schedules to customers. The central input factors influencing the order lead times in a shop floor production are not yet systematically investigated.Therefore, the aim of this project is to identify and quantify central input factors for the determination of planned order lead times in shop floor production from company data of six contract manufacturers. Machine learning (ML) will be used in the project to identify patterns in data and to derive generalized assumptions. The insights into the central cause-effect relationships will help the investigated companies to more precisely forecast planned order lead times and thus improve their ability to meet their customers' deadlines.

在生产结构复杂的企业中，计划订单提前期的确定仍然是一个悬而未决的问题。因此，公司经常难以按时向客户交付产品。除了由于工作过程延迟而未充分遵守时间表外，过早的工作过程还会导致可避免的库存。为了给客户确定一个可行的最后期限，计划生产能力和更好地组织采购，需要精确的计划订单提前期。然而，在实践中，经常使用僵化的方法来确定计划订单提前期，这种方法不能对变化的环境影响做出足够的反应。可能的环境影响例如是短期的病假呼叫或生产负荷的波动。确定计划订单提前期的经典方法尤其包括基于估计、历史值、逻辑模型和模拟的方法。这些方法考虑了用于确定的不同信息和数据。大多数经典方法的局限性尤其是假设过于简单化，这使得高质量的规划难以实现。诸如模拟之类的方法抵消了这些限制，但会导致非常高的应用程序工作量。特别是在复杂的生产环境中，如车间生产(如工具和专用机械制造)，由于工作内容分散和每个作业的操作数量波动，在实践中没有实施精确的计划。此外，对于合同制造商来说，对计划交货期的确定不充分直接影响到对客户时间表的遵守。影响车间生产中订单提前期的中心输入因素尚未得到系统的研究，因此，本项目的目的是从六家合同制造商的公司数据中识别和量化中心输入因素，以确定车间生产中的计划订单提前期。该项目将使用机器学习(ML)来识别数据中的模式，并得出普遍的假设。对核心因果关系的洞察将有助于被调查公司更准确地预测计划订单提前期，从而提高他们满足客户最后期限的能力。