Improving Delivery Performance in High-Mix Low-Volume Manufacturing by Model-Based and Data-Driven Methods

Abstract

In a high-mix and low-volume (HMLV) manufacturing environment where demand fluctuation is the rule rather than the exception, daily production management in face of conflicting key performance indicators such as high delivery precision, short lead time, and maximal resource utilization is a most challenging task. This situation may even be hampered by unreliable supplier performance. This paper presents a generic decision support workflow, which first identifies the most critical external and internal factors which have a serious impact on delivery performance. Next, it suggests a method which combines traditional manufacturing system simulation with advanced machine learning techniques to support the improved daily routine lot-sizing and production scheduling activities in a HMLV company. Argumentation is motivated and illustrated by a detailed industrial case study.