New Strategic and Dynamic Variation Reduction Techniques for Assembly Lines

Variation is inevitable in any process, so it has to be dealt with effectively and economically. It has been the focal point of many researchers to control, predict or reduce the variation of a product to increase its competitiveness in the current global marketplace. Reducing variation can be achieved in assembly lines strategically and dynamically. It can be reduced strategically by developing optimal process, setup and inspection plans. It can be reduced dynamically by utilizing real-time data (inspection data) to effectively assign parts together, and to adjust process settings and design requirements accordingly. Implementing both the strategic and dynamic variation reduction techniques is expected to lead to further reduction in the number of failed final assemblies. The dissertation is divided into three major parts. In the first part, we propose to reduce variation for assemblies by developing efficient inspection plans based on (1) historical data for existing products, or simulated data for newly developed products; (2) Monte Carlo simulation; and (3) optimization search techniques. The cost function to be minimized is the total of inspection, rework, scrap and failure costs. The novelty of the proposed approach is three-fold. First, the use of CAD data to develop inspection plans for newly launched products is new, and has not been introduced in the literature before. Second, frequency of inspection is considered as the main decision variable, instead of considering whether or not to inspect a quality characteristic of a subassembly. Third, we use a realistic reaction plan (rework-scrap-keep) that mimics reality in the sense that not all out-of-tolerance items should be scrapped or reworked. Instead, our reaction plan in the case of out-of-tolerance items is to rework an item if it does not drift significantly