Impact of sharing imprecise information on fixed-bin selective assembly

Abstract

Fixed-Bin Selective Assembly (FBSA) is widely used to improve the assembly quality of single units of different component types. FBSA has parts with similar dimensions, but with slight deviations due to production variances, sorted into pre-determined bins. A selection is made from paired bins for assembly. To ensure enough components, assemblers adopt a proactive or reactive strategy. For the proactive, predictions of component usage quantities are used to adjust processes for the internally manufactured matched component prior to production. With the reactive, component inventory levels are adjusted after assembly. The performance of each can be improved with information about incoming part dimensions. However, the communicated information may be imprecise. We developed a Bayesian Measurement Error Model to evaluate the impact of imprecision, in the supplier’s communicated information, on the efficacy of FBSA with either strategy. The model was tested with parameters obtained from a US assembler of combustion engines. We find that in most scenarios, the proactive approach results in better FBSA efficacy on average. A key exception was when imprecise information about the mean was communicated, resulting in no significant difference. The reactive approach is typically easier to implement, therefore it may be preferred when communicated information about the mean of incoming part dimensions is often inaccurate.