Selective Assembly in Manufacturing: Statistical Issues and Optimal Binning Strategies

Abstract

Selective assembly is a cost-effective approach for reducing the overall variation and thus improving the quality of an assembled product. In this process, components of a mating pair are measured and grouped into several classes (bins) as they are manufactured. The final product is assembled by selecting the components of each pair from appropriate bins to meet the required specifications as closely as possible. This approach is often less costly than tolerance design using tighter specifications on individual components. It leads to high-quality assembly using relatively inexpensive components. In this article we describe the statistical formulation of the problem and develop optimal binning strategies under several loss functions and distributional assumptions. Optimal schemes under absolute and squared error loss are studied in detail. The results are compared with two commonly used heuristic schemes. We consider situations in which only one component of the mating pair is binned, as well as cases in which both components are binned.