An Improved Aggregation Method for Performance Analysis of Bernoulli Serial Production Lines

Aggregation method has been widely utilized to evaluate the performance measures of production lines. However, traditional aggregation method (TAM) has low prediction accuracy in production lines with multiple bottlenecks, such as "inverted bowl" lines and "oscillatory" lines. Therefore, the root causes of the low prediction accuracy are first investigated. Extensive numerical studies indicate that Lambda-units are one of the major causes, where a Lambda-unit is defined as the subsystem between two consecutive bottlenecks. Then, an improved aggregation method (IAM) is proposed to improve the prediction accuracy of TAM. IAM is established by extending the traditional twomachine aggregation building blocks to general multimachine aggregation building blocks in Lambda-units. Specifically, for a smallscale Lambda-unit, an aggregation building block is established for all the machines and buffers in the Lambda-unit. For a large-scale Lambda-unit, a heuristic rule is proposed to divide the Lambda-unit into several smallscale production line segments, where an aggregation building block is established for each segment. Numerical studies indicate that IAM can effectively improve the estimation accuracy of the aggregation method while maintaining a reasonable computational efficiency. Note to Practitioners-Bottleneck machines refer to the machines whose performance impedes the production systems in the strongest manner. They have been widely utilized to improve productivity, energy efficiency, and so on. However, there are few efforts devoted to the impacts of bottlenecks on the aggregation method. The numerical studies indicate that the traditional aggregation method (TAM) has lower estimation accuracy in production lines with multiple bottlenecks. Therefore, in this article, improved aggregation method (IAM) is established to improve the performance of the aggregation method in the production lines. The research contributes to the literature with a generalized aggregation method that is very effective in production lines with and without bottlenecks. It can be utilized by production managers to evaluate production performance, predict the impacts of system changes, and accelerate control and investment decision-making.