The (αX,βX)-precise estimates of production systems performance metrics
研究了在串行生产线中，为达到机器效率、系统产出率等性能指标所需的最小测量次数，并开发了质量参数估计方法，帮助管理者设计改进项目。
Estimates of production systems performance metrics, such as machine efficiency, e, system throughput, TP, lead time, LT, and work-in-process, WIP, are necessary for evaluating effectiveness of potential system modifications. Calculating these estimates requires machines MTBF and MTTR, which can be obtained by measuring up- and downtime realizations on the factory floor. A question arises: What is the smallest number of measurements required to ensure the desired accuracy of the induced estimates eˆ, (TPˆ), (LTˆ) and (WIPˆ)? This paper provides an answer to this question in terms of serial lines with exponential machines. The approach is based on the theory of (α,β)-precise estimates (MTBFˆ) and (MTTRˆ), where α represents estimate's accuracy and β its probability. Specifically, the paper calculates (αX,βX)-precise estimates of X∈{e,TP,LT,WIP} induced by (MTBFˆ) and (MTTRˆ), and evaluates the smallest number of machines' up- and downtime measurements, which ensure the desired precision of Xˆ∈{eˆ,TPˆ,LTˆ,WIPˆ}. In addition, the paper develops a method for evaluating the smallest number of parts quality measurements to ensure (αq,βq)-precise estimate of machines' quality parameter q and the desired (αTPq,βTPq)-precise estimate of good parts throughput, TPˆq. The results obtained are intended for production systems managerial/engineering/research personnel as a tool for designing continuous improvement projects with analytically predicted outcomes.