Capability Flexibility: A Decision Support Methodology for Parallel Service and Manufacturing Systems with Flexible Servers

Abstract

To obtain improved performance, many firms pursue operational flexibility by endowing their production operations with multiple capabilities (e.g., multi-skilled workers, flexible machines and/or flexible plants). This article focuses on the problem of ranking (according to average wait in queue) alternative system designs that vary by capacity and the structure of capabilities for open, parallel queueing networks with partially flexible servers. Prior literature introduced the Structural Flexibility (SF) concept and because the SF method was intended for a strategic context with very little information, it did not incorporate mean service times by demand type, server speeds, or wide ranges in demand arrival rates. This article develops the Capability Flexibility (CF) index methodology to extend the range of operational environments and designs that can be ranked. By showing the effectiveness of a deterministic, second-order approximation of a capability-design's relative flexibility/performance — the CF index — it proved possible to establish the insight that the proposed simple deterministic approximation of these complex stochastic is able to capture the dominant drivers of congestion of one design relative to another.

Keywords: operational flexibility, maxflow algorithm, parallel queueing systems, cross-training