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

Posted: 2 Jul 2013

See all articles by Seyed Iravani

Seyed Iravani

Northwestern University - Department of Industrial Engineering and Management Sciences

Bora Kolfal

University of Alberta - Department of Accounting, Operations & Information Systems

Mark P. Van Oyen

University of Michigan at Ann Arbor

Date Written: June 1, 2010

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

Suggested Citation

Iravani, Seyed and Kolfal, Bora and Van Oyen, Mark P., Capability Flexibility: A Decision Support Methodology for Parallel Service and Manufacturing Systems with Flexible Servers (June 1, 2010). IIE Transactions Volume 43, Issue 5, 2011, University of Alberta School of Business Research Paper No. 2013-1050, Available at SSRN: https://ssrn.com/abstract=2279635

Seyed Iravani (Contact Author)

Northwestern University - Department of Industrial Engineering and Management Sciences ( email )

Evanston, IL 60208-3119
United States

Bora Kolfal

University of Alberta - Department of Accounting, Operations & Information Systems ( email )

Edmonton, Alberta T6G 2R6
Canada

Mark P. Van Oyen

University of Michigan at Ann Arbor ( email )

500 S. State Street
Ann Arbor, MI 48109
United States

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