Author:
Afy-Shararah Mohamed,Rich Nicholas
Abstract
Purpose
Effective operations management systems (OMS) measurement remains a critical issue for theorists and practising managers (Neely, 2005; Bititci et al., 2012). Traditional labor efficiency measures sufficed when all that was made could be sold or when mass production systems filled warehouses with stock and the OMS had little relationship with “the consumer.” Modern manufacturing systems require a different form of flow optimization (beyond labor efficiency) measurement (Schmenner, 2015). The essential unit of measure for all OMS designs is the optimal use of time for process value adding and the flow of materials into and from the conversion process. Timely flow, therefore, satisfies the needs of multiple organizational stakeholders including cash flow (accounting), consumer reaction times (marketing) and the general steady state flow of materials (sales and supply chain). The purpose of this paper is to present the results of testing a new performance measure of operations flow effectiveness (OFE) with ten purposively selected cases.
Design/methodology/approach
The paper is theory building using ten, purposively selected, longitudinal case studies drawn from the UK high-value manufacturing (HVM) sector using a pluralist methodology of interviews, observation and secondary data.
Findings
The OFE measure provides a holistic view of material flow through the input-process-output cycles of a firm. The measure highlights OMS design weaknesses and flow inhibitors that reduce cash flow using a time-based approach to measuring OMS performance. The study validates the OFE measure and has identified six key design elements that enable high flow performance.
Originality/value
The paper tests a new process-focused flow performance measure. The measure supports a holistic approach to the manufacturing enterprise and allows different OMS designs to be evaluated so that organizational learning may be enacted to support performance improvement.
Subject
Management of Technology and Innovation,Strategy and Management,General Decision Sciences
Cited by
18 articles.
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