Applying selected quality management techniques to diagnose delivery time variability

Abstract

Purpose

This research seeks to identify and apply techniques that can be used in a supply chain context to diagnose the causes of variability in delivery lead time.

Design/methodology/approach

A literature review was conducted and a number of quality management (QM), techniques were selected as candidates for diagnosing delivery time variability. A case study of the application of these techniques is provided on the UK‐based defence supply chain that supported UK operations in the Iraq war of 2003.

Findings

Candidate QM techniques for diagnosing delivery time variability were identified, namely: Process Chart; Histogram; Failure Mode and Effect Analysis; and Cause and Effect Analysis. These techniques were successful in enabling the diagnosis of the causes of delivery time variability in the context of the case study investigated.

Practical implications

The work illustrates how QM techniques can be employed to address issues with supply chains, not least with regard to the important problem of variability in delivery leadtime. In practice, this highlights benefits that result to practitioners in order to improve the performance of operations in a dynamic setting, such as the defence supply chain studied here.

Originality/value

This work has value in presenting the findings of an in‐depth case study on the application of QM techniques in a multi‐echelon supply chain setting. It is also original in employing the FMEA technique together with an end‐customer perspective to assess the effect of failure modes in operations across a supply chain. FMEA also provided the means to examine supply chain risk, thus providing a research instrument for deploying risk as a lens. The application of QM techniques in this novel setting provides support for their application beyond the conventional setting of internal operations.

Citation

Chapman, P., Bernon, M. and Haggett, P. (2011), "Applying selected quality management techniques to diagnose delivery time variability", International Journal of Quality & Reliability Management, Vol. 28 No. 9, pp. 1019-1040. https://doi.org/10.1108/02656711111172568