Applying selected quality management techniques to diagnose delivery time variability

Abstract

PurposeThis 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/approachA 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.FindingsCandidate 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 implicationsThe 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/valueThis 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.

Assessment of upstream service quality of supply chain

“…Service quality, improved lead time and flexibility are important factors in improving the performance of supply chain. Chapman et al (2011) in their research work that spans across measuring the variability of delivery time in supply chain have successfully demonstrated the use of quality management tools in multi echelon supply chains. In one of the recent studies, Ghorbani et al (2014) discusses in his paper about selection of distributor based on service quality dimensions.…”

Prioritisation of 14 quality management tools based on quality principles using integrated approach of multi-criteria decision-making and importance-performance analysis - the case of Marjan Tile Company

“…For instance, according to Chapman et al (2011), histogram and cause and effect analysis can be also applied in the diagnosis of the causes of delivery time variability in supply chains. Dale and McQuater (1998) prioritised the application of the seven basic tools of quality management as illustrated in Figure 2.…”

Supply chain risk management processes for resilience: A study of South African grocery manufacturers

“…Xie et al (2011:477) construe the SCRM process to include risk identification, risk assessment, risk mitigation and risk monitoring, as illustrated in Figure 1. These steps equip managers with strategic information to select strategies that mitigate different risks to improve overall performance of the supply chain (Chapman, Bernon & Haggett 2011:1030Xie et al 2011:481). The four phases are discussed in the next sections.…”