Prioritising indicators in improving supply chain performance using fuzzy AHP: insights from the case example of four Indian manufacturing companies

Abstract

In this contribution, we aim to prioritise the indicators to enhance the organisational supply chain’s (SC) effectiveness from an industrial perspective. It will help industries to develop strategies for managing the SC effectively and ensuring improvement in performance continuously. To achieve this, this work proposes to use a two-phase research methodology. First, 36 SC performance improvement indicators are recognised from a literature survey and from field and industrial expert’s inputs. Secondly, a structural model is proposed using the fuzzy analytical hierarchy process (AHP) to prioritise the indicators strategically to improve the SC performance. The fuzzy AHP method helps determine the priority of concerns of the identified indicators under fuzzy surroundings. Inputs in this research are taken from four ancillary Indian plastic manufacturing firms. Research findings indicate that collaborations and information exchange dimension indicators obtained the highest priority in improving SC performance. The model proposed is considered very useful for the SC managers/practitioners/decision-makers to understand better and distinguish the essential SC performance improvement indicators and to take systematic decisions specifically to improve the performance of business in a SC context. Sensitivity analysis was conducted to examine the priority ranking of the indicators.

Keywords:

• Supply chain management (SCM)
• SC performance improvements indicators
• multi criteria decision making (MCDM)
• fuzzy analytic hierarchy process (FAHP)
• sensitivity analysis