Assessment of Lean Six Sigma Readiness (LESIRE) for manufacturing industries using fuzzy logic

• Published in: The International journal of quality & reliability management Vol. 36; no. 2; pp. 137 - 161
• Main Authors: Sreedharan V, Raja, Raju, R, Sunder M, Vijaya, Antony, Jiju
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 21-02-2019; Emerald Group Publishing Limited
• Subjects: Case studies; Decision making; Fuzzy logic; Fuzzy sets; Lean manufacturing; Linguistics; Literature reviews; Material requirements planning; Model testing; Productivity; Six Sigma; Studies; Variables; Weight; Fuzzy logic; FPII; FLSS; Lean Six Sigma (LSS)
• ISSN: 0265-671X; 1758-6682
• DOI: 10.1108/IJQRM-09-2017-0181

Purpose
Many organizations have reported significant benefits after the implementation of Lean Six Sigma (LSS). Embracing LSS requires asking some important questions: How Lean Six Sigma Readiness (LESIRE) can be measured? How can an organization identify the barriers for LESIRE? Answers to these questions are critical to both academicians and practitioners. The paper aims to discuss this issue.
Design/methodology/approach
This study illustrates the development process of a Lean Six Sigma Readiness (LESIRE) evaluation model to assess an organization’s readiness for LSS deployment using the fuzzy approach. The model was developed from 4 enablers, 16 criteria and 46 attributes of LSS, identified through a literature review.
Findings
To demonstrate the efficiency of the model, this study testing the LESIRE evaluation model in three Indian SMEs. Using experts’ ratings and weight, the researchers calculated the Fuzzy Lean Six Sigma index (FLSS) which indicates the LESIRE level of an organization and the Fuzzy Performance Importance Index (FPII) that helps to identify the barriers for LESIRE.
Research limitations/implications
The main limitations of this study are that it did not consider the failure factors of LSS for model development and the LESIRE was only tested in manufacturing industries. Thus, future researchers could focus on developing a model with failure factors. The results obtained from the SMEs show that LESIRE is capable of assessing LESIRE in an industrial scenario and helps practitioners to measure LESIRE for the future decision making process.
Practical implications
The LESIRE model is easy to understand and use without much computation complexity. This simplicity makes the LESIRE evaluation model unique from other LSS models. Further, LESIRE was tested in three different SMEs, and it aided them to identify and improve their weak areas, thereby readying them for LSS deployment.
Originality/value
The main contribution of this study it proposes a LESIRE model that evaluates the organization for FLSS and FPII for LESIRE, which is essential for the organization embarking on an LSS journey. Further, it improves the readiness of the organization that is already practicing LSS.