Lean and Six Sigma in logistics: a pilot survey study in Singapore

Author:

Zhang Abraham,Luo Wen,Shi Yangyan,Chia Song Ting,Sim Zhi Hao Xavier

Abstract

Purpose Lean and Six Sigma are the two most powerful methodologies for process improvement. They have been widely employed in manufacturing and healthcare industries to achieve substantial cost savings and quality improvement. The purpose of this paper is to investigate the implementation of Lean and Six Sigma for improving logistics operations. Design/methodology/approach A survey was conducted among 410 companies registered under the Singapore Logistics Association and returned 32 usable responses. χ2-tests were used to identify factors affecting the implementation of Lean and Six Sigma. Findings It was found that 37.5 per cent of respondents had implemented Lean and the majority of them had implemented Six Sigma together. All implementing companies reported varying degrees of cost savings and productivity improvements. Large logistics companies are more likely to implement Lean and Six Sigma. Companies with a higher service standard are more likely to implement Six Sigma. Research limitations/implications The survey was limited to Singapore. Future studies should explore a broader geographical coverage and improve the response rate. Practical implications Lean and Six Sigma are applicable for improving logistics operations. Senior management must be committed to support an implementation. Effective training and communications are required for overcoming resistance to change and helping employees to understand improvement methodologies and initiatives. Originality/value This research is the very first survey study of Lean and Six Sigma in the logistics industry. It reveals similarities and differences in comparison with the implementation of these methodologies in the manufacturing industry. The findings offer valuable insights for improving logistics operations.

Publisher

Emerald

Subject

Management of Technology and Innovation,Strategy and Management,General Decision Sciences

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