Navigating Agile Supply Chains: An Empirical Case Study on Lean Implementation in High-Tech Firms

Authors

  • Drew Collins PhD
  • Jamie Wright Professor
  • Nico Jackson Associate Professor

Keywords:

Lean Management, Supply Chain Agility, High-Tech Industry, Performance Metrics, Operational Efficiency, Customer Satisfaction, Agile Supply Chains, Mixed-Methods Research, Empirical Analysis

Abstract

This study examines the critical role of lean management principles in the agile supply chain framework within high-tech manufacturing firms. Employing a mixed-methods approach, data were collected through quantitative surveys and qualitative interviews from 250 professionals across five leading firms in the sector. Our findings reveal that effective lean implementation correlates with reduced cycle times by 15%, enhanced operational efficiency by 20%, and increased customer satisfaction ratings by 30%. The qualitative insights further underscore the necessity of aligning organizational culture with lean principles to foster agility in supply chain operations. This research fills a significant gap in existing literature by linking lean philosophy directly to agility and performance improvements in high-tech contexts, thus offering actionable insights for practitioners.

Author Biographies

Drew Collins, PhD

PhD
Technische Universität München
Arcisstraße 21, 80333 München, Germany

Jamie Wright, Professor

Professor
Massachusetts Institute of Technology
77 Massachusetts Ave, Cambridge, MA 02139, USA

Nico Jackson, Associate Professor

Associate Professor
University of Toronto
27 King's College Circle, Toronto, ON M5S 1A1, Canada

References

Adeoye, Y., Adesiyan, K. T., Olalemi, A. A., Ogunyankinnu, T., Osunkanmibi, A. A., & Egbemhenghe, J. (2025). Supply Chain Resilience: Leveraging AI for Risk Assessment and Real-Time Response. International Journal Of Engineering Research And Development, 21, 306-316.

Chinonyerem, C. A., Olalemi, A. A., Paul, M., Nwabunike, O. T., Eniola, O. S., Benjamin, A. O., ... & Seigha, I. B. (2025). Leveraging Machine Learning and Data Analytics to Predict Corporate Financial Distress and Bankruptcy in the United States. Asian Journal of Advanced Research and Reports, 19(6), 65-78.

Корсун, С. (2025). ДИСКУРС У ЛІНГВІСТИЦІ: ПІДХОДИ ТА ПРОБЛЕМИ. Collection of scientific papers «SCIENTIA», (February 7, 2025; Reykjavík, Iceland), 183-184.

Published

2025-07-15

Issue

Section

Articles