Innovative Supply Chain Risk Management: Enhancing Productivity with Industrial Engineering Tools in the US Manufacturing Sector

EasyChair Preprint no. 14115

Abstract

In the dynamic landscape of the U.S. manufacturing sector, supply chain risk management (SCRM) has become increasingly critical to maintaining productivity and competitive advantage. This study explores innovative SCRM strategies that leverage industrial engineering tools to enhance resilience and efficiency in supply chains. By integrating advanced methodologies such as Lean Six Sigma, predictive analytics, and IoT-based monitoring, manufacturers can anticipate, mitigate, and respond to potential disruptions more effectively. Lean Six Sigma, with its focus on waste reduction and process optimization, aligns with SCRM objectives by identifying vulnerabilities and streamlining operations. Predictive analytics provide actionable insights through the analysis of vast data sets, enabling proactive decision-making. IoT-based monitoring systems offer real-time visibility into supply chain activities, improving transparency and enabling swift corrective actions. The research also examines case studies from leading U.S. manufacturers who have successfully implemented these tools, demonstrating significant improvements in operational stability and productivity. By adopting these innovative strategies, U.S. manufacturers can not only mitigate risks but also achieve sustained productivity growth and a competitive edge in the global market. This research underscores the vital role of industrial engineering in transforming SCRM practices and driving the future of manufacturing excellence.

Keyphrases: Analytics, Industrial Engineering, IoT, Lean Six Sigma, Predictive, Productivity, Resilience, Supply Chain Risk Management, U.S. manufacturing

@Booklet{EasyChair:14115,
  author = {Deep Himmatbhai Ajabani},
  title = {Innovative Supply Chain Risk Management: Enhancing Productivity with Industrial Engineering Tools in the US Manufacturing Sector},
  howpublished = {EasyChair Preprint no. 14115},

  year = {EasyChair, 2024}}