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Bayesian Estimation of Supply Chain Innovation Path.

Xin Zhang1, Jian He2, Weiguo Tian3

  • 1Business College, Jiaxing University, Jiaxing 314001, Zhejiang, China.

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This study models supply chain innovation strategy, determining optimal leadership and innovation types based on risk, cost, and market response. It validates a model using Zhejiang

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Area of Science:

  • Operations and Supply Chain Management
  • Innovation Management
  • Business Strategy

Background:

  • The supply chain is evolving from factor-driven to investment-driven and innovation-driven models.
  • Supply chain innovation is increasingly critical for competitive advantage.
  • Existing research lacks a strategic framework for prioritizing supply chain innovation.

Purpose of the Study:

  • To develop a theoretical model for strategic supply chain innovation decision-making.
  • To identify optimal leadership and innovation types under varying market conditions.
  • To analyze supply chain innovation from strategic and behavioral perspectives.

Main Methods:

  • Utilized Bayesian prior probability to model innovation likelihood.
  • Defined supply chain innovation capability as a set of node tasks and assets (Capability Set).
  • Expressed supply chain innovation demand as conditional market demand probability.
  • Developed a decision-making model prioritizing minimum risk, cost, and rapid market response.

Main Results:

  • Determined optimal initiation of supply chain innovation leadership and type based on strategic objectives.
  • Validated the theoretical model using the supply chain of Zhejiang Province's professional market.
  • Decomposed group innovation capability into individual enterprise node capabilities.
  • Revealed the starting point and intensity of product/service innovation demand within the supply chain.

Conclusions:

  • The developed model provides a framework for strategic supply chain innovation decisions.
  • Understanding individual node capabilities is key to enhancing overall supply chain innovation.
  • The study offers insights into optimizing innovation efforts for market responsiveness and efficiency.