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A Stochastically Optimized Two-Echelon Supply Chain Model: An Entropy Approach for Operational Risk Assessment.

Konstantinos Petridis1, Prasanta Kumar Dey2, Amit K Chattopadhyay3

  • 1Department of Business Administration, University of Macedonia, 54006 Thessaloniki, Greece.

Entropy (Basel, Switzerland)
|September 28, 2023
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Summary
This summary is machine-generated.

This study uses an entropy approach to optimize supply chains by accounting for uncertainty. Incorporating stochastic fluctuations helps producers lower costs and increase profits by selecting specific suppliers.

Keywords:
green supply chain managementnoisestochastic modelssupply chain risk model

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Stochastic Optimization

Background:

  • Traditional supply chain optimization often relies on deterministic models.
  • Operational risk minimization requires addressing supply line constraints and uncertainties.
  • Stochastic fluctuations represent randomness, quantifying uncertainty or risk in supply chain decisions.

Purpose of the Study:

  • To demonstrate an entropy-based approach for optimizing manufacturing and distribution supply chains.
  • To incorporate subjective and objective uncertainty into supply chain decision-making.
  • To model a processing production plant to analyze stochastic cost variations.

Main Methods:

  • Adopting an entropy approach to model supply chain uncertainty.
  • Analyzing stochastically varying production and transportation costs.
  • Utilizing stochastic optimization techniques to identify optimal strategies.
  • Comparing Pareto and Gaussian distributions for modeling cost fluctuations.

Main Results:

  • Stochastic optimization enables producers to achieve better financial outcomes.
  • Producers can increase sale prices while reducing optimized production costs.
  • Selecting producers with Pareto-distributed production costs improves optimization.
  • A lower Pareto exponent leads to more accurate supply chain cost optimization predictions.

Conclusions:

  • An entropy-based stochastic approach effectively optimizes supply chains under uncertainty.
  • Pareto distribution offers superior cost optimization predictions, while Gaussian distribution provides simplicity.
  • Strategic supplier selection based on cost distribution is key to improved financial performance.
  • Balancing optimization performance with implementation simplicity is crucial for practical application.