Developing trust among players in a vendor-managed inventory model for random demand under environmental impact

Area of Science:

• Operations Research
• Supply Chain Management
• Environmental Economics

Background:

• Retailers' direct consumer interaction creates data asymmetry in supply chains.
• Unreliable data sharing between retailers and manufacturers impacts system efficiency.
• Environmental concerns necessitate strategies to control carbon emissions from production and transportation.

Purpose of the Study:

• To mitigate supply chain unreliability using a vendor-managed inventory policy.
• To assess the effectiveness of a cap and trade policy in controlling carbon emissions.
• To optimize inventory management under random demand distributions.

Main Methods:

• Implementation of a vendor-managed inventory policy.
• Application of a cap and trade policy for carbon emission control.
• Analysis of random demand following uniform and normal distributions.
• Numerical and sensitivity analyses for parameter optimization.

Main Results:

• The vendor-managed inventory policy effectively addresses data asymmetry and improves reliability.
• The cap and trade policy demonstrates significant control over carbon emissions.
• Optimal retailer lot size, service levels, and reorder points are determined under random demand.
• Numerical findings indicate a 50.24% economic benefit compared to previous methods.

Conclusions:

• The integrated approach of vendor-managed inventory and cap and trade policies enhances supply chain efficiency and environmental sustainability.
• Random demand modeling provides a more realistic approach to inventory optimization.
• The study offers a robust framework for economically beneficial and environmentally conscious supply chain management.