Production Planning at NWC to Support Inventory Management at its Warehouses

This project addresses the complex production and inventory challenges at NWC’s assembly facility, which manages 150 SKUs with 26 requiring specific setups. With finite production capacity and storage constraints, the current system struggles to maintain optimal stock levels.

The primary objective is to develop a dynamic production planning model using Reinforcement Learning (RL). This AI-driven solution will intelligently balance production schedules against capacity limits, warehouse space, and pre-defined target inventory levels.

The key deliverable is an automated system that generates optimized production plans. Success will be measured by the model’s ability to consistently meet target stock levels, minimize excess inventory and waste, and ensure production aligns closely with demand. This project will empower NWC with a robust, data-driven tool to enhance operational efficiency and reduce costs across its supply chain.

Faculty Supervisor:

Uday Venkatadri

Student:

Partner:

Nature's Way

Discipline:

Engineering

Sector:

Manufacturing

University:

Dalhousie University

Program:

Accelerate

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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.