FMCG Case Study: Python-Based Demand Forecasting Solution| Clarion
Enhancing Supply Chain Efficiency with a Python-based Demand Forecasting Solution for A Consumer...
With multiple vendors to supply different parts, the manufacturing company was struggling with its manual order processing system which lead to errors, delays in deliveries and failure of meeting deadlines. It involved a lot of time and effort from everyone involved, resulted in tons of paperwork and took many iterations back and forth. Labored lacking exception-based insights real-time tracking made it impossible to provide timely supplier performance feedback. For example, having to go through inventory levels one-by-one meant that stockouts or overstocking became issues for a contractor, while the contract management was heavy, ever leading to missed deadlines and risks in compliance.
To tackle these issues, the company implemented an AI-driven supplier management system. The AI automated order processing, ensuring accurate and timely orders. It streamlined supplier onboarding, guiding suppliers through documentation and compliance steps in real-time. AI also enabled automated performance monitoring, providing detailed insights and feedback to suppliers. Inventory replenishment was automated by monitoring stock levels and predicting demand, while contract management was improved with AI tracking key dates and compliance requirements, reducing manual oversight.
The AI solution yielded huge benefits, including:
In summary, this solution resulted in the company enjoying improved efficiency of operations, minimization of errors, and better supplier partnerships — essential ingredients for long-term competitiveness in the dynamic manufacturing space.
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Enhancing Supply Chain Efficiency with a Python-based Demand Forecasting Solution for A Consumer...
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