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Supply Chain Optimization

Strategic and operational planning based on our proprietary methodology

•  Is your demand planning based on intuition and on a multitude of excel spreadsheets with thousands of data points?
•  Is it a manual process which takes ages to complete and build consensus?
•  Different teams have different sets of data that they rely on as the source of truth?
•  Does capital tied up in inventory worry you?
•  Are stock outs eating into your profits?
•  Is a significant portion your inventory occupying shelf space without getting sold?

If you answered yes to any of the questions above you can benefit from AI driven supply chain optimization.

BluePi’s proprietary methodology of Demand Planning would add a minimum 5% to your bottom-line by finding the right amount of inventory to carry to ensure both stock outs and Excess Inventory is minimized. Our supply chain solutions driven by AI algorithms perfected for different retail formats like multi-format, grocery, apparel among others.

demand-forecasting

Demand Forecasting

At the foundation of the BluePi Supply chain optimization suite is the AI driven Demand forecasting models. Our AI models considers all known features like holiday, season, trends, weather as well as planned changes in collections, stores and promotions/markdowns. The forecast horizons are tuned to align with the procurement cycles to ensure the use of all available data and yet provide enough head room for logistics.

Automatic Replenishment

Shelf-out-of-stock is one of the leading motivations of technology innovation in Retail sector. Retail stores need to make sure that items are replaced the moment they leave shelves. At the heart of this effort is inventory management, which is a very thin and precarious line between stockout and oversupply.

Inventory Re-balancing

Do you have inventory at one store while a much higher rate of sale in the other? And thousands of SKUs across hundreds of stores compound the problem even further? The problem can be exacerbate by excess reorder while demand could be fulfilled by just moving stocks from one store to another. While the customer demand at each store is independent of each other lack of proper stocks means unmet sales and lost revenue opportunities.

Markdown optimization

Shrinking product lifecycles, seasonal demands, diversified assortments, changes in variety and size of customer base, increase in store count and ever increasing competition have both necessitated and increased the use of markdowns. Optimizing markdowns entails a complex decision for retail managers as if markdowns are ‘too much too soon’ then it will reduce the revenue potential while if they are ‘too little too late’ then there is a risk of ending the season with high level of stocks lying in warehouse.

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