Leveraging Data Science to Increase Forecast Accuracy against Inventory Loss

Leveraging Data Science to Increase Forecast Accuracy against Inventory Loss

About the Client:

Our client is an American multinational food manufacturing company in the food processing business for over 100 years



  • Create a data driven, statistically backed solution that would improve product availability visibility across business units, thereby helping manage inventory with reduced losses to the company
  • Integrate data from multiple sources like Point of Sales, shipment, forecast and market consumption, to use it for effective data analysis and insight generation


The Solution:

TekLink consultants, using SAS Enterprise Guide, Microsoft SQL Server and Tableau Desktop, delivered the following solution to the client:

  • Created a dashboard in Tableau showcasing inventory performance and risk metrics for all of client’s business units regularly tracked by the higher management based on which business decisions are made
  • Developed risk metrics to showcase the inventory exposure of various products to the market (in terms of $) and helped the business unit plan their next steps including estimation of write-off amount
  • Processed the data with SAS from various sources such as SAP, SQL Server and SharePoint, using business logic to create a comprehensive data file, which then can be fed into Tableau for visualization purposes
  • Provided ability to generate customized reports from the final model, based on needs and requirements of individual business units, to track various metrics on a weekly basis
  • Provided regular enhancements and upgrades done to the existing data science solution. The model was tuned to act as a guidance to the actual achieved in comparison to predictions of inventory production and consumption


Outcome and Benefits:

  • Addressed the challenge of inventory management by building a model with a data driven, fact based and statistical approach
  • Identified products having a high risk of inventory build-up due to either inadequate planning (based on demand forecasting) or past shipping performance, planned production (based on actual shipments & planned production) and revenue slippage (actual POS sales comparing with national average consumption)


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