Leveraging Data Science to Increase Forecast Accuracy against Inventory Loss

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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