IDS Case Study: Creating a Forecasting Model to Anticipate Low Inventory

By Keiter Technologies

IDS Case Study: Creating a Forecasting Model to Anticipate Low Inventory

How can retail and distribution efficiency lead to increased revenue?

Data Science can help you achieve your business goals with tailored advice specific to your organization. Learn more about how Keiter Data Solutions solved a similar problem for one of our clients in the below case study.

Challenge and Opportunity

A Keiter Technologies client and technology company was seeking to better coordinate product delivery between retailers, manufacturers, and delivery partners.


Our Approach

  • Direct store delivery logistics is extremely complicated because numerous factors impact the delivery of inventory at retail locations at specific times.
  • Our team enhanced the client’s existing technology by collecting and inputting data, such as seasonality and product grouping, which anticipate inventory need.
  • Using their existing data with new data sets, our team was able to build a forecasting model that anticipated inventory needs more effectively, with less data from the stores.


The technology tool has been a major revenue driver for the client resulting in increased revenue and serving over 35,000 partners.


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About the Author

Keiter Technologies

Keiter Technologies

Keiter Technologies focuses on serving businesses with their strategic technology needs through data science, cybersecurity, and IT audit and consulting.

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The information contained within this article is provided for informational purposes only and is current as of the date published. Online readers are advised not to act upon this information without seeking the service of a professional accountant, as this article is not a substitute for obtaining accounting, tax, or financial advice from a professional accountant.


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