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Discover how Oxford’s solution created a streamlined forecasting process for our client.
INDUSTRY
Government
SERVICES
Natural Language Processing
Data Engineering
Cloud Engineering
DevOps
SKILLS
Data Scientist
Cloud Engineer
Data Engineer
Automation Engineer
The Challenge
A Fortune 500 computer technology firm was struggling with their time consuming process of running financial forecasts of unit shipments. The forecasting process consisted of delivering extremely large statistical models to a large group of resources to manually adjust and tweak, and the model was then custom tailored to one specific data set. On average, the process required two team members more than one day to run the statistical models and a total of seven weeks to complete the entire process. Additionally, the customer was incapable of executing true forecasting across multiple lines of business since the processing engine was not extensible beyond one data set. We were engaged to implement a replacement for our client’s ineffective and outdated processes.
The Solution
Our data scientist worked closely with the client to implement a solution that replaced the client’s time consuming and inefficient method with a processing engine that provided speed and extensibility to other data sets not previously forecastable. The new processing engine can go directly from producing the models, to the data review and clean up all in the same day. In conjunction with the technology, our data scientist worked closely with the client to overhaul the forecasting process, providing a full circle solution.
The Result
Our solution and process decreased forecasting time by 90%. The unique extensibility of the process engine provides forecasts not previously accessible, and business managers are now armed with data that allows them to make decisions that are more informed. Additionally, the solution and process has enabled collaboration among lines of business that were previously separated for each other.