![]() Reduce redundant internal user support request driven tickets by 50% creating opportunities for improved employee engagement Streamlined conversion process with opportunity to lower cost of healthcare purchases in hospitals Reduced time to results from hours to minutes ![]() Impact: Rewrite of the contract matching engine enabled client to handle more complex scenarios and larger datasets with This resulted in a follow up to rewrite the contract matching engine to handle more complex scenarios and larger datasets. Extended an existing time-series algorithm (L1TF) to give an improved forecast that beat human forecasting for the contracts, reduced manual effort and gave the team time to focus on higher-level activity, such as interpreting results and responding to them. Reviewed machine learning literature to find the best solution for this customer. ![]() Standard forecasting models are limited in several ways, including limited ability to deal with trends, seasonality, and momentum. Create a simpler, more accurate matching of thousands of client healthcare purchases to negotiated business contracts.
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