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Contact Us

A Healthcare Provider Improved Order Predictability with NLB ML Model

Why Change was Necessary

A Leading US-based Healthcare Provider was facing the following challenges regarding its order forecasting and expense tracking:

discrepancy in the Purchase Order (PO) and actual bills submitted by hospital partners
no process in place for a detailed PO forecasting
unable to deal with fluctuations in POs across expense categories in Emergency Operations Center (EOC)

Experts at Work

NLB optimized the PO process disruptions by developing an ensemble ML model (XGB + Seasonal ARIMAX) that captures data from existing systems and provides an accurate forecast based on total PO trend, including weekly/monthly growth and seasonality. Our team of specialists also

plugged in last three week’s data into the model
leveraged casual models to estimate EOC level share of the expense
calculated weekly volatility index
balanced data sheets through noise component adjustment
improved weekly performance

Impact We Delivered

correction in demand forecasting accuracy
0 %

nlbadmin

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