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Sales and Operations Planning
Objective
Improve on the current sales forecast process, to
- support robust top-line business planning,
- reduce forecast/ prediction errors at lower granularities and,
- exhibit capability of AutoML solutions to scale the process across customer/ channel segments.
Industry: Healthcare – Wholesaler
Services: Technology Innovation
Technology: Sales Forecasting
Enabled daily sales forecasting cadence (vs. monthly cadence) resulting in improved visibility and actionable insights into month-end sales performance.
Reduced forecasting error by
$16B
per year with baseline dataset
Reduced forecast/prediction errors on ‘problematic’ drug categories – e.g. bio-similar, seasonal or new products.
Our Client
Our client is a Fortune 100 global healthcare wholesaler and one of the largest drug distributor in the U.S., supplying a wide range of pharmaceuticals, as well as medical supplies and equipment.
Challenge
Pharmaceuticals sales forecasting is a complex process, influenced not only by interdependence between entities within the value chain/ecosystem, but also human health trends, seasonality, epidemics, manufacturers’ competitive programs, BRx to GRx conversions, active pharma ingredient relationships & regulation.
Approach
AutoML solutions employed against:
- Enterprise sales data by strategic owner/channel and profit-center/ material-product.
Outcomes
Improved sales forecasting system delivers:
- Robust performance projections & associated financial plans
- Improved inventory planning and stock replenishment decisions
- Informs design of manufacturer rebate strategy and rebate program management
- Decreases customer revenue leakage
Impact
- Enabled daily sales forecasting cadence (vs. currently monthly cadence) resulting in improved visibility to and actionability on month-end sales performance
- Scaled forecasting capabilities across customers and channels
- Reduced forecasting error by $16 billion per year with baseline dataset*
- Reduced forecast/prediction errors on ‘problematic’ drug categories – e.g. bio-similar, seasonal or new products
*Dataset with strategic owners, profit centers, date features and without knowledge of external influencing factors (e.g. acquisitions), business dynamics, contracts re-negotiation, pricing, etc.
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