In the healthcare industry, Artificial Intelligence can dramatically improve the forecasting accuracy of demand estimates.
Demand forecasting is an analytics field that attempts to predict customer demand to optimize supply decisions.
In the Pharmaceutical industry, demand forecasts contribute significantly to production planning, inventory management, and deciding whether to enter a new market.
Demand forecasting notably helps manufacturing companies plan to have inventory available when customer demand spikes. An adequate demand forecast can keep companies from incurring penalties, rush charges, or even putting items on backorder.
Nevertheless, the demand forecast is not an end goal. Forecasting accuracy is a means to increasing value for the company. More accurate demand forecasting improves customer service and inventory management. It also reduces backorders and costs incurred due to breach of contract.
Demand forecasting methods in healthcare can be based on qualitative or quantitative methods.
Qualitative methods for demand forecasts, such as executive opinions or sales force surveys, are based on judgment or opinion.
One popular method of qualitative demand forecasting is the Delphi Technique, which relies on experts’ panel. Delphi is based on the wisdom of the crowd principle: predictions from a group of structured individuals are more accurate than those from a single expert.
Quantitative techniques are based on historical data.
Nowadays, contemporary methods rely on machine learning algorithms that “learn” from experience and analyze many complicated relationships and constituents that influence product demand. Machine learning for demand forecasting has grown to an industry level that can dramatically transform business in pharma.
Mixed or combined models allow the integration of both approaches (qualitative and quantitative).
Here at Konplik, we use hybrid approaches that leverage experts’ forecasts with complex machine learning algorithms to obtain the best results.
In the next post, we will be sharing how we combine different sources and techniques to achieve the best results in demand forecasting for healthcare.