Important Considerations While Forecasting Pharmaceutical Demand

In the context of the supply chains used to distribute medications, forecasting in the pharmaceutical industry refers to the practice of estimating future sales and consumption of pharmaceuticals in order to ensure that sufficient amounts of these products are procured in advance. 

Managers of the supply chain should make it their mission to achieve a state of equilibrium between customer demand and available inventory. If there is an overabundance of medicines, you may incur expensive storage expenses and massive financial losses, but if there is a shortage of medicines, you may lose customers to your rivals.

From 2021–2028, the worldwide pharmaceutical manufacturing market is projected to expand from an initial 2020 valuation of USD 405.52 billion at a CAGR (compound annual growth rate) of 11.34%. 

Therefore, demand forecasting is an essential component of pharmaceutical supply chain management. In this post, we will examine the top 5 criteria that should be considered while doing demand forecasting for pharmaceuticals.

The Most Effective Methods of Predicting Pharmaceutical Sales

• Prioritize demand over customer orders

When it comes to predicting, there are very thin lines that separate what the demand is from what the customers actually order. Instead of relying on customer orders, businesses should analyze actual demand. Since numerous external factors might have an impact on consumer orders, it can be difficult to make accurate predictions.

A medicine wholesaler, for instance, may place an abnormally large order of Panadol tablets not because of an increase in demand from pharmacies, but rather because of an expected scarcity. 

Similarly, if a distributor is having trouble keeping ibuprofen tablets in supply, a wholesaler may place a much smaller purchase. Lastly, there’s always the possibility that customers would want to cancel their orders because of poor quality. 

Because of these factors, it is not a good idea to base forecasting on customer orders; instead, it is best to base it on actual demand, which can vary dramatically from what was expected.

• Forecasts are nearly seldom correct

Forecasting pharmaceutical demand is nearly impossible, even when employing the most cutting-edge statistical methods and consulting the most competent industry experts. 

During the process of conducting business, both conditions and attitudes might shift, which in turn can have an impact on decisions. To quote an old adage, “the only thing constant in life is change,” therefore predictions are always just best guesses.

Through ongoing analysis of forecasting methods, the objective is to reduce the amount of error in the forecasts to the greatest extent possible. Rather than relying on real product demand, several pharmaceutical businesses incorrectly base their projections on what they hope to accomplish. 

This is a serious error. Instead of relying on subjective assumptions or organizational ideals, pharmaceutical projections should be grounded in hard evidence.

• Include a margin of error whenever possible

As a corollary to the second bullet point above, it is possible that mistakes will be made throughout the forecasting process. If you want an accurate cost estimate for your mistake, you need to do some serious statistical study of the demand variations. 

If supply chain managers see a large forecast error estimate, they should revisit the forecasting procedures or reorganize the supply chain to account for the fluctuating demand for their medicines.

• Predict drug categories instead than specific medications

Instead of trying to predict demand for a single drug, pharmaceutical supply chain administrators would be better served by looking at the market as a whole. Risk pooling refers to the practice of making predictions based on a collection of data rather than an individual item. 

According to this line of reasoning, the risks associated with the low forecast items will be balanced out by the risks associated with the high forecast items, which will result in an overall risk that is lower than the pool’s average risk.

In order to reduce inaccuracy, it is preferable to make aggregate antibiotics forecasts rather than amoxicillin-specific ones. Instead of trying to predict which drug each individual will need, it is possible to share the burden of forecasting by doing so for a group of consumers who have all ordered the same thing.

• Short-term projections are superior to their long-term counterparts

Do you ever ponder why banks and other lending institutions tack on so much interest to long-term loans but not shorter-term ones? This is due to the fact that, in theory, long-term plans are more likely to go astray due to random events and shifts in circumstances. 

To give just one example, in 2018 someone who had a four-year demand prediction of a specific class of medications would learn that their calculations were flawed since they didn’t account for the COVID outbreak, which they also hadn’t anticipated.

To that end, it is recommended that shorter-term forecasts be made and that adequate checks of forecast accuracy be conducted. Managers in the pharmaceutical supply chain are urged to prioritize short lead times because doing so shortens the forecasting horizon and, in turn, improves forecast accuracy.