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A Comprehensive Guide on Forecasting in Pharmaceutical Industry

Accurate performance analysis relies on precise forecasting, which boosts trust in operational decision-making. 

Forecasting in the pharmaceutical industry is figuring out how much medicine will be needed down the road so that enough can be manufactured. Supply chain managers should make it their goal to find a balance between what customers want and what they can get.

When you have more customers than you can serve, you risk losing them to the competition. On the other hand, when there are fewer medicines, you risk paying a lot to store them and thereby lose a lot of money. Therefore, forecasting future demand is integral to managing the pharmaceutical supply chain.

Here are a few guidelines to follow while making pharmaceutical forecasts: 

  • Pay more attention to what people want than to what businesses tell them to buy:

Forecasting requires figuring out how to move between the idea of demand and the actual orders people make. In business, meeting real demand is more important than filling orders. This is because several things out of the business’s control could affect customer orders, which can throw off predictions in a big way. 

For example, the medicine distributor will probably place a large order for Panadol pills, not because pharmacies will order more, but because they want to ensure they stay supplied.

In the same way, if a distributor can’t keep enough ibuprofen pills in stock, a wholesaler may make a much smaller order. 

Third, there’s always a chance that people will only accept their orders if the quality needs to improve. Because of these and other things, it is better to use actual demand instead of consumer orders to make projections. 

  • Predictions rarely turn out the way people think they will:

Forecasting how much medicine will be used is quite hard, even when the most advanced statistical methods and knowledgeable industry experts are used. Changes in circumstances and points of view are natural parts of doing business, and they affect how decisions are made.

People have said that change is the only thing that stays the same in life. Because of this, all predictions are just educated guesses.

This ongoing analysis aims to eliminate as many wrong predictions as possible. For example, some pharmaceutical companies make inaccurate predictions about future sales based on what they want to happen instead of what people want.

There’s something wrong. Projections in the pharmaceutical industry should be based on data, not on the goals and plans of organisations. 

  • Always leave some room for error:

No matter what kind of prediction you make, you can always be wrong. A monetary estimate of the error should be made after a thorough statistical analysis of the change in demand. If they discover many incorrect predictions, supply chain managers should reconsider their forecasting methods or reorganise the pharmaceutical distribution network. This will help them deal with the fluctuating demand for their medicines. 

  • Instead of thinking about a specific drug, think about the categories of drugs:

Instead of trying to guess how much demand there will be for a specific drug, managers of the pharmaceutical supply network would do better to look at the market for therapeutic classes. Risk pooling is a way to make predictions based on a group of data points instead of a single data point.

The things with high forecasts will make up for those with low forecasts, making the risk profile more inadequate than the pooled average.

It is easier to predict the demand for antibiotics as a whole than for individual antibiotics like amoxicillin.

Forecasting a drug for a single buyer isn’t the only way. If a lot of people are going to buy the same thing, the risk can be shared by predicting demand for a group of buyers. 

  • Most of the time, shorter-term predictions are more likely to be correct:

Have you ever thought about why long-term loans have higher interest rates than short-term loans? This is because, in theory, random events and changes in circumstances are more likely to throw off long-term plans. For example, if in 2018, you tried to predict how much of a particular class of drugs people would want in the next four years, but you didn’t consider the COVID epidemic, you could have made wrong calculations. Because of this, it is best to make accurate predictions for shorter periods. By ensuring that the pharmaceutical supply chain has short lead times, managers of this chain can reduce the amount of time they have to make predictions.

It’s not easy to guess what will happen in the future, and uncertainty about basic assumptions often makes it hard to trust forecasts. But uncertainty can be turned into a good thing by teaching people who make decisions about it. 


The pharmaceutical market depends more on a healthy balance between supply and demand than any other market. If you store less, you could save a lot on storage. But you must follow the order to keep your customers and competitors from selling. So, people in charge of pharmaceutical supply networks must forecast demand if they want their businesses to do well.

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