Why should Pharma companies use Machine Learning ML for Demand Forecasting?

Authored by chintan on September 27, 2020

The question, why pharma companies should use machine learning ML for demand forecasting, comes from a major concern that the pharma industry has been witnessing for quite some time.

One of the toughest challenges that the pharma industry players face is to recognize and devise market forecasts in order to enhance their customer service levels. 

Demand forecasting is a very crucial aspect of the pharma industry that helps them stay ahead of their competitors and match the supply to demand ratio. Without proper identification of patterns and implementation of technology accordingly, making sure that all the items are produced at the desired time and delivered seamlessly poses a big challenge to the pharma industry.

What Is Demand Forecasting?

Demand forecasting comprises two words i.e. demand and forecasting. When it comes to understanding the actual process of demand forecasting, one can say that it is the use of historical sales data for creating the estimate and forecasting the demand of customers in a timely manner. 

The right kind of demand forecasting provides businesses an edge over their competitors while helping them stock up only the goods and services that their customers would need in the coming times. This follows up with a lot of important business processes like turnover, profit, cash flow, overall expenses, risk assessment, mitigation plans, capacity planning, etc.

How can Machine Learning ML help in demand forecasting?

Machine learning techniques allow predicting the amount of products/services to be purchased during a defined future period. In the case of the Pharma industry, a software system can learn from data collected by the companies from the customer front for improved analysis. Compared to traditional demand forecasting methods, machine learning:

  • Accelerates data processing speed
  • Provides a more accurate forecast
  • Automates forecast updates based on the recent data
  • Analyzes more data
  • Identifies hidden patterns in data
  • Creates a robust system
  • Increases adaptability to changes

With accurate and efficient demand forecasting, the following processes will improve as well:

Supplier relationship management

By having the prediction of customer demand in numbers, it’s possible to calculate how many products to order, making it easy for you to decide whether you need new supply chains or to reduce the number of suppliers.

Customer relationship management

Customers planning to buy something expect the products they want to be available immediately. Demand forecasting allows you to predict which categories of products need to be purchased in the next period from a specific store location. This improves customer satisfaction and commitment to your brand.

Order fulfillment and logistics

Demand forecasting features optimize supply chains. This means that at the time of order, the product will be more likely to be in stock, and unsold goods won’t occupy prime retail space.

Marketing campaigns

Forecasting is often used to adjust ads and marketing campaigns and can influence the number of sales. Sophisticated machine learning forecasting models can take marketing data into account as well.

Manufacturing flow management

Being part of the ERP, time series-based demand forecasting predicts production needs based on how many goods will eventually be sold.

Benefits of implementing Machine Learning for Demand Forecasting 

As the Pharma companies are cringing over the demand-supply problem, they need to realize the importance technology can play in not only simplifying most of their self-made problems but also in finding the right balance between helping their customers and keeping their business principles intact. How can Machine Learning improve the Pharma industry’s business model? Let’s go through this:


For an enterprise level business accuracy is the most important contributor in demand planning and it all depends on the precision in which the demand forecasting is done. Any new method will not be welcome without a certain level of skepticism and resistance since most of the existing methods are proven and have worked for them for years. Unless, the method offered is accurate to the extent that it can make the old methods obsolete.

This is exactly why machine learning is bound to succeed. The accuracy that ML provides comes through proper analysis of all the user data and the economic impact of accurate demand forecasting is what helps these companies lead the game. 

Another reason why machine learning ML comes highly recommended is that it isn’t a method, but the way in which companies can implement and automate their own method within their system to precisely make them work with the help of existing data. This leaves a lot of space and chance for any company to weigh in different methods and implement them within their data forecasting process, finding what’s the most feasible one for their business.

The need and use of data/The thirst for data

An important contributor to Machine Learning forecasting accuracy is the ability of Machine Learning to ingest disparate data and leverage that information at a granular level to improve Stock Keeping Unit forecasting.  Simply stated, if data can be matched to the Stock Keeping Unit at the point of sale or the point of distribution, the data can be leveraged with Machine Learning forecasting.  

Rapid adaptation to change and supply chain disruption

Another quality of Machine Learning forecasting is the ability to be ‘always on’ in the sense that the forecast can be programmed to update automatically on the most recent data.  Typically, this means updating the forecast based on aggregate data on a daily or weekly basis, refreshing the data warehouse with each forecast refresh, and regenerating a running forecast based on the most recent actuals.  New forecast accuracy and bias metrics can be calculated, the base and running forecast can be compared, and the updated results presented for review through Logility dashboards.

In this way, forecast accuracy trends can be leveraged in adjusting demand planning.  This ‘always on’ forecast monitoring, combined with dynamic and customer-level pricing and promotions, can be tuned to identify price sensitivity among customer segments, products that form a market basket, and thus build the foundations of an online recommender system.  Once a daily forecast and customer history is merged with a transactional website recommender system, the value of the recommender system in driving incremental consumer purchases can be unlocked.  This is where forecasting truly becomes an automated learning cycle, nearing AI capabilities, and the method is ideally suited to large-scale pharma businesses.

Analytical processing speed and accelerated corporate learning

An additional advantage of Machine Learning is data processing speed.  Modern Machine Learning packages have been designed to forecast light-speed results.  Machine Learning forecast generation has processing speed of 1.5 million forecasts per hour.

Machine Learning can incorporate additional predictors and Deep Learning, with the analytical results demonstrating the speed / accuracy trade-off as an efficient frontier.  From that point, decision makers are well informed in making necessary trade-offs on where to invest – more data, faster data processing, a larger computational cluster, and so forth – with all of those issues addressable in the highly scalable Microsoft cloud and other cloud computing services.

How can smart Demand Forecasting benefit the Pharma Industry

The ability to set goals based on future predictions

Demand in the pharma industry is driven by a combination of push and pull driven demand, changing regulatory requirements, and competitive pricing pressures. Unlike the other industries, point-of-sale data is not easily accessible in pharma resulting in issues around demand visibility and data management. However, businesses that have leveraged dynamic demand forecasting approaches have been able to achieve their set goals based on future predictions and market trends.

The ability to increase market share

Working with leading pharma companies, we’ve witnessed that most challenges revolve around accurate demand forecasting. Leveraging accurate demand forecasting techniques in such a scenario has helped pharma companies to readjust their inventory plans and ensure its aligned to the actual demand resulting in maximized product sales and market share.

The ability to provide accurate and deep insights into your business

It’s no surprise that pharma leaders are leveraging demand forecasting techniques to gain real-time insights into factors impact demand. Insights such as these can help drive rapid responses to demand surges and enable businesses to make necessary adjustments to the production plans.

How can I incorporate machine learning abilities into my business process?

If you have gone through the benefits of Machine Learning within the pharma business and you have understood the various advantages it can provide then you would probably be interested in knowing how it can be implemented.

Incorporating machine learning abilities into your business lies in the able hands of your technological partners. This is why it is very important to consult technical experts like Archisys who would closely monitor your business processes and come up with practical solutions along with solid system architecture. If you wish to let us help you out in implementing learning abilities as per your business model we would be more than willing to do so. Archisys has always been the technological backbone for all the entrepreneurs and risk-takers for long.

Be a part of the culture that intends to stay ahead of its time. 

Stay tuned for actual technical explanation of machine learning in the pharmaceutical industry’s business processes.

For a detailed reading experience

Demand Forecasting: Understanding the Basics

The Complete Guide to Measuring Forecast Accuracy

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Challenges for Indian Pharma Industry

Technology can Overcome the Key Challenges Faced by the Pharma Industry

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