At a Glance

  • The digital era has given companies various tools and techniques to help pharmaceutical manufacturers optimize and streamline their operations, and predictive analysis is one such highly advanced method.
  • There are different ways with which predictive analytics can integrate with existing software setup and forecast plausible technical glitches and predict future trends, thus helping in enhancing operational efficiency.
  • Predictive analysis can help planning to execution, aftermarket services level to develop better products/ services, improve their response time, and stay ahead of the curve for delivering better customer experience.

The Role of Robust Infrastructure

Database management has become one of the topmost priorities for companies across the globe. This database is used to fabricate trends and patterns for a particular time-frame or a process or a product. Data historians have been around for quite some time now, but manufacturers have recently started to look at them as more than software that stores and retrieves data. Application of predictive analytics is turning out to be a game-changer in terms of predicting the future with maximum accuracy and helping manufacturers make the right calls across various functions- purchase, operations, consumer demands, marketing, and more.

Any business operates with the ultimate motive – to enhance productivity and profits. Optimizing operations thus becomes a must for manufacturers. Companies are riding the digital wave with cutting-edge technologies and tools such as Cloud, Internet of Things (IoT), Machine Learning, and Digital Analytics. By making the most of this digital disruption and using predictive analysis to their advantage, companies can achieve better operational efficiency and higher productivity.

Increased and better predictability for both structured and unstructured data helps companies in planning their operations accordingly for enhanced productivity and a faster pace of work.

Predictive Analytics in Pharmaceutical Operations

Predictive analytics is creating a buzz in the Pharma industry for quite some time now. Different pharmaceutical manufacturing companies are looking to model their business processes by gauging the future requirements. The predictive analysis makes use of data historians to accurately make predictions about future trends, possible glitches, and diversions along the road. While technology has come a long way when it comes to predictive analysis, at an enterprise level, there are many things you can do to make the most of this technique.

93%

of healthcare executives stated that predictive analytics is important to their business’ future.

Source: CIO.com

As major companies are competing to stay ahead, product sales and consumer acceptance of a specific drug are a few contributing factors that help decide on advancing to Predictive analytics.

Here are a few ways that predictive analytics helps Pharmaceutical operations become more streamlined and agile:

01. Predictive analytics assets help in understanding patient needs ahead of time

For years Pharma companies have invested heavily in market research and insight experts to understand various geographies and patient domains. This included research to understand and forecast patient needs and drug usage compliance to help both R&D and manufacturing teams prepare them ahead, thus catering to the requirements of the patient base. Predictive analysis plays a vital role in this domain by taking historian data and mining it to populate trends and patterns that can be used by Pharma companies to decide upon the demand for their product. Advanced digital analytics is also capable of generating models based on consumption density for a particular geography, demographic, and health index of the patient base. A pharmaceutical company, thus, would automatically be empowered by knowing its end customer base better and learn the composition of drugs and approximate quantities to produce. You can, therefore, produce the drugs as per the forecast and restructure your supply chain as per the demand. All this will optimize your operations by streamlining both the production department and your supply chain. This will result in enhanced productivity and reduced risks of stock-outs or inventory influxes.

02. Digital analytics plays an imperative role in predicting plausible manufacturing equipment glitches

Anyone working on a production line can vouch for the fact that faulty equipment can cost fortunes by becoming the reason for slowed down or altogether stopped production for days. What predictive analysis does is that it uses the stored equipment data and runs the algorithms to understand the working patterns of any equipment. This, in turn, helps in generating reports for plausible scenarios of equipment malfunction. The production team can get forewarned and can work on the said equipment beforehand to prevent any glitches. Apart from helping in enhancing the operational efficacy, this can also help in preventing loss due to stalled production.

One can take the predictive analysis a step further and use the trends generated to get into a proactive maintenance mode, rather than a more cumbersome and costlier reactive maintenance option.

9%

uptime improvement can be achieved by ensuring predictive maintenance in factories.

Source: A Report by PWC

03. Predictive analytics enhances operational efficiency by enabling risk assessment

Predicting the actions and production outcome of a batch record has become integral to measuring the performance of a Pharma product line. Predictive analytics helps in this assessment with maximum accuracy. It also helps in proactively foretelling issues risks related to product line performance, which allows Production Managers to mitigate these risks to raise product quality standards, and hence become a key driver in increasing product performance in the market. This could apply to both software and hardware in a production line. The cumulative phenomenon results in better risk assessment. This helps the operations team to proactively plan the course of their actions for a better outcome. The advanced predictive analytics tools integrate with different software used by the manufacturers to recognize patterns, share information with other machines and apply the principles of machine learning to automatically gauge risks and alert the users on a timely basis. For optimized operations, risk aversion plays an important role.

Figure 1Use Cases of Predictive Analytics in Pharma

01

Deriving 360 degree patient journey insights

02

Influencing patient adherence

03

Capturing genomics data to accelerate discovery of precision medicine

04

Speeding up drug discovery and development

05

Improving the efficiency in clinical trials

06

Identifying gaps in compliance to streamline regulations

07

Reducing cost and speeding up time-to-market

08

Improving safety and risk management

09

Managing operations and employee training

10

Taking effective sales and marketing initiatives

04. Advanced analytics is essential in accelerating operations

The ways mentioned above with which advanced predictive analysis helps in operational efficiency have all resulted in more agility in the overall operations. Rapidly delivering drugs to the end customer, is becoming a primary responsibility for Pharma companies within the Pharma Value Chain. Companies see a rapid generation of patterns, demands met with more ease, lower risks in manufacturing processes – advancing production lines, and related functions to be more agile.

Business Scenario

Meridian Medical Technologies, a Pfizer company, continues to experience manufacturing challenges in the production of EpiPen® (epinephrine injection, USP) 0.3 mg and EpiPen Jr® (epinephrine injection, USP) 0.15 mg Auto-Injectors, and the authorized generic versions of these strengths. These challenges are expected to result in tighter supplies and greater variability in pharmacy-level access at this time.

Added to the shortage of EpiPens due to tighter supplies, the U.S. Food and Drug Administration provided additional information on lots that are about to expire creating further risk in Epipen availability for the patient base.

A good model of predictive analytics in the Pharma supply chain that provides a gauge of stock requirements based on patient demand, in-store inventory, and expiration dates would have mitigated this risk of Epipen outage before it is too late. Time to market is an essential factor in deciding the success of a product. Accelerating pharma manufacturing processes will thus help companies stay ahead in the competition.

Key Takeaways

  • Predictive analytics is highly effective in risk assessment, equipment analysis, trend forecasting, and data mining.
  • Every industry has their specific criterion to make use of predictive analysis to boost sales and with better use of such tools, enterprises will be better prepared to serve their customer base.
  • Manufacturers can benefit highly from this advanced digital technology by optimizing their operations and enhancing the speed of production.
  • Speeding up the drug to the market process by predicting demand based on patient demographic enables Pharma companies to be prepared for the increase in end-customer demand and manage stock outages without compromising patient needs.

About XcelPros

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