Pharmaceuticals and Life Sciences
        Recent market research report by leading research agencies suggest that emerging markets are becoming increasingly important for pharma companies. However, to be successful in the market, it becomes essential for companies in the pharmaceutical industry to shift from a marketing and sales-focused model to an access-driven commercial model in the next decade.

Few key industry trend drivers are:

        1. Predictive analytics to fuel precision medicine
        2. Artificial intelligence to gain speed in medicine
        3. Exploration of innovative pricing and reimbursement schemes with payers.
        4. Innovation challenges in the pharma industry
        5. Drug Pricing and Quality
The challenges become more globalized  than localized  for the industry:

•  Healthcare policy reforms
•  Impact of new technologies on pharma
•  Slower growth rate in emerging markets
•  Shift to Value Based Payments
•  Challenging Biosimilars
•  Reengineering to Industry 4.0 standards
•  AI driven drug discovery and design

And hence the focus remains tightly centered on adopting an AI based business model, accurate forecast of shifting demand and appropriate pricing strategy.
Our Pharma & Life Sciences industry services

As mainstream companies look to the future, there is a growing consensus that AI holds the key. With 93% of executives identifying artificial intelligence as the disruptive technology their company is investing in for the future, there appears to be common agreement that companies must leverage cognitive technologies to compete in an increasingly disruptive period. Investment in AI can be expected to increase as organizations position themselves to compete in the future. Those companies that prove themselves to be adept at developing and executing initiatives using big data and AI capabilities will likely be the companies that are best positioned to deflect the threats of agile, data-driven competitors in the decade ahead.

For any successful AI implementation, we have to build the whole skeleton first, called the “AI BUSINESS MODEL”. And then build the organizational business process one after another. That will complete the body of the organization in a balanced manner.

Implementation of AI program in a “Portfolio” mode is the only way that organizations will realize tangible business value from their AI investments. Companies are spending considerable sums on AI technology, and it should not be viewed exclusively as an experiment. It is only when companies step up to production status with AI that it will deliver ROI and productivity for their organizations.

A portfolio approach to AI Business Model adoption and implementation is best served through a “Center of Excellence mode” or a “Competency Center mode”

But “Competency Center mode” doesn’t have an integrated strategy and hence most of the times ends up in compartmentalized limitations unless the top management drives the whole program to its’ logical end. The 2nd limitations of the mode are difficulty in realising a truly integrated delivery network as internal coordination is truly difficult without centralized control.

Considering the above, most of the organisations have taken the “Center of Excellence” path. We strongly believe that this path will lead to successful adoption of AI Business Model in a phased manner.

We follow a very structured and well-planned, phased approach to adopt AI technologies and build an AI Business model through implementation and operationalization of an AI Center of Excellence that will enable you leap frog to the next generation innovations in drug discovery, achieving better operational efficient and manufacturing excellence, higher productivity, adopting IIoT and Pharma 4.0 standards..

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Product forecasting, sales forecasting and demand forecasting has been among the top five challenges for the pharma/biotech industry since long. Expanding an existing business or setting up a new business, we have to forecast the demand for the product, capacity of competitors, expected share in the market, the amount and sources of raising finances, etc. Success of a business will depend upon the accuracy of such forecasts.

Forecasting isn't based on your business performance and predicted performance alone, but also that of the market around you. You need to know your customer base and competition, inside out. Besides, flexibility built into any business forecast is key to a reliable and efficient model.

Our AI based Forecasting approach is based on a business/ market model specific to the dynamics of a particular market segment and it identifies the key influencing factors about which assumptions are made. These assumptions are informed by numerous fact bases, such as primary and secondary research, inquiry analysis and an extensive network of industry data.  

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Negotiation and execution of a successful pricing and reimbursement arrangement for each product that has become the real barometer for success - not only for the specific product but for the business as a whole.. As it emerges more and more as industry norm, it’s critical that organizations begin to consider the mechanics for a pay-for-performance pricing structure and the potential impacts to operations and long-term strategy.

AI adds great value in the areas of price optimization, price analytics and pricing intelligence and also provides critical insight into pharmaceutical pricing process. Appropriate application of supervised learning, unsupervised learning and reinforcement learning in Market Price Prediction, Price Sensitivity (Elasticity) Estimation, Market Mix Modeling (or Promotion Response Modeling), Customer segmentation, Complementary and substitute products pricing optimization processes helps in optimal price setting.
We offer comprehensive AI driven pharmaceutical pricing management that will helps clients discover and set optimal pricing strategy for your product portfolio.

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Industry 4.0 is becoming increasingly important to the continued success and competitiveness of Pharma/Biotech manufacturers. It refers to new tools and processes that are enabling AI data ecosystem, smart & decentralized production, with intelligent factories, integrated IT systems, the Internet of Things (IoT), and flexible, highly integrated manufacturing systems. For the life science manufacturing industry, it’s not about being new—it’s about using proven solutions and approaches to decision making to improve quality, reliability, and reducing waste. Companies in the life science industry have been collecting and using evidentiary data to improve their manufacturing processes for nearly 40 years and have some of the best quality systems in the world.

It is essential for companies to plot their digital journey to Industry 4.0 standards and to identify the common challenges for companies wishing to rise up the maturity levels and adopt necessary transformational changes. We adhere to Industry 4.0 Plant Maturity Model standards in assessing the current maturity level and find out organizational readiness to plan for Pharma 4.0 standards adoption.

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