Smarter supply chains for a healthier India

As a nation known for generations as the “Pharmacy of the World,” India’s reputation was based upon both manufacturing capability and an established system of generic drugs. However, the world of international pharmaceuticals is undergoing significant change. Post-pandemic economic growth, changes in global geopolitics due to the “China+1” model and increasingly stringent international regulation are shifting supply chain management from a basement function to a highlevel corporate concern. To meet projections that have the Indian pharma sector valued at $130 billion by 2030, the industry will need to evolve beyond the traditional models of logistics and move toward becoming what Gartner calls an “Intelligent Enterprise” driven by AI. 

The geopolitical pivot: Capitalising on China+1

India has an extraordinary chance to grow by leveraging the ‘China+1’ strategy—used by multinational pharma companies that are looking for alternatives to China as they create diversified sourcing and manufacturing models. This will require more than simply lower labor costs: India’s pharma industry needs to develop a supply chain that matches the same level of visibility and dependability that can be found in the global marketplace.

AI is a technology that can close this gap. When India’s pharma companies use AIbased systems, they provide the level of transparency required by Western partner companies. Whether it is tracking active pharma ingredients sourced internationally or providing a digital verification of quality and consistency of final mile delivery of a biologic product to a remote village in India, AI creates the digital handshake that ensures the quality and consistency of products produced in India, making India the most attractive “plus one” in the global market.

Beyond spreadsheets: Predictive demand forecasting

The Indian pharma market is unique in its complexity with fragmented market conditions and more than sixty thousand generic brands available to consumers. Because of these conditions, the “bullwhip” effect has occurred historically, caused by minor fluctuations in retail demands that result in large, costly fluctuations in both production and inventory levels for the company providing the product. The traditional method of forecasting is no longer reliable because it has been based on historical sales patterns and simple spreadsheet calculations.

Machine learning (ML) offers an alternative solution to traditional methods of forecasting. Algorithms used in ML are capable of ingesting nontraditional types of data including:

1. Epidemiological trends of disease outbreaks 
2. Weather patterns to predict seasonal increases in respiratory or tropical illnesses 3. Macroeconomic trends to adjust for changes in consumer purchasing power due to inflation

Pharma companies using predictive models instead of reactive models can improve their forecasting accuracy up to 95 per cent, which can ensure that products reach customers at the right location and at the time of need when a surge occurs,  resulting in almost complete elimination of two major problems faced by all pharma companies: stock-outs and expired inventory.

Precision at scale: ML-driven inventory optimisation

The logistics of the pharma industry is limited by only two things: tight profit margins and strict expiration dates (shelf lives). Because of the need to store pharma inventory in a highly controlled environment, the inventory holding costs for pharma inventory are much higher than for typical retail inventory. As a result, the “middle-mile” is transforming to be more efficient by using AI driven smart warehousing.

Unlike traditional warehousing, which often experiences “dwell time,” (the amount of time a product spends idle), AI is used to eliminate dwell time through dynamic slotting and picking. The AI analyses the real-time demand signals and uses them to automatically determine the best place to put high velocity or sensitive items (such as biologics) to pick at the most opportune time possible. This limits the number of times that an item is physically handled and ensures that items with the shortest expiration date will flow quickly through the warehouse, thereby limiting the opportunity for the item to expire.

Beyond the walls of the warehouse, AI allows for dynamic rebalancing to take place nationally. Because India is made up of many different states, stock shortages and surpluses occur regularly. For example, if an AI platform verifies there is an overstock of a particular antibiotic in a warehouse located in Maharashtra and determines there will be a shortage of that same antibiotic in a warehouse located in Karnataka, it can autonomously issue a transfer order to redistribute the excess to the area experiencing a shortage.

As a result of the real-time rebalancing of stock that takes place between warehouses in each state, the supply chain becomes a fluid, responsive system rather than a collection of separate silos. Pharma companies can also reduce the amount of capital being held in “safety stock” and assure that life-saving medications are available when they are needed, long before a crisis arises.

The cold chain frontier: IoT and AI integration

Because of India’s tropically warm environment, maintaining the “golden mean” temperature for vaccines and other specialised biologic drugs has been notoriously challenging. This is why most of the total lost value occurs during the cold chain process. Traditional data loggers are merely recording devices that report on a failure once it has occurred; however, with the integration of AI and the Internet of Things (IoT), thermal integrity for vaccines and biologic drugs is now evolving alongside proactive preventative systems.

When utilising a continuous flow of real-time data on temperature, humidity and light exposure, AI converts the cold chain from a passive monitoring mode to an active preventive mode. As an example, AI can recognise the slight vibration patterns of a compressor in a refrigerated truck as indicative of an impending mechanical failure. Once this information is identified by the system, the system alerts the driver to take action to move the shipment to the closest validated cold storage facility. The potential risk associated with the shipment has been eliminated.

The sophistication of the monitoring systems used in this type of process culminates in a comprehensive digital audit trail. Although anecdotal assurances may have been sufficient in the past, global regulatory bodies such as the US Food and Drug Administration (FDA) and the European Union Good Manufacturing Practices (EU GMP) require a data-driven based approach to ensure compliance with environmental regulations. A digital audit trail creates an unalterable and verifiable record of quality for each point of contact in the supply chain. Manufacturers can certify their compliance with regulations; make audit processes easier; and, most importantly, they can ensure that the efficacy of the drug remains uncompromised when going from the manufacturing site to the patient by tracking the journey of the finished product back to the raw materials used to create it.

The road ahead: Building the intelligent enterprise

The implementation of AI in logistics operations requires more than technological updates because it stands as a fundamental requirement for Indian pharma leaders to achieve success in the upcoming 10 years. The future business model of Indian pharma leaders requires them to establish AI as their core operational foundation.

Organisations need to achieve three critical development stages to transform into an “intelligent enterprise”: 

1. Data democratisation: The current obstacles that block manufacturing from sharing data with sales and logistics departments need to be eliminated.
2. “Pilot scaling” refers to the method that allows AI testing to move from limited small-scale research to complete organization-wide implementation of AI systems. 
3. The talent upstreaming program teaches logistics professionals to work with automation systems through analytical training, which enables them to achieve effective system collaboration.

To create a healthier India, the intelligent capabilities of our supply chains will determine whether healthcare will be accessible to all citizens. The Indian pharma industry will maintain its position as the Pharmacy of the World through AI implementation, which will also establish the nation as the world’s leading technologically advanced and dependable partner

References 

1. EY and Organization of Pharmaceutical Producers of India. “Reimagining Pharma and Healthcare for India@100,” 2023. Available at: https://www.ey.com/en_in/insights/health/pharma-andhealthcare-for-india-100-a-century-of-change-on-the-horizon 
2. Gartner. “Gartner Glossary: The Intelligent Enterprise,” 2024. Available at: https://www.gartner.com/en/information-technology/glossary/intelligent-enterprise 
3. Indian Pharmaceutical Alliance. “Indian Pharma 2030: From Volume to Value,” 2021. Available at: https://www.ipaindia.org/static-files/pdf/publications/ipa-mckinsey-report2030.pdf 
4. McKinsey & Company. “India: The Promise and Possibilities for Global Companies,” 2023. Available at:https://www.mckinsey.com/in dustries/industrials/our-insights/india-the-promise-andpossibilities-for-global-companies 
5. U.S. Food and Drug Administration. “CFR – Code of Federal Regulations Title 21, Part 11: Electronic Records,” 2024. Available at: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?CFRP art=11 
6. European Commission. “EudraLex Volume 4: Good Manufacturing Practice Guidelines,” 2022. Available at: https://health.ec.europa.eu/medicinal-products/eudralex/eudralex-volume-4_en