Role Of AI In Pharmaceutical Supply Chain Management


Supply Chain Management is the process of collecting the raw materials required to create a product or service and delivering the final product or service to the consumers. It is the management of flow of goods or services. SCM has interconnected, interrelated or interlinked channels and node businesses that are involved in delivering final products from the point of origin to point of consumption.

SCM holds an important place in pharmaceutical industry in order to ensure effective supply of the drugs to the consumers. However, there are a few challenges faced by SCM.

Challenges in pharmaceutical supply chain management:

  • Environmental Pressures: Environment regulators are imposing strict controls to curb carbon emission and reduce plastic, waste water, etc.
  • New Medicines: New complex biological drugs and gene therapies are highly sensitive with short lifecycle. This poses as a challenge for manufacturing and distributing channels.
  • Demographic Shifts: Ageing of populations across the world has increased the prevalence of associated chronic diseases such as diabetes, cancer, dementia, etc.
  • Falsification: Rise in criminal market for falsified medicine has created the need for development of tamper-proof packaging and protection of medicine quality for safety of consumers.
  • Demand From Emerging Markets: Demand from developing markets such as BRIC economies creates a need for pharmaceutical industry to invest in & implement global supply chain.

Artificial Intelligence offers a great solution to fight against these SCM challenges effectively.

Leveraging AI In Pharmaceutical Supply Chain

There are 2 distinct types of AI solutions that facilitate pharmaceutical Supply Chain Management:

  • Augmentation- Assisting technology to workforce, boosting efficiency, reducing human error.
  • Automation- AI has no human intervention and works completely autonomously.

Assuring drug efficacy, patient identity and chain of custody integrated with supply chain agility is where the true value of AI lies for the drug industry.

Examples of AI Implementation

  • DataRobot – It is an AI platform powered by open source algorithms that are able to model automation by using historical drug delivery data.
  • OptumRx- It uses AI/ML to manage data collected from healthcare setting to reduce number of shortages or excess inventory of drugs.

Inventory Management As Part Of Supply Chain Management:

AI-based inventory management can determine which product is most likely to be needed (and how often), track exactly when it’s delivered to a patient, and provide delivery time and delays or incidents that might trigger replacement shipment within hours.

Benefits Of AI In Pharmaceutical Supply Chain

  • Forecasting Demand: AI-based software helps in analyzing big data related to diseases and drugs consumption to drive patterns and forecast demand.
  • Fake Drug Detection: Serialization of drugs coupled with AI/ML capabilities helps in tracking drugs around the globe and detecting fake drugs. This imposes safety of consumers.
  • Supply Chain Optimization: AI/ML tools improve collaboration between manufacturer/supplier and retailer for demand-driven SCM.

SCM Amid COVID-19:

As the world markets are facing lockdowns, pharmaceutical industry is required to step up the efforts to manage the supply chain for providing consumers with required drugs. COVID-19 has resulted in disruptions in supply chain in following ways:

  • Supply Disruption- Impact on supply of raw materials or products sourced from areas heavily impacted with COVID-19.
  • Demand Shocks- With implementation of lockdowns due to deepening of pandemic crisis, consumers are stocking up the required drugs leading to systemic demand shocks.

How To Implement AI In Pharmaceutical SCM?

AI can be used to power solutions for managing these disruptions in following ways:

  • Chatbots: Chatbots can be used for operational procurements such as speaking to suppliers during trivial conversations, setting & sending actions to suppliers regarding compliance material, placing purchase orders, researching & answering internal questions, receiving/filing/documenting invoices, etc.
  • ML For Supply Chain Planning: It could help the SCP professionals in providing best solutions based upon intelligent algorithms and machine-to-machine analysis of big data. This facilitates optimized delivery of goods while balancing supply & demand.
  • Autonomous Vehicles For Logistics & Shipping: Incorporation of AI for developing autonomous vehicles for logistics & shipping will result in faster & more accurate shipping, reducing lead time & transportation expenses, reducing labor costs, facilitating environmental friendly operations, etc.
  • NLP For Data Cleansing & Building Data Robustness: Natural Language Processing deciphers large amounts of foreign language data in streamlined manner. It drops the language barrier to build supplier data sets and streamline auditing/ compliance actions.

To know more about Advanced Artificial Intelligence based Supply Chain Systems for Pharmaceutical companies, contact us.