By Dr Francesca Properzi, PhD. Research Manager, and Maria João Cruz, Research Analyst, Centre for Health Solutions

Pharma

This week we launched the fourth in our series of reports highlighting the impact of artificial intelligence (AI) across the biopharma value chain. Our report, Intelligent drug supply chain: Creating value from AI, explores the digital transformation of biopharma’s global supply chains and how AI can help improve value and manage risks more effectively. Given the unprecedented challenges as a result of the COVID-19 pandemic, the report also considers the role that AI can play in helping supply chains respond, recover and thrive.

The rationale for transforming the biopharma supply chain

The biopharma supply chain involves the complex set of steps required to produce a drug, from sourcing and supply of materials, through manufacturing and distribution, to delivery to the consumer. This forms a golden thread that links discovery of new therapies to the patients who receive them (figure 1).

Figure 1: The different steps in the biopharma supply chain

Fig1

Biopharma supply chains have to meet the expectations of a complex range of stakeholders, comprising governments, payers, healthcare providers, national and international regulators, and patients with complex and varied needs. The global nature of the supply chain and its role in ensuring populations get access to lifesaving and life-enhancing products, means protecting its integrity is a high priority. However, while life sciences companies have explored the opportunities that digital technologies offer, many are yet to make consistent, sustained and bold moves to take advantage of the new capabilities.

How AI can augment supply chain transformation

A huge amount of data is generated across the biopharma supply chain but historically has been underutilised. Using AI technologies to process these data will be critical to supporting real-time decision making, orchestrating operational efficiency and, ultimately, creating a cost-effective and thriving supply chain. We have identified five areas where AI is likely to have the highest impact.

  1. End-to-end visibility
    Supply chain visibility means having access to data relating to every transaction and demand trigger, across all steps and tiers, and the logistics movements in between. This concept can be realised through supply chain control towers that function as centralised hubs collecting information from disparate systems to be used for monitoring, auditing and generating insights.
  2. Demand forecasting, inventory management and logistics
    Accurate, real-time, inventory levels are needed to unlock the value of the supply chain and provide patients with timely, reliable access to their therapies. Leveraging advanced AI technologies, including predictive analytics, can help track drugs throughout the supply chain and enable proactive and timely interventions when any issues arise.
  3. Intelligent automation enabling Industry 4.0 and the Internet of Things
    Digitalisation and intelligent process automation (IPA) can help companies establish cost-effective, reliable and robust processes that are coordinated across the supply chain. IPA can mimic human interaction and make advanced decisions based on the outputs of robotic inputs, minimising human errors, improving performance metrics and generating strategic insights.
  4. Optimising predictive maintenance
    Business disruption, due to compliance, quality or safety-related manufacturing issues, is a common challenge for biopharma companies. This can be minimised using AI-enabled predictive maintenance that provides insights about manufacturing performance, including forecasting equipment faults or other issues to improve operational effectiveness, including machine uptime.
  5. Protecting the integrity of the supply chain
    Counterfeit or substandard drugs are a problem for the industry, as well as for international health organisations and society in general. However, for biopharma the importance of supply chain integrity goes beyond counterfeit products, as key product types need ‘chain of identity’ and ‘chain of custody’. Consequently, companies are investing in blockchain and AI technologies to improve security, transparency and traceability.

AI’s role in helping supply chains respond, recover and thrive after COVID-19

During 2020, the COVID-19 pandemic has affected most aspects of biopharma’s global supply chain, from sourcing raw materials to distributing finished products. With much of the global population in quarantine, plant closures and supply shortages across the extended supply network are leading to significant global supply chain disruption.
Deloitte considers that business recovery comprises three phases (see figure 2).

Figure 2. Managing biopharma’s supply chain risk disruption due to COVID-19

Fig2

Companies that have developed and implemented supply chain risk management and digital transformation strategies alongside business continuity strategies will be better prepared to mitigate the impact of disruptions from COVID-19. AI-enabled business processes should help biopharma companies to respond, recover and thrive more effectively to current and future disruptions.

Roadmap for implementing an intelligent supply chain

In the next few years, AI will transform the operating models of the industry. However, full digitalisation takes time and strategic thinking, and involves a fundamental shift from a linear supply chain to a dynamic, interconnected and open AI-enabled digital supply network (DSNs) (figure 3). An AI technology roadmap can be used to support biopharma’s digital transformation journey (figure 4).

Figure 3: The evolution towards the Digital Supply Network: from linear to network thinking

Fig3

Figure 4: A roadmap to support digital transformation of the biopharma supply chain

Fig4

Start small, scale fast, think big
Biopharma companies should identify and prioritise pilot projects to achieve quick wins and build confidence and buy-in. Once an implementation is proven, companies can then scale the project across the supply chain.

Identify internal cost and value drivers
DSNs can help biopharma deliver more value and lead to lower costs, higher efficiencies and better use of capacity across its ’plan – source – make – deliver’ operations. Companies with a robust knowledge of their supply chain cost drivers in each function will better placed to decide which AI technology pilot projects should be prioritised.

Build a blueprint of your data architecture
Smart and optimised data management is essential. Currently, inadequate IT infrastructures and a lack of interoperability standards present significant barriers to biopharma’s DSN implementation. Overcoming these hurdles using AI-enabled technologies can increase connectivity and insights while ensuring data privacy.

Collaborate and learn from other industries
In recent years, digital technology experts from other industries have established several collaborations to develop AI solutions for biopharma manufacturing. Biopharma can also learn from other industries by acquiring their expertise and adopting their innovation models.

Acquire and build the right skills and talent
The implementation of DSNs will require changes to roles and responsibilities, including employing a more diverse workforce. As technology and capabilities evolve, biopharma employees will need to balance the acquisition of new skills with the application of their current skill sets. For drug manufacturers moving into an end-to-end digital supply chain, hiring experts is a priority, especially AI design thinking.

Create a win-to-win approach between industry and regulators
The regulatory environment continues to increase in complexity, and failure to comply can damage a company’s reputation and have important legal and financial consequences. Many biopharma companies are beginning to see their own regulatory functions as a strategic asset and are streamlining clinical, quality and regulatory processes to eliminate functional siloes and improve compliance.

The future of AI-enabled supply chains

The life sciences sector is ripe for adoption of AI to harness their large datasets effectively and generate actionable insights. Biopharma companies should also embrace advanced digital solutions to meet future market demands for more precise, personalised therapeutics. We consider that shifting from a traditional linear supply chain to an AI-enabled interconnected DSN, together with radically interoperable data, will help companies thrive. We also expect that, in response to the COVID-19 pandemic, the digital transformation of the supply chain will accelerate at an unparalleled rate and scale.

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Dr Francesca Properzi (PhD) - Research Manager, Deloitte UK Centre for Health Solutions

Francesca is a new Research Manager for the Deloitte UK Centre for Health Solutions. She was previously a Principal Investigator at the Italian Institute of Health, supporting research on neurodegenerative diseases and on novel diagnostic and therapeutic nanotechnologies. She also worked previously at the MRC Prion Unit in London as a Senior Scientist, where she joined the team of Prof. Charles Weissmann working on cell-based assays and drug screening of prion diseases. Francesca has a PhD in neuronal regeneration from Cambridge University, and she is currently completing an executive MBA at the Imperial College Business School London focused on innovation and healthcare. 

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Maria João Cruz - Research Analyst, Centre for Health Solutions

Maria João is a Research Analyst for The Centre for Health Solutions, the independent research hub of the Healthcare and Life Sciences team. At the Centre she conducts rigorous analysis and research to generate insights that support the practice across Life Sciences and Healthcare. She loves a good challenge and is always passionately curious. Before joining Deloitte, Maria João was a postgraduate researcher in Bioengineering at Imperial College London, jointly working with Instituto Superior Técnico, University of Lisbon. She holds a BSc and MSc in Biological Engineering from IST, Lisbon.

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