Today’s healthcare, pharma and life sciences supply chains face continuous disruption. But data and AI can tackle the biggest logistical challenges.
From demand fluctuations to recent global supply challenges, optimizing supply chain management remains a particular tough task. But for organizations in healthcare, pharmaceuticals and life sciences, the stakes are even higher, with disruptions leading to delayed treatments, reduced access to critical therapies and compromised patient care.
Regulatory compliance, product shortages, spiraling costs and the need to maintain quality standards across ever-more-complex supply chains are just some of the obstacles to be navigated.
In our daily work delivering custom solutions for clients in these sectors, the most common supply chain challenges can be distilled into:
Optimizing inventory management to prevent revenue loss
Responding to continuous global disruptions
Scaling augmented supply chain capabilities across the organization
Our latest guide, Unleash the power of Data & AI: Streamlining supply chains in healthcare, pharma and life sciences, explores in detail how tackling underlying issues with data and AI can empower companies to build a more efficient, resilient and strategically valuable supply chain — backed by real-world client examples.
Data and AI have the potential to help solve some of the biggest and most persistent challenges faced across pharmaceutical, life sciences and healthcare supply chains. But their ability to do that depends on exactly where and how you implement them.
Created by Ammara Gafoor, healthcare and life sciences domain lead at Thoughtworks Europe, this guide covers:
Why you can’t tackle underlying inventory management issues with “band-aid” solutions
Take safety stock as an example. Produced to counter low inventory levels or unexpected disruption, it’s an essential component of a healthy supply chain. But if poorly managed or over-produced, it can quickly become dead stock and lead to revenue loss.
To mitigate this, organizations need full end-to-end supply chain visibility rather than just local optimization. This is alongside clear insight from the huge volumes of data generated by the supply chain ecosystem that will enable informed decisions.
This is brought to life in work for our client, a leading multinational healthcare company. The organization lacked a systematic way to manage its safety stock levels, which led to understocking and deadstock. You’ll discover how we used data and AI to create a series of bespoke data products that provided complete visibility of safety stock and the ability to forecast how levels will change in the future.
How to build digital resilience when disruption is continuous
Volatile markets, seasonal fluctuations, changing regulations and geopolitical unrest mean that supply chain environments are in a permanent state of turbulence. And that’s without the massive disruption from unforeseen events such as COVID-19, which had an even greater impact in the healthcare, pharma and life sciences sectors.
Being able to react quickly, respond and bring new products to market demands that organizations adopt diversified supply strategies and automation, but also that they build digital resilience. Once again, effective use of data and AI is at the heart of developing capabilities across automation, real-time processes and predictive analytics.
As our work at one of the largest national commercial health insurance companies in the U.S. demonstrated, introducing Data Mesh transformed it into a modern digital business. By creating data products that provided better user experiences and enabled scenario planning and reliable forecasting, it was able to rapidly support its customers when the pandemic hit.
Why creating an organization where data and AI can be used both autonomously and consistently is possible
Typically, most companies within healthcare, life sciences and pharmaceutical industries follow a domain-driven approach, which creates separation between business functions and IT, hindering information flow and cross-team collaboration.
While individual teams can get good results with data and AI, wider organizational efficiency gains can be hard to achieve across end-to-end processes such as supply chains.
As this guide explains, creating a core data and AI team is the solution. With responsibility for developing an end-to-end understanding of the supply chain, results and solutions can then be shared across individual domains, empowering individual teams to apply data and AI to solve their own challenges consistently.
Everyone wants to harness the potential value of AI. But in the rush to seize this opportunity, it’s important that you don’t slip into adopting AI for AI’s sake. Like any enabling technology, it must be applied strategically to augment your existing capabilities, teams and operations.
Unleash the power of Data & AI on your supply chain - download your copy now.
Disclaimer: The statements and opinions expressed in this article are those of the author(s) and do not necessarily reflect the positions of Thoughtworks.