Revolutionising supply chain optimisation with Generative AI (GenAI)
Creating value through data-driven decision-making
One of our global healthcare and life sciences clients, a Fortune Group 500 organisation, asked us to help test the transformative potential of GenAI to unlock value in their complex supply chains.
We collaborated with our client to develop a scalable inventory optimisation solution, starting in one business area. This strategic initiative aimed to help it make smarter, faster and more data-driven decisions.
Navigating the complexities of multiple processes and systems
Our client manages vast and intricate supply chains that generate a constant influx of data encompassing demand forecasts, inventory levels, and lead times. This data is often contained in various systems and data-sets, making it difficult to rapidly extract meaningful insights and drive coordinated action with confidence.
Our client was seeking a way to improve the speed and precision of its inventory optimisation processes within its planning cycles. It was also seeking to develop a broader tool to accelerate its supply chain continuous improvement diagnostics. GenAI has proven itself as an obvious choice to make this possible.
Angela Bowden, Life Sciences supply chain lead, Deloitte UK
Our team has helped our client to embed trustworthy AI principles throughout the entire project lifecycle, including prioritising data integrity and the exclusive use of verified internal data sources, and minimising the risk of misinterpretations or biased outputs. It’s been a significant achievement to prove the value of using GenAI within a supply chain focus and how it could be scaled for the wider benefit of our client’s businesses.
Angela Bowden, Life Sciences supply chain lead, Deloitte UK
Creating a user-friendly approach to interpreting data
We designed and built an AI agent with a user-friendly interface that helped the organisation’s stakeholders at any level to interrogate complex data using natural everyday language, eliminating the need for specialised data analysis skills.
The vision is that targeted user groups will be able to ask questions in their own words – like "Where are my inventory levels the highest?" or "What are the top five significant changes in forecast volatility compared to last month?" – to which the AI agent will rapidly analyse the relevant data, providing clear, concise, and actionable responses. With this insight, people can then apply their own judgement and experience to complete the next best action.
The joint team also factored in significant time for training and knowledge transfer so teams at our client can manage the solution themselves.
Success leads to further deployment across product categories and geographies
The initial proof of concept for one limited business category yielded significant working capital improvement opportunities. This success provided a compelling case for broader deployment across our client’s businesses, encompassing a more comprehensive range of product categories and geographies.
The AI agent is also being integrated into our client’s more comprehensive supply chain operating system, ensuring that data-driven insights become embedded within the company's essential diagnostic and continuous improvement processes. This strategic integration positions the AI agent as a central tool for driving continuous improvement and unlocking new value across our client’s global supply chain.
Helping unlock value through a more human GenAI interface
This case study shows Deloitte’s commitment to using new technology to address complex business challenges. Deep industry expertise and a human-centric approach to GenAI helped our client confirm and unlock its significant value potential and enhance its ability to deliver life-changing healthcare products to patients worldwide.
This project demonstrates how the transformative potential of GenAI is implemented responsibly and strategically, paving the way for a more resilient, agile, and data-driven future for our client and the broader healthcare industry.
