Supply chains are under pressure in the UK due to coronavirus, leaving the EU and the new legislation that is being implemented. These supply chains are highly fragmented, with data stored in silos across many organisations that at present, do not see a business case for sharing the data.

This leaves companies blind to the impact of their actions on other actors and commercial power, rather than data, normally provides the justification for decisions. When one actor reduces costs, they then transfer and often increase costs for others diminishing the resilience of the whole system.

The current tools and models available in the market are designed to optimise the processes of single companies not whole supply chains. This means they cannot analyse the complex product and information flows and activities within and between companies to generate the required financial and resource efficiencies that reflects the interdependence of the actors.

For over a decade, Incept have been working at the forefront of this field with companies like Carrefour, Unilever, SPAR, CHEP and Coca-Cola and have developed the Network Value Model (NVM) that, when applied, has delivered millions of pounds of savings equivalent to between 10%-30% of total costs.

The NVM model runs a full analysis in minutes, however setup of the exhaustive model is a manual exercise which takes weeks to run, the analysis requires expert interpretation meaning it can take months to realise savings, by which time the operating environment has changed.

The study will use data from 20,000 products shipped in over 100 million cases through supply chains including 1,200 suppliers, 1,400 stores and 11 distribution nodes. This will provide a valid representation of potential benefits of supply chain integration and optimisation for UK grocery logistics across the whole of the country.

The hypothesis that this feasibility study will test is that the latest techniques in AI can be used to optimise the existing NVM model to deliver cost savings of between 10-30% for the users. This will in turn reduce carbon emissions at a similar level and significantly reduce waste whilst improving resource utilisation.

It will also develop a business and governance model that will users will trust to enshrine the IP for the tool into a social enterprise structure that is governed by its users and reduce the barriers to adoption for all stakeholders in the UK manufacturing sector.