Aim: to develop a standard framework and approach for the automotive spare parts supply chain.

Inconsistent identification and naming standards present a significant challenge across supply chains, including automotive, amplifying supply and demand risks and impacting efficiency, productivity, and lead times.  Inconsistencies in the names of similar parts or products with identical attributes and descriptions are prevalent.  A solution is required to standardise in order to improve supply chain visibility, asset tracking and inventory management which in turn will reduce these inefficiencies.

Develop a standard framework and approach for part identification within the automotive spare parts supply chain.  Supported by AI, transform legacy records into a uniform format  enabling seamless part identification and tracking by multiple automotive spare parts manufacturers, distributors and end-users. Utilise actual industry data including part names, descriptions, part numbers, attributes & metadata (assembly data, manufacturing data).

Develop a Minimum Viable Product (MVP, TRL 6-8) solution & demonstrate its value in the Spares Testbed environment.

The solution should deliver a uniform and consistent approach to the identification of parts and should demonstrate the value to the supply chain through enhanced visibility, traceability and supplier collaboration. The MVP should be scalable for adoption in the automotive supply chain and have the potential to transfer to other sectors with similar identification challenges.  Furthermore, it is expected that the solution will become a key enabler for further enhancements of supply chain efficiency, resilience and sustainability which rely on a common view of the materials in the supply chain.

Aim: to develop a financing/market modelling tool that enables effective capital deployment decisions and highlights commercial opportunities in the hydrogen supply chain.

In the emerging Hydrogen supply chain, the future demand picture is unclear to investors and producers, making investment decisions in new manufacturing capacity difficult. Market information to inform the viability and location of proposed production facilities is not readily available. Meanwhile, the availability of low-cost hydrogen remains a concern for potential users.

The aim of this challenge is to develop a project-financing tool for potential new hydrogen production facilities, incorporating dynamic market modelling and full supply chain analysis to inform commercial viability. Other key elements are to:

• Establish the capability to capture local & regional market supply and demand, generate projections of viability, return on investment, and highlight key supply chain dependencies.
• Support the analysis of multiple demand and supply scenarios, proposal optimisation benchmarking of the proposal. The outputs from the solution should directly support the preparation of a business case to inform decision making on project financing.
• Work alongside the Hydrogen Testbed Consortium and Challenge Sponsor to utilise available industry data and supplement it with use case specific data to provide visibility to manufacturing companies and potential investors. Develop a Minimum Viable Product (MVP, TRL 6-8) solution and demonstrate its value in the Hydrogen Testbed environment.

The solution should establish an effective model and user facing tools for industry players to analyse the market and create robust business cases for new hydrogen production. Expected benefits are that producers/suppliers will benefit from faster and better investment decisions, accelerating projects to provide new products & services to the emerging hydrogen market. Longer term this helps to underpin future growth in the sector, increasing availability and ultimately reducing prices for users. Investors will benefit from improved and transparent analysis resulting in more robust business cases.

The MVP should be scalable for adoption in the hydrogen supply chain and have the potential to transfer to other emerging sectors with similar challenges

Aim: to track the flow of materials in different products through multiple facilities across the supply chain, making data available through permissions to relevant stakeholders in order to prove compliance with EPR.

Circular textiles are able to charge a premium to retailers due to both higher costs, low availability and strong demand from consumers. However, there is a high risk of fraud where linear products are resold as circular ones as the data quality and availability regarding provenance of circular textiles is very weak.

In addition to this, Extended Producer Responsibility (“EPR”) legislation is likely to be extended from plastic packaging meaning that charges are likely to be imposed on apparel brands and retailers of ‘white label’ apparel. The business case for the collection of non-reusable textiles is so they can be recycled into new fibre and yarn.

Users should be able to use the tool to track and claim rebates against EPR charges – if the regulations permit – tracking the non-reusable textiles collected via kiosks at stores and other logistic pathways sponsored by apparel brands and retailers. This benefit would accrue financially even if the apparel companies have not been able to achieve full digital tracking from fibre to finished garment in their store.

Develop a Minimum Viable Product solution & demonstrate its value in the Circular Textiles Testbed environment.

Aim: to develop a tool to simplify how companies across supply chains can efficiently, accurately, and discretely aggregate Life Cycle Analysis (LCA) performance measurements from their successive supply chain tiers.

The business case for sustainability is currently advancing more quickly than companies’ ability to measure their sustainability performance meaning that the demand for proving sustainability performance is not being met by supply.

Life Cycle Analysis is a methodology increasingly being used for assessing environmental impacts such as energy use, water consumption, land use, emissions, and resource depletion, associated with all stages of a product, process or service. In Textiles, impacts are assessed from raw material extraction and processing through to manufacture distribution. When recycled, the resulting product can therefore compare its improved LCA to that of virgin fibres providing the business case for circular textiles compared to linear.

However, for a given garment product supply chain, each designated tier partner might use a different LCA firm using different standards and metrics.  The resulting lack of interoperability means that providing accurate results for a full LCA is very difficult, and as a result, often inaccurate.

The aim of the solution will:

• Enable interoperability between existing LCA systems.
• Provide product or material Level LCA Assessment.
• Interpret each tier partner’s production and resource usage data with their respective LCA company’s.
• Providing necessary permission control for underlying data.

Develop a Minimum Viable Product solution & demonstrate its value in the Circular Textiles Testbed environment.

Aim: to develop a tool that enables logistics providers to effectively incentivise their clients through a dynamic pricing engine to optimise truck fill and improve Co2 footprint and costs of shipments.

Logistics typically accounts for around 10-15% of a manufacturer’s costs yet 26% of vehicle miles run completely empty. logistic providers are facing considerable pressure from increased fuel and labour costs, cost and time pressures from clients, as well as new legislation and customer demands to reduce congestion and the impact of road freight on our environment.

Multiple challenges prevent optimised and synchronised logistics – dynamic routing optimisation, variable delivery density and transport availability, cost and time constraints, and siloed business models. In recycled Textiles, logistics providers collecting manufacturers’ waste from suppliers for recycling do not have an aligned business model with their clients to optimise the efficiency of logistics. This means that they are often requested to pick up collections at clients manufacturing sites at times that make optimising truck fill impossible resulting in high CO2 footprint and low margins.

The solution should develop a tool that enables logistics providers to effectively incentivise their clients through a dynamic pricing engine to optimise truck fill and improve Co2 footprint and costs of shipments. It should also enable users to:

• Increase pricing when a customer insists on more restrictive terms and/or scheduling and possibly reducing price when customer flexibility releases truck resources for other incremental business.
• Access price-setting tools that are intuitive to the customer and nuanced to address the needs and fluctuating constraints of both the logistics provider and its customer.
• Estimate truck fill to present the estimated CO2e impact of the shipment along side the price.
• Provides other insights to maximising the value of the user’s truck fleet.

The solution should develop a Minimum Viable Product (MVP, TRL 7-8) solution & demonstrate its value in the Circular Textiles Testbed environment. The expected outcome for logistics companies will be able to provide dynamic incentives to their clients through to improve truck fill, reduce CO2e emissions and improve profitability whilst presenting accurate Scope 3 emissions scores to clients.

Aim: to create a data driven approach to define fruit ‘best before dates’ enabling the optimisation of logistics processes to reduce waste and increase sell through.

The best before date of produce is not determined by the actual conditions of produce moving through the supply chain, but on a generic score determined in a lab.  This means that understanding the effects of ripening and statistical variance within produce life-cycles is very limited across the supply chain often resulting in only 30-45% of goods making it through to the shelf from the initial farm shipment.  Given that the UK imports more than 50% of its produce, it is crucial to overcome this issue to meet netzero commitments.

The challenge’s aim is to develop a data driven approach to generate a best before date that reflects the actual condition of the produce in the fruit supply chain. Using data from farms and the environmental conditions it has been exposed along the supply chain, model how the produce performs against differing environmental conditions during transit.  Utilising all available data create a dynamic best before date incorporating the conditions detected in transit.

To summarise, the aim is to develop a Minimum Viable Product (MVP, TRL 7-8) solution & demonstrate its value in the Food Testbed environment. And, the solution should provide a reliable best-before date based on the characteristics of the produce and the conditions it has been exposed to. It should enable improved decision making in the handling, storage and transport of produce in order to improve yield, sell through, supply continuity and sustainability.  This value should be demonstrated to stakeholders in the fruit supply chain, particularly the trader/processor.

The MVP should be scalable for adoption in the fruit supply chain and have the potential to transfer to other sectors with similar challenges.

Aim: develop a minimum viable product solution and demonstrate its value in the Food Testbed environment.

At present, ca. 90% of the sustainability impacts of fresh produce supply chains are borne within the supply chain with the majority at farming level.  The true costs of sustainable farming are rarely internalised into the price of produce meaning that the farmers often bear the brunt of the risk and costs for sustainable farming.  This can reduce the  incentives for farms to farm sustainably. At the same time, investors searching for sustainable impact programmes are unable to access sufficient deal flow to satisfy the requirements of their portfolios meaning that the funding is available to invest in sustainable farming but it is not put to work.

Develop a solution to:

• Analyse farm performance data and provide farmers with recommendations to determine optimum farming strategies from a cost, yield & sustainability performance perspective.
• Present these optimum farming strategy recommendations to investors as sustainable investment opportunities.  
• Monitor the implementation and transition of farms to sustainable farming practices and measure the impact.

Develop a Minimum Viable Product (MVP, TRL 7-8) solution & demonstrate its value in the Food Testbed environment.

On one side, the solution will demonstrate how a farmer could receive recommendations & funding options to improve farming performance and transition to more sustainable and regenerative farming.  On the other, investors access a wide range of high quality sustainable investment opportunities that can be delivered on the farm. The solution will demonstrate to all stakeholders the progress and impacts of the implementation of sustainable farming practices. 

The MVP should be scalable for adoption in the fruit supply chain and have the potential to transfer to other food sub sectors with similar challenges.