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A real-time forecast decision support system for the food supply chain

Periodic Reporting for period 2 - FreshProof (A real-time forecast decision support system for the food supply chain)

Reporting period: 2019-12-01 to 2020-11-30

Food security is a current worldwide challenge faced by the uncertainty of meeting future food demand for the ever-increasing population. Simply increasing food production is challenging due to land and climate constraints, and reports have revealed that reducing food waste has the potential to address the food security challenges. It is believed that about 30 to 50% of the food produced in the world never reaches the consumer. A key cause of commercial food waste is under-addressed supply chain issues. In the case of fresh fruit and vegetables, almost 50% of produce are lost due to temperature related spoilage.

Freshproof is an innovative systems approach aiming to address the existing food supply chain losses/waste and the overall shortcomings in food safety, integrity, and traceability. Current commercial product waste reducing strategies have many limitations (the interlinked complexity of qualitative and nutritive aspects of food is ignored, constant postharvest environmental conditions are assumed, and pre-harvest environment is completely disregarded). Current commercial systems offer fragmented solutions and lack the capability to apply a holistic perspective to supply chain integrity.

Freshproof aims to address these issues and provide a system capable of predicting the remaining shelf-life of products as they progress through a full supply chain. It will be based on First Expired First Out strategy (FEFO), incorporate pre- and postharvest conditions, and exploit novel data capture sensor units combined with advanced modelling algorithms.
The main objective of this project is to develop a cloud based forecast decision support system to deliver real-time food product shelf-life prediction along the farm-consumer supply chain. Initially FreshProof was developed for strawberries due to their highly perishable nature; forming an ideal product to evaluate system robustness and integrity. However, the functionality of FreshProof, once demonstrated, can be applied across a wide range of agri-food products and related supply chains.

The results of Freshproof highlighted the importance of supply chain monitoring and individually determined the impact each step can have on product quality and shelf-life. Critical factors (pre- and post-harvest) were identified, analysed, and used in prediction models, generating system critical knowledge for supply chain design. This has resulted in a non-destructive system to predict shelf-life of strawberries along the supply chain with the potential to be used in real-time allowing stakeholders to make informed decisions and take required actions to prevent product, quality, and monetary losses.

The main findings of Freshproof support the development of a decision support system and a pathway for the digitisation of global food supply chains promoting positive change across the European food industry. A system such as FreshProof will advance protection of the environment through promoting sustainability and increased efficiency in food distribution chains; strengthen food security by reducing existing waste and losses through the supply chain; and enable sustainable food systems through digitization of the supply chain. The consumer will also be positively impacted through the development of safe, secure, responsive global food systems.
Work performed during the duration of the project facilitated the completion of the specific objectives set out for Freshproof. The project was successful in determining the impact level of each step along the supply chain of perishable products, their effect and cumulative effect on quality and critical aspects of quality that can lead to waste, and finally build models to accurately predict shelf-life based on this information.
Freshproof has been successful in identifying critical steps in the supply chain of perishable products (strawberries and blueberries), where a decline in physical and/or biochemical quality occurred and potentially generated waste/loss of product. The impact of each step was measured and the conditions responsible for negative impact identified. Important product attributes linked to shelf-life and quality deterioration were identified. Based on those attributes, prediction models were developed using machine learning algorithms that accurately predicted shelf-life and quality scores.
Furthermore, a non-destructive system was developed using hysperspectral imaging technology for the accurate estimation of shelf-life of strawberries. The shelf-life prediction model built, demonstrated the possibility of using spectra collected from any given point along the supply chain and predicting the remaining shelf-life from that point onwards.

Activities relating to outreach and dissemination and exploitation of the results were undertaken, including lectures to undergraduate and postgraduate students both in European and US establishments, peer reviewed publications and conference presentations, public engagement at harvest events, student mentoring, and engagement with supply chain stakeholders in Europe and internationally to discuss food waste challenges and avenues of exploiting the results.
It is envisaged that FreshProof will progress the state of the art by integrating multiple environmental, quality, safety, and nutritional parameters of perishable food products into a single decision support tool. The unique approach of combining both pre- and postharvest conditions in a single decision support tool and implement prediction models to determine the remaining shelf life and nutritional value of a product to consumer level, will support smart agri-food supply and sustainability in food systems globally. The potential positive economic impact stems from successfully addressing supply chain inefficiencies which can reduce preventable food waste (€235 billion p.a. potential saving), and address food security challenges relating to accessibility and availability of food for the increasing population.

In addition, the results from the hyperspectral imaging technology trial have the potential to both establish and validate this technology as a tool for quick, non-destructive, and accurate self-life estimation. This will be a turning point in global supply chain management, as will remove current subjective industrial practices of estimating shelf-life and eliminate flawed supply chain management systems. It will enable more informed, timely and accurate decision making and strategy building that will positively benefit all stakeholders across global food supply chains.
Hyperspectral camera
Supply chain simulation