This study considered 41 case studies to assess the different business models adopted and how they utilise big data analytics. The researchers say that businesses focusing on resource efficiency typically make better use of big data than those which create value from waste.
Food waste is a major sustainability concern, with 1.3 billion tons generated globally each year, and the UN Food and Agriculture Organisation estimating the total societal cost in 2014 at US$2.6 trillion (€2.5 trillion). Many small businesses aim to address this issue by operating at different points in the supply chain – from suppliers to consumers; and at different levels in the food-waste hierarchy – from prevention through redistribution, reuse and recycling to energy recovery.
Big data analytics – the use of large, complex data sets manipulated by sophisticated software – is increasingly employed by businesses across a wide range of sectors. This research involved a case study analysis to provide a framework for understanding different food-waste reduction business models and how they might benefit from the use of big data.
The researchers began by identifying relevant startup businesses around the world through a combination of internet searching and expert referral. They found 41 companies that met their criteria and gathered information from the companies’ own websites and other business-related web pages in order to have an extensive map of the different business models to tackle the food waste issue along agri-food supply chains. The researchers categorised each business according to its position in the supply chain and in the food-waste hierarchy. They also assigned each business to one or more sustainable-business model archetypes based on an established framework of eight archetypes (developed with support from the EC’s 7th Framework Programme1. Then they scored each business on a binary scale of high or low, according to both the current degree of big data use and the future potential for big data to support the business.
The case studies represented almost every combination of supply chain and waste hierarchy positions, according to the researchers. The retail/wholesale supply chain stage was the most represented, they say, with household consumption the least. The key business archetypes identified were maximising material and energy efficiency (optimising a linear supply chain) and creating value from waste (building a circular economy), the researchers report. They add that one example of creating value from waste can be found by assigning new value to produce which has previously been considered waste – such as by selling food products with aesthetic defects, transforming food into a new edible product (e.g. a condiment) or a completely different product such as food packaging or energy. All businesses in their sample adopted one of these archetypes either alone or in combination with others.
The researchers identified four groups of business models – depending on current big data analysis use and future big data potential for an even greater reduction of food waste. Businesses with high current use of big data and low future potential were generally those for which big data was fundamental to their approach (with business models that aimed to maximise material efficiency); while those with low use and low potential were all packaging providers. But businesses with low current use and high potential formed the largest group, according to the researchers, and were typically focused on reuse and recycling levels of the waste hierarchy2. In this context, reuse would involve using surplus food for human consumption either by redistributing food to people in need, or by transforming it into an edible product; while recycling would convert food waste into animal feed, compost, fertilisers or other non-edible products.
The researchers say that, in their sample, businesses that work to maximise resource efficiency typically use big data as a key part of their operations, although some also have the potential to expand this for further benefit. However, they highlight that businesses which create value from waste typically do not make significant use of big data, although most could achieve significant benefits from doing so. They suggest this is because resource-efficiency businesses work from the outset within a linear supply chain, for which data gathering and analysis is relatively straightforward with clear benefits.
However, they argue that value-creation businesses collaborate with a range of people in other sectors (such re-processing and recycling) and face uncertainty over factors such as waste supply, and, therefore, may rule out a big data approach.
The study’s research framework demonstrates that each supply chain stage, whether farmers, processing companies, retailers or consumers may be generating waste. The researchers suggest that all the different supply chain stages should be mindful of the causes of food waste. Therefore, the framework also addresses business models for sustainability to better deal with the causes of food waste. The researchers suggest that the frameworks developed in this study could be useful for food waste businesses in developing their business models. They posit that future research could make use of primary information sources and consider ways for food waste businesses to start adopting big data analytics.
This subjectisaligned with the Horizon Europe Work Programme 2021/2022 and the upcoming 2023/2024 Work Programme. See: Horizon Europe work programmes (europa.eu)
- N.M.P. Bocken, S.W. Short, P. Rana and S. Evans (2014) A literature and practice review to develop sustainable business model archetypes. Journal of Cleaner Production 65: 42–56, ISSN 0959–6526.
- For more information see: Papargyropoulou, E. et al. (2014) The food waste hierarchy as a framework for the management of food surplus and food waste. Journal of Cleaner Production 76: 106–115.
Ciccullo, F., Fabbri, M., Abdelkafi, N., and Pero, M. (2022) Exploring the potential of business models for sustainability and big data for food waste reduction. Journal of Cleaner Production 340: 130673.
To cite this article/service:
“Science for Environment Policy”: European Commission DG Environment News Alert Service, edited by the Science Communication Unit, The University of the West of England, Bristol.
Notes on content:
The contents and views included in Science for Environment Policy are based on independent, peer reviewed research and do not necessarily reflect the position of the European Commission. Please note that this article is a summary of only one study. Other studies may come to other conclusions.
- Paskelbimo data
- 9 lapkritis 2022
- Aplinkos generalinis direktoratas