Connecting food supply chains

Abstract

Food Science and TechnologyVolume 36, Issue 3 p. 28-32 FeaturesFree Access Connecting food supply chains First published: 01 September 2022 https://doi.org/10.1002/fsat.3603_6.xAboutSectionsPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Wayne Martindale of the National Centre for Food Manufacturing at the University of Lincoln discusses the potential of the Internet of Things to unlock food industry innovation and increase connectivity of supply chains by recording and reporting environmental and sustainability data. There are over half a million food and beverage businesses in the UK alone and they are all becoming increasingly dependent on Internet of Things (IoT) devices and technologies. Each one of them will be linked to IoT for sourcing utilities and materials or direct selling to customers and consumers1. IoT devices and protocols will be making sure systems do not fail and delivering service even if they are not entirely visible in a business. With IoT, we are not only talking about robotics and automation systems but considering how data connects and flows across enterprises. This article aims to demonstrate why the continued use of IoT will enable the development of trusted Environmental Social Governance (ESG), sustainability and net zero carbon reporting. The methodology for measuring these parameters has been known for many years, for example carbon footprinting has been delivered for over ten years now and we have seen the publication of sustainability or ESG reports for over twenty years. The use of IoT devices is making this reporting more transparent to customers as well as consumers and is also creating turn-key opportunities to implement reporting in real time. Impact of IoT All businesses collect data and it is widely reported that most food companies do not have a greenhouse gas (GHG) reduction plan or a formal means of reporting ESG and the sustainability impacts of their business activities. Business surveys often limit their data collection to publicly listed companies and even these highly resourced businesses have a muted response to GHG reporting and climate change planning2. This is changing in response to several drivers, including new guidance encouraging SMEs and micro-companies, with under 250 and 50 employees respectively, to report GHG emission impacts2. Transparency of information is critical if these organisations are to report ESG data robustly so as to enable a more sustainable food system. The many thousands of companies in the service and retail supply sectors, which deliver foods and beverages to millions of consumers, will need to know how to do this. IoT can deal with this level of complexity in data flow. The complexity increases rapidly because connectivity between operators is dynamic and before the advent of IoT there was little motivation for companies to report sustainability performance. The ambition to do this has changed; it is no longer a graveyard of greenwashing because collecting big data in close to real time has been made possible. Projections of food system sustainability and resilience are also dependent upon changes made in product choice, restrictions of supply and new demands creating an increase in the jeopardy of making sustainability claims. Supply chain outcomes are often uncertain and this is uncomfortable for businesses because responding to uncertainty can lead to losses if the only data available is not current or is inaccurate. This produces well known impacts, such as the bullwhip effect, which has been known for many years in the automotive industry and has been incisively explained for the food industry by Tom Hollands4. Emissions from sugar cane facility IoT devices will reduce uncertainty by enabling the collection of greater volumes of business data in manufacturing spaces so that it can be connected and assessed within the context of business goals. IoT has step-changed the ambition to assess the whole food system because it can deliver this capability to collect and structure big data. Scope 1, 2 and 3 emissions Big data for food is collected all the time for declarations, claims and assurances but the amount of data that is utilised in a business remains low because it is stored to de-risk any future liability issues associated with products. Most of this data is owned by suppliers in the food supply chains. IoT has begun to offer the ability to identify the usefulness of this latent data and a promising application is ESG reporting, where claims of sustainable practice need to be based on traceable evidence. An example is the reporting of GHG emissions across a business enterprise; these emissions are now categorised into three scopes or lenses (Table 1). If GHG emission reporting is currently in place, it is usually associated with Scope 1 and 2 emissions, which are those from sources owned by companies, such as vehicles and factories (Scope 1), and those from sources indirectly produced by companies, such as purchased utilities (Scope 2). Scope 1 and 2 emissions are accounted for in the UK using BEIS (Department of Business, Energy and Industrial Strategy) conversion factors for these purchases and assets; this data is publicly available5. Table 1. The Scope 1, 2, 3 categories of greenhouse gas emissions associated with a business enterprise Greenhouse Gas emission category Scope 1 Scope 2 Scope 3 Fuel combustion Purchased electricity, heat, and steam Purchased goods and services Company vehicles Use of sold products Leaks and fugitive emissions Waste disposal Transportation and distribution Scope 3 GHG emissions are more challenging; their reporting is potentially reliant on IoT devices recording and collecting data. Food service and retail companies are currently asking suppliers to account for these emissions because they are built into supply chains and the data associated with them is owned by suppliers. Scope 3 GHG emissions are not the result of activities from assets owned or controlled by the company, they are those that the enterprise is indirectly responsible for, up and down its value chain. As well as the processes in Table 1, Scope 3 GHG emissions also include those associated with business travel, employee commuting, investments and leased assets including franchises. This is a complex network of data that must be accounted for; its measurement needs an IoT structure to make sense of it. Table 1 demonstrates how the measurements associated with GHG emission data can be accounted for by tracing (back) and tracking (forward) supply chain emissions of companies to eventually inform the reporting of net carbon zero outcomes across the food system. Any notion of reaching net carbon zero for all three GHG emission categories means that mapping resource flows is essential. If we do not know where the sources of emissions are or how much GHG is emitted, then there is no way of reaching robust disclosure of this data – each emission will have a time and location fingerprint or tag associated with it. In the case of food and beverage production, Scope 1 and 2 emissions are associated with point source processes in factories, but Scope 3 emissions are associated with fast-moving consumer goods. Consequently, protocols for collecting data and measuring outcomes for the movement of goods need to be carried out in a different way to that used previously for carbon footprinting of static labels on products. This is where IoT devices that continuously collect and secure supply chain data as immutable proof of a product being in a specific place at a specific time become very useful. Examples of such technology include digital sensors in manufacturing that report product volume and Stock Keeping Units (SKUs or product type) data to a secure storage framework, such as a blockchain. IoT offers a defence against greenwashing by being able to map where resources and products are in real time. These technologies are of high value in reporting Scope 3 GHG emissions because they become owned by specific enterprises and products. Glossary of Acronyms DTC – Digital Twin for Consumption BEIS – (Department of) Business, Energy and Industrial Strategy ESG – Environmental Social Governance GHG – Greenhouse Gas IoT – Internet of Things LSOA – Lower Layer Super Output Areas NDNS – National Dietary and Nutrition Survey Digital twins The metrics and calculations required to report emissions associated with specific ingredients are often available because the industry has had over a decade of reporting carbon footprints. As an example, carbon footprint data reported by Swedish researchers in 2004 is available to use for conversion constants, but a lot has changed since then, notably the volume of footprinting data has grown and become more detailed6. IoT provides a mechanism to package these types of data into GHG emission maps; this can be demonstrated as a Digital Twin for Consumption (DTC), which helps food and beverage companies report Scope 3 GHG emissions. It also helps to define how activities align to the UN Sustainable Development Goals, in particular the SDG 12 Responsible Production and Consumption Goal. The DTC uses National Census data which provides population data for over 30,000 areas or population samples called Lower Layer Super Output Areas (LSOA) for England. The consumption of food and beverage in these areas is projected using baseline Office of National Statistics (ONS) data7. This enables the development of consumption maps, such as Figure 1, for the Sheffield City Region of 1.3m people. While maps are useful, the DTC demonstration tests the data analysis by scaling this regional map to the national UK population to test whether the DTC projection aligns to reported ONS data (Table 2). It enables the DTC to project GHG emission and waste data for different diets, such as the National Dietary and Nutrition Survey (NDNS) diet reported by the UK Government against the Livewell diet, which provides a 10% reduction in livestock product consumption. The data reported by the DTC is compared to reported UK ONS data. The DTC test has provided a demonstration of how modelled data can confidently project dietary change scenarios. Table 2, shows how the DTC can provide robust projections of GHG emissions, waste production and expenditure based on population demographics, demonstrating how big datasets can be used to project consumption. This can be used to project Scope 3 emissions associated with the use and waste of products if we have the real-time data that IoT devices can provide1, 2 Figure 1Open in figure viewerPowerPoint Figure 1 and Table 2. The digital twin for consumption and utilisation for the Sheffield City Region (Local Enterprise Partnership region) 1.3m people scaled to UK national population data for benchmarking/testing methods used in Table 1. The figure shows the carbon footprint of food consumption in the region at a 0.5km2 resolution and the data shows GHG emission and waste data for different diets with benchmarking to UK population. The typical UK citizen diet was constructed for 1 week of consumption as reported by the NDNS and projected as consumption areas on a 0.5km square grid; the red 0.5km square accounts for a maximum of 30t GHG, the blue 0.5km square accounts for a minimum of 20t GHG, green and yellow squares are 20-30t GHG. The shaded circles are the location of large retail stores. Data sources for benchmarking – ONS. Applications of big data Of course, using large datasets is not only about projecting outcomes, such as Scope 3 GHG emissions, but geospatial data can also be used to connect operations so that manufacturers can identify how they can de-risk supply chains and construct robust vulnerability assessments for business operations. We have demonstrated how this can be achieved by identifying control points in supply chains, such as mills that need to process small grains. Essentially there are more producers of grain than mills so mapping proximity to mills can help to define any vulnerability in supply8. Big data analysis has been used to assess vulnerability in the Ukrainian agricultural system to provide a strategic view for global food supplies9. This study utilised remote sensing data from the EC Copernicus and MODIS programmes to assess vegetation cover and associated it with agricultural production intelligence so that vulnerability assessments could be delivered. It demonstrated how very different datasets can be associated to provide insight and give meaningful guidance for resilient food systems. The use of IoT devices to collect real time data from specific supply chains and product processes holds even greater opportunity to demonstrate highly specific vulnerability assessments for supply chains. The final example in this article is concerned with health outcomes and shows how big data utilisation can be transformed using IoT devices. My current research is developing geospatial analyses of public health and nutritional data. Figure 2 shows the reported hypertension of adults for Clinical Commissioning Groups across England. This data is openly available, and it reports the percentage of adults with self-reported drug treatment for high blood pressure and those with recorded high blood pressure and no prescribed medication10. The data has been obtained from 12m adults and it provides an example of how plotting data geospatially gives insight into how healthy populations are (Figure 2). The demonstrator raises many questions; rural areas seem to have greater levels of hypertension reported for example and how such relationships relate to lifestyle and nutrition are our current research goals. Figure 2Open in figure viewerPowerPoint A map of reported hypertension of adults for Clinical Commissioning Groups (CCGs) across England. The sample population is 11,862,750 adults, and the Rag scheme shows the prevalence of hypertension in each Clinal Commissioning Group area from 16-34% of the population sample for each of the 191 CCGs. The number of adults in the population sample is seen as a grey symbol that is layered behind the Rag symbol layer and is visible when the sample is between 150,000-300,000 adults. CCG Boundaries-Source: Office for National Statistics licensed under the Open Government Licence v.3.0, Contains OS data © Crown copyright and database 2022 What is particularly exciting in reviewing this data is to consider how IoT devices that regularly monitor health might be used in future to improve lifestyles and disease management. These are complex interactions of data and what is apparent in dealing with big data relevant to the food industry is that access to data is rarely a barrier; it is obtaining timely and relevant data that confounds progress. This is the very thing that IoT can solve if confidentiality is controlled or maintained. As with everything in the food industry, the difficult part of getting the IoT to work well is harnessing the human perspectives, not the technological challenges! Dr Wayne Martindale, National Centre for Food Manufacturing University of Lincoln, Park Rd, Holbeach, Spalding PE12 7PT email wmartindale@lincoln.ac.uk web lincoln.ac.uk/holbeach/ References 1 Food Standards Agency. 2021. Consolidated annual report and accounts 2020/21. Available from: https://www.food.gov.uk/about-us/reports-and-accounts (accessed 26 July 2022) 2 ClientEarth. 2021. Accountability emergency: a review of UK-listed companies’ climate change-related reporting (2019-20). Available from: https://www.clientearth.org/latest/documents/accountability-emergency-a-review-of-uk-listed-companies-climate-change-related-reporting-2019-20/ (accessed 26 July 2022) 3 DEFRA. 2021. Small business user guide: guidance on how to measure and report your greenhouse gas emissions. Available from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/69494/pb13310-ghg-small-business-guide.pdf 4IFST, Raynor Foods Ltd. 2021. Smart futures with Blockchain: showcasing the digital sandwich. Available from: https://www.youtube.com/watch?v=jfOMrALYK1c&t=204s (accessed 26 July 2022) 5 BEIS. 2022. Government conversion factors for company reporting of greenhouse gas emissions. Available from: https://www.gov.uk/government/collections/government-conversion-factors-for-company-reporting (accessed 26 July 2022) 6Wallén, A., Brandt, N., Wennersten, R. 2004. Does the Swedish consumer's choice of food influence greenhouse gas emissions? Environmental Science and Policy 7: 525-535. Available from: https://doi.org/10.1016/j.envsci.2004.08.004 7 DEFRA. 2022. Family food 2019/20. Available from: https://www.gov.uk/government/statistics/family-food-201920 (accessed 26 July 2022) 8Martindale, W., Duong, L., Hollands T Æ., Swainson, M. 2020. Testing the data platforms required for the 21st century food system using an industry ecosystem approach. Science of the Total Environment 724: 137871. Available from: https://doi.org/10.1016/j.scitotenv.2020.137871 9Jagtap, S., Trollman, H., Trollman, F., Garcia-Garcia, G., Parra-López, C., Duong, L. et al. 2022. The Russia-Ukraine Conflict: its implications for the global food supply chains. Foods 11: 2098. Available from: https://doi.org/10.3390/foods11142098 10 Public Health England. 2019. Cardiovascular disease data and analysis: guide for health professionals. Available from: https://www.gov.uk/guidance/cardiovascular-disease-data-and-analysis-a-guide-for-health-professionals Volume36, Issue3September 2022Pages 28-32 FiguresReferencesRelatedInformation