Intrinsic value of food chain data

Food Science and TechnologyVolume 35, Issue 3 p. 44-47 FeaturesFree Access Intrinsic value of food chain data First published: 16 September 2021 https://doi.org/10.1002/fsat.3503_11.xAboutSectionsPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text full-text accessPlease review our Terms Conditions Use check box below share version article.I have read accept the Wiley Online Library UseShareable LinkUse link a this article with your friends colleagues. Learn more.Copy URL Raymond Obayi University Manchester explains how assess intrinsic inform adoption smart technologies. Data [not technology] is new frontier The agri-food industry experiencing digital revolution recent advances in precision agriculture manufacturing. It estimated that technologies could potentially unlock an additional $500bn GDP contribution by 20301. To benefit from revolution, businesses need potential assets produced through technology investments (1) digitising analog processes, (2) digitalising existing for predictive analytics, (3) transforming business models advanced connectivity network technologies2. Corporate are assessed using economic or market indicators value. In contrast, information pretty challenging appraise because embedded context derived its use3. This presents straightforward two-step approach appraising inherent underpin investment decisions Step 1: Appraising support Determine perhaps most valuable asset economy. Today's successful companies like Amazon, Google Facebook invested developing capabilities harnessing utilising virtually all processes decisions. However, quite it considered intangible generally viewed as public good no stipulated also non-rival asset, which means does not diminish based on number users4. Consequently, ascribe fixed data, different users would same differently depending intended application. Despite these challenges, actors reflect benefits justify investing technologies5. assumption end flawed any linked generation processing capability affords6. There several approaches prescribed literature assessing data. Cost-based income-based cost future cash flows production, respectively. Market-based estimating price be willing pay access. Benefit monetisation impact-based methods evaluate particular impact availability economic, social regulatory outcomes. helpful they assume varying implications actors7. Thus, stakeholders must collaboratively ascribed key owners, processors users8. plays crucial role underpins performance safety, quality provenance supports compliance requirements. While promising, whether given will generate sufficient stakeholders9. determining generated technologies, less emphasis should placed much available required. Instead, identify stakeholders10. Understanding before exploring required avoid pitfall chasing solutions search problems. is, therefore, so about is. Value really advantages affords. Discussions around importance rather than can help arrive at consensus regarding needs, priorities perceived selecting right deliver such Table 1 provides series questions guide focus group discussion among nature require assets. Actors improving specific outcomes understand requirements form measures11. 1. Questions Potential safety12 What monitor, analyse report critical production controls minimise risks associated safety levels? do we record-keeping parameters procedures chain? categories enable us run comparative hazard exposure assessments line surveillance database requirements, e.g. EU Rapid Alert System Feed (RASFF), US Import Refusal Report (IRR), Inspection Classification Database (ICD) China State Administration Market Regulation (SAMR)? 13 Can used improve monitoring HACCP management requirements? real-time produce records transferrable machine-readable formats? Is possible store reports control points what stored? customers operators robust system? fraud prevention14 types (adulteration, tampering, product overrun, theft, diversion, simulation counterfeiting fraud) product, consumer identification? authenticity assessment partners captured Fraud Vulnerability Assessment susceptible operations across Quality (nutrition, sensory, convenience, functional, ethical aesthetical)15 stakeholders’ multiple dimensions quality? allow demonstrate communicate stakeholders? use subjective perceptual measures objective specification outputs nodes Evaluate actors’ readiness deployment Once identified prioritised, next step technologies16. Different embrace paces due levels preparedness usage operations. These differences sometimes lead uneven utility, overall generated17. Digital ease transitioning manual digitised workflows adopting into derive returns investment18. aggregate measure firm's proficiency terms level behavioural technical competencies facilitate meaningful adoption19. For many upstream chains, priority digitise sensors (RFID, barcodes QR codes) capture transmission quality. They tend enhance accuracy, conciseness, integrity, availability, detail, timeliness, urgency, relevance, applicability plausibility, clarity, objectivity uniqueness20. Other invest deploying analytics systems analytics21. connectivity, storage distributed ledgers, contracts cloud computing. transformation enterprise-wide endeavour22. their appropriating (not themselves) by: Assessing parties against strategy, operational process maturity, employee capabilities, security, interoperability. 2 each node chain. Identifying gaps measure. 4 Highlighting regulatory, security propriety issues may hinder strategy. 5 Developing cases applications digitisation, digitalisation investments. As shown Figure 1, such, trends 1Open figure viewerPowerPoint streams 2: Digitisation, provide forms utility. digitisation takes assets, improvement innovation Technology expensive, begin ascribing capture, technologies23. Laney's entitled ‘how monetize, manage asset’24 proposed some valuation technology. Information (IVI) (Figure 2) completeness, utility access, scarcity aspects (safety, quality, prevention). 2Open :Intrinsic Formula After perspectives, rank areas improvements. Business (BVI) 3) 3Open Transforming requires comprehensive extensive (KPI) changed (e.g. inventory management, reporting, optimisation, etc.). Performance (PVI) 4) assesses yield scenario analysis twins processes)25. IVI BVI leading deployment, while PVI lagging indicator26. 4Open KPI ratio modelled post-digital indicates accruable (increase KPI) after introduced. time formula increase attributable improvements Conclusions highlights steps evaluating achieve expected ends utilities. With technological disruptions horizon, mindset, views tools current splendour Obayi, Alliance School Manchester, Booth Street West, M15 6PB Email [email protected] References 1Goedde,L., Katz, J., Ménard, A. Revellat, J. 2020. Agriculture's connected future: How growth [online] https://www.mckinsey.com/industries/agriculture/our-insights/agricultures-connected-future-how-technology-can-yield-new-growth () 2Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, Q., Fabian, N., Haenlein, M. 2021. transformation: A multidisciplinary reflection research agenda. Journal Research 122: 889- 901 3Short, Todd, S. 2017. What's worth? MIT Sloan Management Review, 58(3), 17 4Nagorny, K., Lima-Monteiro, P., Barata, & Colombo, W. (2017). Big manufacturing: review. International Communications, Network Sciences: 10(3), 31- 58 5Grover, V., Chiang, R.H., Liang, T.P., Zhang, D. 2018. Creating strategic big analytics: framework. Systems 35(2): 388- 423 6Laney, D.B. Infonomics: manage, competitive advantage. Routledge 7Mayhew, 2010. Practical early stage life sciences valuations. Commercial Biotechnology 16(2): 120- 134 8Araújo, S.O., Peres, R.S., Lidon, F., Ramalho, J.C. Characterising Agriculture 4.0 Landscape—Emerging Trends, Challenges Opportunities. Agronomy 11(4): 667 9Felin, E., Jukola, Raulo, S., Heinonen, Fredriksson-Ahomaa, 2016. Current insufficient modern meat inspection pigs. Preventive Veterinary Medicine 127: 113- 120 10Magnin, C. revolutionize global https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/how-big-data-will-revolutionize-the-global-food-chain# 11Belaud, J.P., Prioux, Vialle, Sablayrolles, 2019. 4.0: Application sustainability by-products supply Computers Industry 111: 41-50. Also, see: Markovic, M., Jacobs, Dryja, Edwards, Strachan, N. Integrating internet things, blockchain trust last mile deliveries. Frontiers Sustainable 12Panghal, Chhikara, Sindhu, Jaglan, Role Safety safe production: 38(4): e12464 13Wallace, C.A., Manning, L. Provenance: assuring integrity identity. CAB Reviews. 14Robson, Dean, Haughey, Elliott, terminologies mitigation guides. Control, 107516 15Jiménez-Carvelo, A.M., González-Casado, Bagur-González, M.G., Cuadros-Rodríguez, Alternative mining/machine learning analytical evaluation – 25- 39 16Petrenko, S.A., Makoveichuk, K.A., Chetyrbok, P.V., Petrenko, A.S. About In: 2017 IEEE II Conference Control Technical (CTS), pp. 96-99. 17Jin, Bouzembrak, Zhou, van den Bulk, Gavai, Marvin, H. - Opinion Science. Also Kusiak, Smart manufacturing Nature News 544(7648): 23 18Soomro, M.A., Hizam-Hanafiah, Abdullah, N.L. models: systematic Compusoft 19For detailed framework readiness, Mutula, S.M. E-Readiness Methods Tools. Economies: SMEs E-Readiness, 87-110. IGI Global. 20Miranda, Ponce, Molina, Wright, Sensing, sustainable Agri-Food 4.0. 108: 21- 36 21Annosi, M.C., Brunetta, Bimbo, Kostoula, Digitalization within chains prevent waste. Drivers, barriers collaboration practices. Industrial Marketing 93: 208- 220 22Tao, Qi, Liu, Data-driven Manufacturing 48: 157- 169 23Corallo, Latino, M.E., Menegoli, From voluntary traceability. Nutrition Engineering 12(5): 146- 150 24Laney, Routledge. 25Qi, Tao, F. twin towards 360 degree comparison. 6: 3585- 3593 26Garifova, L.F. 2015. Infonomics Economy. Procedia Economics Finance 23: 738- 743 Volume35, Issue3September 2021Pages FiguresReferencesRelatedInformation