| Sonstiges: |
- Nachgewiesen in: MEDLINE
- Sprachen: English
- Publication Type: Journal Article
- Language: English
- [Environ Sci Pollut Res Int] 2023 Apr; Vol. 30 (18), pp. 52304-52320. <i>Date of Electronic Publication: </i>2023 Feb 24.
- MeSH Terms: Data Science* ; Industry* ; Humans ; Turkey ; Sustainable Development ; Food Supply
- References: Akhtar P, Tse YK, Khan Z, Rao-Nicholson R (2016) Data-driven and adaptive leadership contributing to sustainability: global agri-food supply chains connected with emerging markets. Int J Prod Econ 181:392–401. https://doi.org/10.1016/j.ijpe.2015.11.013. (PMID: 10.1016/j.ijpe.2015.11.013) ; Alharthi A, Krotov V, Bowman M (2017) Addressing barriers to big data. Bus Horiz 60(3):285–292. https://doi.org/10.1016/j.bushor.2017.01.002. (PMID: 10.1016/j.bushor.2017.01.002) ; Ali SM, Moktadir MA, Kabir G, Chakma J, Rumi MJU, Islam MT (2019) Framework for evaluating risks in food supply chain: implications in food wastage reduction. J Clean Prod 228:786–800. https://doi.org/10.1016/j.jclepro.2019.04.322. (PMID: 10.1016/j.jclepro.2019.04.322) ; Awan U, Shamim S, Khan Z, Zia NU, Shariq SM, Khan MN (2021) Big data analytics capability and decision-making: the role of data-driven insight on circular economy performance. Technol Forecast Soc Chang 168:120766. https://doi.org/10.1016/j.techfore.2021.120766. ; Bag S, Gupta S, Wood L (2022) Big data analytics in sustainable humanitarian supply chain: barriers and their interactions. Ann Oper Res 319:721–760. https://doi.org/10.1007/s10479-020-03790-7. (PMID: 10.1007/s10479-020-03790-7) ; Bag S, Pretorius JHC (2022) Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework. Int J Organ Anal 30(4):864–898. https://doi.org/10.1108/IJOA-04-2020-2120. ; Batista L, Bourlakis M, Liu Y, Smart P, Sohal A (2018) Supply chain operations for a circular economy. Prod Plan Control 29(6):419–424. https://doi.org/10.1080/09537287.2018.1449267. (PMID: 10.1080/09537287.2018.1449267) ; Chauhan C, Parida V, Dhir A (2022) Linking circular economy and digitalisation technologies: a systematic literature review of past achievements and future promises. Technol Forecast Soc Chang 177:121508. https://doi.org/10.1016/j.techfore.2022.121508. ; Lezoche M, Hernandez JE, Alemany Díaz M del ME, Panetto H, Kacprzyk J (2020) Agri-food 4.0: a survey of the supply chains and Technologies for the future agriculture. Comput Ind 117:103187. https://doi.org/10.1016/j.compind.2020.103187. ; de Sousa L, Jabbour AB, Jabbour CJC, Godinho Filho M, Roubaud D (2018) Industry 4.0 and the circular economy: a proposed research agenda and original roadmap for sustainable operations. Ann Oper Res 270:273–286. https://doi.org/10.1007/s10479-018-2772-8. (PMID: 10.1007/s10479-018-2772-8) ; Doolun IS, Ponnambalam SG, Subramanian N, Kanagaraj G (2018) Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: automotive green supply chain empirical evidence. Comput Oper Res 98:265–283. https://doi.org/10.1016/j.cor.2018.01.008. (PMID: 10.1016/j.cor.2018.01.008) ; Dubey R, Gunasekaran A, Childe SJ, Papadopoulos T, Luo Z, Wamba SF, Roubaud D (2019) Can big data and predictive analytics improve social and environmental sustainability? Technol Forecast Soc Chang 144:534–545. https://doi.org/10.1016/j.techfore.2017.06.020. (PMID: 10.1016/j.techfore.2017.06.020) ; EMF (Ellen MacArthur Foundation) (2013) Towards a circular economy-economic and business rationale for an accelerated transition. https://emf.thirdlight.com/link/x8ay372a3r11-k6775n/@/preview/1?o . Accessed 10 October 2021. ; Esposito B, Sessa MR, Sica D, Malandrino O (2020) Towards circular economy in the agri-food sector A systematic literature review. Sustainability 12(18):1–21. https://doi.org/10.3390/su12187401. (PMID: 10.3390/su12187401) ; Etzion D, Aragon-Correa JA (2016) Big data, management, and sustainability: strategic opportunities ahead. Organ Environ 29(2):147–155. https://doi.org/10.1177/1086026616650437. (PMID: 10.1177/1086026616650437) ; FAO (Food and Agriculture Organization of the United Nations) (2013) Food wastage footprint impacts on natural resources. Summary Report 63. https://www.fao.org/3/i3347e/i3347e.pdf . Accessed 15 October 2021. ; Farooque M, Zhang A, Liu Y (2019) Barriers to circular food supply chains in China. Supply Chain Manag 24(5):677–696. https://doi.org/10.1108/SCM-10-2018-0345. (PMID: 10.1108/SCM-10-2018-0345) ; Faulkner A, Cebul K (2014) Agriculture gets smart: the rise of data and robotics. Cleantech Agriculture Report. https://www.cleantech.com/wp-content/uploads/2014/07/Agriculture-Gets-Smart-Report.pdf . Accessed 17 September 2021. ; Genovese A, Acquaye AA, Figueroa A, Koh SCL (2017) Sustainable supply chain management and the transition towards a circular economy: evidence and some applications. Omega 66:344–357. https://doi.org/10.1016/j.omega.2015.05.015. (PMID: 10.1016/j.omega.2015.05.015) ; Ghisellini P, Cialani C, Ulgiati S (2016) A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems. J Clean Prod 114:11–32. https://doi.org/10.1016/j.jclepro.2015.09.007. (PMID: 10.1016/j.jclepro.2015.09.007) ; Govindan K, Hasanagic M (2018) A systematic review on drivers, barriers, and practices towards circular economy: a supply chain perspective. Int J Prod Res 56(1–2):278–311. https://doi.org/10.1080/00207543.2017.1402141. (PMID: 10.1080/00207543.2017.1402141) ; Grisham T (2009) The Delphi technique: a method for testing complex and multifaceted topics. Int J Manag Proj Bus 2(1):112–130. https://doi.org/10.1108/17538370910930545. (PMID: 10.1108/17538370910930545) ; Gul M, Ak MF (2018) A comparative outline for quantifying risk ratings in occupational health and safety risk assessment. J Clean Prod 196:653–664. https://doi.org/10.1016/j.jclepro.2018.06.106. (PMID: 10.1016/j.jclepro.2018.06.106) ; Gunasekaran A, Yusuf YY, Adeleye EO, Papadopoulos T (2018) Agile manufacturing practices: the role of big data and business analytics with multiple case studies. Int J Prod Res 56(1–2):385–397. https://doi.org/10.1080/00207543.2017.1395488. (PMID: 10.1080/00207543.2017.1395488) ; Gupta S, Chen H, Hazen BT, Kaur S, Santibanez Gonzalez EDR (2019) Circular economy and big data analytics: a stakeholder perspective. Technol Forecast Soc Chang 144:466–474. https://doi.org/10.1016/j.techfore.2018.06.030. (PMID: 10.1016/j.techfore.2018.06.030) ; Hassoun A, Boukid F, Pasqualone A, Bryant CJ, García GG, Parra-López C, Jagtap S, Trollman H, Cropotova J, Barba FJ (2022) Emerging trends in the agri-food sector: digitalisation and shift to plant-based diets. Curr Res Food Sci 5:2261–2269. https://doi.org/10.1016/j.crfs.2022.11.010. (PMID: 10.1016/j.crfs.2022.11.010) ; Himesh S, Rao EVSP, Gouda KC, Ramesh KV, Rakesh V, Mohapatra GN, Rao BK, Sahoo SK, Ajilesh P (2018) Digital revolution and big data: a new revolution in agriculture. CAB Rev 13:1–7. (PMID: 10.1079/PAVSNNR201813021) ; Iakovou E, Vlachos D, Achillas C, Anastasiadis F (2014) Design of sustainable supply chains for the agrifood sector: a holistic research framework. Agric Eng Int. CIGR J: Special Issue: 1–10. ; Ilbahar E, Karaşan A, Cebi S, Kahraman C (2018) A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Safety Sci 103:124–136. https://doi.org/10.1016/j.ssci.2017.10.025. (PMID: 10.1016/j.ssci.2017.10.025) ; Irani Z, Sharif AM (2016) Sustainable food security futures: perspectives on food waste and information across the food supply chain. J Enterp Inf Manag 29(2):171–178. https://doi.org/10.1108/JEIM-12-2015-0117. (PMID: 10.1108/JEIM-12-2015-0117) ; Jabbour CJC, Fiorini PDC, Ndubisi NO, Queiroz MM, Piato ÉL (2020) Digitally-enabled sustainable supply chains in the 21st century: a review and a research agenda. Sci Total Environ 725:138177. https://doi.org/10.1016/j.scitotenv.2020.138177. ; Ji G, Hu L, Tan KH (2017) A study on decision-making of food supply chain based on big data. J Syst Sci Syst Eng 26:183–198. https://doi.org/10.1007/s11518-016-5320-6. (PMID: 10.1007/s11518-016-5320-6) ; Jin C, Bouzembrak Y, Zhou J, Liang Q, van den Bulk LM, Gavai A, Liu N, van den Heuvel LJ, Hoenderdaal W, Marvin HJP (2020) Big data in food safety- a review. Curr Opin Food Sci 36:24–32. https://doi.org/10.1016/j.cofs.2020.11.006. (PMID: 10.1016/j.cofs.2020.11.006) ; Kache F, Seuring S (2017) Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management. Int J Oper Prod Manag 37(1):10–36. https://doi.org/10.1108/IJOPM-02-2015-0078. (PMID: 10.1108/IJOPM-02-2015-0078) ; Kamble SS, Gunasekaran A, Gawankar SA (2020) Achieving sustainable performance in a data-driven agriculture supply chain: a review for research and applications. Int J Prod Econ 219:179–194. https://doi.org/10.1016/j.ijpe.2019.05.022. (PMID: 10.1016/j.ijpe.2019.05.022) ; Kamble SS, Gunasekaran A (2020) Big data-driven supply chain performance measurement system: a review and framework for implementation. Int J Prod Res 58(1):65–86. https://doi.org/10.1080/00207543.2019.1630770. (PMID: 10.1080/00207543.2019.1630770) ; Kamble SS, Belhadi A, Gunasekaran A, Ganapathy L, Verma S (2021) A large multi-group decision-making technique for prioritizing the big data-driven circular economy practices in the automobile component manufacturing industry. Technol Forecast Soc Chang 165:120567. https://doi.org/10.1016/j.techfore.2020.120567. ; Kamilaris A, Kartakoullis A, Prenafeta-Boldú FX (2017) A review on the practice of Big Data analysis in agriculture. Comput Electron Agric 143:23–37. https://doi.org/10.1016/j.compag.2017.09.037. (PMID: 10.1016/j.compag.2017.09.037) ; Kazancoglu Y, Ozbiltekin Pala M, Sezer MD, Luthra S, Kumar A (2021) Drivers of implementing Big Data Analytics in food supply chains for transition to a circular economy and sustainable operations management. J Enterp Inf Manag. https://doi.org/10.1108/JEIM-12-2020-0521. ; Kshetri N (2014) The emerging role of Big Data in key development issues: opportunities, challenges, and concerns. Big Data Soc 1(2):1–20. https://doi.org/10.1177/2053951714564227. (PMID: 10.1177/2053951714564227) ; Kul C, Zhang L, Solangi YA (2020) Assessing the renewable energy investment risk factors for sustainable development in Turkey. J Clean Prod 276:124164. https://doi.org/10.1016/j.jclepro.2020.124164. ; Kusi-Sarpong S, Orji IJ, Gupta H, Kunc M (2021) Risks associated with the implementation of big data analytics in sustainable supply chains. Omega 105:102502. https://doi.org/10.1016/j.omega.2021.102502. ; Lahane S, Kant R, Shankar R (2020) Circular supply chain management: a state-of-art review and future opportunities. J Clean Prod 258:120859. https://doi.org/10.1016/j.jclepro.2020.120859. ; Maroufkhani P, Tseng ML, Iranmanesh M, Ismail WKW, Khalid H (2020) Big data analytics adoption: determinants and performances among small to medium-sized enterprises. Int J Inf Manag 54:102190. https://doi.org/10.1016/j.ijinfomgt.2020.102190. ; Marvin HJP, Janssen EM, Bouzembrak Y, Hendriksen PJM, Staats M (2017) Big data in food safety: an overview. Crit Rev Food Sci Nutr 57(11):2286–2295. https://doi.org/10.1080/10408398.2016.1257481. (PMID: 10.1080/10408398.2016.1257481) ; McAfee A, Brynjolfsson E (2012) Big data: the management revolution. Harv Bus Rev 90(10):60–68. ; Mishra N, Singh A (2018) Use of twitter data for waste minimisation in beef supply chain. Ann Oper Res 270:337–359. https://doi.org/10.1007/s10479-016-2303-4. (PMID: 10.1007/s10479-016-2303-4) ; Modgil S, Gupta S, Sivarajah U, Bhushan B (2021) Big data-enabled large-scale group decision making for circular economy: an emerging market context. Technol Forecast Soc Chang 166:120607. https://doi.org/10.1016/j.techfore.2021.120607. ; Moktadir MA, Ali SM, Paul SK, Shukla N (2019) Barriers to big data analytics in manufacturing supply chains: a case study from Bangladesh. Comput Ind Eng 128:1063–1075. https://doi.org/10.1016/j.cie.2018.04.013. (PMID: 10.1016/j.cie.2018.04.013) ; Okoli C, Pawlowski SD (2004) The Delphi method as a research tool: an example, design considerations and applications. Inf Manag 42(1):15–29. https://doi.org/10.1016/j.im.2003.11.002. (PMID: 10.1016/j.im.2003.11.002) ; Raguseo E (2018) Big data technologies: an empirical investigation on their adoption, benefits and risks for companies. Int J Inf Manag 38(1):187–195. https://doi.org/10.1016/j.ijinfomgt.2017.07.008. (PMID: 10.1016/j.ijinfomgt.2017.07.008) ; Raut RD, Yadav VS, Cheikhrouhou N, Narwane VS, Narkhede BE (2021) Big data analytics: implementation challenges in Indian manufacturing supply chains. Comput Ind 125:103368. https://doi.org/10.1016/j.compind.2020.103368. ; Razzaq A, Sharif A, Ozturk I, Skare M (2022) Inclusive infrastructure development, green innovation, and sustainable resource management: evidence from China’s trade-adjusted material footprints. Resour Policy 79:103076. https://doi.org/10.1016/j.resourpol.2022.103076. ; Shahzad K, Lu B, Abdul D (2022) Entrepreneur barrier analysis on renewable energy promotion in the context of Pakistan using Pythagorean fuzzy AHP method. Environ Sci Pollut Res 29:54756–54768. https://doi.org/10.1007/s11356-022-19680-3. (PMID: 10.1007/s11356-022-19680-3) ; Shankarnarayan VK, Ramakrishna H (2020) Paradigm change in Indian agricultural practices using Big Data: challenges and opportunities from field to plate. Inf Process Agric 7(3):355–368. https://doi.org/10.1016/j.inpa.2020.01.001. (PMID: 10.1016/j.inpa.2020.01.001) ; Shukla M, Mattar L (2019) Next generation smart sustainable auditing systems using big data analytics: understanding the interaction of critical barriers. Comput Ind Eng 128:1015–1026. https://doi.org/10.1016/j.cie.2018.04.055. (PMID: 10.1016/j.cie.2018.04.055) ; Shukla M, Tiwari MK (2017) Big-data analytics framework for incorporating smallholders in sustainable palm oil production. Prod Plan Control 28(16):1365–1377. https://doi.org/10.1080/09537287.2017.1375145. (PMID: 10.1080/09537287.2017.1375145) ; Sivarajah U, Kamal MM, Irani Z, Weerakkody V (2017) Critical analysis of big data challenges and analytical methods. J Bus Res 70:263–286. https://doi.org/10.1016/j.jbusres.2016.08.001. (PMID: 10.1016/j.jbusres.2016.08.001) ; Song M, Cen L, Zheng Z, Fisher R, Liang X, Wang Y, Huisingh D (2015) Improving natural resource management and human health to ensure sustainable societal development based upon insights gained from working within ‘Big Data Environments.’ J Clean Prod 94:1–4. https://doi.org/10.1016/j.jclepro.2015.02.010. (PMID: 10.1016/j.jclepro.2015.02.010) ; Stahel WR (2016) The circular economy. Nature 531:435–438. https://doi.org/10.1038/531435a. (PMID: 10.1038/531435a) ; Surya P, Aroquiaraj IL, Kumar MA (2016) The role of big data analytics in agriculture sector: a survey. Int J Adv Res Biol Eng Sci Technol 2(10):830–838. ; Talwar S, Kaur P, Wamba SF, Dhir A (2021) Big Data in operations and supply chain management: a systematic literature review and future research agenda. Int J Prod Res 59(11):3509–3534. https://doi.org/10.1080/00207543.2020.1868599. (PMID: 10.1080/00207543.2020.1868599) ; Wamba SF, Akter S, Edwards A, Chopin G, Gnanzou D (2015) How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study. Int J Prod Econ 165:234–246. https://doi.org/10.1016/j.ijpe.2014.12.031. (PMID: 10.1016/j.ijpe.2014.12.031) ; Weersink A, Fraser E, Pannell D, Duncan E, Rotz S (2018) Opportunities and challenges for big data in agricultural and environmental analysis. Annu Rev Resour Economics 10:19–37. https://doi.org/10.1146/annurev-resource-100516-053654. (PMID: 10.1146/annurev-resource-100516-053654) ; Wolfert S, Ge L, Verdouw C, Bogaardt MJ (2017) Big data in smart farming-a review. Agric Syst 153:69–80. https://doi.org/10.1016/j.agsy.2017.01.023. (PMID: 10.1016/j.agsy.2017.01.023) ; Yager RR (2014) Pythagorean membership grades in multicriteria decision making. IEEE Trans Fuzzy Syst 22(4):958–965. https://doi.org/10.1109/TFUZZ.2013.2278989. (PMID: 10.1109/TFUZZ.2013.2278989) ; Zeng M, Lu J (2021) The impact of information technology capabilities on agri-food supply chain performance: the mediating effects of interorganizational relationships. J Enterp Inf Manag 34(6):1699–1721. https://doi.org/10.1108/JEIM-08-2019-0237. (PMID: 10.1108/JEIM-08-2019-0237) ; Zeng S, Chen J, Li X (2016) A hybrid method for pythagorean fuzzy multiple-criteria decision making. Int J Inf Technol Decis Mak 15(02):403–422. https://doi.org/10.1142/S0219622016500012. (PMID: 10.1142/S0219622016500012) ; Zhang X, Xu Z (2014) Extension of TOPSIS to multiple criteria decision making with pythagorean fuzzy sets. Int J Intell Syst 29(12):1061–1078. https://doi.org/10.1002/int.21676. (PMID: 10.1002/int.21676) ; Zhong RY, Newman ST, Huang GQ, Lan S (2016) Big Data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives. Comput Ind Eng 101:572–591. https://doi.org/10.1016/j.cie.2016.07.013. (PMID: 10.1016/j.cie.2016.07.013)
- Contributed Indexing: Keywords: Agri-food supply chain (AFSC); Analytic hierarchy process (AHP); Big data analytics (BDA); Circular economy; Pythagorean fuzzy sets (PFSs)
- Entry Date(s): Date Created: 20230224 Date Completed: 20230424 Latest Revision: 20230424
- Update Code: 20240513
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