Skip to main content

Advertisement

Log in

A Fuzzy ISM approach for modeling electronic traceability in agri-food supply chain in India

  • S.I. : Business Analytics and Operations Research
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

The purpose of this paper is to explore the enablers for the implementation of electronic traceability in agri-food supply chain in India. In several agri-food supply chains, the lack of any form of traceability or the presence of paper-based traceability impacts the trade of the concerned food product. Electronic traceability (e-traceability) will assist agri-food firms in improving their performance, minimize food fraud activities, ensure efficient recall of the products and contribute in overall agri-food supply chain management. With the help of literature review and expert opinions, enablers of e-traceability are modelled and analyzed using Fuzzy ISM and FUZZY MICMAC. The combination of both these techniques helps in identifying the essential drivers in the implementation of e-traceability in agri-food supply chains. The proposed approach found that that electronic form of traceability is better than paper-based traceability in agri-food supply chains. The significant drivers in e-traceability implementation, particularly in agri-food supply chain are appropriate technology for e-traceability, competitive advantage, coordination and transparency and management support. The identified enablers would guide the managers or decision-makers in the adoption of e-traceability in their existing supply chains in the agri-food sector.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Alfaro, J. A., & Rábade, L. A. (2009). Traceability as a strategic tool to improve inventory management: A case study in the food industry. International Journal of Production Economics, 118, 104–110.

    Google Scholar 

  • Aung, M. M., & Chang, Y. S. (2014). Traceability in a food supply chain: Safety and quality perspectives. Food Control, 39, 172–184.

    Google Scholar 

  • Balamurugan, S., Ayyasamy, A., & Joseph, K. S. (2020). Enhanced petri nets for traceability of food management using internet of things. Peer-to-Peer Networking and Applications 1–14.

  • Behzadi, G., O’Sullivan, M. J., Olsen, T. L., & Zhang, A. (2018). Agribusiness supply chain risk management: A review of quantitative decision models. Omega, 79, 21–42.

    Google Scholar 

  • Bhanot, N., Qaiser, F. H., Alkahtani, M., & Rehman, A. U. (2020). An integrated decision-making approach for cause-and-effect analysis of sustainable manufacturing indicators. Sustainability, 12, 1517.

    Google Scholar 

  • Büyüközkan, G., & Göçer, F. (2018). Digital supply chain: Literature review and a proposed framework for future research. Computers in Industry, 97, 157–177.

    Google Scholar 

  • Cai, S., & Jun, M. (2018). A: qualitative study of the internalization of ISO 9000 standards: The linkages among firms’ motivations, internalization processes, and performance. International Journal of Production Economics, 196, 248–260.

    Google Scholar 

  • Charlebois, S., Sterling, B., Haratifar, S., & Naing, S. K. (2014). Comparison of global food traceability regulations and requirements. Comprehensive Reviews in Food Science and Food Safety, 13, 1104–1123.

    Google Scholar 

  • Chauhan, A., Kaur, H., Yadav, S., et al. (2020). A hybrid model for investigating and selecting a sustainable supply chain for agri-produce in India. Annals of Operation Research, 290, 621–642.

    Google Scholar 

  • Corallo, A., Latino, M. E., & Menegoli, M. (2018). From industry 4.0 to agriculture 4.0: A framework to manage product data in agri-food supply chain for voluntary traceability. International Journal of Nutrition and Food Engineering, 12, 146–150.

    Google Scholar 

  • Dabbene, F., Gay, P., & Tortia, C. (2014). Traceability issues in food supply chain management: A review. Biosystems Engineering, 120, 65–80.

    Google Scholar 

  • Dalvit, C., De Marchi, M., & Cassandro, M. (2007). Genetic traceability of livestock products: A review. Meat Science, 77, 437–449.

    Google Scholar 

  • Dandage, K., Badia-Melis, R., & Ruiz-García, L. (2017). Indian perspective in food traceability: A review. Food Control, 71, 217–227.

    Google Scholar 

  • Dania, W. A. P., Xing, K., & Amer, Y. (2018). Collaboration behavioural factors for sustainable agri-food supply chains: A systematic review. Journal of Cleaner Production, 186, 851–864.

    Google Scholar 

  • Diabat, A., Govindan, K., & Panicker, V. V. (2012). Supply chain risk management and its mitigation in a food industry. International Journal of Production Research, 50(11), 3039–3050.

    Google Scholar 

  • Duan, Y., Miao, M., Wang, R., Fu, Z., & Xu, M. (2017). A framework for the successful implementation of food traceability systems in China. The Information Society, 33, 226–242.

    Google Scholar 

  • Faisal, M. N., & Talib, F. (2016). Implementing traceability in Indian food-supply chains: An interpretive structural modeling approach. Journal of Foodservice Business Research, 19, 171–196.

    Google Scholar 

  • Feng, H., Wang, X., Duan, Y., Zhang, J., & Zhang, X. (2020). Applying blockchain technology to improve agri-food traceability: A review of development methods, benefits and challenges. Journal of Cleaner Production, 260, 121031.

    Google Scholar 

  • Food and Agricultural Organization of the United Nations and World Health Organization Codex Alimentarius Commission Procedural Manual (2013) Twenty-first edition, Codex Alimentarius Commission procedural manual.

  • Fulponi, L. (2006). Private voluntary standards in the food system: The perspective of major food retailers in OECD countries. Food Policy, 31, 1–13.

    Google Scholar 

  • Galvão, J. A., Margeirsson, S., Garate, C., Viðarsson, J. R., & Oetterer, M. (2010). Traceability system in cod fishing. Food Control, 21, 1360–1366.

    Google Scholar 

  • Galvez, J. F., Mejuto, J. C., & Simal-Gandara, J. (2018). Future challenges on the use of blockchain for food traceability analysis. TrAC Trends in Analytical Chemistry, 107, 222–232.

    Google Scholar 

  • Gautam, R., Singh, A., Karthik, K., Pandey, S., Scrimgeour, F., & Tiwari, M. K. (2017). Traceability using RFID and its formulation for a kiwifruit supply chain. Computers & Industrial Engineering, 103, 46–58.

    Google Scholar 

  • Haleem, A., Khan, S., & Khan, M. I. (2019). Traceability implementation in food supply chain: A grey-DEMATEL approach. Information Processing in Agriculture, 6, 335–348.

    Google Scholar 

  • Hastig, G. M., & Sodhi, M. S. (2020). Blockchain for supply chain traceability: Business requirements and critical success factors. Production and Operations Management, 29, 935–954.

    Google Scholar 

  • Hobbs, J. E. (2004). Information asymmetry and the role of traceability systems. Agribusiness: An International Journal, 20, 397–415.

    Google Scholar 

  • Hong, E., Lee, S. Y., Jeong, J. Y., Park, J. M., Kim, B. H., Kwon, K., & Chun, H. S. (2017). Modern analytical methods for the detection of food fraud and adulteration by food category. Journal of the Science of Food and Agriculture., 97(12), 3877–3896.

    Google Scholar 

  • Hsu, C. W., Kuo, T. C., Chen, S. H., & Hu, A. H. (2013). Using DEMATEL to develop a carbon management model of supplier selection in green supply chain management. Journal of Cleaner Production, 56, 164–172.

    Google Scholar 

  • Ianculescu, M., Alexandru, A., & Tudora, E. (2017). A RFID-based tracking approach for building up smart solutions for consumer's safety. In 2017 5th international symposium on electrical and electronics engineering (ISEEE) (pp. 1–7). IEEE.

  • Jakhar, S. K., & Barua, M. K. (2014). An integrated model of supply chain performance evaluation and decision-making using structural equation modelling and fuzzy AHP. Production Planning & Control, 25, 938–957.

    Google Scholar 

  • Kamble, S. S., Gunasekaran, A., & Sharma, R. (2018). Analysis of the driving and dependence power of barriers to adopt industry 40 in Indian manufacturing industry. Computers in Industry, 101, 107–119.

    Google Scholar 

  • Kannan, G., Pokharel, S., & Kumar, P. S. (2009). A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resources, Conservation and Recycling, 54, 28–36.

    Google Scholar 

  • Karipidis, P., Athanassiadis, K., Aggelopoulos, S., & Giompliakis, E. (2009). Factors affecting the adoption of quality assurance systems in small food enterprises. Food Control, 20, 93–98.

    Google Scholar 

  • Khan, S., Imran Khan, M., Haleem, A., & Shuaib, M. (2018). Selection of traceable technology in food supply chain. In IOP conference series: Materials science and engineering (Vol. 404, p. 012010).

  • Khoifin, K., & Nimsai, S. (2018). Investigating traceability costs and benefits in food supply chain: Case study in Serang City, Indonesia. International Journal of Supply Chain Management, 7, 153.

    Google Scholar 

  • Lamba, K., & Singh, S. P. (2018). Modeling big data enablers for operations and supply chain management. The International Journal of Logistics Management, 29, 629–658.

    Google Scholar 

  • Li, D., Wang, X., Chan, H. K., & Manzini, R. (2014). Sustainable food supply chain management. International Journal of Production Economics, 152, 1–8.

    Google Scholar 

  • Li, Y., Sankaranarayanan, B., Kumar, D. T., & Diabat, A. (2019). Risks assessment in thermal power plants using ISM methodology. Annals of Operations Research, 279(1–2), 89–113.

    Google Scholar 

  • Luthra, S., Mangla, S. K., Shankar, R., Prakash Garg, C., & Jakhar, S. (2018). Modelling critical success factors for sustainability initiatives in supply chains in Indian context using Grey-DEMATEL. Production Planning & Control, 29, 705–728.

    Google Scholar 

  • Mangla, S. K., Kumar, P., & Barua, M. K. (2015). Flexible decision modeling for evaluating the risks in green supply chain using fuzzy AHP and IRP methodologies. Global Journal of Flexible Systems Management, 16, 19–35.

    Google Scholar 

  • Mangla, S. K., Luthra, S., Rich, N., Kumar, D., Rana, N. P., & Dwivedi, Y. K. (2018). Enablers to implement sustainable initiatives in agri-food supply chains. International Journal of Production Economics, 203, 379–393.

    Google Scholar 

  • Mattevi, M., & Jones, J. A. (2016). Traceability in the food supply chain: Awareness and attitudes of UK Small and Medium-sized Enterprises. Food Control, 64, 120–127.

    Google Scholar 

  • Matzembacher, D. E., do Carmo Stangherlin, I., Slongo, L. A., & Cataldi, R. (2018). An integration of traceability elements and their impact in consumer’s trust. Food Control, 92, 420–429.

    Google Scholar 

  • New Zealand Ministry for Primary Industries. (2013). http://www.biosecurity.govt.nz/biosec/camp-acts/nait. Accessed October 2, 2020.

  • Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156, 445–455.

    Google Scholar 

  • Panigrahi, S. S., & Sahu, B. (2018). Analysis of interactions among the enablers of green supply chain management using interpretive structural modelling: an Indian perspective. International Journal of Comparative Management, 1, 377–399.

    Google Scholar 

  • Patelli, N., & Mandrioli, M. (2020). Blockchain technology and traceability in the agrifood industry. Journal of Food Science, 85, 3670–3678.

    Google Scholar 

  • Ragasa, C., Thornsbury, S., & Bernsten, R. (2011). Delisting from EU HACCP certification: Analysis of the Philippine seafood processing industry. Food Policy, 36, 694–704.

    Google Scholar 

  • Raut, R. D., Narkhede, B., & Gardas, B. B. (2017). To identify the critical success factors of sustainable supply chain management practices in the context of oil and gas industries: ISM approach. Renewable and Sustainable Energy Reviews, 68, 33–47.

    Google Scholar 

  • Senneset, G., Forås, E., & Fremme, K. M. (2007). Challenges regarding implementation of electronic chain traceability. British Food Journal, 109, 805–818.

    Google Scholar 

  • Shankar, R., Gupta, R., & Pathak, D. K. (2018). Modeling critical success factors of traceability for food logistics system. Transportation Research Part E: Logistics and Transportation Review, 119, 205–222.

    Google Scholar 

  • Singh, R. K., & Gupta, A. (2020). Framework for sustainable maintenance system: ISM–fuzzy MICMAC and TOPSIS approach. Annals of Operations Research, 290, 643–676.

    Google Scholar 

  • Thakur, M., & Donnelly, K. A. M. (2010). Modeling traceability information in soybean value chains. Journal of Food Engineering, 99, 98–105.

    Google Scholar 

  • Violino, S., Antonucci, F., Pallottino, F., Cecchini, C., Figorilli, S., & Costa, C. (2019). Food traceability: A term map analysis basic review. European Food Research and Technology, 245, 2089–2099.

    Google Scholar 

  • Wang, E. S. T., & Tsai, M. C. (2019). Effects of the perception of traceable fresh food safety and nutrition on perceived health benefits, affective commitment, and repurchase intention. Food Quality and Preference, 78, 103723.

    Google Scholar 

  • Wilson, T. P., & Clarke, W. R. (1998). Food safety and traceability in the agricultural supply chain: Using the Internet to deliver traceability. Supply Chain Management: An International Journal, 3, 127–133.

    Google Scholar 

  • Yadav, S., & Singh, S. P. (2020). An integrated fuzzy-ANP and fuzzy-ISM approach using blockchain for sustainable supply chain. Journal of Enterprise Information Management. https://doi.org/10.1108/JEIM-09-2019-0301.

    Article  Google Scholar 

  • Yu, Z., Jung, D., Park, S., Hu, Y., Huang, K., Rasco, B. A., Wang, S., Ronholm, J., Lu, X., & Chen, J. (2020). Smart traceability for food safety. Critical Reviews in Food Science and Nutrition. https://doi.org/10.1080/10408398.2020.183:0262.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ayushi Srivastava.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Srivastava, A., Dashora, K. A Fuzzy ISM approach for modeling electronic traceability in agri-food supply chain in India. Ann Oper Res 315, 2115–2133 (2022). https://doi.org/10.1007/s10479-021-04072-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-021-04072-6

Keywords

Navigation