Purpose : This study sought to define and organize key performance indicators (KPIs) pertinent to agricultural supply chain management (SCM) and to investigate their interrelationships for the assessment of supply chain performance within the agricultural sector.

Methodology : A comprehensive analysis of literature was conducted using the Scopus database, covering the period from 2014 to 2023. We used predefined inclusion and exclusion criteria to screen relevant papers and conducted thematic and keyword co-occurrence analyses to derive KPIs. We consulted experts to ensure that the shortlisted KPIs were applicable to agricultural supply chains.

Findings : The research delineated a thorough KPI system organized into financial, operational, quality, and environmental performance categories. The results showed that metrics of financial stability, operational efficiency, and sustainability were critical for assessing the performance of the agricultural supply chain. The framework also showed how these characteristics were interconnected, supporting a more holistic evaluation of performance.

Practical Implications : The proposed KPI framework provided managers with a formal framework for monitoring, comparing, and improving supply chain performance across different phases of agricultural operations.

Originality : This research developed a sector-specific KPI framework for agricultural supply chains, grounded in bibliometric and expert-validated evidence. This feature differed from other studies that provided general indicators of supply chain performance.

1. Aivazidou, E., Tsolakis, N., Iakovou, E., & Vlachos, D. (2016). The emerging role of water footprint in supply chain management: A critical literature synthesis and a hierarchical decision-making framework. Journal of Cleaner Production, 137, 1018–1037. https://doi.org/10.1016/j.jclepro.2016.07.210
2. Akyuz, G. A., & Erkan, T. E. (2010). Supply chain performance measurement: A literature review. International Journal of Production Research, 48(17), 5137–5155. https://doi.org/10.1080/00207540903089536
3. Aramyan, L. H., Lansink, A. G., van der Vorst, J. G., & van Kooten, O. (2007). Performance measurement in agrifood supply chains: A case study. Supply Chain Management, 12(4), 304–315. https://doi.org/10.1108/13598540710759826
4. Beamon, B. M. (1999). Measuring supply chain performance. International Journal of Operations & Production Management, 19(3), 275–292. https://doi.org/10.1108/01443579910249714
5. Bhat, S. A., Huang, N.-F., Sofi, I. B., & Sultan, M. (2022). Agriculture-food supply chain management based on blockchain and IoT: A narrative on enterprise blockchain interoperability. Agriculture, 12(1), 40. https://doi.org/10.3390/agriculture
6. Brint, A., Genovese, A., Piccolo, C., & Taboada-Perez, G. J. (2021). Reducing data requirements when selecting key performance indicators for supply chain management: The
7. case of a multinational automotive component manufacturer. International Journal of Production Economics, 233, Article no. 107967. https://doi.org/10.1016/j.ijpe.2020.107967
8. Cabral, I., Grilo, A., & Cruz-Machado, V. (2012). A decision-making model for lean, agile, resilient and green supply chain management. International Journal of Production Research, 50(17), 4830–4845. https://doi.org/10.1080/00207543.2012.657970
9. Cai, J., Liu, X., Xiao, Z., & Liu, J. (2009). Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment. Decision Support Systems, 46(2), 512–521. https://doi.org/10.1016/j.dss.2008.09.004
10. Castellano, D., Gallo, M., Grassi, A., & Santillo, L. C. (2019). Batching decisions in multi-item production systems with learning effect. Computers & Industrial Engineering, 131, 578–591. https://doi.org/10.1016/j.cie.2018.12.068
11. Chae, B. (2009). Developing key performance indicators for supply chain: An industry perspective. Supply Chain Management, 14(6), 422–428. https://doi.org/10.1108/13598540910995192
12. Chand, P., Thakkar, J. J., & Ghosh, K. K. (2020). Analysis of supply chain performance metrics for Indian mining & earthmoving equipment manufacturing companies using hybrid MCDM model. Resources Policy, 68, Article ID 101742. https://doi.org/10.1016/j.resourpol.2020.101742
13. Chavadi, C. A., Kokatnur, S. S., & Sirothiya, M. (2024). Role of 'Q'-commerce instant gratification on customer satisfaction: The moderating effect of green packaging. Indian Journal of Marketing, 54(8), 30–50. https://doi.org/10.17010/ijom/2024/v54/i8/174185
14. Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146–166. https://doi.org/10.1016/j.joi.2010.10.002
15. Das, S., Mohapatra, A. K., Pandey, S., & Nayak, Y. (2025). A meta-analytical approach to cognize the pooled effect of customer relationship management on firm performance. Indian Journal of Marketing, 55(1), 44–63. https://doi.org/10.17010/ijom/2025/v55/i1/174687
16. de Janvry, A., & Sadoulet, E. (2020). Using agriculture for development: Supply- and demand-side approaches. World Development, 133, Article no. 105003. https://doi.org/10.1016/j.worlddev.2020.105003
17. Gardner, T. A., Benzie, M., Börner, J., Dawkins, E., Fick, S., Garrett, R., Godar, J., Grimard, A., Lake, S., Larsen, R. K., Mardas, N., McDermott, C. L., Meyfroidt, P., Osbeck, M., Persson, M., Sembres, T., Suavet, C., Strassburg, B., Trevisan, A., … Wolvekamp, P. (2019). Transparency and sustainability in global commodity supply chains. World Development, 121, 163–177. https://doi.org/10.1016/j.worlddev.2018.05.025
18. Giuseppe, A., Mario, E., & Cinzia, M. (2014). Economic benefits from food recovery at the retail stage: An application to Italian food chains. Waste Management, 34(7), 1306–1316. https://doi.org/10.1016/j.wasman.2014.02.018
19. Gong, W., & Fu, Z. (2010). ABC-ACO for perishable food vehicle routing problem with time windows. In 2010 International Conference on Computational and Information Sciences, (pp. 1261–1264). IEEE. https://doi.org/10.1109/ICCIS.2010.311
20. Gupta, C. P., & Kumar, V. V. (2025). Sentiment analysis: Using different models for monitoring and analyzing customer reviews. Indian Journal of Marketing, 55(5), 8–25. https://doi.org/10.17010/ijom/2025/v55/i5/175017
21. Hobbs, J. E. (2020). Food supply chains during the COVID-19 pandemic. Canadian Journal of Agricultural Economics, 68(2), 171–176. https://doi.org/10.1111/cjag.12237
22. Homrich, A. S., Galvão, G., Abadia, L. G., & Carvalho, M. M. (2018). The circular economy umbrella: Trends and gaps on integrating pathways. Journal of Cleaner Production, 175, 525–543. https://doi.org/10.1016/j.jclepro.2017.11.064
23. IEEE World Forum on Internet of Things. (2018). In IEEE 4th World Forum on Internet of Things WF-IoT 2018. http://archive.ieee-sensors.org/conference-websites-archive/wfiot2018/index.html
24. Joghee, S., Kabiraj, S., Ramakrishnan, S., & Alzoubi, H. M. (2024). Consumer decision-making study regarding the SUV market in the Indian context. Indian Journal of Marketing, 54(11), 8–25. https://doi.org/10.17010/ijom/2024/v54/i11/174628
25. Jones, C., & Clark, J. (1990). Effectiveness framework for supply chain management. Computer Integrated Manufacturing Systems, 3(4), 196–206. https://doi.org/10.1016/0951-5240(90)90059-N
26. Joshi, N. A., Joshi, M., & Trada, S. (2024). A bibliometric and thematic analysis of the 'Indian Journal of Marketing': A study of 13 years. Indian Journal of Marketing, 54(4), 8–30. https://doi.org/10.17010/ijom/2024/v54/i4/173711
27. Joshi, S. P., Saha, S., Pendse, M., & Ursal, R. S. (2025). Figs, farmers, and futures: A case study on Purandar Highlands' expansion dilemma. Prabandhan: Indian Journal of Management, 18(10), 25–42. https://doi.org/10.17010/pijom/2025/v18i10/174988
28. Jothimani, D., & Sarmah, S. P. (2014). Supply chain performance measurement for third party logistics. Benchmarking, 21(6), 944–963. https://doi.org/10.1108/BIJ-09-2012-0064
29. Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179–194. https://doi.org/10.1016/J.IJPE.2019.05.022
30. Kamble, S. S., Gunasekaran, A., Ghadge, A., & Raut, R. (2020). A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation. International Journal of Production Economics, 229, Article ID 107853. https://doi.org/10.1016/j.ijpe.2020.107853
31. Khan, S. A., Kusi-Sarpong, S., Arhin, F. K., & Kusi-Sarpong, H. (2018). Supplier sustainability performance evaluation and selection: A framework and methodology. Journal of Cleaner Production, 205, 964–979. https://doi.org/10.1016/j.jclepro.2018.09.144
32. Khurana, A., Ahuja, V., & Adidam, P. T. (2023). Estimating demand for passenger cars: A model for the Indian market. Prabandhan: Indian Journal of Management, 16(5), 37–56. https://doi.org/10.17010/pijom/2023/v16i5/170124
33. Kourav, V., & Sharma, A. (2023). Exploring success factors for new product selling in fast - moving consumer goods. Indian Journal of Marketing, 53(3), 8–25. https://doi.org/10.17010/ijom/2023/v53/i3/172653
34. Kumar, S., Khan, A., Lochab, A., Gupta, V. P., & Arora, A. K. (2023). Boundaryless career: A bibliometric analysis. Prabandhan: India Journal of Management, 16(8), 24–44. https://doi.org/10.17010/pijom/2023/v16i8/173063
35. Kusrini, E., Safitri, K. N., & Fole, A. (2020). Design key performance indicator for distribution sustainable supply chain management. In 2020 International Conference on Decision Aid Sciences and Application, DASA 2020, (pp. 738–744). IEEE. https://doi.org/10.1109/DASA51403.2020.9317289
36. Lagarda-Leyva, E. A., Bueno-Solano, A., Vea-Valdez, H. P., & Machado, D. O. (2020). Dynamic model and graphical user interface: A solution for the distribution process of regional products. Applied Sciences, 10(13), 4481. https://doi.org/10.3390/app10134481
37. Makhija, R., Aggarwal, S., & Singh, Y. (2025). AI-driven hybrid learning for sustainable development: A bibliometric analysis and systematic literature review. Prabandhan: Indian Journal of Management, 18(9), 29–53. https://doi.org/10.17010/pijom/2025/v18i9/174842
38. Malik, A., & Dangi, H. K. (2021). A qualitative inquiry on information search behaviour for services in India. Indian Journal of Marketing, 51(3), 8–20. https://doi.org/10.17010/ijom/2021/v51/i3/158059
39. Melacini, M., Perotti, S., Rasini, M., & Tappia, E. (2018). E-fulfilment and distribution in omni-channel retailing: A systematic literature review. International Journal of Physical Distribution and Logistics Management, 48(4), 391–414. https://doi.org/10.1108/IJPDLM-02-2017-0101
40. Merriam-Webster. (n.d.). Agriculture. Merriam-Webster.com dictionary. https://www.merriam-webster.com/dictionary/agriculture
41. Meynard, J.-M., Jeuffroy, M.-H., Le Bail, M., Lefèvre, A., Magrini, M.-B., & Michon, C. (2017). Designing coupled innovations for the sustainability transition of agrifood systems. Agricultural Systems, 157, 330–339. https://doi.org/10.1016/j.agsy.2016.08.002
42. Mishra, A., & Agrawal, A. (2022). Investigating report cards to predict the academic performance of new MBA students. Prabandhan: Indian Journal of Management, 15(3), 8–23. https://doi.org/10.17010/pijom/2022/v15i3/168846
43. Mittal, S., & Kumar, V. (2024). Analyzing the role of technological capabilities and digital marketing on the performance of e-commerce-based SMEs. Indian Journal of Marketing, 54(12), 45–60. https://doi.org/10.17010/ijom/2024/v54/i12/174658
44. Ondersteijn, C. M., Wijnands, J. H., Huirne, R. B., & van Kooten, O. (2006). Quantifying the agrifood supply chain (1st ed.). Springer. https://doi.org/10.1007/1-4020-4693-6
45. Piotrowicz, W., & Cuthbertson, R. (2015). Performance measurement and metrics in supply chains: An exploratory study. International Journal of Productivity and Performance Management, 64(8), 1068–1091. https://doi.org/10.1108/IJPPM-04-2014-0064
46. Pistolesi, F., Lazzerini, B., Mura, M. D., & Dini, G. (2018). EMOGA: A hybrid genetic algorithm with extremal optimization core for multiobjective disassembly line balancing. IEEE Transactions on Industrial Informatics, 14(3), 1089–1098. https://doi.org/10.1109/TII.2017.2778223
47. Rathi, S., & Kumar, P. (2023). Work-life balance and work-life conflict: A bibliometric analysis. Prabandhan: Indian Journal of Management, 16(8), 45–64. https://doi.org/10.17010/pijom/2023/v16i8/173064
48. Sardana, V., Mohapatra, A. K., Singh, A. K., & Singhania, S. (2023). Unveiling insurance and risk management insights through bibliometric and cluster analysis. Prabandhan: Indian Journal of Management, 16(11), 8–26. https://doi.org/10.17010/pijom/2023/v16i11/173213
49. Sharma, A., Kaurav, R. P., & Koul, S. (2024). Understanding customer confusion in the marketplace – A systematic literature review. Indian Journal of Marketing, 54(12), 8–28. https://doi.org/10.17010/ijom/2024/v54/i12/174656
50. Sharma, R., Datt, S., & Sachdeva, K. (2025). Impact of website design and product image quality on efficient online shopping experiences. Indian Journal of Marketing, 55(6), 29–47. https://doi.org/10.17010/ijom/2025/v55/i6/175109
51. Shekhar, Singh, P., & Shekhar, S. (2023). Sustainable tourism research in India: A review study. Prabandhan: Indian Journal of Management, 16(4), 8–27. https://doi.org/10.17010/pijom/2023/v16i4/170747
52. Singh, R., & Arora, N. (2025). The application of artificial intelligence in law: A bibliometric analysis. Prabandhan: Indian Journal of Management, 18(7), 58–71. https://doi.org/10.17010/pijom/2025/v18i7/174565
53. Singh, S., Dutta, S., & Sharma, D. (2024). Mapping the landscape of sustainability reporting: A bibliometric analysis. Prabandhan: Indian Journal of Management, 17(11), 61–77. https://doi.org/10.17010/pijom/2024/v17i11/174023
54. Sinha, G. K., Dhingra, D., & Chattopadhyay, N. (2023). Fuzzy AHP approach for supply chain strategy selection: A post-pandemic scenario. Prabandhan: Indian Journal of Management, 16(3), 8–26. https://doi.org/10.17010/pijom/2023/v16i3/169913
55. Sokolov, B., Ivanov, D., Dolgui, A., & Pavlov, A. (2016). Structural quantification of the ripple effect in the supply chain. International Journal of Production Research, 54(1), 152–169. https://doi.org/10.1080/00207543.2015.1055347
56. Soysal, M., Bloemhof-Ruwaard, J. M., Meuwissen, M. P., & van der Vorst, J. G. (2012). A review on quantitative models for sustainable food logistics management. International Journal on Food System Dynamics, 3(2), 136–155. https://doi.org/10.18461/ijfsd.v3i2.324
57. Tajbakhsh, A., & Hassini, E. (2015). Performance measurement of sustainable supply chains: A review and research questions. International Journal of Productivity and Performance Management, 64(6), 744–783. https://doi.org/10.1108/IJPPM-03-2013-0056
58. Tripathi, S., & Gupta, M. (2019). A current review of supply chain performance measurement systems. In K. Shanker, R. Shankar, & R. Sindhwani (eds.), Advances in industrial and production engineering. Lecture notes in mechanical engineering (pp. 27–39). Springer. https://doi.org/10.1007/978-981-13-6412-9_4
59. Turner, S. J., Cai, W., & Gan, B. P. (2000). Adapting a supply-chain simulation for HLA. In Proceedings Fourth IEEE International Workshop on Distributed Simulation and Real-Time Applications (DS-RT 2000), (pp. 71–78). IEEE. https://doi.org/10.1109/DISRTA.2000.874065
60. Weersink, A., von Massow, M., Bannon, N., Ifft, J., Maples, J., McEwan, K., McKendree, M. G., Nicholson, C., Novakovic, A., Rangarajan, A., Richards, T., Rickard, B., Rude, J., Schipanski, M., Schnitkey, G., Schulz, L., Schuurman, D., Schwartzkopf-Genswein, K., Stephenson, M., … Wood, K. (2021). COVID-19 and the agrifood system in the United States and Canada. Agricultural Systems, 188, Article ID 103039. https://doi.org/10.1016/j.agsy.2020.103039
61. Wu, I.-L., Chuang, C.-H., & Hsu, C.-H. (2014). Information sharing and collaborative behaviors in enabling supply chain performance: A social exchange perspective. International Journal of Production Economics, 148, 122–132. https://doi.org/10.1016/j.ijpe.2013.09.016
62. Yadav, S., Luthra, S., & Garg, D. (2021). Modelling Internet of Things (IoT)-driven global sustainability in multi-tier agrifood supply chain under natural epidemic outbreaks. Environmental Science and Pollution Research, 28, 16633–16654. https://doi.org/10.1007/s11356-020-11676-1
63. Yadav, V. S., Singh, A. R., Gunasekaran, A., Raut, R. D., & Narkhede, B. E. (2022). A systematic literature review of the agro-food supply chain: Challenges, network design, and performance measurement perspectives. Sustainable Production and Consumption, 29, 685–704. https://doi.org/10.1016/j.spc.2021.11.019
64. Zhang, J., Wang, P., Yan, R., & Gao, R. X. (2018). Long short-term memory for machine remaining life prediction. Journal of Manufacturing Systems, 48, 78–86. https://doi.org/10.1016/j.jmsy.2018.05.011