The Indian Dairy Market: Forecast, Risk and Strategic Roadmap to 2030

Neha Patvardhan

Symbiosis Institute of International Business, Symbiosis International (Deemed University), Pune 411057, India

Cheenu Rathi

Symbiosis Institute of International Business, Symbiosis International (Deemed University), Pune 411057, India

Ashok Chopra

Amity University Dubai, DAIC (Dubai International Academic City), Dubai P.O. Box 345019, UAE

DOI: https://doi.org/10.36956/rwae.v7i1.2527

Received: 24 July 2025 | Revised: 19 September 2025 | Accepted: 25 September 2025 | Published Online: 24 December 2025

Copyright © 2025 Neha Patvardhan, Cheenu Rathi, Ashok Chopra. Published by Nan Yang Academy of Sciences Pte. Ltd.

Creative Commons LicenseThis is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.


Abstract

The Indian dairy sector is a vital industry experiencing significant transformation due to innovation, increased brand competition, new entrants, and retail evolution. A resource-informed roadmap is crucial for guiding investors, marketers, and policymakers in capitalising on opportunities in this dynamic landscape. In this regard, the current study aims to provide a roadmap for the traditional Indian cooperatives. It utilises comprehensive trend and forecasting analysis of the Indian dairy market. By leveraging data on brand share, company share, retail channels, and product ingredients used, the current study employs multiple models to examine the growth patterns of market leaders and emerging players. The study also includes growth analysis of the emerging hypermarket format in retail and market shares of various products. The best-fitting model is validated using residual analysis and cross-validation techniques. The study reveals that emerging players exhibit unstable yet aggressive growth, indicating their potential for disruption. The hypermarket assessment shows the retail industry's robust linear growth. This study employs the comprehensive GE-Matrix to analyse the strategic positioning of various players. Its novelty lies in integrating different model confidences into the strategic GE Matrix concerning the Indian dairy market. The findings suggest that traditional market players must innovate to maintain their stable growth, while investors must address and understand the challenges posed by the aggressive growth of emerging players. This research provides comprehensive data analysis, predictive insights into future trends, and strategic guidance for traditional market players in the Indian dairy industry.

Keywords: Indian Dairy Market; Regression Analysis; Brand Share; Company Share; Hypermarkets; Market Share; GE Matrix Framework


References

[1] Press Information Bureau, 2025. Dairy value chain. Available from: https://www.pib.gov.in/PressReleasePage.aspx?PRID=2101849&utm (cited 18 July 2025).

[2] The Bullvine, 2025. US-India dairy standoff: why American producers face a brick wall in the world’s largest milk market. Available from: https://www.thebullvine.com/news/us-india-dairy-standoff-why-american-producers-face-a-brick-wall-in-the-worlds-largest-milk-market/?utm_source (cited 18 July 2025).

[3] FAOSTAT, 2025. FAOSTAT database. Available from: https://www.fao.org/faostat/en/#home (cited 18 July 2025).

[4] Agriculture times.co.in, 2025. Dairy news. Available from: https://agritimes.co.in/dairy/coop-model-pivots-indias-emergence-as-worlds-largest-milk-producer-milma-chief-at-idf-world-dairy-summit?utm_source (cited 18 July 2025).

[5] Burt, R.S., Soda, G., 2021. Network Capabilities: Brokerage as a Bridge Between Network Theory and the Resource-Based View of the Firm. Journal of Management. 47(7), 1698–1719. DOI: https://doi.org/10.1177/0149206320988764

[6] Nayak, B., Bhattacharyya, S.S., Krishnamoorthy, B., 2023. Integrating the Dialectic Perspectives of Resource-Based View and Industrial Organization Theory for Competitive Advantage – A Review and Research Agenda. Journal of Business and Industrial Marketing. 38(3), 656–679. DOI: https://doi.org/10.1108/JBIM-06-2021-0306

[7] Ramana, M.V., Kumari, C.P., Karthik, R., et al., 2025. Integrated Farming Systems Improve the Income of Small Farm Holdings—An Overview of Earlier Findings in the Indian Context. Food and Energy Security. 14(2), e70064. DOI: https://doi.org/10.1002/fes3.70064

[8] National Dairy Development Board, 2025. Welcome to nddb.coop. Available from: https://www.nddb.coop/ (cited 18 July 2025).

[9] Gaillard, C., Dervillé, M., 2022. Dairy Farming, Cooperatives and Livelihoods: Lessons Learned from Six Indian Villages. Journal of Asian Economics. 78, 101422. DOI: https://doi.org/10.1016/j.asieco.2021.101422

[10] Malik, M., Gahlawat, V.K., Mor, R.S., et al., 2024. Towards White Revolution 2.0: Challenges and Opportunities for the Industry 4.0 Technologies in Indian Dairy Industry. Operations Management Research. 17(3), 811–832. DOI: https://doi.org/10.1007/s12063-024-00482-4

[11] Kona, S.S.R., Ravikiran, G., Sasidhar, P.V.K., et al., 2025. Perspectives in Milk Production in India. Theriogenology. 231, 116–126. DOI: https://doi.org/10.1016/j.theriogenology.2024.10.001

[12] Gerhart, B., Feng, J., 2021. The Resource-Based View of the Firm, Human Resources, and Human Capital: Progress and Prospects. Journal of Management. 47(7), 1796–1819. DOI: https://doi.org/10.1177/0149206320978799

[13] Chen, M.J., Michel, J.G., Lin, W., 2021. Worlds Apart? Connecting Competitive Dynamics and the Resource-Based View of the Firm. Journal of Management. 47(7), 1820–1840. DOI: https://doi.org/10.1177/01492063211000422

[14] Barney, J., 1991. Firm Resources and Sustained Competitive Advantage. Journal of Management. 17(1), 99–120. DOI: https://doi.org/10.1177/014920639101700108

[15] Wernerfelt, B., 1984. A Resource-Based View of the Firm. Strategic Management Journal. 5(2), 171–180.

[16] Shankar, V., Kalyanam, K., Setia, P., et al., 2021. How Technology is Changing Retail. Journal of Retailing. 97(1), 13–27. DOI: https://doi.org/10.1016/j.jretai.2020.10.006

[17] Kazancoglu, Y., Ozbiltekin-Pala, M., Sezer, M.D., et al., 2022. Circular Dairy Supply Chain Management Through Internet of Things-Enabled Technologies. Environmental Science and Pollution Research. 1–13. DOI: https://doi.org/10.1007/s11356-021-17697-8

[18] Belany, P., Hrabovsky, P., Sedivy, S., et al., 2024. A Comparative Analysis of Polynomial Regression and Artificial Neural Networks for Prediction of Lighting Consumption. Buildings. 14(6), 1712. DOI: https://doi.org/10.3390/buildings14061712

[19] Fu, S. (Q.), Dimotakis, N., Koopman, J., 2025. Mediation Testing With Polynomial Regression: A Critical Review of Extant Approaches and a Researcher’s Toolkit for the Future. Journal of Applied Psychology. DOI: https://doi.org/10.1037/APL0001302

[20] Mishra, P., Matuka, A., Abotaleb, M.S.A., et al., 2022. Modeling and Forecasting of Milk Production in the SAARC Countries and China. Modeling Earth Systems and Environment. 8, 947–959. DOI: https://doi.org/10.1007/s40808-021-01138-z

[21] Chandravanshi, S., Krishi, J.N., Vidyalaya, V., 2001. Trend, Growth and Forecasting of Milk Production of Various Animal Species in India [Master of Science Thesis]. Jawaharlal Nehru Krishi Vishwa Vidyalaya: Jabalpur, India.

[22] Bihola, A., Chaudhary, M.B., Bumbadiya, M.R., et al., 2025. Milk Procurement System in India. International Journal of Dairy Technology. 78(2), e70019. DOI: https://doi.org/10.1111/1471-0307.70019

[23] Van Dam, I., Allais, O., Vandevijvere, S., 2022. Market Concentration and the Healthiness of Packaged Food and Non-Alcoholic Beverage Sales Across the European Single Market. Public Health Nutrition. 25(11), 3131–3136. DOI: https://doi.org/10.1017/s1368980022001926

[24] Van Eenennaam, A.L., 2025. Current and Future Uses of Genetic Improvement Technologies in Livestock Breeding Programs. Animal Frontiers. 15(1), 80–90. DOI: https://doi.org/10.1093/af/vfae042

[25] Wang, Y., Liu, S., Xie, Q., et al., 2024. Carbon Footprint of a Typical Crop–Livestock Dairy Farm in Northeast China. Agriculture. 14(10), 1696. DOI: https://doi.org/10.3390/agriculture14101696

[26] Kewuyemi, Y.O., Kesa, H., Adebo, O.A., 2022. Trends in Functional Food Development With Three-Dimensional (3D) Food Printing Technology: Prospects for Value-Added Traditionally Processed Food Products. Critical Reviews in Food Science and Nutrition. 62(28), 7866–7904. DOI: https://doi.org/10.1080/10408398.2021.1920569

[27] Dairy Business Middle East & Africa, 2025. Gut-Friendly Dairy Market Gains Traction in India. Available from: https://dairybusinessmea.com/2025/05/02/gut-friendly-dairy-market-gains-traction-in-india/?utm_source (cited 18 July 2025).

[28] Koutsandreas, D., Spiliotis, E., Petropoulos, F., et al., 2022. On the Selection of Forecasting Accuracy Measures. Journal of the Operational Research Society. 73, 937–954. DOI: https://doi.org/10.1080/01605682.2021.1892464

[29] Mugiyo, H., Chimonyo, V.G.P., Sibanda, M., et al., 2021. Multi-Criteria Suitability Analysis for Neglected and Underutilised Crop Species in South Africa. PLoS One. 16(10), e0244734. DOI: https://doi.org/10.1371/journal.pone.0244734

[30] Ahmed, N., 2015. Resource Review: Euromonitor International’s Passport. Media Industries Journal. 2(2), 117–121. DOI: https://doi.org/10.3998/mij.15031809.0002.207

[31] Ng, H.M.E., Xu, J., Liu, Q., et al., 2022. Changes in Package Sizes of Savoury Snacks Through Exploration of Euromonitor and Industry Perspectives. International Journal of Environmental Research and Public Health. 19(15), 9359. DOI: https://doi.org/10.3390/ijerph19159359

[32] Putera, G.A., Heikal, J., 2021. Business Strategy of Indah Kiat Pulp and Paper Perawang Mill, Riau, Indonesia Using PESTLE, Porter’s Five Forces, and SWOT Analysis Under SOSTAC® Framework. International Journal of Scientific Research in Science and Technology. 8(6), 252–270. DOI: https://doi.org/10.32628/IJSRST218624

[33] Jia, Y., 2025. Research on Macroeconomic Analysis and Forecasting Based on Regression Models. Advances in Economics, Management and Political Sciences. 168, 131–137. DOI: https://doi.org/10.54254/2754-1169/168/2025.21720

[34] Kumari, P., Kumar, M.S., Vekariya, P., et al., 2025. Predicting Potato Prices in Agra, UP, India: An H2O AutoML Approach. Potato Research. 68, 127–142. DOI: https://doi.org/10.1007/s11540-024-09726-z

[35] Vadar, S., Moharekar, T., Pol, R., 2024. Comparative Analysis of Automated Machine Learning Libraries: PyCaret, H2O, TPOT, Auto-Sklearn, and FLAML. International Journal of Scientific Research in Engineering and Management. 8(11), 1–8.

[36] Madni, H.A., Umer, M., Ishaq, A., et al., 2023. Water-Quality Prediction Based on H2O AutoML and Explainable AI Techniques. Water. 15(3), 475. DOI: https://doi.org/10.3390/w15030475

[37] Bera, D., Chatterjee, N.D., Bera, S., 2021. Comparative Performance of Linear Regression, Polynomial Regression and Generalized Additive Model for Canopy Cover Estimation in the Dry Deciduous Forest of West Bengal. Remote Sensing Applications. 22, 100502. DOI: https://doi.org/10.1016/j.rsase.2021.100502

[38] Renaud, O., Victoria-Feser, M.P., 2010. A Robust Coefficient of Determination for Regression. Journal of Statistical Planning and Inference. 140(7), 1852–1862. DOI: https://doi.org/10.1016/j.jspi.2010.01.008

[39] Wang, Q., Yang, Q., 2025. A Scenario-Based Requirement Analysis of R&D Projects From the Cross-Efficiency Perspective. International Transactions in Operational Research. 32(5), 2981–3007. DOI: https://doi.org/10.1111/itor.13427

[40] Guo, L., Liang, J., Chen, T., et al., 2023. Scenario-Driven Methodology for Cascading Disasters Risk Assessment of Earthquake on Chemical Industrial Park. Processes. 11(1), 32. DOI: https://doi.org/10.3390/pr11010032

[41] Barko, O., Lami, I., 2025. Estimation of the Upper Limit of Confidence Interval With the Monte Carlo Method. Balkan Journal of Interdisciplinary Research. 11(1), 17–34. DOI: https://doi.org/10.2478/bjir-2025-0002

[42] Thomas, A., Shaheen, N.A., Hussein, M.A., 2023. An Efficient Confidence Interval Estimation for Prevalence Calculated From Misclassified Data. Biostatistics and Epidemiology. 7(1), e2076530 . DOI: https://doi.org/10.1080/24709360.2022.2076530

[43] Abramo, G., D’Angelo, C.A., Gzoyan, E., et al., 2025. Benchmarking Research Performance in a Post-Soviet Science System: The Case of Armenia. Scientometrics. 130, 2213–2235. DOI: https://doi.org/10.1007/s11192-025-05312-3

[44] Krajcsák, Z., 2021. Researcher Performance in Scopus Articles (RPSA) as a New Scientometric Model of Scientific Output: Tested in Business Area of V4 Countries. Publications. 9(4), 50. DOI: https://doi.org/10.3390/publications9040050

[45] Wang, Y.A., Huang, Q., Yao, Z., et al., 2024. On a Class of Linear Regression Methods. Journal of Complexity. 82, 101826. DOI: https://doi.org/10.1016/j.jco.2024.101826

[46] Li, Y., Li, X., Guo, M., et al., 2024. Regression Analysis and Its Application to Oil and Gas Exploration: A Case Study of Hydrocarbon Loss Recovery and Porosity Prediction, China. Energy Geoscience. 5(4), 100333. DOI: https://doi.org/10.1016/j.engeos.2024.100333

[47] Larsen, W.A., McCleary, S.J., 1972. The Use of Partial Residual Plots in Regression Analysis. Technometrics. 14(3), 781–790. DOI: https://doi.org/10.2307/1267305

[48] Law, M., Jackson, D., 2017. Residual Plots for Linear Regression Models With Censored Outcome Data: A Refined Method for Visualizing Residual Uncertainty. Communications in Statistics — Simulation and Computation. 46(4), 3159–3171. DOI: https://doi.org/10.1080/03610918.2015.1076470

[49] Corrente, S., Garcia-Bernabeu, A., Greco, S., et al., 2023. Robust Measurement of Innovation Performances in Europe With a Hierarchy of Interacting Composite Indicators. Economics of Innovation and New Technology. 32, 305–322. DOI: https://doi.org/10.1080/10438599.2021.1910815

[50] European Union, 2008. Handbook on Constructing Composite Indicators: Methodology and User Guide. OECD Publishing: Paris, France. DOI: https://doi.org/10.1787/9789264043466-en

[51] Yin, S., Li, J., Liang, J., et al., 2020. Optimization of the Weighted Linear Combination Method for Agricultural Land Suitability Evaluation Considering Current Land Use and Regional Differences. Sustainability. 12(23), 10134. DOI: https://doi.org/10.3390/su122310134

[52] Ilic, I., Görgülü, B., Cevik, M., et al., 2020. Explainable Boosted Linear Regression for Time Series Forecasting. Pattern Recognition. 120, 108144. DOI: https://doi.org/10.1016/j.patcog.2021.108144

[53] Götze, T., Gürtler, M., Witowski, E., 2023. Forecasting Accuracy of Machine Learning and Linear Regression: Evidence From the Secondary CAT Bond Market. Journal of Business Economics. 93, 1629–1660. DOI: https://doi.org/10.1007/s11573-023-01138-8

[54] Hewamalage, H., Ackermann, K., Bergmeir, C., 2023. Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices. Data Mining and Knowledge Discovery. 37, 788–832. DOI: https://doi.org/10.1007/s10618-022-00894-5

[55] Das, S., Shukla, S., Kailasam, A.S., et al., 2025. Predicting and Mitigating Agricultural Price Volatility Using Climate Scenarios and Risk Models. Available from: https://arxiv.org/pdf/2503.24324 (cited 18 July 2025).

[56] Gauly, M., Ammer, S., 2020. Review: Challenges for Dairy Cow Production Systems Arising From Climate Changes. Animal. 14(Supplement 1), S196–S203. DOI: https://doi.org/10.1017/S1751731119003239

[57] Banerjee, K., van den Bijgaart, H., Holroyd, S., et al., 2024. Food Safety Challenges in the Dairy Supply Chain in India: Controlling Risks and Developing a Structured Surveillance System. International Dairy Journal. 157, 106004. DOI: https://doi.org/10.1016/j.idairyj.2024.106004

Online ISSN: 2737-4785, Print ISSN: 2737-4777, Published by Nan Yang Academy of Sciences Pte. Ltd.