Enhancing Sustainable Dairy Industry Growth through Cold-Supply-Chain-Integrated Production Forecasting

Abhishek Kashyap, Om Ji Shukla, Bal Krishna Jha, Bharti Ramtiyal and Gunjan Soni ()
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Abhishek Kashyap: Department of Mechanical Engineering, National Institute of Technology Patna, Patna 800005, India
Om Ji Shukla: Department of Mechanical Engineering, National Institute of Technology Patna, Patna 800005, India
Bal Krishna Jha: Indian Council of Agricultural Research-Research Complex for Eastern Region (ICAR-RCER), Farming System Research Centre for Hill and Plateau Region Ranchi, Ranchi 834010, India
Bharti Ramtiyal: Department of Management Studies, Graphic Era (Deemed to Be University), Dehradun 248002, India
Gunjan Soni: Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur, Jaipur 302017, India

Sustainability, 2023, vol. 15, issue 22, 1-17

Abstract: Cold supply chains (CSCs) are critical for preserving the quality and safety of perishable products like milk, which plays a vital role in the daily lives of a vast population, especially in countries like India. This research centers on sustainable milk production in Northern India, with priorities of ensuring efficiency and waste reduction within the cold supply chain. Leveraging data from a prominent North India-based dairy company, Company ‘X’, an ARIMA model is applied for predicting monthly milk production trends. Utilizing the Statistical Package for the Social Sciences (IBM SPSS STATISTICS 20) software, the study forecasts Company ‘X’s monthly milk production and identifies four distinct ARIMA models based on the autocorrelation function (ACF) and the partial autocorrelation function (PACF). By comparing predicted and actual milk production values (April–October 2021), sustainability metrics are integrated into ARIMA forecasts. Implications for the dairy sector’s sustainability and alignment with the Sustainable Development Goals (SDGs) are assessed through error terms such as R squared (R 2 ) and mean absolute percentage error (MAPE). The study promotes sustainable milk production practices in Northern India’s dairy sector, resonating with the SDGs to optimize demand–supply dynamics and foster a more environmentally conscious dairy industry.

Keywords: cold supply chain; time-series analysis; ARIMA; forecasting; milk production forecasting; SPSS; SDGs; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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