Advaita Menon,
Midhir Nambiar,
Aman Mishra,
Aryan Tiwari,
Aditya Kasar,
- Student, School of Technology, Management & Engineering (STME), NMIMS University, Navi Mumbai, Maharashtra, India
- Student, School of Technology, Management & Engineering (STME), NMIMS University, Navi Mumbai, Maharashtra, India
- Student, School of Technology, Management & Engineering (STME), NMIMS University, Navi Mumbai, Maharashtra, India
- Student, School of Technology, Management & Engineering (STME), NMIMS University, Navi Mumbai, Maharashtra, India
- Assistant Professor, School of Technology, Management & Engineering (STME), NMIMS University, Navi Mumbai, Maharashtra, India
Abstract
‘]
This paper introduces a comprehensive study aimed at enhancing the forecasting of perishable food item demand. Focusing on solving the critical issue of waste management within the supply chain of food products, the research undertakes a comparative analysis of various machine-learning models. The development of an optimized model that is capable of accurately forecasting the demand for perishable food items is the focus of this research. The research includes a review of the literature on demand, pricing, and production prediction studies carried out in India. It argues that machine-learning algorithms can generate accurate forecasts and emphasizes the need for improved supply chain management to reduce waste in the perishable foods business. By underlining the need for modeling techniques in the optimization of supply chains for perishable items, this work increases the understanding of demand forecasting. Each entity involved in the supply chain can reduce waste and maximize inventory management with the help of the paper’s insightful recommendations and proposed mechanism, thereby benefiting through a reduction in the cost of maintenance of the perishable food products.
Keywords: Data Analytics, Machine Learning, Regression, Demand Forecasting, Supply Chain, Food Waste, Perishable food commodities.
[This article belongs to Journal of Software Engineering Tools & Technology Trends (josettt)]
Advaita Menon, Midhir Nambiar, Aman Mishra, Aryan Tiwari, Aditya Kasar. Demand Forecasting for Perishable Food Commodities using Data Analytics. Journal of Software Engineering Tools & Technology Trends. 2024; 11(03):-.
Advaita Menon, Midhir Nambiar, Aman Mishra, Aryan Tiwari, Aditya Kasar. Demand Forecasting for Perishable Food Commodities using Data Analytics. Journal of Software Engineering Tools & Technology Trends. 2024; 11(03):-. Available from: https://journals.stmjournals.com/josettt/article=2024/view=172133
Fetching IP address…
References ‘]
[1] Carbonneau R, Laframboise K, Vahidov R. Application of machine learning techniques for supply chain demand forecasting. European journal of operational research. 2008 Feb 1;184(3):1140-54.
[2] Negi S, Anand N. Factors leading to losses and wastage in the supply chain of fruits and vegetables sector in India. Energy Infrastructure and Transportation “Challenges and Way Forward”; Dhingra, T., Ed. 2016:89-105.
[3] Sahoo A, Dwivedi A, Madheshiya P, Kumar U, Sharma RK, Tiwari S. Insights into the management of food waste in developing countries: with special reference to India. Environmental Science and Pollution Research. 2024 Mar;31(12):17887-913.
[4] Ali SM, Moktadir MA, Kabir G, Chakma J, Rumi MJ, Islam MT. Framework for evaluating risks in food supply chain: Implications in food wastage reduction. Journal of cleaner production. 2019 Aug 10;228:786-800.
[5] Feizabadi J. Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications. 2022 Feb 1;25(2):119-42.
[6] Thole V, Vain P, Martin C. Effect of elevated temperature on tomato post-harvest properties. Plants. 2021 Nov 1;10(11):2359.
[7] Indore HD. EFFECT OF PACKAGING MATERIALS AND STORAGE CONDITIONS ON THE SHELF LIFE AND QUALITY OF OKRA (Doctoral dissertation, Mahatma Phule Krishi Vidyapeeth (Kolhapur)). Int. J. of Adv. Res. 2016;4(11): 257-265. Available from https://www.journalijar.com/uploads/158_IJAR-13238.pdf
[8] Birkmaier A, Imeri A, Riester M, Reiner G. Preventing waste in food supply networks-a platform architecture for AI-driven forecasting based on heterogeneous big data. Procedia CIRP. 2023 Jan 1;120:708-13.
[9] Rodrigues M, Miguéis V, Freitas S, Machado T. Machine learning models for short-term demand forecasting in food catering services: A solution to reduce food waste. Journal of Cleaner Production. 2024 Jan 5;435:140265.
[10] Llanes RP, Sala HV, García AO. Models for predicting perishable products demands in food trading companies. Revista Cubana de Ciencias Informáticas. 2020;14(1):110-35.
[11] Raju Y, Kang PS, Moroz A, Clement R, Hopwell A, Duffy A. Investigating the demand for short-shelf life food products for SME wholesalers. International Journal of Economics and Management Engineering. 2015 Jul 1;9(6):2051-5.
[12] Azzam A, Salsabila SE, Miranda S. Multi-item time series prediction using autoregressive integrated moving average and long short term memory on perishable products. InAIP Conference Proceedings 2023 Feb 21 (Vol. 2482, No. 1). AIP Publishing.
[13] Kumar MN, Snehalatha S, Nageswari CS, Raveena C, Rajan S. Optimized Warehouse Management of Perishable Goods. Alinteri Journal of Agriculture Sciences. 2021 Jan 1;36(1).
[14] Du XF, Leung SC, Zhang JL, Lai KK. Demand forecasting of perishable farm products using support vector machine. International journal of systems Science. 2013 Mar 1;44(3):556-67.
[15] Shukla M, Jharkharia S. ARIMA models to forecast demand in fresh supply chains. International Journal of Operational Research. 2011 Jan 1;11(1):1-8. [16] Huber J, Gossmann A, Stuckenschmidt H. Cluster-based hierarchical demand forecasting for perishable goods. Expert systems with applications. 2017 Jun 15;76:140-51.
[17] Sankaran S. Demand forecasting of fresh vegetable product by seasonal ARIMA model. International Journal of Operational Research. 2014 Jan 1;20(3):315-30.
[18] Shukla M, Jharkharia S. Applicability of ARIMA models in wholesale vegetable market: An investigation. International Journal of Information Systems and Supply Chain Management (IJISSCM). 2013 Jul 1;6(3):105-19.
[19] Almbaidin D, Etaiwi W. Predicting Wheat Supply and Demand: A Systematic Literature Review of Machine Learning Approaches. In2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) 2023 Jul 19 (pp. 1-8). IEEE.
[20] Priyadarshi R, Panigrahi A, Routroy S, Garg GK. Demand forecasting at retail stage for selected vegetables: a performance analysis. Journal of Modelling in Management. 2019 Oct 4;14(4):1042-63.
[21] Sakhare KV, Kulkarni I. Predictive Analysis of End to End Inventory Management System for Perishable Goods. In2022 3rd International Conference for Emerging Technology (INCET) 2022 May 27 (pp. 1-5). IEEE.
[22] Balaji M, Arshinder K. Modeling the causes of food wastage in Indian perishable food supply chain. Resources, Conservation and Recycling. 2016 Nov 1;114:153-67.
[23] Pandey NK, Mishra AK, Kumar V, Kumar A, Diwakar M, Tripathi N. Machine Learning based Food Demand Estimation for Restaurants. In2023 6th International Conference on Information Systems and Computer Networks (ISCON) 2023 Mar 3 (pp. 1-5). IEEE.
[24] Bozdoğan U, Alptekin GI. Demand Forecasting for Daily Retail Orders in Fresh Food Market. In2023 4th International Informatics and Software Engineering Conference (IISEC) 2023 Dec 21 (pp. 1-5). IEEE.
[25] Welfare F. Ministry of Agriculture and Farmers Welfare. Government of India. 2020. Available from https://www.tamilanjobs.com/wp-content/uploads/2020/02/5e462c52d0a9e-1-3.pdf
Journal of Software Engineering Tools & Technology Trends
| Volume | 11 |
| Issue | 03 |
| Received | June 28, 2024 |
| Accepted | August 21, 2024 |
| Published | September 14, 2024 |