Forecasting stochastic consumer portability visitation pattern in fair price shops of India
*Archana SasiCorresponding authorarchana.sasi2k8@gmail.comDepartment of Computer Science and EngineeringBig Data Analytics LabPresidency UniversityBangalore, Karnataka, 560064, IndiaView full profile → , Thiruselvan Subramanianthirulic@gmail.comDepartment of Computer Science and EngineeringBig Data Analytics LabPresidency UniversityBangalore, Karnataka, 560064, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 11 Aug 2023
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1364
- Pages:
- 439–454
Abstract
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References
[1] Dreze, J., & Khera, R. Understanding Leakages in the Public Distribution System. Economic and Political Weekly, 50(7), 7-8 (2015).
[2] Pingali, P., Mittra, B., & Rahman, A. The bumpy road from food to nutrition security – Slow evolution of India’s food policy. Global Food Security, 15, 77-84 (2017). https://doi.org/10.1016/j.gfs.2017.05.002.
[3] Banerjee, A., Hanna, R., Kyle, J., Olken, B. A., & Sumarto, S. Tangible Information and Citizen Empowerment: Identification Cards and Food Subsidy Programs in Indonesia. Journal of Political Economy, 126(2), 451-491 (2017). https://doi.org/10.1086/696226.
[4] Sharma, N., & Gupta, S. An investigation of IT-intervention adoption in public distribution system: A stakeholder and agency theory perspective. Information Development, 35(2), 203-219 (2017).
[5] Sargar, R., Kumar, A., Nakade, V., & Borkar, N. Public Distribution System in Solapur District of Maharashtra: A Case Study. International Journal of Research in Engineering & Advanced Technology, 2(3), 8 (2014).
[6] Allu, R., Deo, S., & Devalkar, S. Alternatives to Aadhaar-based Biometrics in the Public Distribution System. Economic & Political Weekly, 54(12), 30-37 (2019).
[7] Christopher, M., & Holweg, M. Supply chain 2.0 revisited: a framework for managing volatility-induced risk in the supply chain. International Journal of Physical Distribution & Logistics Management, 47(1), 2-17 (2017).
[8] Syntetos, A. A., Babai, Z., Boylan, J. E., Kolassa, S., & Nikolopoulos, K. Supply chain forecasting: Theory, practice, their gap and the future. European Journal of Operational Research, 252(1), 1-26 (2016).
[9] Torkul, O., Yılmaz, R., Selvi, I., & Cesur, M. R. A real-time inventory model to manage variance of demand for decreasing inventory holding cost. Computers & Industrial Engineering, 102, 435-439 (2016).
[10] Meindl, P., & Chopra, S. Supply chain management: Strategy, planning, and operation (2001). Prentice Hall.
[11] Aye, G. C., Balcilar, M., Gupta, R., & Majumdar, A. Forecasting aggregate retail sales: The case of South Africa. International Journal of Production Economics, 160, 66-79 (2015).
[12] Alon, I., Qi, M., & Sadowski, R. J. Forecasting aggregate retail sales: A comparison of artificial neural networks and traditional methods. Journal of Retailing and Consumer Services, 8(3), 147-156 (2001).
[13] Huang, T., Fildes, R., & Soopramanien, D. The value of competitive information in forecasting fmcg retail product sales and the variable selection problem. European Journal of Operational Research, 237(2), 738-748 (2014).
[14] Huang, T., Fildes, R., & Soopramanien, D. Forecasting retailer product sales in the presence of structural change. European Journal of Operational Research (2019).
[15] Makridakis, S., Spiliotis, E., & Assimakopoulos, V. The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting, 34(4), 802-808 (2018).
[16] Huang, M.-G., Chang, P.-L., & Chou, Y.-C. Demand forecasting and smoothing capacity planning for products with high random demand volatility. International Journal of Production Research, 46(12), 3223-3239 (2008).
[17] Rajan, P., Chopra, S., Somasekhar, A. K., & Laux, C. Designing for food security: Portability and the expansion of user freedoms through the COREPDS in Chhattisgarh, India. Information Technologies & International Development (2016).
[18] Joshi, A., Sinha, D., & Patnaik, B. Credibility and Portability? : Lessons from CORE PDS Reforms in Chhattisgarh. Economic and Political Weekly, 51(37), 51-59 (2016).
[19] Sasi A, Subramanian T, Kumar Ravichandran S. Systematic Literature Review on Industry Revolution 4.0 to Enhance Supply Chain Operation Performance. In2022 5th International Conference on Computers in Management and Business (ICCMB), pp. 173-179 (2022 Jan 21).
[20] Ravichandran SK, Sasi A. Optimal Arrangement of Ration Items into Container Using Modified Forest Optimization Algorithm.
[21] Xie, N., Pearman, A.D. Forecasting energy consumption in China following instigation of an energy-saving policy. Nat Hazards 74, 639-659 (2014). https://doi.org/10.1007/s11069-014-1200-x.
[23] Archana Sasi & Thiruselvan Subramanian (2022) Comparative analysis of ARIMA and double exponential smoothing for forecasting rice sales in fair price shop, Journal of Statistics and Management Systems, 25:7, 1601-1619, DOI: 10.1080/09720510.2022.2130572.
[24] YaLi Yuan. Forecasting method for import and export trade on the basis of GMDH network model, Journal of Discrete Mathematical Sciences and Cryptography, 20:4, 755-766 (2017), DOI: 10.1080/09720529.2017.1358859.




