Evaluating the Effectiveness of Business Analytics in Demand Forecasting: A Study of the Inventory Department at Spencer's Retail, Vijayawada

Author

Kota Jaswanth Kumar, M. Raja Pavan Kumar


Abstract

 This research investigates business analytics for demand forecasting in the inventory department of Spencer's Retail, Vijayawada. Analytics can contribute to demand forecasting by allowing retailers to use historical sales and current demand information to predict future demand, optimize stock levels, and reduce stockouts and wastage. This research aims to identify the analytics tools and techniques that are used for demand forecasting, to assess their perceived impact on forecast accuracy and inventory management, and to identify the practical implementation issues of using such techniques. A quantitative cross-sectional descriptive study was employed with a structured questionnaire and the resultant analysed data set of 128 records of student respondents, 112 simulated from 16 actual responses this way illustrating the analytical process and indicating trends rather than practice at Spencer's Retail. Respondents expressed an optimistic attitude to analytics, and their perceived usefulness was the primary predictor of predicted demand forecasting results.



Keywords

business analytics; demand forecasting; inventory management; retail; Spencer's Retail; forecast accuracy



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References


  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  • Cramér, H. (1946). Mathematical methods of statistics. Princeton University Press.
  • Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334.
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
  • Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583-621.
  • Kumar, D., & Bansal, M. (2023). Use of demand forecasting techniques adopted in the retail industry: A comparative analysis of traditional versus analytics-based approaches. Journal of Operations and Supply Chain Management.
  • Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 1-55.
  • Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
  • Ramesh, S., Nair, A., & Iyer, P. (2024). Business analytics maturity and supply chain responsiveness in retail: A survey-based study. International Journal of Business Analytics.
  • Sharma, R., & Patel, K. (2023). Adoption of predictive analytics in retail inventory management: A study of organised retail chains in India. Journal of Retail and Consumer Studies.
  • Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15(1), 72-101.
  • Verma, S., & Rao, A. (2024). Inventory improvement through business analytics: A study of Indian retail chains. South Asian Journal of Business Studies.
  • Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80-83.

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