Demand forecasting at retail stage for selected vegetables: a performance analysis

Abstract

Purpose

The purpose of this study is to select the appropriate forecasting model at the retail stage for selected vegetables on the basis of performance analysis.

Design/methodology/approach

Various forecasting models such as the Box–Jenkins-based auto-regressive integrated moving average model and machine learning-based algorithms such as long short-term memory (LSTM) networks, support vector regression (SVR), random forest regression, gradient boosting regression (GBR) and extreme GBR (XGBoost/XGBR) were proposed and applied (i.e. modeling, training, testing and predicting) at the retail stage for selected vegetables to forecast demand. The performance analysis (i.e. forecasting error analysis) was carried out to select the appropriate forecasting model at the retail stage for selected vegetables.

Findings

From the obtained results for a case environment, it was observed that the machine learning algorithms, namely LSTM and SVR, produced the better results in comparison with other different demand forecasting models.

Research limitations/implications

The results obtained from the case environment cannot be generalized. However, it may be used for forecasting of different agriculture produces at the retail stage, capturing their demand environment.

Practical implications

The implementation of LSTM and SVR for the case situation at the retail stage will reduce the forecast error, daily retail inventory and fresh produce wastage and will increase the daily revenue.

Originality/value

The demand forecasting model selection for agriculture produce at the retail stage on the basis of performance analysis is a unique study where both traditional and non-traditional models were analyzed and compared.

Citation

Priyadarshi, R., Panigrahi, A., Routroy, S. and Garg, G.K. (2019), "Demand forecasting at retail stage for selected vegetables: a performance analysis", Journal of Modelling in Management, Vol. 14 No. 4, pp. 1042-1063. https://doi.org/10.1108/JM2-11-2018-0192