Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect

Keun Hee Lee, Mali Abdollahian (), Sergei Schreider and Sona Taheri
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Keun Hee Lee: School of Sciences, RMIT University, Melbourne 3000, Australia
Mali Abdollahian: School of Sciences, RMIT University, Melbourne 3000, Australia
Sergei Schreider: Rutgers Business School, Rutgers University, Newark, NJ 07102, USA
Sona Taheri: School of Sciences, RMIT University, Melbourne 3000, Australia

Mathematics, 2023, vol. 11, issue 11, 1-28

Abstract: Determining the optimal price of products is essential, as it plays a critical role in improving a company’s profitability and market competitiveness. This requires the ability to calculate customers’ demand in the Fast Moving Consumer Goods (FMCG) industry as various effects exist between multiple products within a product category. The substitution effect is one of the challenging effects at retail stores, as it requires investigating an exponential number of combinations of price changes and the availability of other products. This paper suggests a systematic price decision support tool for demand prediction and price optimise in online and stationary retailers considering the substitution effect. Two procedures reflecting the product price changes and the demand correlation structure are introduced for demand prediction and price optimisation models. First, the developed demand prediction procedure is carried out considering the combination of price changes of all products reflecting the effect of substitution. Time series and different well-known machine learning approaches with hyperparameter tuning and rolling forecasting methods are utilised to select each product’s best demand forecast. Demand forecast results are used as input in the price optimisation model. Second, the developed price optimisation procedure is a constraint programming problem based on a week time frame and a product category level aggregation and is capable of maximising profit out of the many price combinations. The results using real-world transaction data with 12 products and 4 discount rates demonstrate that including some business rules as constraints in the proposed price optimisation model reduces the number of price combinations from 11,274,924 to 19,440 and execution time from 129.59 to 25.831 min. The utilisation of the presented price optimisation support tool enables the supply chain managers to identify the optimal discount rate for individual products in a timely manner, resulting in a net profit increase.

Keywords: price optimisation; demand prediction; demand correlation; substitution effect; data driven machine learning; retailing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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