Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect

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.

Impacts of COVID-19 and the Russian–Ukrainian Conflict on Food Supply Chain: A Case Study from Bread Supply Chain in Egypt

“…By mid-January 2024, about three months since the start of the war, the shipping rates for a 40-foot container surged by more than 600% from North Asia to Europe, 137% from North Asia to the US East Coast, and 131% from North Asia to the US West Coast [74]. Since such disruptions do not usually happen suddenly, it may be a proper strategy to use non-traditional forecasting techniques to avoid the bullwhip effect, e.g., rolling forecasting [75], risk aversion [76], and alternative food networks and short food supply chains [77].…”

Integration of machine learning in the supply chain for decision making: A systematic literature review

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