Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/11/2502 |
| _version_ | 1797597140982169600 |
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| author | Keun Hee Lee Mali Abdollahian Sergei Schreider Sona Taheri |
| author_facet | Keun Hee Lee Mali Abdollahian Sergei Schreider Sona Taheri |
| author_sort | Keun Hee Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:02:35Z |
| format | Article |
| id | doaj.art-605f18f2ee194b8a9fe874710fc47a5f |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T03:02:35Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-605f18f2ee194b8a9fe874710fc47a5f2023-11-18T08:12:54ZengMDPI AGMathematics2227-73902023-05-011111250210.3390/math11112502Supply Chain Demand Forecasting and Price Optimisation Models with Substitution EffectKeun Hee Lee0Mali Abdollahian1Sergei Schreider2Sona Taheri3School of Sciences, RMIT University, Melbourne 3000, AustraliaSchool of Sciences, RMIT University, Melbourne 3000, AustraliaRutgers Business School, Rutgers University, Newark, NJ 07102, USASchool of Sciences, RMIT University, Melbourne 3000, AustraliaDetermining 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.https://www.mdpi.com/2227-7390/11/11/2502price optimisationdemand predictiondemand correlationsubstitution effectdata driven machine learningretailing |
| spellingShingle | Keun Hee Lee Mali Abdollahian Sergei Schreider Sona Taheri Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect Mathematics price optimisation demand prediction demand correlation substitution effect data driven machine learning retailing |
| title | Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect |
| title_full | Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect |
| title_fullStr | Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect |
| title_full_unstemmed | Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect |
| title_short | Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect |
| title_sort | supply chain demand forecasting and price optimisation models with substitution effect |
| topic | price optimisation demand prediction demand correlation substitution effect data driven machine learning retailing |
| url | https://www.mdpi.com/2227-7390/11/11/2502 |
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