Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail

Henzel, Joanna; Sikora, Marek

doi:10.15439/2020f118

Cited by 8 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Compared to the 'bagging' algorithm that only controls high variance in the model, the 'boosting' algorithm is known to control both bias and variance [45]. Several studies used Gradient Boosting for forecasting demand, as it is more robust to noisy data and uses less memory [46,47].…”

Section: Existing Modelsmentioning

confidence: 99%

Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Section: Existing Modelsmentioning

confidence: 99%

Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Both the papers talk about applications of marketing intelligence for the fast moving consumer goods. (ii) Application of Modern Machine Learning Methods: Benefits of machine learning methods are described in the work of (lcio, Getúlio, Camilo, Jose, & Douglas, 2019), (Pereira & Frazzon, 2021), (Joanna & Marek, 2020), and (Arvan, Fahimnia., Reisi, & Siemsen, 2018). The paper (lcio, Getúlio, Camilo, Jose, & Douglas, 2019) discusses the use of machine learning techniques in predicting demand for various fast moving consumer goods.…”

Section: Literature Surveymentioning

confidence: 99%

Effective Selection of Entities From Heterogeneous and Large Resources Using a Cooperative Neuro-Fuzzy System

Sajja

Mishra²

2022

International Journal of System Dynamics Applications

View full text Add to dashboard Cite

This paper focuses on using the cooperative neuro-fuzzy system for the effective and customised selection of entities from large and heterogeneous resources by presenting a general architecture. An experiment is carried out with the fast-moving consumer goods to prove the utility of the architecture. It is observed that most consumers go for the frequent purchase of fast-moving consumer items. Further, various brands, costs, discounts, schemes, quantities, and reviews might make it challenging. Hence, such decisions need to be intelligent and practically feasible in terms of time and effort. The paper discusses neural networks to categorise the entities, type-1 & 2 fuzzy membership functions with rules, training sets, and graphical views of the fuzzy rules and the experiment details. Besides the generic approach and experiment, the paper also discusses the work done so far with their limitations and applications in other domains. At the end, the paper presents the limitations and possible future enhancements.

show abstract

“…The study also indicated that the incorporation of additional explanatory variables can minimize forecasting errors. Similarly, GBM and LightGBM were assessed for their utility in forecasting future sales and promotions, demonstrating decent accuracy [16][17][18]. XGBoost, a widely used model in demand forecasting due to its strong performance in sales forecasting for retail, was found to be a favorable choice [19].…”

Section: Introductionmentioning

confidence: 99%

A Sales Forecasting Model for New-Released and Short-Term Product: A Case Study of Mobile Phones

Hwang,

Yoon,

Baek

et al. 2023

Electronics

View full text Add to dashboard Cite

In today’s competitive market, sales forecasting of newly released and short-term products is an important challenge because there is not enough sales data. To address these challenges, we propose a sales forecasting model for new-released and short-term products and study the case of mobile phones. The main approach is to develop an integrated sales forecasting model by training the sales patterns and product characteristics of the same product category. In particular, we analyze the performance of the latest 12 machine learning models and propose the best performance model. Machine learning models have been used to compare performance through the development of Ridge, Lasso, Support Vector Machine (SVM), Random Forest, Gradient Boosting Machine (GBM), AdaBoost, LightGBM, XGBoost, CatBoost, Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). We apply a dataset consisting of monthly sales data of 38 mobile phones obtained in the Korean market. As a result, the Random Forest model was selected as an excellent model that outperforms other models in terms of prediction accuracy. Our model achieves remarkable results with a mean absolute percentage error (MAPE) of 42.6258, a root mean square error (RMSE) of 8443.3328, and a correlation coefficient of 0.8629.

show abstract

Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail

Cited by 8 publications

References 34 publications

Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect

Supply Chain Demand Forecasting and Price Optimisation Models with Substitution Effect

Effective Selection of Entities From Heterogeneous and Large Resources Using a Cooperative Neuro-Fuzzy System

A Sales Forecasting Model for New-Released and Short-Term Product: A Case Study of Mobile Phones

Contact Info

Product

Resources

About