Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion

Abolghasemi, Mahdi; Beh, Eric J.; Tarr, Garth; Gerlach, Richard

doi:10.1016/j.cie.2020.106380

Cited by 108 publications

(57 citation statements)

References 54 publications

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“…When looking at studies where promotional activities-especially advertisements-are used in forecasting demand, it is seen that in general, traditional forecasting methods have been used (Divakar et al, 2005;Ma et al, 2016;Ramanathan & Muyldermans, 2010Van Donselaar et al, 2016;Van Heerde et al, 2002). However, a few studies using ML-based demand prediction methods have been used involving promotions as a variable (Ali et al, 2009;Di Pillo et al, 2013, Ferreira et al, 2015Trapero et al, 2015;Makridakis et al, 2018;Abolghasemi et al, 2020;Aguilar-Palacios et al, 2019;Ali & Gürlek, 2020;Wolters & Huchzermeier, 2021). Other research has been conducted that aims to forecast demand based on advertising.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Besides, current studies generally examine the promotion or only the advertising effect on demand/sales using traditional methods (Divakar et al, 2005;Trapero et al, 2015;Van Donselaar et al, 2016). A few studies have used ML methods to estimate demand based on promotions (Ferreira et al, 2016;Abolghasemi et al, 2020;Ali & Gürlek, 2020). The number of studies using ML methods and estimating demand by focusing on just advertisements is even smaller (Bollapragada et al, 2008;Güler, 2019;Rai et al, 2019).…”

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confidence: 99%

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The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

et al. 2022

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In recent years, machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting, there is a lack of research about how marketing strategies such as advertising and other promotional activities affect demand. Therefore, an accurate demand-forecasting model can make significant academic and practical contributions for business sustainability. The purpose of this article is to evaluate machine learning methods to provide accuracy in forecasting demand based on advertising expenses. The study focuses on a prediction mechanism based on several Machine Learning techniques-Support Vector Regression (SVR), Random Forest Regression (RFR) and Decision Tree Regressor (DTR) and deep learning techniques-Artificial Neural Network (ANN), Long Short Term Memory B Yigit Kazancoglu

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Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

et al. 2022

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“…Though NNs have been successfully applied to diverse SC contexts, Abolghasemi et al [10] provide evidences that simple statistical forecasting models can outperform some ML approaches when demand series is highly volatile. Nikolopoulos et al [24] also show that forecast accuracy performance can be deteriorated when using nearest neighbor approaches to forecast demand series with high levels of intermittence.…”

Section: Related Workmentioning

confidence: 99%

“…However, it is worth pointing out that since ML-based algorithms have not been fully explored in the context of SCM [10], company managers need to understand the complexities inherent to their application in real-life environments with multiple components, as well as to identify the resources with the necessary skills to successfully implement them in a productive system. Bridging this gap is critical for the overall success of the proposed forecasting initiative.…”

Section: Practical and Managerial Implicationsmentioning

confidence: 99%

“…Following an unconditional (ex ante) forecasting setup, we explore several Machine Learning (ML) models together with the statistical ARIMAX model to assess the viability of the proposed approach over the component's life-cycle. Interestingly, note that the ARI-MAX model has recently achieved good forecasting results on series with different levels of volatility [10]. To empirically evaluate our forecasting approach, we joined forces with a major automotive electronics organization -Bosch Automotive Electronics, Portugal -considered to be one of the largest business units belonging to the Bosch group.…”

Section: Introductionmentioning

confidence: 99%

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A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain

Gonçalves

Cortez

Carvalho

et al. 2021

Decision Support Systems

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Demand forecasting works as a basis for operating, business and production planning decisions in many supply chain contexts. Yet, how to accurately predict the manufacturer's demand for components in the presence of end-customer demand uncertainty remains poorly understood. Assigning the proper order quantities of components to suppliers thus becomes a nontrivial task, with a significant impact on planning, capacity and inventoryrelated costs. This paper introduces a multivariate approach to predict manufacturer's demand for components throughout multiple forecast horizons using different leading indicators of demand shifts. We compare the autoregressive integrated moving average model with exogenous inputs (ARIMAX) with Machine Learning (ML) models. Using a real case study, we empirically evaluate the forecasting and supply chain performance of the multivariate regression models over the component's life-cycle. The experiments show that the proposed multivariate approach provides superior forecasting and inventory performance compared with traditional univariate benchmarks. Moreover, it reveals applicable throughout the component's life-cycle, not just to a single stage. Particularly, we found that demand signals at the beginning of the life-cycle are predicted better by the ARIMAX model, but it is outperformed by ML-based models in later life-cycle stages.

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Toward a smart forecasting model in supply chain management: A case study of coffee in Vietnam

Nguyen,

Bekrar,

et al. 2024

Journal of Forecasting

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Forecasting is a crucial part of supply chain management. Accurate forecasts have a strong influence on supply chain performance. Many forecasting methods have been developed and adapted in various domains and industries. However, none are perfect in all contexts due to the data's characteristics and the methods' strength. Hence, we propose a new ARIMAX‐LSTM hybrid forecasting model that integrates ARIMAX and LSTM models to improve the ability to capture different combinations of linear and nonlinear patterns in time series. Our proposed model is validated in a case study of coffee demand in Vietnam. The case study results show that our proposed model outperforms the well‐known single and current hybrid models regarding performance measures and degree of association. Moreover, to prove the model's robustness, we test and compare our proposed model to the previous study for Thailand's agricultural products (pineapple, corn, and cassava). Computational results demonstrate that our hybrid model is superior in the majority of experiments. It has a strong capability of predicting complex time series data. Furthermore, our proposed method increases forecasting accuracy and enhances supply chain performance (measured by the bullwhip effect; net‐stock amplification, and transportation cost.

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Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion

Cited by 108 publications

References 54 publications

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain

Toward a smart forecasting model in supply chain management: A case study of coffee in Vietnam

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