Analogue-based demand forecasting of short life-cycle products: a regression approach and a comprehensive assessment

Basallo-Triana, Mario José; Rodríguez-Sarasty, Jesús A.; Benítez-Restrepo, Hernán Darío

doi:10.1080/00207543.2016.1241443

Cited by 16 publications

(11 citation statements)

References 30 publications

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“…Application of traditional time-series methods such as exponential smoothing, ARIMA, was quite popular for demand forecasting in the offline retail (Basallo-Triana, Rodríguez-Sarasty, and Benitez-Restrepo 2017). Recently, data-driven approaches like neural networks (Alon, Qi, and Sadowski 2001) and random forests for multivariate data were also explored by the researchers (Ferreira, Lee, and Simchi-Levi 2016).…”

Section: The Forecasting In Retailmentioning

confidence: 99%

Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail

Punia

Νικολόπουλος

Singh

et al. 2020

International Journal of Production Research

111

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This paper proposes a novel forecasting method that combines the deep learning method -long shortterm memory (LSTM) networks and random forest (RF). The proposed method can model complex relationships of both temporal and regression type which gives it an edge in accuracy over other forecasting methods. We evaluated the new method on a real-world multivariate dataset from a multichannel retailer. We benchmark the forecasting performance of the new proposition against neural networks, multiple regression, ARIMAX, LSTM networks, and RF. We employed forecasting performance metrics to measure bias, accuracy, and variance, and the empirical evidence suggests that the new proposition is (statistically) significantly better. Furthermore, our method ranks the explanatory variables in terms of their relative importance. The empirical evaluations are replicated for longer forecasting horizons, and online and offline channels and the same conclusions hold; thus, advocating for the robustness of our forecasting proposition as well as the suitability in multi-channel retail demand forecasting.

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Section: The Forecasting In Retailmentioning

confidence: 99%

Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail

Punia

Νικολόπουλος

Singh

et al. 2020

International Journal of Production Research

111

View full text Add to dashboard Cite

show abstract

“…Please note that the accuracy of the estimations relates to several factors. Firstly, customer preferences and demand for newly developed products are extremely uncertain [21,22], and this issue, due to several reasons, is even more serious for fashion industry [23]. Secondly, only two manufacturers were considered by the proposed model to simplify the modeling and solving processes, while there are more manufacturers in the market.…”

Section: Implementing the Solutionsmentioning

confidence: 99%

Launch Strategies for Newly Developed Short Life Cycle Products

et al. 2022

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Nowadays, due to the rapid pace of technology enhancement, growing consumer expectations, and market competitiveness, life cycle of products is shortening faster than it used to be. In such situations, where new generations of products lead to the obsolescence of those currently available, the simultaneous pricing of both newly developed and available products is a challenging task. In this paper, using game theory approaches, we investigate various possible conditions in which two firms may introduce new generations of products with short life cycles. Optimum price of newly developed and planned obsolescence products are determined by the proposed method. The effectiveness of the proposed method is verified using various numerical calculations and sensitivity analyses. A real case study from textile industry illustrates the application of the proposed approach in industry.

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“…Analogue forecasting is often used in forecasting new product sales based on past sales of similar products. This approach was presented for instance by Basallo-Triana, Rodríguez-Sarasty and Benitez-Restrepo [49]. An analogous approach was applied also to the forecasting of sales of steel products [50,51].…”

Section: Literature Reviewmentioning

confidence: 99%

Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree

Gaweł

Paliński

2021

Energies

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Classic forecasting methods of natural gas consumption extrapolate trends from the past to subsequent periods of time. The paper presents a different approach that uses analogues to create long-term forecasts of the annual natural gas consumption. The energy intensity (energy consumption per dollar of Gross Domestic Product—GDP) and gas share in energy mix in some countries, usually more developed, are the starting point for forecasts of other countries in the later period. The novelty of the approach arises in the use of cluster analysis to create similar groups of countries and periods based on two indicators: energy intensity of GDP and share of natural gas consumption in the energy mix, and then the use of fuzzy decision trees for classifying countries in different years into clusters based on several other economic indicators. The final long-term forecasts are obtained with the use of fuzzy decision trees by combining the forecasts for different fuzzy sets made by the method of relative chain increments. The forecast accuracy of our method is higher than that of other benchmark methods. The proposed method may be an excellent tool for forecasting long-term territorial natural gas consumption for any administrative unit.

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Analogue-based demand forecasting of short life-cycle products: a regression approach and a comprehensive assessment

Cited by 16 publications

References 30 publications

Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail

Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail

Launch Strategies for Newly Developed Short Life Cycle Products

Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree

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