Innovating knowledge and information for a firm-level automobile demand forecast system: A machine learning perspective

Kim, Sehoon

doi:10.1016/j.jik.2023.100355

Cited by 13 publications

(4 citation statements)

References 52 publications

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“…The article contributes to the research field by proposing a framework that explains the impact of COVID-19 on electricity demand and produces accurate demand forecasts [39]. Kim (2023) aimed to develop a machine learning-supported hybrid input model for automobile demand forecasting. To achieve the research objective, the study analyzed the forecasting performance of a machine learning algorithm based on hybrid micro/firm-level (internal) and macro-level (external) factors.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To achieve the research objective, the study analyzed the forecasting performance of a machine learning algorithm based on hybrid micro/firm-level (internal) and macro-level (external) factors. The work contributes to the field by exploring the synergistic interaction between business processes and new analytical techniques, particularly in the context of demand forecasting in the automobile industry [40]. Viverit et al (2023) studied daily hotel demand forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

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Forecasting Retail Sales for Furniture and Furnishing Items through the Employment of Multiple Linear Regression and Holt–Winters Models

İnce,

Taşdemir

2024

Systems

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Global economic growth, marked by rising GDP and population, has spurred demand for essential goods including furniture. This study presents a comprehensive demand forecasting analysis for retail furniture sales in the U.S. for the next 36 months using Multiple Linear Regression (MLR) and Holt–Winters methods. Leveraging retail sales data from 2019 to 2023, alongside key influencing factors such as furniture imports, consumer sentiment, and housing starts, we developed two predictive models. The results indicated that retail furniture sales exhibited strong seasonality and a positive trend, with the lowest forecasted demand in April 2024 (USD 9118 million) and the highest in December 2026 (USD 13,577 million). The average annual demand for 2024, 2025, and 2026 is projected at USD 12,122.5 million, USD 12,522.67 million, and USD 12,922.17 million, respectively, based on MLR, while Holt–Winters results are slightly more conservative. The models were compared using the Mean Absolute Percentage Error (MAPE) metric, with the MLR model yielding a MAPE of 3.47% and the Holt–Winters model achieving a MAPE of 4.21%. The study’s findings align with global market projections and highlight the growing demand trajectory in the U.S. furniture industry, providing valuable insights for strategic decision-making and operations management.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Retail Sales for Furniture and Furnishing Items through the Employment of Multiple Linear Regression and Holt–Winters Models

İnce,

Taşdemir

2024

Systems

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“…The random forest regression algorithm solves the problem of overfitting, and hyperparameter tuning, can handle a large dataset, and gives more accurate predictions, due to which numerous researchers have used it to solve their problems easily. For instance, [46] conducted a comparative study of different machine learning algorithms, namely Linear Regression, Random Forest, SGD, and ANN, for demand prediction in an automobile firm to find that the Random Forest algorithm yielded accurate results after SGD. In addition, [47] employed the random forest algorithm to forecast demand for newly introduced products, thereby enhancing the operational decisions of a supply chain.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Improved Demand Forecasting of a Retail Store Using a Hybrid Machine Learning Model

Taparia,

Mishra,

Gupta

et al. 2023

JGEU

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Accurate demand forecasting is a competitive advantage for all supply chain components, including retailers. Approaches like naïve, moving average, weighted average, and exponential smoothing are commonly used to forecast demand. However, these simple approaches may lead to higher inventory and lost sales costs when the trend in demand is non-linear. Additionally, price strongly influences demand, and we can’t neglect the impact of price on demand. Similarly, the demand for a stock keeping unit (SKU) depends on the price of the competitor for the same SKU and the price of the competitive SKU. We thus propose a demand prediction model that considers historical demand data and the SKU price to forecast the demand. Our approach uses different machine-learning regressor algorithms and identifies the best machine-learning algorithm for the SKU with the lowest forecasting error. We further extend the forecasting model by training a hybrid model from the best two regression algorithms individually for each SKU. Forecasting error minimisation is the driving criterion for our literature. We evaluated the approach on 1000 SKUs, and the result showed that the Random Forest is the best-performing regressor algorithm with the lowest mean absolute percentage error (MAPE) of 8%. Furthermore, the hybrid model resulted in a lower inventory and lost sales cost with a MAPE of 7.74%. Overall, our proposed hybrid demand forecasting model can help retailers make informed decisions about inventory management, leading to improved operational efficiency and profitability.

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“…Finance requires adequate forecasting to project cash flow and capital needs; while Human Resources needs them to anticipate hiring and training needs [1]. Even more, having advanced demand forecasting capabilities, by allowing you to minimize costs, time, and optimize resources, can be an important source of competitive advantage; while inaccurate forecasts can cause damage such as excess inventories, lack of supplies for production, high labor costs and loss of reputation [2]. The strategic importance of having adequate forecasts is clear, then.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of the Literature on the Use of Artificial Intelligence in Forecasting the Demand for Products and Services in Various Sectors

Villar,

Lengua

2024

IJACSA

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This systematic review, carried out under the PRISMA methodology, aims to identify the recently proposed artificial intelligence models for demand forecasting, distinguishing the problems they try to overcome, recognizing the artificial intelligence methods used, detailing the performance metrics used, recognizing the performance achieved by these models and identifying what is new in them. Studies in the manufacturing, retail trade, tourism and electric energy sectors were considered in order to facilitate the transfer of knowledge from different sectors. 33 articles were analyzed, with the main results being that the proposed models are generally ensembles of various artificial intelligence methods; that the complexity of data and its scarcity are the main problems addressed; that combinations of simple machine learning, "bagging", "boosting" and deep neural networks, are the most used methods; that the performance of the proposed models surpasses the classic statistical methods and other reference models; and that, finally, the proposed novelties cover aspects such as the type of data used, the pattern extraction techniques used, the assembly forms of the applied models and the use of algorithms for automating the adjustment of the models. Finally, a forecast model is proposed that includes the most innovative aspects found in this research.

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Innovating knowledge and information for a firm-level automobile demand forecast system: A machine learning perspective

Cited by 13 publications

References 52 publications

Forecasting Retail Sales for Furniture and Furnishing Items through the Employment of Multiple Linear Regression and Holt–Winters Models

Forecasting Retail Sales for Furniture and Furnishing Items through the Employment of Multiple Linear Regression and Holt–Winters Models

Improved Demand Forecasting of a Retail Store Using a Hybrid Machine Learning Model

A Systematic Review of the Literature on the Use of Artificial Intelligence in Forecasting the Demand for Products and Services in Various Sectors

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