Demand Forecasting and Inventory Prediction for Apparel Product using the ARIMA and Fuzzy EPQ Model

Vo, Tran Thi Bich Chau; Le, Phan Hung; Nguyen, Nhut Tien; Nguyen, Thi Le Thuy; Hien, Ngoc

doi:10.25103/jestr.142.11

Cited by 7 publications

(7 citation statements)

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“…From the indicators in terms of bias and accuracy they concluded that there is no level of superiority between both sets of methods, however, it is worth mentioning that in recent years Machine Learning methods have gained notoriety, being proposed as "an advantageous alternative to traditional methods", due to the use of non-linear algorithms able to learn by trial and improve their performance by observing historical data in order not to make assumptions. Vo et al (2021) proposed solutions to improve business supply capacity by selecting forecasting models and a policy framework to ensure optimal inventory. First, the chosen research looked at the forecasting model with the lowest error based on a collection of 60 periods; where the ARIMA model was positioned as the most optimal model for forecasting demand after comparing it with the Holt Winters regression model.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-product inventory modeling with demand forecasting

Valencia Olivares,

Mendoza Bojórquez,

Taquía Gutiérrez

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Many wholesale companies do not use an adequate control of their inventory level, and variables such as product demand tend to vary continuously due to seasonality and market trends, generating costs that reduce profitability and eventually produce customer dissatisfaction with the service offered. In this paper we propose the application of a method to reduce the level of uncertainty and risk in sales and to simulate the implementation of a policy of continuous inventory review. Using the monthly demand of the wholesale company and considering the maximization of profit, the ARIMA time series model was chosen, developed through the R programming language, and then modeled in the Arena software the results of the forecast found. The relevance of the study is to present inventory improvement tools that encourage a favorable management in terms of inventory management costs.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-product inventory modeling with demand forecasting

Valencia Olivares,

Mendoza Bojórquez,

Taquía Gutiérrez

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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“…The next category of tools used is the models with auto-regressive components, that is, auto-regressive moving average (ARMA), auto-regressive integrated moving average (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). ARIMA and/or ARMA models are used to extract the historical trends in the time-series data and use this information for forecasting purposes (Erdogdu, 2007;Ho & Xie, 1998;Huang & Shih, 2003;Vo et al, 2021;Wang et al, 2019). The only difference between the two models is that, unlike ARMA models, the ARIMA models can be used only with stationary time-series (Valipour et al, 2013).…”

Section: Energy Demand/consumption Forecastingmentioning

confidence: 99%

Incorporating causality in energy consumption forecasting using deep neural networks

2022

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Forecasting energy demand has been a critical process in various decision support systems regarding consumption planning, distribution strategies, and energy policies. Traditionally, forecasting energy consumption or demand methods included trend analyses, regression, and auto-regression. With advancements in machine learning methods, algorithms such as support vector machines, artificial neural networks, and random forests became prevalent. In recent times, with an unprecedented improvement in computing capabilities, deep learning algorithms are increasingly used to forecast energy consumption/demand. In this contribution, a relatively novel approach is employed to use long-term memory. Weather data was used to forecast the energy consumption from three datasets, with an additional piece of information in the deep learning architecture. This additional information carries the causal relationships between the weather indicators and energy consumption. This architecture with the causal information is termed as entangled long short term memory. The results show that the entangled long short term memory outperforms the state-of-the-art deep learning architecture (bidirectional long short term memory). The theoretical and practical implications of these results are discussed in terms of decision-making and energy management systems.

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“…A first smoothing method is the ARIMA model or Autoregressive Integrated Moving Average Model, which according to Vo et al (2021) is considered an effective methodology for forecasting trends and fluctuations with seasonality. This method, by eliminating the non-stationary variation of the historical data record, is able to predict future values.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Where: p: order of the autoregressive model q: order of mean movement d: degree of differentiation On the other hand, there is the Holt Winters smoothing method or Triple Exponential Smoothing Method, which according to Vo et al (2021) is an ideal methodology for analyzing a series of seasonal and trend data. Similarly, this…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, due to such uncertainty, two unfavorable and recurring problems related to inventory demand arise: destocking and overstocking. For Vo et al (2021), these problems require companies to pay attention to forecasting work because this is the most effective method to know the market demand. In fact, demand forecasting is a necessary and indispensable activity for any business.…”

Section: Introductionmentioning

confidence: 99%

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Demand and Inventory Simulation using Time Series

Salsavilca,

Niño,

Taquía Gutiérrez

2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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The importance of implementing an adequate demand and inventory management process improves coordination in supply chain models. The purpose of this research is to analyze and propose the application of a forecasting technique and the implementation of a continuous inventory review policy by designing a simulation for a company in the veterinary commercial sector. Currently, companies without this coordination obtains an inadequate inventory level generating losses in profits and a low demand level of service. This study presents as a result an optimization model based on the ARIMA technique and an inventory model simulated in Arena software.

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Demand Forecasting and Inventory Prediction for Apparel Product using the ARIMA and Fuzzy EPQ Model

Cited by 7 publications

References 0 publications

Multi-product inventory modeling with demand forecasting

Multi-product inventory modeling with demand forecasting

Incorporating causality in energy consumption forecasting using deep neural networks

Demand and Inventory Simulation using Time Series

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