On the Application of ARIMA and LSTM to Predict Order Demand Based on Short Lead Time and On-Time Delivery Requirements

Wang, Chien-Chih; Chien, Chun-Hua; Trappey, Amy J.C.

doi:10.3390/pr9071157

Cited by 33 publications

(21 citation statements)

References 26 publications

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“…Nowadays, the time series analysis is widely applied in various sectors, including consumption and businesses [5,9,10,22,28,29], demand forecasting and supply chains [1][2][3]6,7,11,12], economics [30], industrial applications [4], traffic and automatic system controls [8,21,31], meteorology and the environment [17], epidemiology [19,20,[23][24][25], and others [26,32,33]. In the above studies, the authors have proposed assumptions regarding time series data, the corresponding modeling methods, and evaluation indicators for various contexts.…”

Section: Analysis Of Time Series With Missing Datamentioning

confidence: 99%

“…The classic time series forecasters are based on statistical learning and include the Naive forecasting [4,7,50], the moving average [2][3][4]7,22,32,33], the exponential smoothing, the ARMA [14,28,51], and the ARIMA [6,[14][15][16][17][18]24,26,[28][29][30][50][51][52] processes. The modern time series forecasters include machine learning and deep learning algorithms such as the support vector regression [6,10,11], k-nearest neighbor [10,31], artificial neural network [1,7,33], recurrent neural network (RNN) [6,9,10,12], and LSTM [6,9,10,29,30,53] algorithms. Recently, a comparison between the statistical learning and modern approaches in either simulated or real datasets with or without missing data has been reported in the literature [1,…”

Section: Classic and Modern Time Series Forecastermentioning

confidence: 99%

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An Empirical Comparison of the Sales Forecasting Performance for Plastic Tray Manufacturing Using Missing Data

et al. 2022

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The problem of missing data is frequently met in time series analysis. If not appropriately addressed, it usually leads to failed modeling and distorted forecasting. To deal with high market uncertainty, companies need a reliable and sustainable forecasting mechanism. In this article, two propositions are presented: (1) a dedicated time series forecasting scheme, which is both accurate and sustainable, and (2) a practical observation of the data background to deal with the problem of missing data and to effectively formulate correction strategies after predictions. In the empirical study, actual tray sales data and a comparison of different models that combine missing data processing methods and forecasters are employed. The results show that a specific product needs to be represented by a dedicated model. For example, regardless of whether the last fiscal year was a growth or recession year, the results suggest that the missing data for products with a high market share should be handled by the zero-filling method, whereas the mean imputation method should be for the average market share products. Finally, the gap between forecast and actual demand is bridged by employing a validation set, and it is further used for formulating correction strategies regarding production volumes.

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Section: Analysis Of Time Series With Missing Datamentioning

confidence: 99%

Section: Classic and Modern Time Series Forecastermentioning

confidence: 99%

An Empirical Comparison of the Sales Forecasting Performance for Plastic Tray Manufacturing Using Missing Data

et al. 2022

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“…A model can predict the quality of finished products using Auto-Regressive Integrated Moving Average (ARIMA), which is machine learning [7]. A recent study has proven that Long Short-Term Memory (LSTM) is suitable for predicting inventory supply and demand in the manufacturing process [8]. There are also various attempts to use Bidirectional Long Short Term Memory (BI-LSTM) for intelligent manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers

et al. 2022

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Recently, interest in the Cyber-Physical System (CPS) has been increasing in the manufacturing industry environment. Various manufacturing intelligence studies are being conducted to enable faster decision-making through various reliable indicators collected from the manufacturing process. Artificial intelligence (AI) and Machine Learning (ML) have advanced enough to give various possibilities of predicting manufacturing time, which can help implement CPS in manufacturing environments, but it is difficult to secure reliability because it is difficult to understand how AI works, and although it can offer good results, it is often not applied to industries. In this paper, Bidirectional Long Short Term Memory (BI-LSTM) is used to predict process execution time, which is an indicator that can be used as a basis for CPS in the manufacturing process, and the Shapley Additive Explanations (SHAP) algorithm is used to explain how artificial intelligence works. The experimental results of this paper, applying manufacturing data, prove that the results derived from SHAP are effective for workers and AI to collaborate.

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“…Since customer demand is the basis for all activity planning, it is time-varying. Thus, accurate demand forecasting will prevent stock-outs and increase customer satisfaction [3].…”

Section: Introductionmentioning

confidence: 99%

Hybrid LSTM-ARMA Demand-Forecasting Model Based on Error Compensation for Integrated Circuit Tray Manufacturing

2022

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Demand forecasting plays a crucial role in a company’s operating costs. Excessive inventory can increase costs and unnecessary waste can be reduced if managers plan for uncertain future demand and determine the most favorable decisions. Managers are demanding increasing accuracy in forecasting as technology advances. Most of the literature discusses forecasting results’ inaccuracy by suspending the model and reloading the data for model retraining and correction, which is extensively employed but causes a bottleneck in practice since users do not have the sufficient ability to correct the model. This study proposes an error compensation mechanism and uses the individuals and moving-range (I-MR) control chart to evaluate the requirement for compensation to solve the current bottleneck using forecasting models. The approach is validated using the case companies’ historical data, and the model is developed using a rolling long short-term memory (LSTM) to output the predicted values; then, five indicators are proposed for screening to determine the prediction statistics to be subsequently employed. Root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) compare the LSTM, rolling LSTM combined index, and LSTM-autoregressive moving average (ARMA) models. The results demonstrate that the RMSE, MAPE, and MAE of LSTM-ARMA are smaller than those of the other two models, indicating that the error compensation mechanism that is proposed in this study can enhance the prediction’s accuracy.

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On the Application of ARIMA and LSTM to Predict Order Demand Based on Short Lead Time and On-Time Delivery Requirements

Cited by 33 publications

References 26 publications

An Empirical Comparison of the Sales Forecasting Performance for Plastic Tray Manufacturing Using Missing Data

An Empirical Comparison of the Sales Forecasting Performance for Plastic Tray Manufacturing Using Missing Data

Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers

Hybrid LSTM-ARMA Demand-Forecasting Model Based on Error Compensation for Integrated Circuit Tray Manufacturing

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