Neural network approach to lumpy demand forecasting for spare parts in process industries

Amin-Naseri, Mohammad Reza; Rostami-Tabar, Bahman

doi:10.1109/iccce.2008.4580831

Cited by 23 publications

(8 citation statements)

References 14 publications

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“…Amin-Naseri & Rostami Tabar (2008) proposed the use of Recurrent Neural Networks (RNN). The network is composed from four layers: an input layer, a hidden layer, a context layer and an output layer.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparison between Neural Networks and Traditional Forecasting Methods: A Case Study

Mitrea

Lee

2009

International Journal of Engineering Business Management

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Section: Literature Reviewmentioning

confidence: 99%

A Comparison between Neural Networks and Traditional Forecasting Methods: A Case Study

Mitrea

Lee

2009

International Journal of Engineering Business Management

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“…However, forecasting is hardly difficult with traditional methods when the demand of item has changing values [8]. At this point, the artificial neural network (ANN) method is a logical choice to handle these limitations [9]. ANN can capture interactions between the non-zero demand and the inter-arrival rate of demand events [10].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similarly, Gutierrez et al [14] applied the ANN method to forecast lumpy demand. Amin-Naseri and Tabar [9] used the recurrent ANN for lumpy demand forecasting of spare parts. Croston's method and Syntetos & Boylan's approximation were also used to evaluate the proposed method.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Artificial Intelligence Based Methodology for the Inventory Routing Problem

et al. 2019

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In this paper, a new hybrid method including simulation optimization and artificial intelligence based simulation is created to solve the inventory routing problem (IRP) in which three different routing strategies are evaluated for uneven demand patterns including intermittent, erratic, and lumpy demand. The proposed method includes two phases. In the first phase, a nondominated sorting genetic algorithm II based simulation is employed to perform a multi-objective search for the IRP where the objectives of the method are total supply chain cost minimization and average service level maximization. In the second phase, artificial neural network based simulation is used to adjust the reorder point and order-up-to-level by forecasting the customer demand at each replenishment time. The results of the study demonstrated that the average service level is at least 98.54% in the supply chain. From this, it can be concluded that the proposed method can provide a tremendous opportunity to improve the average service level under uncertain environments. In addition, it is determined that different routing strategies can be selected for different demand patterns according to the considered performance measures.

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“…Another research [51] proposed the use of Recurrent Neural Networks (RNN). A RNN is basically a FFNN with the outputs forwarded back to the inputs.…”

Section: Multilayer Feedforward Neural Networkmentioning

confidence: 99%

Design and development of a knowledge discovery system in inventory management

Mitrea

Lee

2009

2009 IEEE International Conference on Industrial Engineering and Engineering Management

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The ability to learn inductively from examples is an important feature of intelligent systems. These learning methods and algorithms, which are able to generate a model of a system using the observations (data) available, are applied in a specific domain usually with the following goals: to create a system that can carry out a task and to obtain a better understanding of the available data. In this thesis, function approximation or pattern recognition is used to predict inventory level so as to enhance the performance of supply chain.Inventory management plays a crucial role in logistics and its performance is shaped dramatically by uncertainty in both demand and supply. Although demand is difficult to predict due to its stochastic behavior, it is necessary to have accurate forecasting so as to fulfill the customer needs and maintain the corporate competitive edge. Achieving accurate demand forecasting and representing knowledge in rules attract the attention of both academic and practitioners. Knowledge is regarded as a valuable asset for enterprises and knowledge can be manipulated through Artificial Intelligence (AI) techniques. Among those AI techniques, Artificial Neural Networks (ANN) has been proven to be very useful in approximating functions and recognizing patterns by adjusting their weights and biases during training process, but it cannot explain how they arrived at a conclusion in solving a problem. This is the reason why ANN is considered in literature as black boxes due to the lack of clear explanation. To overcome these shortages, TREes PArroting Networks(TREPAN) algorithm is used to extract knowledge from the trained networks in the form of decision trees.The TREPAN algorithm utilizes the IF M-of-N THEN conditions rule instead of the common IF-THEN-ELSE rule to construct the decision tree which can be used to understand previously unknown relationship between input variables for forecasting so as to improve inventory management. The experimental results show that forecasting accuracy using ANN are superior to traditional methods like moving average and autoregressive integrated moving average (ARIMA). Also, the knowledge extracted from trained ANNs using TREPAN algorithm is represented in a comprehensible way and can be used to facilitate human decision-making. The significance of this study is to design and develop a knowledge discovery system, thereby enhancing the inventory management.

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Neural network approach to lumpy demand forecasting for spare parts in process industries

Cited by 23 publications

References 14 publications

A Comparison between Neural Networks and Traditional Forecasting Methods: A Case Study

A Comparison between Neural Networks and Traditional Forecasting Methods: A Case Study

A Novel Hybrid Artificial Intelligence Based Methodology for the Inventory Routing Problem

Design and development of a knowledge discovery system in inventory management

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