A compound-Poisson Bayesian approach for spare parts inventory forecasting

Babaï, Mohamed Zied; Chen, Huijing; Syntetos, Aris A.; Lengu, David

doi:10.1016/j.ijpe.2020.107954

Cited by 19 publications

(8 citation statements)

References 34 publications

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“…All variables other than the response can be predictors. We apply the compound distributions to analyze the sparse data with zero-inflated problem [14,17,[36][37][38][39][40][41]. In the compound distribution, a random variable Y is defined as (5) [17]:…”

Section: Proposed Modelmentioning

confidence: 99%

Zero-Inflated Patent Data Analysis Using Compound Poisson Models

Park

Jun

2023

Applied Sciences

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A large part of big data consists of text documents such as papers, patents or articles. To analyze text data, we have to preprocess the text documents and build a structured data based on a document-word matrix using various text mining techniques. This is because statistics and machine learning algorithms used in text analysis require structured train data. The row and column of the matrix are document and word, respectively. The element of the matrix represents the frequency value of the word occurring in each document. In general, because the number of words is much larger than the number of documents, most elements have zero values. Due to the sparsity problem caused by inflated zeros, the performance of the predictive model has decreased. In this paper, we propose a method to solve the sparsity problem and improve the model performance in text data analysis. We perform compound Poisson linear modeling to make the proposed method. To show the performance of our proposed method, we collect and analyze the patent documents from patent databases. In our experimental results, we compared the value of the Akaike information criterion (AIC) of the proposed model with traditional models, such as linear model, generalized linear model and zero-inflated Poisson model. Additionally, we illustrated that the AIC value of our proposed model is smaller than others. Therefore, we verify the validity of this paper.

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Section: Proposed Modelmentioning

confidence: 99%

Zero-Inflated Patent Data Analysis Using Compound Poisson Models

Park

Jun

2023

Applied Sciences

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“…A misspecified Bayesian model can yield an ill-behaved asymptotic posterior distribution [23][24][25]. The work in [26] finds that the outperformance of nonparametric methods increases with higher demand variability.…”

Section: Review Of Demand Forecastingmentioning

confidence: 99%

“…Assuming that the demand distribution is a multistage Poisson distribution, Seeger et al constructed a nonparametric Bayesian model [2]. Babai et al proposed a nonparametric Bayesian method (CPB), assuming that the demand follows a compound Poisson-geometric distribution [26]. However, there is no known conjugate prior that leads to a posterior distribution in a closed form.…”

Section: Review Of Demand Forecastingmentioning

confidence: 99%

Bayesian Network Demand-Forecasting Model Based on Modified Particle Swarm Optimization

Hu,

2023

Applied Sciences

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With the increasing variety of products, the increasing substitutability of products, and the trend of customized products, the volatility of market demand is increasing, which poses a challenge to make accurate demand forecasting. The Bayesian method is particularly promising and appealing when the data fluctuate greatly. This paper proposes a product-demand forecasting model based on multilayer Bayesian network, which introduces hidden layer variables and volatility factors to meet the time series connection and volatility of the demand data. However, most studies use sampling methods to estimate the parameters. We use Bayesian maximum a posteriori estimation to estimate the model parameters and introduce an improved particle swarm optimization algorithm (MPSO) to optimize the objective function. In order to increase the diversity of the particle population and accelerate the convergence, an adaptive particle velocity, position updating strategy, and nonlinear changing inertia weight are introduced in the algorithm. Finally, RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error) are used as the evaluation criterion to conduct experiments on six different datasets, and the experimental results are compared with the results of the ARIMA (autoregressive integrated moving average model) method and PSO algorithm. The experimental results show that the method has a good prediction effect. It provides a new idea for demand forecasting in the supply chain.

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“…Figure 1 visualises four resulting demand patterns that can be identified using these two metrics and their associated threshold values. Demand is typically characterised as intermittent for patterns that have infrequent demand with low variability in quantity (with cut-off values in the literature suggesting an ADI > 1.32 and a CV 2 < 0.49), and as lumpy for demand that is highly variable in both frequency and quantity (ADI > 1.32 and CV 2 > 0.49) [20,21]. As stated in Section 1, various studies have found that spare part demand in aviation tends to be intermittent or lumpy [1,4,14].…”

Section: Spare Part Demand Characterisation and Forecastingmentioning

confidence: 99%

The Influence of Repair Quality on Aircraft Spare Part Demand Variability

Heijenrath,

Verhagen

2023

Aerospace

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Accurate estimation of spare part demand is challenging in the case of intermittent or lumpy demand, characterised by infrequent demand occurrence and variability in demand size. While prior research has considered the effect of exogenous variables on spare part demand, there is a lack of research considering the effects of repair quality and aggregated spare part demand behaviour across fleets of assets under the influence of multiple simultaneously acting drivers of failure. This research provides new insights towards the problem of estimating variable spare part demand through modelling and simulation of the effects of multiple, simultaneously considered spare part demand drivers. In particular, a contribution to the state of the art is introduced by the use of a Branching Poisson Process (BPP) to model repair quality effects for spare part demand generation in conjunction with several demand drivers. The approach is applied in a numerical study which involves component failure characteristics based on real-life data from an aircraft maintenance, repair and overhaul (MRO) provider. It is shown that repair quality improvements drive down the variance in the demand and the total number of failures over time, and outperform the effect of environmental drivers of failure in terms of demand generation.

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A compound-Poisson Bayesian approach for spare parts inventory forecasting

Cited by 19 publications

References 34 publications

Zero-Inflated Patent Data Analysis Using Compound Poisson Models

Zero-Inflated Patent Data Analysis Using Compound Poisson Models

Bayesian Network Demand-Forecasting Model Based on Modified Particle Swarm Optimization

The Influence of Repair Quality on Aircraft Spare Part Demand Variability

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