scholarly journals ANÁLISE DO PROCESSO DE PREDIÇÃO DE DEMANDA DE PEÇAS DE REPOSIÇÃO COM DEMANDA INTERMITENTE / ANALYSIS OF THE SPARE PARTS DEMAND PREDICTION PROCESS WITH INTERMITTENT DEMAND

2020 ◽  
Vol 6 (9) ◽  
pp. 66882-66904
Author(s):  
Sérgio Neri de Almeida ◽  
José Helvécio Martins
Keyword(s):  
Spare Parts ◽  
Intermittent Demand ◽  
Demand Prediction

scholarly journals Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kyle C. McDermott ◽  
Ryan D. Winz ◽  
Thom J. Hodgson ◽  
Michael G. Kay ◽  
Russell E. King ◽  
...  
Keyword(s):  
Supply Chain ◽  
Additive Manufacturing ◽  
Production Capacity ◽  
Spare Part ◽  
Spare Parts ◽  
Mixed Integer ◽  
Network Configuration ◽  
Intermittent Demand ◽  
Content Type ◽  
Demand Patterns

PurposeThe study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns.Design/methodology/approachThis work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network.FindingsThis research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance.Research limitations/implicationsThis research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements.Originality/valueThis research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.

Exploiting elapsed time for managing intermittent demand for spare parts

2017 ◽  
Vol 258 (3) ◽  
pp. 958-969 ◽  
Author(s):  
Clint L.P. Pennings ◽  
Jan van Dalen ◽  
Erwin A. van der Laan
Keyword(s):  
Spare Parts ◽  
Intermittent Demand ◽  
Elapsed Time

scholarly journals Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259284
Author(s):  
Hailan Ran
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Inventory Management ◽  
Management System ◽  
Prediction Method ◽  
Spare Parts ◽  
Collaborative Computing ◽  
Demand Prediction ◽  
Industrial Enterprises ◽  
Inventory Management System

The present work aims to strengthen the core competitiveness of industrial enterprises in the supply chain environment, and enhance the efficiency of inventory management and the utilization rate of inventory resources. First, an analysis is performed on the supply and demand relationship between suppliers and manufacturers in the supply chain environment and the production mode of intelligent plant based on cloud manufacturing. It is found that the efficient management of spare parts inventory can effectively reduce costs and improve service levels. On this basis, different prediction methods are proposed for different data types of spare parts demand, which are all verified. Finally, the inventory management system based on cloud-edge collaborative computing is constructed, and the genetic algorithm is selected as a comparison to validate the performance of the system reported here. The experimental results indicate that prediction method based on weighted summation of eigenvalues and fitting proposed here has the smallest error and the best fitting effect in the demand prediction of machine spare parts, and the minimum error after fitting is only 2.2%. Besides, the spare parts demand prediction method can well complete the prediction in the face of three different types of time series of spare parts demand data, and the relative error of prediction is maintained at about 10%. This prediction system can meet the basic requirements of spare parts demand prediction and achieve higher prediction accuracy than the periodic prediction method. Moreover, the inventory management system based on cloud-edge collaborative computing has shorter processing time, higher efficiency, better stability, and better overall performance than genetic algorithm. The research results provide reference and ideas for the application of edge computing in inventory management, which have certain reference significance and application value.

scholarly journals Improving intermittent demand forecasting based on data structure

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
S. Fatemeh Faghidian ◽  
◽  
Mehdi Khashei ◽  
Mohammad Khalilzadeh ◽  
◽  
...  
Keyword(s):  
Hybrid Model ◽  
Prediction Models ◽  
Moving Average ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Percentage Error ◽  
Complex Nature ◽  
Intermittent Demand ◽  
Classical Models ◽  
The Cost

Forecasting spare parts requirements is a challenging problem, because the normally intermittent demand has a complex nature in patterns and associated uncertainties, and classical forecasting approaches are incapable of modeling these complexities. The present study introduces a hybrid model that can impressively overcome the limitations of classical models while simultaneously using their unique advantages in dealing with the complexities in intermittent demand. The strategy of the proposed hybrid model is to use the three individual autoregressive moving average (ARMA), single exponential smoothing (SES), and multilayer perceptron (MLP) models simultaneously. Each of them has the potential of modeling a different structure and patterns of behavior among the data. The accuracy in forecasting ability is also increased by the suitable examination of these in the intermittent data. Croston’s method is the backbone of the suggested model. The proposed hybrid model is based on CV2 and ADI criteria, which improve its efficacy in examining inappropriate structures by reducing the cost of inappropriate modeling while increasing the prediction model accuracy. Using these results prevents the hybrid model from being confused or weakened in the modeling of all groups and reduces the risk of choosing the disproportionate model. The accuracy of prediction models was evaluated and compared using mean absolute percentage error (MAPE) by implementing an example, and promising results were achieved.

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