The inventory management system for automobile spare parts in a central warehouse

Li, S. G.; Kuo, Xin

doi:10.1016/j.eswa.2006.12.003

Cited by 75 publications

(34 citation statements)

References 15 publications

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“…Thus, iterative algorithms are used to find a solution close to the optimal one. Many studies have presented some efficient solution procedures to obtain the approximating decision rules for such problems (Bhattacharya, 2005;Chen, 2005;Ghezavati, Jabal-Ameli, & Makui, 2009;Hausman et al, 1998;Li & Kuo, 2008;Lin, Shie, & Tsai, 2009;Swaminathan & Tayur, 1999;Thomas, 1991).…”

Section: Introductionmentioning

confidence: 99%

Heuristic method on solving an inventory model for products with optional components under stochastic payment and budget constraints

Wang

2010

Expert Systems with Applications

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Section: Introductionmentioning

confidence: 99%

Heuristic method on solving an inventory model for products with optional components under stochastic payment and budget constraints

Wang

2010

Expert Systems with Applications

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“…Therefore, Li and Kuo (2008) introduced neural network technology into inventory model to predict the demand for spare parts [28]. In order to meet all the time based service level limited and cost constraints limited, Kutanoglu and Mahajan (2009) developed implicit enumeration method to determine the optimal basic local warehouse inventory levels [29].…”

Section: Literature Reviewmentioning

confidence: 99%

The Laptop Spare Parts Studying under Considering Users’ Repair Willingness

Lu¹,

Wang²

2015

Int. j. simul. model.

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With the development of science and technology, laptop becomes very popular. Laptop manufacturing firms are concerning how much spare parts they shall prepare to meet the demand of repairing. The quantities of spare parts are not only related to laptop life time, but also concern with users' repair willingness. We propose an approach to calculate the repair probability of laptop parts combined Poisson Process with users' repair willingness. In this study, the users' repair willingness relates laptop parts failure frequency. The new approach's verification is carried on by simulation. The significant test results show that simulation data fit theoretical value very well. By survey data and simulation result, we find that after four times broken for a laptop, a user will give up repairing it. The calculation result of new approach shows that spare parts quantities are entirely lower than traditional theory. From this point, if laptop manufacturers know their each parts lifetime, with the help of this paper, they can cut down their spare parts and save their service costs reasonable than before.

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“…Such investigation takes the advantage of the direct availability of historic information about the repairs of components implying the demand of the specific parts for which the forecast is being performed. Li [20] conducted a study where a decision-making system was developed based on an EFNN (enhanced fuzzy neural network), which is used to forecast the spare-parts demand on the basis of a causal perspective. The aforementioned study takes advantage of the great quantity of information available to the enterprise, since this handles not only the distribution of new automobiles but also the late spare-parts supply itself.…”

Section: Spare-parts Demand Forecastingmentioning

confidence: 99%

Automobile spare-parts forecasting: A comparative study of time series methods

Vargas¹,

Cortés²

2017

Int. J. Automot. Mech. Eng.

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In Mexico, the automotive industry is considered to be strategic in the industrial and economic development of the country because it generates production, employment and foreign exchange. Good demand forecasts are needed for better manufacturing management. The time series modelling tools applied to the monthly demand forecasting of automobile spare parts in Mexico are assessed, for the case of a transnational enterprise, considering affordability. The classic methods of moving averages, final value and exponential smoothing, the prestigious autoregressive integrated models of moving averages (ARIMA), the rarely implemented artificial neural networks (ANNs) and the very little explored ARIMA-ANNs hybrid models are compared. A good performance of the models involving ANNs is observed, but they were not as steady as the ARIMA models in the post-sample periods. The mean absolute percentage error (MAPE) was reduced from an original 57% to 32.65%. The obtained results could help demonstrate the importance of improving industrial forecasting methodologies for better planning.

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The inventory management system for automobile spare parts in a central warehouse

Cited by 75 publications

References 15 publications

Heuristic method on solving an inventory model for products with optional components under stochastic payment and budget constraints

Heuristic method on solving an inventory model for products with optional components under stochastic payment and budget constraints

The Laptop Spare Parts Studying under Considering Users’ Repair Willingness

Automobile spare-parts forecasting: A comparative study of time series methods

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