Forecasting Spare Parts Demand Using Statistical Analysis

Hemeimat, Raghad; Al-Qatawneh, Lina; Arafeh, Mazen; Masoud, Shadi

doi:10.4236/ajor.2016.62014

Cited by 16 publications

(9 citation statements)

References 10 publications

(12 reference statements)

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“…The results obtained by Hemeimat et al [31] show that moving averages can provide very competitive forecasts, compared with other techniques, even in the case of exponential smoothing, which has been found not …”

Section: Resultsmentioning

confidence: 97%

“…It is also important to identify the nature of the item under study. This can be achieved when classifying them under different criteria, according to our needs and several studies on this matter have been performed, with classifications based on the periodicity of the demand [28], the manufacturing volume [29], or even more complex classifications [30] that should be of help for selecting proper methods and tools for modeling spare-parts behaviour [31]. Based on the literature review up to this point, it was possible to decide that the best tool for modelling the automobile spare-parts demand, given its behaviour and little availability of information, is by means of a time series perspective, so that it has been decided to develop the ARIMA and ANNs models using only the information available in order to obtain forecasts.…”

Section: Spare-parts Demand Forecastingmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 97%

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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“…The current method is moving average which is used to forecast for every pattern of demand whether it is intermittent, lumpy, erratic, or smooth, but different spare parts associate diverse demand patterns and forecasting methods (Hemeimat et al, 2016). The aim of current study is to investigate the prevailing forecasting approaches for intermittence demand and select the demand estimations to upsurge forecasting performance of the selected auto service station.…”

Section: Methodsmentioning

confidence: 99%

A comparative study of forecasting methods for sporadic demand in an auto service station

Mor

Nagar

Bhardwaj

2019

IJBFMI

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Spare parts are essential in the automobile sector and forecasting of spare parts has always been the vital prospects in an automobile service parts station. In this paper, a comparison and efforts have been made at the service station of a reputed organisation to reduce the errors in demand forecasting of intermittent demand items. The errors are compared for various methods using mean absolute scaled error (MASE) and Syntetos and Boylan approximation (SBA) method which exhibited the least error for intermittent demand and lumpy demand pattern. While single exponential smoothing method is used for smooth and erratic demand pattern. All calculations are done in MS Excel and solver tool to find optimal values of smoothing parameters.

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“…(TSB). The results indicated that Root Mean Square Error (RMSE) showed closer performances for all the five approaches [12]. Another study on spare parts of aircraft revealed that it is meaningful to measure performance based on the quality of repairs rather than the level of spare parts.…”

Section: Literature Backgroundmentioning

confidence: 97%

Procurement Methodologies to Optimize the Inventory Levels of Spare Parts

Anbuudayasankar¹,

N²,

Deepan³

et al. 2019

IJRTE

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Inventory is essential for the smooth running of any organization. It acts as a link between procurement and sales. Inventory constitutes of current assets/ working capital of the organization. Thus, it is very essential to have suitable control over inventories. The purpose of inventory management is to ensure availability of parts in sufficient numbers as and when required and also to minimize the accumulation. The main objective of this paper is to devise an inventory management method for spare parts management system in order to utilize the space effectively and to stabilize FIFO system which would help the company personnel to keep record of spares precisely. A three dimensional approach is performed using sales order line frequency, seasonal frequency and sales value through ABC analysis. In addition to this, four different procurement techniques are adopted viz., one bin, two bins, multi bins and zero bin. Demand variation is measured for a period of last one year and this parameter is included during the calculation of the inventory maintenance. It is evident that a large amount of money is saved without compromising the customer order fulfilment.

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Forecasting Spare Parts Demand Using Statistical Analysis

Cited by 16 publications

References 10 publications

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

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

A comparative study of forecasting methods for sporadic demand in an auto service station

Procurement Methodologies to Optimize the Inventory Levels of Spare Parts

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