Electrical Spare Parts Demand Forecasting

This research was conducted at one of the motorcycle dealers in Indonesia. Besides selling motorcycles, this dealer also provides services to repair motorcycles and sells genuine motorcycle parts. Inventory management which the company carried out is still not good enough because there are still demand for spare parts from consumers that cannot be fulfilled by the company. The purpose of this study is to draw up a plan to control spare parts by paying attention to the spare parts that need to be considered, estimating the exact number of spare parts demand, knowing the smallest total inventory cost, knowing the amount of safety stock needed, and knowing when to reorder. In preparing the spare parts control, the methods used are ABC analysis, demand forecasting method, and EOQ method. The results of this study are plans to control the inventory of Tire, Rr. such as the forecasting sales of Tire, Rr. as many as 17338, economic order quantity of Tire Rr are 2158 units, the number of safety stocks of Tire, Rr. needed in 2020 are 1738 units, and the reorder point in 2020 is 8 times with the total inventory cost for Tire, Rr. in 2020 is Rp. 30,009,005.

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Improving intermittent demand forecasting based on data structure

Journal of Engineering Research ◽

10.36909/jer.v9i1.8667 ◽

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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