Optimizing Automotive Spare Parts Inventory: A Comparative Study of Quantitative Forecasting Techniques (original) (raw)
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Automobile spare-parts forecasting: A comparative study of time series methods
International Journal of Automotive and Mechanical Engineering, 2017
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.
Demand forecasting and inventory control: A simulation study on automotive spare parts
International Journal of Production Economics, 2015
This paper presents results of a large-scale simulation study on spare parts demand forecasting and inventory control to select best policies within each SKU category. Simulations were conducted over 10,032 SKUs of an automaker that operates in Brazil, considering six years of demand data. Literature review drove the selection of different models simulated. The study included three alternatives to record demand data (individual orders data, weekly and monthly time buckets), three demand forecasting models (SMA -Simple Moving Average, SBA -Syntetos-Boylan Approximation and Bootstrapping) and six models for demand distribution during lead-time (Normal, Gamma, NBD-Negative Binomial Distribution, compound Poisson-Normal, compound Poisson-Gamma and Bootstrapping) resulting in 17 "combined" policies. These policies were applied under (s, nQ) inventory control (reorder point, multiples of fixed order quantity), considering two alternative frequencies for model parameters revision (monthly and semi-annually) and four Target-Fill-Rates (TFR¼ 80%, 90%, 95% and 99%), totalizing 136 simulation runs over each SKU. Parameter values (s, Q) were calculated towards TFR using methods from literature. Performance of each combined policy was measured by total costs and RFR -Realized-Fill-Rate. Major contributions of the research are the policy recommendations within each SKU category, a new Bootstrapping procedure and the highlight of Single Demand Approach (SDA) as a promising area for future theoretical and empirical studies. Results shall be used as guideline for practitioners under similar operations.
Journal of Management Science & Engineering Research
The primary intent of the current research is to provide insights regarding the management of spare parts within the supply chain, in conjunction with offering some methods for enhancing forecasting and inventory management. In particular, to use classical forecasting methods, the use of weak and unstable demand is not recommended. Furthermore, statistical performance measures are not involved in this particular context. Furthermore, it is expected that maintenance contracts will be aligned with different levels. In addition to the examination of some literature reviews, some tools will guide us through this process. The article proposes new performance analysis methods that will help integrate inventory management and statistical performance while considering decision maker priorities through the use of different methodologies and parts age segmentation. The study will also identify critical level policies by comparing different types of spenders according to the inventory manageme...
An integrated approach for demand forecasting and inventory management optimisation of spare parts
International Journal of Simulation and Process Modelling, 2015
In this paper, we develop and test an advanced model, based on discrete-event simulation, whose purpose is to forecast the demand of spare parts during the whole lifetime of a complex product, such as, for instance, an industrial machine. To run the model, the relevant data of the product manufactured by a targeted company should be collected. With those data, the model provides an estimate of the optimal level of spare parts inventory the company should keep available. The data provided by the model are subsequently applied to a case example, referring to a hypothesised company, manufacturing industrial plants. The application is carried out considering two scenarios, i.e., a 'traditional' and an 'advanced' approach for demand forecasting, this latter reflecting the circumstance where the company makes use of the proposed forecasting method. The comparison of the outcomes obtained in the two scenarios highlights the efficiency and resolution capacity of the model developed.
Emerging Markets : Business and Management Studies Journal
The global economic crisis has reached the world today, forcing many customers to become more cost aware in their search for better quality and service, and forcing corporate organizations to discover more effective and efficient ways to compete among them. The main objective of this research is to choose the best forecasting method to predict the demandfor spare parts at PT. XYZ highly fluctuating, and to avoid or minimize stockouts. The demand for high-priced spare partsand capital goods is considered discontinuous if it is random and contributes a large part of the inventory value. Fluctuating demand for goods will be difficult to predict, and inaccurate estimates can cause huge losses for the company due to obsolescence of spare parts or unfulfilled demand for spare parts. Running a successful company operation today requires organizational strength to supply the needs of its customers. This study discusses the appropriate demand forecasting method for the fluctuation demand for...
Modeling and long-term forecasting demand in spare parts logistics businesses
International Journal of Production Economics
In order to provide high service levels, companies competing in the electronics manufacturing sector need to ensure the availability of spare parts for repair and maintenance operations. This paper examines the purchase life-cycles of electronic spare parts and presents a new way of modeling and forecasting spare part demand for electronic commodities in the spare parts logistics services. The presented modeling methodology is founded on the assumption that the purchase life-cycles of spare parts can be described by a curve with short term fluctuations around it. For this purpose, a flexible Demand Model Function is introduced. The proposed forecasting method uses a knowledge discovery-based approach that is built upon the combined application of analytic and soft computational techniques and is able to indicate the turning points of the purchase life-cycle curve. The novelty lies in the fact that the model function has certain characteristics which support describing and interpreting the demand trend as a function of time. The application of our methodology is mainly advantageous in long-term forecasting, it can be especially useful in supporting purchase planning decisions in the ramp-up and declining phases of purchase life-cycles of product specific spare parts. A demonstrative example is used to illustrate the applicability of the proposed methodology. Its forecasting capability is
Forecasting vehicle's spare parts price and demand
Journal of Quality in Maintenance Engineering
PurposeThese days vehicles' spare parts (SPs) are a very big market, and there is a very high demand for these parts. Forecasting vehicles' SPs price and demand are difficult because of the lack of data and the pricing of the SPs is not following the normal value chain methods like normal products.Design/methodology/approachA proposed model using multiple linear regression was developed as a guide to forecasting demand and price for vehicles' SPs. A case study of selected hybrid vehicle is held to validate the results of the research. This research is an original study depending on quantitative and qualitative methods; some factors are generated from realistic data or are calculated using numerical equations and the analytic hierarchy process (AHP) method; online questionnaire and expert interview survey.FindingsThe price and demand for SPs have a linear relationship with some independent variables is the hypothesis that is tested. Even though the proposed models are gen...
International Journal of Business Administration, 2020
In order to improve the operations planning of two companies, whose main business is to be chemical products suppliers in Mexico, it was made the sales forecast of a fourth year of operations, using the monthly sales data information of the three previous years. The objective of the chemical suppliers forecast was to be in a better position to satisfy the multiple and varied needs of their clients, which demand different quantities of products and have different consumption patterns. The sales forecast was made by the next six techniques: Simple Moving Average (SMA), Weighted Moving Average (WMA), Trend Projection (TP), Exponential Smoothing (ES), Simple Linear Regression (SLR), and the recently proposed (Castillo, et al. 2016) technique called: Double-Weighted Moving Average (DWMA). The three years monthly sales data of 61 products, handled by the two companies, were processed in order to obtain the monthly forecast of the fourth year. After the fourth year, the forecasted data wer...
Inventory control performance of various forecasting methods when demand is lumpy
2010
This study evaluates a number of methods in forecasting lumpy demand – single exponential smoothing, Croston’s method, the Syntetos-Boylan approximation, an optimally-weighted moving average, and neural networks (NN). The first three techniques are well-referenced in the intermittent demand forecasting literature, while the last two are not traditionally used. We applied the methods on a time series dataset of lumpy demand. We found a simple NN model to be superior overall based on several scalefree forecast accuracy measures. Various studies have observed that demand forecasting performance with respect to standard accuracy measures may not translate into inventory systems efficiency. We simulate on the same dataset a periodic review inventory control system with forecast-based order-up-to levels. We analyze resulting levels of on-hand inventory, shortages, and fill rates, and discuss our findings and insights.
Electrical Spare Parts Demand Forecasting
Elektronika ir Elektrotechnika, 2014
In this paper is presented a research of electrical spare parts demand forecasting through application of conventional (moving average, exponential smoothing and naive theory), more sophisticated forecasting techniques (support vector regression, feed-forward neural networks) and adaptive model selection methodologies. Electrical spare parts demand forecasting is a fundamental task that should be performed in order to improve SCM (supply chain management). If it would be possible to know what the demand for electrical parts will be in the future, the logistics of the companies that manufacture electrical parts or retailers could be managed more accurately: selection of appropriate warehouse safety limits for each part and ability to plan the resources more precisely. Customer sales and marketing departments always perform formal forecasts, this is usually done through application of conventional methods in order to prepare future plans. Experimental results reveal that application of SVR technique guarantees the best and precise results of forecasting of weekly and daily demand of electrical parts. Furthermore, application of adaptive methodology in order to select adaptive model allowed substantially to increase forecasting accuracy.