Forecasting vehicle's spare parts price and demand

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...

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

International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020

In this paper, we research the use of regulated AI method to foresee the cost of trade-in vehicles or vehicles. The expectations depend on authentic information gathered from a legitimate source. Distinctive method numerous straight relapse examination have been utilized to make the Prediction. The expectation depends on certain highlights that aren't mulled over at the hour of estimation. In this work, the estimation depends on a blend of highlights that will help to decide the cost for utilized vehicles. The investigation has demonstrated great proficiency over datasets to predict the model. I. INTRODUCTION Predicting the cost of trade-in vehicles is both a significant and fascinating issue in vehicle selling market. As per information acquired from the National Transport Authority [3], the quantity of vehicles enlisted somewhere in the range of 2003 and 2013 has seen a staggering increment of 234%. From 68, 524 vehicles enrolled in 2003, this number has now arrived at 160, 701. With troublesome financial conditions, almost certainly, deals of secondhand imported (reconditioned) vehicles and trade-in vehicles will increment. It is accounted for in [2] that the deals of new vehicles have enrolled an abatement of 8% in 2013. In many created nations, it is entirely expected to rent a vehicle as opposed to getting it altogether. A rent is a coupling contract between a purchaser and a dealer (or an outsider-typically a bank, protection firm or other budgetary foundations) in which the purchaser must compensation fixed portions for a pre-characterized number of months/years to the merchant/financer. After the rent time frame is finished, the purchaser has the likelihood to purchase the vehicle at its lingering esteem, for example its normal resale esteem. In this manner, it is of business enthusiasm to vender/financers to have the option to anticipate the rescue esteem (remaining estimation) of vehicles with precision. On the off chance that the remaining worth is under-evaluated by the merchant/financer toward the start, the portions will be higher for the customers who will absolutely then decide on another dealer/financer. In the event that the remaining worth is over-assessed, the portions will be lower for the customers however then the merchant/financer may have a lot of trouble at selling these expensive trade-in vehicles at this over-evaluated lingering esteem. Subsequently, we can see that assessing the cost of trade-in vehicles is of high business significance too. Anticipating the resale estimation of a vehicle is definitely not a straightforward undertaking. It is trite information that the estimation of trade-in vehicles relies upon various variables. The most significant ones are generally the age of the vehicle, its make (and model), the beginning of the vehicle (the first nation of the producer), its mileage (the quantity of kilometers it has run) and its pull. Because of rising fuel costs, efficiency is likewise of prime significance. Sadly, practically speaking, a great many people don't know precisely how much fuel their vehicle expends for every km driven. Different factors, for example, the kind of fuel it utilizes, the inside style, the slowing mechanism, quickening, the volume of its chambers (estimated in cc), wellbeing file, its size, number of entryways, paint shading, weight of the vehicle, shopper surveys, renowned honors won by the vehicle producer, its physical state, regardless of whether it is a games vehicle, whether it has journey control, whether it is programmed or manual transmission, whether it had a place with an individual or an organization and different alternatives, for example, forced air system, sound framework, power guiding, astronomical wheels, GPS pilot all may impact the cost too. Some uncommon elements which purchasers append significance are the neighborhood of past proprietors, regardless of whether the vehicle had been associated with genuine mishaps and whether it is a woman driven vehicle. The look and feel of the vehicle absolutely contributes a great deal to the cost. As should be obvious, the cost relies upon an enormous number of variables. Sadly, data pretty much every one of these variables are not constantly accessible and the purchaser must settle on the choice to buy at a specific cost dependent on hardly any components as it were. In this work, we have considered only a small subset of the factors mentioned above. More details are provided in Section III. This paper is organized as follows. In the next section, a review of related work is provided. Section III describes the methodology while in section IV, we describe the result of machine learning technique to predict the price of used cars. Finally, we end the paper with a conclusion with some pointers towards future work.

2016

In the South Korean Navy the demand for many spare parts is infrequent and the volume of items required is irregular. This pattern, known as non-normal demand, makes forecasting difficult. This paper uses data obtained from the South Korean Navy to compare the performance of various forecasting methods that use hierarchical and direct forecasting strategies for predicting the demand for spare parts. A simple combination of exponential smoothing models was found to minimise forecasting errors. A simulation experiment verified that this approach also minimised inventory costs.

2013

Abstract––In this paper the objective is to determine the optimal allocation of spares for replacement of defective parts on-board of a usage. The minimization of the total supply chain cost can only be achieved when optimization of the base stock level is carried out at each member of the supply chain. A serious issue in the implementation of the same is that the excess stock level and shortage level is not static for every period.This has been achieved by using some forecasting and optimization techniques. Optimal inventory control is one of the significant tasks in supply chain management. The optimal inventory control methodologies intend to reduce the supply chain cost by controlling the inventory in an effective manner, such that, the supply chain members will not be affected by surplus as well as shortage of inventory.We focus specifically on determining the most probable excess stock level and shortage level required for inventory optimization in the supply chain so that the...

International Journal of Production Research, 2018

Accurate demand forecasts are essential to the inventory control of spare parts. There is a plethora of statistical methods developed in the academic literature to deal with the forecasting of spare parts demand. These methods belong to the parametric and the non-parametric approaches. Within the second approach, the bootstrapping methods are the most considered ones. Despite that bootstrapping methods have shown a good empirical performance in comparison with their parametric counterparts, none of the available studies highlight the necessity to bring together its related state of knowledge and critically review the relevant research advancements. The present paper bridges this gap by reviewing the literature that deals with the bootstrapping approach and by discussing some of its statistical properties. This yields a better understanding of its framework, and hence, retrieves more robust explanations of the observed mixed-performances of the available bootstrap-based forecasting methods. This paper reviews as well the service level models associated with the bootstrapping approach with an emphasis on the fill rate models.

Industrial Engineering Journal, 2018

Sales forecasting is the most important activity of the Production planning and control and success of the firm is highly driven by accuracy of the forecasting. Sales forecasting aims to determine the demand of the product which helps in determining the available facilities and resources with the firm to meet the demand and procure more if necessary. In this research work, various critical success factors were identified and their overall and individual impact on sales forecasting was determined on the basis of real-time primary data and secondary data of the automobile firm situated at Pithampur specifically in the commercial vehicle segment study were done. In this paper, an attempt is made to apply regression analysis on external and internal factors to forecast sales of the company. This method was chosen as the best among others as it analyzes all driving factors of the sales unlike time-series forecasting technique. This study adds to the literature of sales forecasting, an approach that analyze and compare various macro and micro factor's sensitivity towards the forecasting accuracy.

International Journal of Production Economics, 2013

The performance of alternative forecasting methods that use hierarchical and direct forecasting strategies for predicting spare parts demand depends on the demand features. This paper uses data obtained from the South Korean Navy to identify the demand features of the spare parts that influence on the relative performance of the alternative forecasting methods. A logistic regression classification model for predicting the relative performance of the alternative forecasting methods for the spare parts demand by multivariate demand features was developed. This classification model minimised forecasting errors and inventory costs.

European Journal of Operational Research, 2017

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IP innovative publication pvt. ltd, 2019

Spare part inventory will be forecasted at higher level if historical demand has spike in few of the months. These few months spiked order lines need to be removed or streamline if it is not genuine for better forecasting. But, in spare part inventory forecasting, number of parts & order lines are very high. It is not practical to remove these order lines one by one. These order lines have been generated mainly due to retro fitment, filed fix, one-time order etc. Means these demands are less probable to generate once again. In this paper, researcher has shown new method of demand normalization which help to streamline the data instead of removing spiked order lines one by one. New method of demand normalization helps to improve the forecasting. Researcher has applied new method on spare part inventory data which was received from one of the large automobile company & result of it shown improvement in forecast accuracy. Researcher mainly done the experiment on fast and medium mover parts as per company requirement.

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.

OISAA Journal of Indonesia Emas, 2019

PT Riung Mitra Lestari adalah salah satu perusahaan kontraktor penambangan baru di Indonesia yang didirikan pada tahun 2006. Diidentifikasi bahwa Riung membuat prediksi permintaan (bulanan) untuk menentukan jumlah stok yang 'tepat'. Namun, 'tingkat akurasi' metode peralaman yang digunakan oleh Riung tidak dapat diukur, sementara meningkatkan akurasi peralaman dapat meningkatkan tingkat kinerja perusahaan (yaitu meminimalkan kemungkinan kehabisan persediaan dan inventaris yang berlebihan). Laporan ini bertujuan untuk menemukan metode prediksi terbaik bagi Riung untuk mengelola kontrol inventaris suku cadangnya; yang dimulai dengan mengelompokkan suku cadang menggunakan metode analisis ABC untuk menentukan penanganan pengendalian persediaan yang tepat untuk setiap kelompok. Data menunjukkan bahwa ada 37 suku cadang di Riung yang termasuk dalam kategori A, oleh karena itu diperlukan kebijakan yang lebih ketat. Hal ini menjadi dasar untuk memilih kategori A sebagai objek utama analisis dalam penelitian ini. Penelitian ini akan membandingkan tiga metode prediksi, Moving Average (MA), Weight Moving Average (WMA) dan Single Exponential Smoothing (SES). Kinerja setiap metode prediksi dinilai dengan menghitung nilai kesalahan prediksi (MAD, MSE, MAPE, TS). Diketahui bahwa ada empat suku cadang dengan metode Moving Average sebagai metode prediksi terbaik, delapan suku cadang terbaik dengan menggunakan WMA dan sisanya 25 suku cadang terbaik dengan metode Single Exponential Smoothing (SES).

West African Journal of Industrial and Academic Research, 2012

The immense dynamics and criticality of spare parts and the large revenues accrued, as essential motivating factors for providing control in manufacturing companies has never showed any sign of decrease. In fact, in the vast technological environment of today the complexities of Spare Parts Inventory Control enjoys more insights from analysts (in the Management Science, Information Technology and Industrial/Mechanical Engineering fields) as inventory policies get modeled to ensure customer satisfaction. In other words manual ways of handling inventory has failed to cope with factors like stochastic demands, better service levels, and shorter lead times and providing perfect heuristics for Inventory-related decision making. To this end, significant results for forecasting spare parts requirements can be achieved through the use of novel decision models. Besides the selling of vehicles, the spare parts of various models of heavy duty vehicles are stored and managed by the company. The...