Parts Demand Forecasting Research Articles

Innovative framework for effective service parts management in the automotive industry

Effective service parts management and demand forecasting are crucial for optimizing operations in the automotive industry. However, existing literature lacks a comprehensive framework tailored to the specific context of the Thai automotive sector. This study addresses this gap by proposing a strategic approach to service parts management and demand forecasting in the Thai automotive industry. Drawing on a diverse set of methodologies, including classical time series models and advanced machine learning techniques, various forecasting models were assessed to identify the most effective approach for predicting service parts demand. Categorization of service parts based on demand criteria was conducted, and decision rules were developed to guide stocking strategies, balancing the need to minimize service disruptions with cost optimization. This analysis reveals substantial cost savings potential through strategic stocking guided by the developed decision rules. Furthermore, evaluation of the performance of different forecasting models recommends the adoption of Support Vector Regressor (SVR) as the most accurate model for forecasting service parts demand in this context. This research contributes to the automotive service industry by providing a nuanced framework for service parts management and demand forecasting, leading to cost-effective operations and enhanced service quality. The findings offer valuable insights for practitioners and policymakers seeking to improve efficiency and sustainability in the Thai automotive sector.

A Predictive Model with Data Scaling Methodologies for Forecasting Spare Parts Demand in Military Logistics

This study addresses the challenge of accurately forecasting demand for maintenance-related spare parts of the K-X tank, influenced by high uncertainty and external factors. Deep learning models with RobustScaler demonstrate significant improvements, achieving an accuracy of 86.90% compared to previous methods. RobustScaler outperforms other scaling models, enhancing machine learning performance across time series and data mining. By collecting eight years’ worth of demand data and utilising various consumption data items, this study develops accurate forecasting models that contribute to the advancement of spare parts demand forecasting. The results highlight the effectiveness of the proposed approach, showcasing its superiority in accuracy, precision, recall, and F1-Score. RobustScaler particularly excels in time series analysis, further emphasizing its potential for enhancing machine learning performance on diverse datasets. This study provides innovative techniques and insights, demonstrating the effectiveness of deep learning and data scaling methodologies in improving forecasting accuracy for maintenance spare parts demand.

Short-term auto parts demand forecasting based on EEMD—CNN—BiLSTM—Attention—combination model

Demand forecasting of auto parts is an essential part of inventory control in the automotive supply chain. Due to non-stationarity, strong randomness, local mutation, and non-linearity in short-term auto parts demand data, and it is difficult to predict accurately. In this regard, this paper proposes a combination prediction model based on EEMD-CNN-BiLSTM-attention. First, the model uses the ensemble empirical mode decomposition method to decompose the original data into a series of eigenmode functions and a residual item to extract more feature information. And then uses the CNN-BiLSTM-attention model to analyze each mode separately. The components are predicted, and the prediction results are summed to obtain the final prediction result. The attention mechanism is introduced to automatically assign corresponding weights to the BiLSTM hidden layer states to distinguish the importance of different time load sequences, which can effectively reduce the loss of historical information and highlight the input of critical historical time points. Finally, the final auto parts demand prediction results are output through the fully connected layer. Then, we conduct an experimental analysis of the collected short-term demand data for auto parts. Finally, the experimental results show that the prediction model proposed in this paper has more minor errors, higher prediction accuracy, and the model prediction performance is better than the other nine comparison models, thus verifying the EEMD-CNN-BiLSTM-attention model for short-term parts demand forecasting effectiveness.

Design and Development of Integrated Platform for Automobile Sales Model under the Background of Big Data

Based on the integration of the big data background and the exploration of related technologies in the enterprise service bus, this article focuses on the management strategy of auto parts, carries out theoretical analysis on parts classification, parts demand forecast, parts inventory control, and combines the characteristics of the auto parts industry. Research. At the same time, combined with the demand analysis of the actual business process of the car dealer, the car dealer business system based on the SOA integrated platform is designed. In the system design stage, the current more popular model-view-controller (MVC) three-tier design pattern is adopted to better focus on the hierarchical development of each level.

An Improved Stacking Model for Equipment Spare Parts Demand Forecasting Based on Scenario Analysis

The purpose of spare parts management is to maximize the system’s availability and minimize the economic costs. The problem of cost availability trade-off leads to the problem of spare parts demand prediction. Accurate and reasonable spare parts demand forecasting can realize the balance between cost and availability. So, this paper focuses on spare parts management during the equipment normal operation phase and tries to forecast the demand of spare parts in a specific inspection and replacement cycle. Firstly, the equipment operation and support scenarios are analyzed to obtain the supportability data related to spare parts requirements. Then, drawing on the idea of ensemble learning, a new feature selection method has been designed, which can overcome the limitations of a single feature selection method. In addition, an improved stacking model is proposed to predict the demand for spare parts. In the traditional stacking model, there are two levels of learning, base-learning, and meta-learning, in which the outputs of base learners are taken as the input of the meta learner. However, the proposed model brings the initial feature together with the output of the base learner layer as the input of the meta learner layer. And experiments have shown that the performance of the improved stacking model is better than the base learners and the traditional stacking model on the same data set.

Spare parts demand forecasting method of modern enterprises based on digital twin model

Accurate forecast of spare parts demand is of great significance for modern enterprises to provide accurate support and improve market competitiveness. In most studies, mathematical laws are used to forecast, without enough consideration of the actual operation of equipment and the fact that the accuracy of spare parts demand forecasting is not high, which cannot adapt to the new characteristics of complex equipment use environment and fierce market competition in modern enterprises. The digital twin model can be used to forecast the demand for spare parts more accurately and guide modern enterprises to carry out accurate support. By analyzing the current spare parts demand of modern enterprises, the paper puts forward the forecasting ideas of spare parts demand based on the digital twin model by using the digital twin model of equipment maintenance management in modern enterprises. In the digital twin model, the theoretical demand forecasting model of spare parts based on life distribution of replaceable units is introduced, and the sensitivity coefficient system of spare parts demand of replaceable units to operation and environment is constructed. The digital twin model is used to feedback train the sensitivity coefficient to obtain the reliable spare parts demand rules. Based on the theoretical demand and sensitivity coefficient of spare parts, the forecasting method of spare parts demand is given, and the spare parts demand forecasting model is established. Through case analysis, the feasibility and accuracy of the forecasting model are verified.

Spare Parts Demand Forecasting During Covid 19 pandemic

The research was conducted to analyze the calculation of demand forecasting and inventory of spare parts . The data collection method uses descriptive quantitative by conducting literature studies, interviews, and collecting sample data of 140 spare parts from June 2019 to June 2020. The data classification process with FSN method, obtained 13 fast moving spare parts use for calculating demand forecasting based on the Triple Exponential Smoothing method. The results from the trial error to get the smoothing value of alpha, beta and gamma errors prioritize to best MAPE <5%. Some MAPE errors above 5% are X000000 000254 MAPE = 5.68% , X000000 005566 MAPE = 21.12, X400 401 10 72 MAPE = 13.14, and X400 401 26 71 MAPE = 16.70. The calculation result of safety stock and reorder point find 4 spare parts have low level inventory have to reorder.

RNN / LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting

The spare parts demand forecasting is very much essential for the organizations to minimize the cost and prevent the stock outs. The demand of spare parts/ car sales distribution is an important factor in inventory control. The valuation of the demand is challenging as the automobile spare parts/car sales demand are often recurrent. The renowned empirical method adopts historical demand data to create the distribution of lead time demand. Although it works reasonably well when service requirements are relatively low, it has difficulty reaching high target service levels. In this paper, we proposed Recurrent Neural Networks/ Long-Short Term Memory (RNN / LSTM) with modified Adam optimizer to predict the demand for spare parts. In this LSTM, weight vectors are generated respectively. These weights are optimized using the Modified-Adam algorithm. The accuracy of the forecast and the performance of the inventory are considered in the experimental result. Experimental results confirm that RNN / LSTM with a Modified-Adam works well with minimal error compared to other existing methods. We conclude that the proposed RNN/LSTM with Modified-Adam algorithm is well suited for the prediction of automobile spare parts.

머신러닝 기반 Lumpy 수요형태의 항공기 수리부속 수요예측 정확도 개선 연구

For the spare parts of aircraft "A" operated by ROKAF(Republic of Korea Air Force), this research suggests the application of machine learning technique rather than that of time series technique in order to improve demand forecast accuracy of spare parts whose demand pattern is lumpy. Demand patterns are divided into four categories by applying Average Inter-Demand Interval(ADI) and Coefficient of Variation(CV). Only for the lumpy parts, this study applies various analysis models selected from demand history, and the probability of demand occurrence for each period for lumpy parts is predicted by applying machine learning techniques. As a result of the experiment, lumpy or erratic pattern appeared to be higher in aircraft spare parts, and smooth parts with stable demand appeared relatively lower. In addition, logistics regression, linear discriminant analysis, and decision tree technique showed a relatively better accuracy, and machine learning technique improved prediction accuracy about 5% higher than time series technique, thus proving that machine learning technique is an effective technique to for lumpy spare parts demand forecast.

Maintenance Spare Parts Demand Forecasting for Automobile 4S Shop Considering Weather Data

Maintenance spare parts demand forecasting is an important foundation of maintenance spare parts inventory control, which is an essential responsibility for managers of an automobile 4S shop. Although the existence of the effect of weather conditions on maintenance spare parts demand has been verified, the study on maintenance spare parts demand forecasting for an automobile 4S shop considering weather data has not been found. In this paper, a novel method is proposed for maintenance spare parts demand forecasting for an automobile 4S shop considering weather data. By taking into account three dimensions of weather data (i.e., temperature, visibility, and slipperiness) and delayed effects of weather conditions on maintenance spare parts demand, a vector of numerical weather data with 30 weather factors is constructed to represent the related weather conditions of a given day. Then, a back propagation (BP) neural network is trained and the weights of the 30 weather factors are determined. Similar historical cases of the target case are extracted, and two forecasting models are respectively trained based on extreme learning machine (ELM) and support vector machines (SVM) using the similar historical cases. The final forecasting model is determined by comparing the fitting precisions of the two forecasting models. The experimental study is conducted based on the real data of an automobile 4S shop. The results show that weather data is critical to maintenance spare parts demand forecasting for an automobile 4S shop, and the extraction of similar historical cases is an effective approach to capture the complex effect mechanism of weather data on maintenance spare parts demand.

데이터마이닝 기반 육군 수리부속 수요예측

우리 군에서 사용하고 있는 수리부속은 약72만 종으로, 연간 예산은 전체 국방예산의 3% (약1.3조원)을 차지하고 있다. 이와 같은 예산이 집행됨에도 불구하고 현재 우리 육군의 수리부속 예측정확도는 품목기준 70%, 수량기준 60%를 보이고 있으며, 이는 목표 장비가동률 저하에 악영향을 미친다. 따라서 본 연구에서는 수리부속 수요예측 정화도 제고를 위해 현재 육군에서 운용중인 장비정비정보체계 시스템의 에이타킴스 (ATCMS), K-9 자주포, ARTHUR-K 총 3개 장비에 대한 2012~2016년까지의 ASL (authorized storage list) 598개 수리부속 품목의 연도별 소모개수, 표준단가의 데이터 수집을 통해 데이터마이닝 예측모형 (회귀나무, random forest, 인공신경망, 다중선형회귀)을 활용하여 2017년 수요 (Y)를 예측하였으며, 예측력척도는 RMSE (평균제곱근오차)와 MAE (평균절대오차)를 사용하였다. 연구결과 데이터마이닝 예측모형이 현재 육군에서 활용중인 시계열 예측 모형에 비해 예측정확도가 높았으며, 이어서 K-means 군집분석을 통해서 예측정확도를 더욱 높일 수 있는 군집을 찾을 수 있었다.

Cost-optimal spare parts inventory planning for wind energy systems

For safe and reliable machine operation, maintenance, repair and overhaul (MRO) activities are required. Spare parts demand forecasting and inventory planning, which is an important part of MRO activities, must be accurate to avoid costs because of surplus spare parts or machine downtimes. The restriction of reduced accessibility to wind turbines during the winter months also has to be taken into account when planning maintenance activities and spare part inventories for wind farms. The presented model provides the most economic stock quantity under given environmental conditions. It is based on the proportional hazards model, which is extended to calculate the remaining useful component life time and derive required spare parts inventory levels. The presented model is validated, using condition monitoring data and environmental data of an onshore wind farm. Comparison of the spare part inventory prediction to wind farm’s failure data proves the model’s accuracy. Parameter analyses show that the model can be applied for spare parts inventory planning under consideration of environmental conditions.

Electrical Spare Parts Demand Forecasting

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. DOI: http://dx.doi.org/10.5755/j01.eee.20.10.8870

Demand forecasting and inventory control: A simulation study on automotive spare parts

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 (TFR=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.

A risk‐based approach to manage non‐repairable spare parts inventory

Purpose – The purpose of this paper is to propose a risk‐based approach for spare parts demand forecast and spare parts inventory management for effective allocation of limited resources. Design/methodology/approach – To meet the availability target and to reduce downtime, process facilities usually maintain inventory of spare parts. The maintaining of non‐optimized spare parts inventory claims more idle investment. Even if it is optimized, lack of attention towards the critical equipment spares could threaten the availability of the plant. This paper deals with the various facets of spare parts inventory management, mainly risk‐based spare parts criticality ranking, forecasting, and effective risk reduction through strategic procurement policy to ensure spare parts availability. A risk‐based approach is presented that helps managing spare parts requirement effectively considering the criticality of the components. It also helps ensuring the adequacy of spare parts inventory on the basis of equipment criticality and dormant failure without compromising the overall availability of the plant. Findings – The paper proposes a risk‐based approach that used conjugate distribution technique with the capability to incorporate historical failure rate as well as expert judgment to estimate the future spare demand through posterior demand distribution. The approach continuously updates the prior distribution with most recent observation to give posterior demand distribution. Hence the approach is unique in its kind. Practical implications – Appropriate spare parts unavailability could have great impact on process operation and result in costly downtime of the plant. Following proposed approach the availability target can be achieved in process industry having limited maintenance resources, by forecasting spare parts demand precisely and maintaining inventory in good condition. Originality/value – Adopting the approach proposed in the paper, risk level can be minimized and plant availability can be maximized within the financial constraint. The resources are allocated to the most critical components and thereby increased availability, and reduce risk.

Stochastic model of forecasting spare parts demand

Stochastic model of forecasting spare parts demand

Numerical method for estimating multivariate conditional distributions

A computational framework for estimation of multivariate conditional distributions is presented. It allows the forecast of the joint distribution of target variables in dependence on explaining variables. The concept can be applied to general distribution families such as stable or hyperbolic distributions. The estimation is based on the numerical minimization of the cross entropy, using the Multi-Level Single-Linkage global optimization method. Nonlinear dependencies of conditional parameters can be modeled with help of general functional approximators such as multi-layer perceptrons. In applications, the information about a complete distribution of forecasts can be used to quantify the reliability of the forecast or for decision support. This is illustrated on a case study concerning the spare parts demand forecast. The improvement of the forecast error due to using non-Gaussian distributions is presented in another case study concerning the truck sales forecast.

Sources of intermittent demand for aircraft spare parts within airline operations

Owing to the sporadic nature of demand for aircraft maintenance repair parts, airline operators perceive difficulties in parts demand forecasting. In this paper we investigate the sources of demand lumpiness, as a function of flying hours, that may affect the parts demand rate. Experimental results of demand lumpiness, measured by the square coefficient of variation (CV 2) and the average inter-demand interval (ADI), are examined and clarified through statistical analysis. The general linear model approach is used to explain the variation attributable to the various experimental factors and their interactions. Actual historical data for hard-time and condition-monitoring components from an airlines operator are used. This study shows that aircraft utilization rate can be a major source of lumpiness since it increases and decreases the square coefficient of variation and the average inter-demand interval respectively for the observed demand. This assumes a strictly linear relationship between demand and flying hours/landings.