Dissertations / Theses on the topic 'Spares Demand Forecasting'

Poor input data quality used in repairable spare parts forecasting by aerospace small and midsize enterprises (SME) suppliers results in poor inventory practices that manifest into higher costs and critical supply shortage risks. Guided by the data quality management (DQM) theory as the conceptual framework, the purpose of this exploratory multiple case study was to identify the key strategies that the aerospace SME repairable spares suppliers use to maximize their input data quality used in forecasting repairable spare parts. The multiple case study comprised of a census sample of 6 forecasting business leaders from aerospace SME repairable spares suppliers located in the states of Florida and Kansas. The sample was collected via semistructured interviews and supporting documentation from the consenting participants and organizational websites. Eight core themes emanated from the application of the content data analysis process coupled with methodological triangulation. These themes were labeled as establish data governance, identify quality forecast input data sources, develop a sustainable relationship and collaboration with customers and vendors, utilize a strategic data quality system, conduct continuous input data quality analysis, identify input data quality measures, incorporate continuous improvement initiatives, and engage in data quality training and education. Of the 8 core themes, 6 aligned to the DQM theory's conceptual constructs while 2 surfaced as outliers. The key implication of the research toward positive social change may include the increased situational awareness for SME forecasting business leaders to focus on enhancing business practices for input data quality to forecast repairable spare parts to attain sustainable profits.

The control of an inventory where spare parts demand is infrequent has always been complex to manage because of the randomness of the demand, as well as the existence of a large proportion of zero values in the demand pattern. However, considering the importance of spare parts demand forecasting in production manufacturing and inventory management, several forecasting methods have been developed over the years to allow decision makers in industry to optimize the management of inventory where the demand pattern is infrequent. The Croston method is one of the traditional forecasting method, known because of its ability to take into consideration periods with zero demands. Yet, despite the Croston method’s advantage over other traditional methods, there are still shortcomings in the method because it does not consider the condition of the components to be replaced. This dissertation proposes an alternative forecasting method to the traditional methods, by means of condition monitoring. This method overcomes the Croston method’s shortcomings by considering the condition information of the component under operation. A statistical model, the so-called proportional hazards model (PHM), which is a regression model, blending event and condition monitoring data, is used to estimate the risk of failure for the component under analysis, while subjected to condition monitoring. To obtain optimal decision making on spare parts demand, a blending of the hazard or risk with the economics is performed, and an optimal risk point is specified. The optimal risk point guides optimal decision making on spare parts policy for the component under analysis. To generate the data needed to construct the proportional hazards model, a numerical investigation was performed on a fan axial bade where a crack was inserted and propagated to estimate the fatigue crack life and corresponding natural frequencies. The simulation was run using MSC.MARC/MENTAT 2016 software. To validate the finite element model, an experiment was run by using a 50kN Spectral Dynamics electrodynamics shaker to apply base excitation to the fan axial blade specimens. The treatment and computation of data generated from experimental and numerical approaches allowed the construction of the proportional hazards model, with the fatigue lifetime as event data and the blade natural frequencies as covariates or condition monitoring information. The baseline Weibull parameters were estimated by maximizing the likelihood function using the Newton Raphson method and the MATLAB package. This allowed the computation of an objective function to determine the shape, scale and location parameters. Instead of defining the covariate behaviour needed to build the cost function by means of the Markov process, a simulation procedure was utilized to define the cost function and determine the optimal risk which minimizes the cost. Furthermore, as the proportional hazards model depends on both, time and covariates, it was also shown how the PHM behaves when time or covariates carry more weight. The added value of the proportional hazard model as forecasting spare parts method lies in the fact that it allows one to proactively gather failure information which enables a ‘just in time’ supply of spare parts as well as an optimal maintenance plan. Forecasting spare parts demand, using condition information, performs better than traditional methods because it reduces an overly large spare parts stock pile. By allowing a ‘just in time’ part availability, the spare parts management becomes more related to the condition of the asset. Additionally, the supply chain management and maintenance cost are optimized, and the preventive replacement of components is optimized compared to the time-based method where a component can be replaced while still having a useful life.
Dissertation (MSc)--University of Pretoria, 2018.
Mechanical and Aeronautical Engineering
MSc
Unrestricted

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 research uses data obtained from the South Korean Navy to compare the performance of forecasting methods that use hierarchical and direct forecasting strategies for predicting the demand for spare parts. Among various forecasting methods tested, a simple combination of exponential smoothing models, which uses a hierarchical forecasting strategy, was found to minimise forecasting errors and inventory costs. This simple combination forecasting method was generated by a simple combination between an exponential smoothing model with quarterly aggregated data adjusted for linear trend at group level and an exponential smoothing model with monthly aggregated unadjusted data at item level. Logistic regression classification model for predicting the relative performance of alternative forecasting methods (Le. a direct forecasting method vs. a hierarchical forecasting method) by multivariate demand features of spare parts was developed. Logistic regression classification model is generalisable, because it is based on relationships between the relative performance of alternative forecasting methods and demand features. This classification model reduced inventory costs, compared to the results of only using the simple combination forecasting method. This classification model is likely to be a promising model to guide the selection of a forecasting method between alternative forecasting methods for predicting spare parts demand in the South Korean Navy, so that it could maximise the operational availability of weapon systems.

La presente investigación consiste en Proponer una Solución para Reducir los Reclamos en el Abastecimiento de Repuestos de Productos de Línea Blanca. Para ello, se aplicó principalmente Métodos de Clasificación ABC, Diagramas de Análisis de Actividades, Distribución por Mezcla de Familias, Métodos de Pronósticos de la Demanda, entre otras herramientas de la Ingeniería Industrial. Finalmente, se concluyó que al mejorar la Productividad del “Picking” (Sacado) y del Embalaje, al mejorar la Identificación y Reconocimiento Visual de los Repuestos y de los Espacios y al realizar una mejor Planificación de la Demanda, un adecuado Control del Inventario, una mejor Planificación del Abastecimiento, se reducirán los Reclamos en el Abastecimiento de Repuestos de productos de línea blanca. The present research is to propose a solution to Reduce Claims in supply of spare parts Products Appliances. To do this, we will mainly apply ABC classification methods, diagrams Analysis Activities, Distribution mix of families Methods demand forecast and other tools of industrial engineering. Finally, it was concluded that by improving the productivity of the "Picking" (Taken) and packaging, improving the identification and Visual Recognition of parts and spaces and improving planning Demand with an adequate control of inventory and with a better supply planning, Claims will be reduced in the Supply of white goods´ spare parts.

Finding the proper balance between availability and cost efficiency is an important concern within spare part management. Spare part suppliers need to respond quickly to customer demand as a stock-out can have severe consequences for both the customer and the supplier. It is critical to identify what items to keep in stock and where to allocate the inventory to avoid stock-outs. This case study was performed at a large high-tech company producing manufacturing equipment to be used in the electronics industry. The aim was to lower the stock-levels of spare parts while not impairing the availability by combining item classification, demand forecasting, and distribution network optimization. A decision diagram for classifying spare parts was constructed using the analytical hierarchy process. Twenty items were classified using the diagram, and the demand for them was forecasted using the Syntetos Boylan Approximationmethod. The shipping cost for spare parts within one region was minimized using a linear optimization model. The analysis showed that equipment criticality, annual usage value, and installed base are critical when classing spare parts. Instead of using five distribution centers in the European region, it was discovered that the shipping costs would decrease if only three warehouses made up the distribution network. The spare parts investigated appeared to follow the typical characteristics for spare parts, showing a low and irregular demand. Hence, demand forecasting seemed to be unnecessary, considering the difficulties in getting satisfactory results. Instead of combining the results from classification, forecasting, and inventory allocation, we suggest that the processes affecting stocking decisions should cooperate and work towards a common objective, namely to satisfy the customer demand in a cost-efficient way. Thus, widening the meaning of taking on an integrated approach to spare part management.
Inom hanteringen av reservdelar är det en stor utmaning att hitta rätt avvägning mellan tillgänglighet och kostnadseffektivitet. Leverantörer av reservdelar måste snabbt kunna möta kundefterfrågan eftersom uteblivna leveranser av kritiska reservdelar kan få allvarliga konsekvenser för både kund och leverantör. Vilka artiklar som ska lager-hållas och var de ska lagerhållas är avgörande beslut för att undvika att artiklar rest-noteras. I den här fallstudien, som utfördes på ett stort teknikföretag som tillverkarproduktionsutrustning till elektronikindustrin, var syftet att sänka lagernivåerna av reservdelar utan att göra avkall på tillgängligheten. Detta genom att kombineragruppering av artiklar, beräkning av kommande efterfrågan och optimering av distributionsnätverket. För att klassificera artiklar i grupper med liknande egenskaper skapades ett schematiskt beslutsdiagram med hjälp av metoden AHP. Tjugo artiklar ur sortimentet valdes ut som beslutsdiagrammet testades på. För samma tjugo artiklar gjordes prognoser för den kommande efterfrågan med metoden Syntetos-Boylan-Approximation. Distributionsnätverket i den europeiska regionen optimerades medavseende på fraktkostnad genom att applicera en linjär optimeringsmodell. Hur kritisk en reservdel är för den relaterade maskinens funktionalitet, reservdelensårliga förbrukningsvärde och den geografiska placeringen av installerade maskinervisade sig vara kritiska för att kunna klassificera artiklarna effektivt. Analysen av distributionsnätverket i Europa visade att fraktkostnaderna kan minskas om nätverket utgjordes av tre lager istället för fem som det gör i dagsläget. De tjugo undersökta reservdelarna uppvisade de typiska egenskaperna för reservdelar som har rapporterats i litteraturen som låg och oregelbunden efterfrågan. Att sätta prognoser på efterfrågan verkar obefogat med tanke på komplexiteten i beräkningarna och att de ger få tillfredsställande resultat. Istället för att kombinera resultaten från klassificering, prognoser på efterfrågan och lageroptimering föreslår vi att alla de funktioner i ett företag som arbetar med att tillgodose kundefterfrågan bör samarbeta i högre grad och jobba mot ett gemensamt mål, nämligen att tillgodose kundernas efterfrågan på ett kostnadseffektivt sätt. Således vill vi utvidga betydelsen av att ta en integrerad strategi för reservdelshantering

碩士
國立臺灣科技大學
工業管理系
107
This paper deals with spare parts demand forecasting problem in energy industry. Forecasting parts demand has its own challenges because in general spares demand is characterized by high variation in its demand size and in its inter-demand interval. In this study, two forecasting approaches to deal with spare parts demand are proposed: in the base approach, traditional time series forecasting methods and machine learning methods are combined using stacked generalization; in the improved approach, external information is utilized to improve the predictions from the base approach, resulting in more accurate predictions. To test the performance of these approaches, a case study in a natural gas liquefaction company is provided in this research. In the case study, these approaches are employed to forecast the monthly demand of parts used in the company’s maintenance operations. Several traditional time series forecasting methods (including Simple Moving Average, Single Exponential Smoothing, Croston’s Method, Syntetos-Boylan’s Approximation, and Teunter-Syntetos-Babai’s Method) and several machine learning methods (including Multiple Linear Regression, Elastic Net, Neural Network, Support Vector Machine, and Random Forests) are also utilized in the case study to compare the performance of the proposed approaches. In the end, results showed that the approaches proposed in this paper are promising.

碩士
國立臺灣大學
商學研究所
105
This study focuses on the demand forecast for the spare parts. With practical data, we use the monthly data of spare parts to create the index for clustering. Let the spare parts which have similar demand pattern in the same group. Next, this study then observes the results after clustering. Eliminate the group which has the fewest demand. Leave the group with more demand for analysis. At last, we will forecast the remaining group. The forecasting method is calculated by the current demand forecasting method and the method proposed in this study. The predictive value will compare with the actual value to measure the forecasting performance. The results obtained by comparing the current demand forecasting methods of the case companies show that the forecasting method proposed in this study can accurately predict the demand of spare parts, and confirmed the different demand patterns of spare parts applicable to different demand forecasting methods, the current demand forecasting method encountered greater demand’s variation of spare parts will have a poor performance. Therefore, this study proposed a demand forecasting method to provide a new way to predict the spare parts, so that enterprises can choose a better demand forecasting method according to the spare parts demand pattern.

碩士
國立清華大學
統計學研究所
107
After-sales service is an increasingly important issue nowadays. In order to provide their customers good after-sales service, companies need to ensure the amount of spare parts needed in maintenance service. After the product ends its manufacturing, the corresponding parts are not available for ordering. The suppliers give the last chance to the company to order spare parts before the end of the supply. The time horizon of the final order includes a three-year warranty and a four-month buffer, a total of forty months. Our model predicts the demand of the final order over the next forty months. The model combines the concept of the moving average model with random forest algorithm to introduce the latest information, making the prediction more accurate. Compared with moving average, the conventional method for demand forecasting, the prediction of our model progresses significantly.

碩士
國立交通大學
交通運輸研究所
98
The importance of demand forecasting and inventory management for aircraft spare parts for reducing the airline operating cost is increasing. It will not only cause the increasing of inventory cost when spare parts are excessive but also great loss result from flight delay or cancellation when those are in shortage. Therefore, the department of maintenance expects to establish the accurate demand forecasting system and inventory management system. The types of aircraft spare parts demand are mostly intermittent demand and lumpy demand. This thesis applies Back-Propagation Neural Network (BPN) and Support Vector Regression (SVR) to forecast the intermittent demand and lumpy demand of aircraft spare parts. In addition, the forecasting results of BPN and SVR are compared to that of traditional methods, Single Exponential Smoothing (SES) and two types of Weighted Moving Average (WMA). From the results, BPN and SVR outperform the traditional methods, particularly, the accuracy of BPN is better. Finally, according to the best result of intermittent demand forecasting and lumpy demand forecasting, the reorder level and economic order quantity are computed to establish the inventory model of aircraft spare parts.

碩士
國立交通大學
管理學院運輸物流學程
100
The current commercial environment is rapidly changed and diversified.In order to meet the request of customers, forecasting is important in the response to dynamic changes of market demand and to effectively curtail the inventory cost. However, it is well recognized that demand forecasting for different products is dramatically different. Especially, the successful inventory management of the spare parts of short-lived digital products extremely rely on the accurate forecast and dynamic inventory adjustment mechanism. Based on this, this study aims to propose effective inventory management strategies for the critical spare parts of digital cameras, Printed Circuit Board (PCB), based on an integrated demand forecasting model and dynamic stock adjustment mechanism. According to the real maintenance records (quantity demanded of PCB) of a digital camera maintenance center, three regression models under various planning horizons: weekly, monthly, and seasonal, are respectively estimated by regressing quantity demanded of PCB on the sales quantity of digital cameras in previous periods. These models are then further compared in terms of MAPE (mean absolute percentage error). Furthermore, a dynamic stock adjustment model based on the forecasted demand is then developed along with the related parameters optimally tuned by evolutionary computation so as to minimize the total inventory cost, including the holding cost, ordering cost, shortage cost, and transportation cost. The results show that the integrated inventory model based on the monthly forecasting technique performs best, which can effectively curtail inventory cost, suggesting the applicability of the proposed model.

碩士
國立臺北科技大學
工業工程與管理系
107
In recent years, due to the slowdown in the growth of notebook computers and tablet PCs, the performance of major brands has fallen into a bottleneck in research and developments. Since notebook computers are still high-priced products, and products become more sophisticated than desktop computers. In addition, the differences in products make assembly and maintenance to different degrees of difficulty, leading to an extremely high competition in the notebook market. In the past decade, notebook computer repair components often suffered from out of stock or uneven distribution, resulting in significant cost increases for major companies in spare-parts preparation. Until recently, with the rapid developments and remarkable breakthrough of machine learning technology, various very-large scale corporate decision-making problems can be dealt with without undue difficulty. In this thesis, we will use the extreme gradient boosting method to propose a new prediction model for the demand of important components of the notebook computer for maintenance/repair, which can also be used to trace down the demand tendency during the future warranty period of important components for outdated products. Namely, demand forecasting for important components of notebook manufacturers with an extension to over 12 months is the major objective of this research. By doing so, the personnel of the procurement department is able to take appropriate decisions based on the prediction results in an attempt to provide consumers with prompt and high-quality after-sales service.

碩士
臺中技術學院
流通管理系碩士班
98
Demand forecasting plays an important role in motorcycle supply chain, especially for maintaining certain after-sale service level. While there is a need of forecasting, most of companies choose to use simple time series method, and refer to their past experiences. As to the characteristics of spare parts, however, due to lots of undetermined factors which affect the accuracy of demand predicting, it is hard to find a certain regulation, and could easily result in an inaccurate demand predicting. In view of the fact that past studies, when discuss this problem, rarely focus on motorcycle industry, the purpose of the study is, on the motorcycle spare parts supplier’s point of view, to construct a forecasting model that is expected to minimize total predicting errors. The model considers the affect of ages of different spare parts toward demand, and is run an estimation process through Markov Chain Monte Carlo method (MCMC) to have the best parameters. Before that, the model is derived by Bayes’ theorem. We choose 16 spare parts from the database of chef dealer of the country for the purpose of evaluating and comparing the forecasting power of our model and time series models (Moving average, Exponential smoothing, Croston, and SY Croston (Syntetos (2001)). As a result, our model could better fit the real demand patterns and has lower predicting errors than time series models mentioned above. The study compares parameter estimation methods of both MCMC and Gene Algorithm as well. We found that MCMC is much straightforward and simple that we just need to change sampling numbers to run the estimation process, while Gene Algorithm has to use different settings to make sure if the estimated parameters are nearly the best.

碩士
國立臺中科技大學
流通管理系碩士班
100
In response to recent economic turmoil and can’t fast-growing, high-quality, low-cost and short-term delivery are business survival basic conditions, in order to lower the costs, mostly in the inventory to the extent permitted mass production. Unlike ordinary products, the characteristics of motorcycle spare parts is intermittent demand, the demand for and intervals are rather unstable, companies need to afford to maintain a certain standard of service under the premise of high inventory and the risk of damage, accurate spare parts demand forecasting model is particularly important. Many scholars for such issue, in addition to using different methods construction mode to reduce the error, also try to assumption normal distribution fit the spare parts product life curve. There are many types of spare parts and the impact factors, some of the important parameters is not easy to obtain, therefore only explore the normal product life curve model is inadequate. In this study, the first attempt to fit motorcycle spare parts product life curve with different distributions, using lognormal and mirror lognormal distribution to construct the demand forecasting model. According to the empirical results, our models decrease the MSE up to 42.99% in some spare parts. It indicates the product life curve is not always the Normal distribution. When the product life curve is more close to the actual situation, the estimated value of utilization of genuine parts will be more in line with the practice. It can provide enterprise to develop strategies and prepare inventory with higher confidence.

碩士
國立臺中科技大學
流通管理系碩士班
100
The repair quality and efficiency of notebook repair center are the key factors for consumers to choose notebook brand. Notebook spare parts forecasting accuracy is definitely the reason of influencing notebook repair speed. Spare parts are usually belonging to intermittent demand. Intermittent demand is usually random, and most part of its demand is zero. Because of demand of non-zero may exist tremendous variability, hence it''s hard to find a specific regular pattern for intermittent demand forecasting. Therefore, this study applied Back-propagation Neural Network (BPN) to build up a demand forecasting pattern of notebook spare parts. The results of demand forecasting pattern will be compared with those of the method generally using like Moving Average, Exponential Smoothing, and Grey Prediction. Using root mean square error (RMSE) to be a key index, and it will proceed the performance evaluation of forecasting. The result of experiments shows that the most excellent performance of forecasting is BPN, and the next is Exponential Smoothing. Grey Prediction using on forecasting the bulk of zero demand is unsatisfactory performance.

碩士
國立成功大學
工業與資訊管理學系碩士在職專班
103
While researchers are paying more attention to production capacity in production management, less attention were paid to facility maintenance and supplement of spare parts. A maintenance department, aiming to maintain a high volume of output, should ensure sufficient quantities of spare parts are available to repair machines when malfunctions occur. This is to minimize any adverse effects on the production process and production capacity. The aim of this study is to investigate the demand forecasts for spare parts for repairing packaging machines in the artificial fiber manufacturing industry. Real data is used to compare differences among the time series methods, regression, an artificial neural network, and the rules of thumb in case company. The accuracy of the model is assessed by the Root Mean Square Error. The results show that the artificial neural networks method provides the best level of accuracy and goodness of fit. On the contrary, a multiple regression method performs poorly as far as accuracy and goodness of fit concerned. The time series method and the heuristic rule provide the worst level of accuracy and goodness of fit.

碩士
臺中技術學院
流通管理系碩士班
98
Manufacturers have been rolling out new products constantly for the market competition, and at the same time they must provide the maintenance service of the products after selling in a period of time. As many as the products, the scales of spare parts inventories are larger, and definitely become a burden of inventory management. Hence, how to scheme out a method well for stock is deserved to be concerned in practice. This research is directed to the demand of spare parts brought by selling and mending of motorcycles. Considering the factor of the hazard rate of parts, and supposing that the hazard function of spare parts conform to the bathtub curve, the study applies the property of Weibull distribution to fit each stage of the hazard function into bathtub curve. By this means we construct a demand forecasting model of spare parts and use genetic algorithm to help find out global optimum. Then we use the real data from case company to carry on the examination to verify the accuracy of demand forecasting of the parts. From the empirical study we can find that the result of the forecasting model presented in the research is better than current methods used by case company. And while there is a wider range of demand variations, the forecasting model can make a much more exact future demand forecast than time series models used by case company.

碩士
國立臺中科技大學
流通管理系碩士班
105
Inventory control of spare parts has been an essential to many organizations since it is one of the most expensive assets. Most of the spare parts are to belong to intermittent demand and Bootstrapping has been claimed to be of great value for forecasting. While a small proportion of spare parts are regard to regular demand, Moving Average, frequently used to deal with this type of demand. We address combination forecasting model by the Moving Bootstrap based on Bootstrapping and Moving Average to classify the appropriate method. Then use the Back-Propagation Neural Network to construct the classification model which can be used to automatically select the better approach of forecasting. We find that the main explanatory variables about consumption of daily average, the ratio of days with zero consumption and standard deviation of daily consumption can exact classify the demand forecasting approach. In the future, enterprise arranges to purchase new spare parts, this combination model will assist in concluding the forecasting method and reducing the forecast error. Moreover, it leads to lower stock costs and improves operational performance.

碩士
臺中技術學院
流通管理系碩士班
99
The environment of motorcycle industry is changing currently. If the spare parts in inventory can be control well, it will make the motorcycle maintain service to keep the quality. Among them, the spare parts demand forecasting accuracy will be particularly important. Therefore, the random demand and uncertainty for spare parts make the demand forecasting more difficult. This study discuss this problem and hope to find the most appropriate method to forecast demand for spare parts. This research considers to introduce the usage rate of genuine spare parts and seasonal factors to construct the demand models, and we through Bayesian statistics with Markov Chain Monte Carlo method (MCMC) to estimate model parameters. This research choose 40 spare parts, and Comparing the forecasting abilities of our models and time series forecasting methods(moving average method, exponential smoothing method, CR method, and the modification CR method), and the normal life time model. As a result, our models have the best performance for demand forecasting, and our models are quite appropriate for forecasting intermittent demand. In addition, using the information of genuine spare parts ratio in this research, can help the motorcycle industry managers to adjust their market strategy.

碩士
國立臺中科技大學
流通管理系碩士班
107
Spare parts are not intermediate products or final products that are sold to consumers, spare parts are an essential part of maintaining smooth operation of production equipment during production activities. The function of the spare part is to ensure that the production equipment is kept in operation, and the analysis of the demand forecast is a key element of the inventory management of the spare parts. Most spare parts are expensive that too high spare parts inventory levels may lead to the company''s capital turnover problem. Therefore, effectively controlling the inventory level of spare parts is an important issue for enterprises. This study collects two-year historical data of spare parts of a manufacturing company specializing in the production of industrial raw materials. We propose classification model of Back-Propagation Neural Network and Decision Tree in demand forecasting model of spare parts. We analyze the influence of different period database on both Moving Average forecasting model and Moving Bootstrap model. The results show that the accuracy of both Back-Propagation Neural Network classification model and Decision Tree classification model are more than 70%, showing that both two classification models can be used to classify the optimal demand forecasting model for spare parts. This study also found that both the Moving Average forecasting model and Moving Bootstrap model performed best in the model with a period of twelve.