Relevant bibliographies by topics / Spares Demand Forecasting

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Spares Demand Forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Spares Demand Forecasting.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Spares Demand Forecasting"

1

Rosienkiewicz, Maria, Edward Chlebus, and Jerzy Detyna. "A hybrid spares demand forecasting method dedicated to mining industry." Applied Mathematical Modelling 49 (September 2017): 87–107. http://dx.doi.org/10.1016/j.apm.2017.04.027.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Lelo, Nzita Alain, P. Stephan Heyns, and Johann Wannenburg. "Forecasting spare parts demand using condition monitoring information." Journal of Quality in Maintenance Engineering 26, no. 1 (September 9, 2019): 53–68. http://dx.doi.org/10.1108/jqme-07-2018-0062.

Full text
Abstract:
Purpose The control of an inventory where spare parts demand is infrequent has always been difficult 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. The purpose of this paper is to propose a just-in-time (JIT) spare parts availability approach by integrating condition monitoring (CM) with spare parts management by means of proportional hazards models (PHM) to eliminate some of the shortcomings of the spare parts demand forecasting methods. Design/methodology/approach In order to obtain the event data (lifetime) and CM data (first natural frequency) required to build the PHM for the spares demand forecasting, a series of fatigue tests were conducted on a group of turbomachinery blades that were systematically fatigued on an electrodynamic shaker in the laboratory, through base excitation. The process of data generation in the numerical as well as experimental approaches comprised introducing an initial crack in each of the blades and subjecting the blades to base excitation on the shaker and then propagating the crack. The blade fatigue life was estimated from monitoring the first natural frequency of each blade while the crack was propagating. The numerical investigation was performed using the MSC.MARC/2016 software package. Findings After building the PHM using the data obtained during the fatigue tests, a blending of the PHM with economic considerations allowed determining the optimal risk level, which minimizes the cost. The optimal risk point was then used to estimate the JIT spare parts demand and define a component replacement policy. The outcome from the PHM and economical approach allowed proposing development of an integrated forecasting methodology based not only on failure information, but also on condition information. Research limitations/implications The research is simplified by not considering all the elements usually forming part of the spare parts management study, such as lead time, stock holding, etc. This is done to focus the attention on component replacement, so that a just-in-time spare parts availability approach can be implemented. Another feature of the work relates to the decision making using PHM. The approach adopted here does not consider the use of the transition probability matrix as addressed by Jardine and Makis (2013). Instead, a simulation method is used to determine the optimal risk point which minimizes the cost. Originality/value This paper presents a way to address some existing shortcomings of traditional spare parts demand forecasting methods, by introducing the PHM as a tool to forecast spare parts demand, not considering the previous demand as is the case for most of the traditional spare parts forecasting methods, but the condition of the parts in operation. In this paper, the blade bending first mode natural frequency is used as the covariate in the PHM in a laboratory experiment. The choice of natural frequency as covariate is justified by its relationship with structural stiffness (and hence damage), as well as being a global parameter that could be measured anywhere on the blade without affecting the results.
APA, Harvard, Vancouver, ISO, and other styles
3

Anglou, Fiorentia Zoi, Stavros Ponis, and Athanasios Spanos. "A machine learning approach to enable bulk orders of critical spare-parts in the shipping industry." Journal of Industrial Engineering and Management 14, no. 3 (July 19, 2021): 604. http://dx.doi.org/10.3926/jiem.3446.

Full text
Abstract:
Purpose: The main purpose of this paper is to propose a methodological approach and a decision support tool, based on prescriptive analytics, to enable bulk ordering of spare parts for shipping companies operating fleets of vessels. The developed tool utilises machine learning and operations research algorithms, to forecast and optimize bulk spare parts orders needed to cover planned maintenance requirements on an annual basis and optimize the company’s purchasing decisions.Design/methodology/approach: The proposed approach consists of three discrete methodological steps, each one supported by a decision support tool based on clustering and machine learning algorithms. In the first step, clustering is applied in order to identify high interest items. Next, a forecasting tool is developed for estimating the expected needs of the fleet and to test whether the needed quantity is influenced by the source of purchase. Finally, the selected items are cost-effectively allocated to a group of vendors. The performance of the tool is assessed by running a simulation of a bulk order process on a mixed fleet totaling 75 vessels.Findings: The overall findings and approach are quite promising Indicatively, shifting demand planning focus to critical spares, via clustering, can reduce administrative workload. Furthermore, the proposed forecasting approach results in a Mean Absolute Percentage Error of 10% for specific components, with a potential for further reduction, as data availability increases. Finally, the cost optimizer can prescribe spare part acquisition scenarios that yield a 9% overall cost reduction over the span of two years.Originality/value: By adopting the proposed approach, shipping companies have the potential to produce meaningful results ranging from soft benefits, such as the rationalization of the workload of the purchasing department and its third party collaborators to hard, quantitative benefits, such as reducing the cost of the bulk ordering process, directly affecting a company’s bottom line.
APA, Harvard, Vancouver, ISO, and other styles
4

Mayur, R., and Baibhav Kumar. "Demand Forecasting of Spare Parts of Automobiles using Gaussian Support Vector Machine." IJOSTHE 6, no. 1 (February 10, 2019): 4. http://dx.doi.org/10.24113/ojssports.v7i1.114.

Full text
Abstract:
Reordering motor vehicle spare parts for the purposes of stock replenishment is an important function of the parts manager in the typical motor dealership. Meaningful reordering requires a reliable forecast of the future demand for items. Production planning and control in remanufacturing are more complex than those in traditional manufacturing. Developing a reliable forecasting process is the first step for optimization of the overall planning process. In remanufacturing, forecasting the timing of demands is one of the critical issues. The current article presents the result of examining the effectiveness of demand forecasting by time series analysis in auto parts remanufacturing. A variety of alternative forecasting techniques were evaluated for this purpose with the aim of selecting one optimal technique to be implemented in an automatic reordering module of a real time computerized inventory management system.
APA, Harvard, Vancouver, ISO, and other styles
5

Vasumathi, B., and A. Saradha. "Forecasting Intermittent Demand for Spare Parts." International Journal of Computer Applications 75, no. 11 (August 23, 2013): 12–16. http://dx.doi.org/10.5120/13154-0805.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Sulistyo, Sinta Rahmawidya, and Alvian Jonathan Sutrisno. "LUMPY DEMAND FORECASTING USING LINEAR EXPONENTIAL SMOOTHING, ARTIFICIAL NEURAL NETWORK, AND BOOTSTRAP." Angkasa: Jurnal Ilmiah Bidang Teknologi 10, no. 2 (October 29, 2018): 107. http://dx.doi.org/10.28989/angkasa.v10i2.362.

Full text
Abstract:
Lumpy demand represents the circumstances when a demand for an item has a large proportion of periods having zero demand. This certain situation makes the time series methods might become inappropriate due to the model’s inability to capture the demand pattern. This research aims to compare several forecasting methods for lumpy demand that is represented by the demand of spare part. Three forecasting methods are chosen; Linear Exponential Smoothing (LES), Artificial Neural Network (ANN), and Bootstrap. The Mean Absolute Scaled Error (MASE) is used to measure the forecast performance. In order to gain more understanding on the effect of the forecasting method on spare parts inventory management, inventory simulation using oil and gas company’s data is then conducted. Two inventory parameters; average inventory and service level; are used to measure the performance. The result shows that ANN is found to be the best method for spare part forecasting with MASE of 0,761. From the inventory simulation, the appropriate forecasting method on spare parts inventory management is able to reduce average inventory by 11,9% and increase service level by 10,7%. This result justifies that selecting the appropriate forecasting method is one of the ways to achieve spare part inventory management’s goal.
APA, Harvard, Vancouver, ISO, and other styles
7

Hemeimat, Raghad, Lina Al-Qatawneh, Mazen Arafeh, and Shadi Masoud. "Forecasting Spare Parts Demand Using Statistical Analysis." American Journal of Operations Research 06, no. 02 (2016): 113–20. http://dx.doi.org/10.4236/ajor.2016.62014.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Milojevic, Ivan, and Rade Guberinic. "Stochastic model of forecasting spare parts demand." Vojnotehnicki glasnik 60, no. 1 (2012): 216–34. http://dx.doi.org/10.5937/vojtehg1201216m.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Hu, Yao Guang, Shuo Sun, and Jing Qian Wen. "Agricultural Machinery Spare Parts Demand Forecast Based on BP Neural Network." Applied Mechanics and Materials 635-637 (September 2014): 1822–25. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1822.

Full text
Abstract:
With the rapid development of agricultural machinery, forecasting the demand for spare parts is essential to ensure timely maintenance of agricultural machinery. Based on features of spare parts, BP neural network is chosen to forecast the demand. First, this paper analyzes factors that affect the demand for spare parts. Second, steps and processes of neural network prediction are described. The third part of this paper is case study based on certain brand of agricultural machinery spare parts. BP neural network turns out suitable for forecasting the demand for spare parts.
APA, Harvard, Vancouver, ISO, and other styles
10

Wang, Wenbin, and Aris A. Syntetos. "Spare parts demand: Linking forecasting to equipment maintenance." Transportation Research Part E: Logistics and Transportation Review 47, no. 6 (November 2011): 1194–209. http://dx.doi.org/10.1016/j.tre.2011.04.008.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Spares Demand Forecasting"

1

Eguasa, Uyi Harrison. "Strategies to Improve Data Quality for Forecasting Repairable Spare Parts." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/3155.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
2

Lowas, Albert Frank III. "Improved Spare Part Forecasting for Low Quantity Parts with Low and Increasing Failure Rates." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1432380369.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Lelo, Nzita Alain. "Forecasting spare parts demand using condition monitoring information." Diss., University of Pretoria, 2009. http://hdl.handle.net/2263/67760.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
4

Moon, Seongmin. "Hierarchical forecasting for predicting spare parts demand in the South Korean Navy." Thesis, University of Newcastle Upon Tyne, 2010. http://hdl.handle.net/10443/1834.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
5

Moscoso, Rios Yves Igor, and Zanabria Henry Alcántara. "Propuesta para reducir reclamos en el abastecimiento de repuestos de productos de línea blanca." Bachelor's thesis, Universidad Ricardo Palma, 2015. http://cybertesis.urp.edu.pe/handle/urp/1303.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
6

Boström, Emma, and Julia Lundell. "Availability vs. Cost Efficiency : A Case Study Taking on an Integrated Approach to Spare Part Distribution in the High-Tech Industry." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279641.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
7

Yaqin, Alvin Muhammad Ainul, and Alvin Muhammad Ainul Yaqin. "Spare Parts Demand Forecasting in Energy Industry." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/qkvc4v.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
8

Chang, Wei-Yu, and 張維友. "Demand Clustering and Forecasting Model for Auto Spare Parts." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/azrwbk.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
9

Chen, Kai-Chien, and 陳愷謙. "Spare Parts Demand Forecasting in the Final Order Problem." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/qz2748.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
10

Chan, Chen-Wei, and 陳朝偉. "Demand Forecasting and Inventory Management for Aircraft Spare Parts." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/15113123934048163307.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Spares Demand Forecasting"

1

L, Adams John. Modeling and forecasting the demand for aircraft recoverable spare parts. Santa Monica, CA: Rand, 1993.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Altay, Nezih, and L. A. Litteral. Service parts management: Demand forecasting and inventory control. London: Springer-Verlag, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Altay, Nezih, and Lewis A. Litteral. Service Parts Management: Demand Forecasting and Inventory Control. Springer, 2014.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Altay, Nezih, and Lewis A. Litteral. Service Parts Management: Demand Forecasting and Inventory Control. Springer, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Browning, Judkin, and Timothy Silver. An Environmental History of the Civil War. University of North Carolina Press, 2020. http://dx.doi.org/10.5149/northcarolina/9781469655383.001.0001.

Full text
Abstract:
This sweeping new history recognizes that the Civil War was not just a military conflict but also a moment of profound transformation in Americans' relationship to the natural world. To be sure, environmental factors such as topography and weather powerfully shaped the outcomes of battles and campaigns, and the war could not have been fought without the horses, cattle, and other animals that were essential to both armies. But here Judkin Browning and Timothy Silver weave a far richer story, combining military and environmental history to forge a comprehensive new narrative of the war's significance and impact. As they reveal, the conflict created a new disease environment by fostering the spread of microbes among vulnerable soldiers, civilians, and animals; led to large-scale modifications of the landscape across several states; sparked new thinking about the human relationship to the natural world; and demanded a reckoning with disability and death on an ecological scale. And as the guns fell silent, the change continued; Browning and Silver show how the war influenced the future of weather forecasting, veterinary medicine, the birth of the conservation movement, and the establishment of the first national parks. In considering human efforts to find military and political advantage by reshaping the natural world, Browning and Silver show not only that the environment influenced the Civil War's outcome but also that the war was a watershed event in the history of the environment itself.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Spares Demand Forecasting"

1

Özbay, Elif, Banu Hacialioğlu, Büşra İlayda Dokuyucu, Hakan Şahin, Mehmet Mukan Saçlı, Merve Nur Genç, Efthymia Staiou, and Mert Paldrak. "Developing a Spare Parts Demand Forecasting System." In Lecture Notes in Mechanical Engineering, 676–91. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31343-2_58.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Zhang, Junni L., and John Bryant. "Bayesian Disaggregated Forecasts: Internal Migration in Iceland." In Developments in Demographic Forecasting, 193–215. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42472-5_10.

Full text
Abstract:
Abstract Local-level demographic forecasts are in high demand. Constructing local-level forecasts requires confronting the problems of random variation and sparse data. Bayesian methods offer promising solutions to both these problems. We illustrate using the example of inter-regional migration in Iceland.
APA, Harvard, Vancouver, ISO, and other styles
3

Si, Xiao-Sheng, Zheng-Xin Zhang, and Chang-Hua Hu. "An Adaptive Spare Parts Demand Forecasting Method Based on Degradation Modeling." In Springer Series in Reliability Engineering, 405–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54030-5_15.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Zhang, Lianwu, Fanggeng Zhao, Jiangsheng Sun, and Xiaoyan Shi. "The Demand Forecasting Method for Repairable Spare Parts Based on Availability." In Lecture Notes in Electrical Engineering, 291–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34522-7_32.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Rosienkiewicz, Maria. "Accuracy Assessment of Artificial Intelligence-Based Hybrid Models for Spare Parts Demand Forecasting in Mining Industry." In Advances in Intelligent Systems and Computing, 176–87. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30443-0_16.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

APAK, SINAN. "SPARE PART DEMAND FORECASTING WITH BAYESIAN MODEL." In Uncertainty Modeling in Knowledge Engineering and Decision Making, 851–56. WORLD SCIENTIFIC, 2012. http://dx.doi.org/10.1142/9789814417747_0136.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

de Oliveira, Alexandre Crepory Abbott, Jéssica Mendes Jorge, Andrea Cristina dos Santos, and Geraldo Pereira Rocha Filho. "Neural Network with Specialized Knowledge for Forecasting Intermittent Demand." In Advances in Transdisciplinary Engineering. IOS Press, 2020. http://dx.doi.org/10.3233/atde200113.

Full text
Abstract:
Demand forecasting is an essential part of an efficient inventory control system. However, when the demand has an intermittent or lumpy behavior, forecasting it becomes a challenging task. Several methods have been developed to solve this issue, but nonetheless, they only consider the information about the occurrence of demand, failing to assess the drivers of the data behavior. With the current digitalization of the industry, more data is available and, therefore, the chances of finding a causal relationship between the available data and the demand increases. Considering that, this paper proposes a single-hidden layer neural network for forecasting irregularly spaced time series with attributes conveying information about the past demand, seasonality of the data and specialized knowledge about the process. The neural network proposed is compared with benchmark neural networks and traditional forecasting methods for intermittent demand using three different performance measures on actual demand data from an industry operating in the aircraft maintenance sector. Statistical analysis is conducted on comparison results to identify significant differences in the forecasting methods according to each performance measure.
APA, Harvard, Vancouver, ISO, and other styles
8

Mahuzier, Ignacio Aranís, Pablo A. Viveros Gunckel, Rodrigo Mena Bustos, Christopher Nikulin Chandía, and Vicente González-Prida Díaz. "Innovation in Scientific Knowledge Based on Forecasting Assessment." In Advances in Human and Social Aspects of Technology, 247–63. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7152-0.ch013.

Full text
Abstract:
This chapter presents a study of forecasting methods applicable to the spare parts demand faced by an automotive company that maintains a share of nearly 25% of the automotive market and sells approximately 13,000 parts per year. These parts are characterized by having intermittent demand and, in some cases, low demand, which makes it difficult for such companies to perform well and to obtain accurate forecasts. Therefore, this chapter includes a study of methods such as the Croston, Syntetos and Boylan, and Teunter methods, which are known to resolve these issues. Furthermore, the rolling Grey method is included, which is usually used in environments with short historical series and great uncertainty. In this study, traditional methods of prognosis, such as moving averages, exponential smoothing, and exponential smoothing with tendency and seasonality, are not neglected.
APA, Harvard, Vancouver, ISO, and other styles
9

Jamieson, Kathleen Hall. "Introduction." In Cyberwar, 1–16. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190058838.003.0001.

Full text
Abstract:
Imagine a strategy memo forecasting cyberattacks by Russian hackers, trolls, and bots designed to roil social discontent and damage the electoral prospects of a major party US presidential nominee, or, if she winds up winning, to sabotage her ability to govern by seeding allegations of Democratic voter fraud. Guaranteed payoff. No fingerprints. No keystroke record. No contrails in the cloud. To ensure that Americans will believe that disparaging messages about her were made in the United States, use Bitcoin to buy space and set up virtual private networks (VPNs) on American servers. Distribute hacked content stolen from the accounts of her staff and associates through an intermediary, WikiLeaks. Use identity theft, stolen Social Security numbers, and appropriated IDs to circumvent Facebook and PayPal’s demand for actual names, birth dates, and addresses. On platforms such as Instagram and Twitter, register under assumed names. Diffuse and amplify your attack and advocacy through posts on Facebook, tweets and retweets on Twitter, videos on YouTube, reporting and commentary on RT, blogging on Tumblr, news sharing on Reddit, and viral ...
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Spares Demand Forecasting"

1

Guo, Feng, Bin Zhou, Chen-yu Liu, and Heng-xin Wang. "Spares demand combined forecasting based on grey model and exponential smoothing." In 2012 International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII). IEEE, 2012. http://dx.doi.org/10.1109/iciii.2012.6339838.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Zhang, Chen, Tao Yang, Wei Gao, Weiqiu Chen, Jing He, and Xingwang Yang. "A Spare Parts Demand Prediction Method for Wind Farm Based on Periodic Maintenance Strategy." In ASME 2017 Power Conference Joint With ICOPE-17 collocated with the ASME 2017 11th International Conference on Energy Sustainability, the ASME 2017 15th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2017 Nuclear Forum. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/power-icope2017-3077.

Full text
Abstract:
Nowadays, the management level and information construction of wind power industry are still relatively backward, for example, the existing maintenance models for wind farm are much too single, and corrective maintenance strategy is the most commonly used, which means that maintenance measures are initiated only after a breakdown occurs in the system. Moreover, the wind farm spare parts management is out-dated, no practical and accurate spares demand assessment method is available. In order to enrich the choices of maintenance methods and eliminate the subjective influence in the demand analysis of spare parts, a spare parts demand prediction method for wind farm based on periodic maintenance strategy considering combination of different maintenance models for wind farms is proposed in this paper, which consists of five major steps, acquire the reliability functions of components, establish the maintenance strategy, set the maintenance parameters, maintenance strategy simulation and spare parts demand prediction. The discrete event simulation method is used to solve the prediction model, and results demonstrate the operability and practicality of the proposed demand forecasting method, which can provide guidance for the actual operation and maintenance of wind farms.
APA, Harvard, Vancouver, ISO, and other styles
3

Tsao, Yu-Chung, Nani Kurniati, I. Nyoman Pujawan, and Alvin Muhammad 'Ainul Yaqin. "Spare Parts Demand Forecasting in Energy Industry." In the 2019 International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3335550.3335573.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Si, Xiao-Sheng, Chang-Hua Hu, and Donghua Zhou. "Forecasting spare parts demand based on degradation modeling." In 2013 25th Chinese Control and Decision Conference (CCDC). IEEE, 2013. http://dx.doi.org/10.1109/ccdc.2013.6561806.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

"Forecasting the spare part demands for mobile phones." In 2017 2nd International Conference on Mechatronics and Information Technology. Francis Academic Press, 2017. http://dx.doi.org/10.25236/icmit.2017.22.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Christensen, Carissa Bryce. "Forecasting the demand for commercial telecommunications satellites." In Space technology and applications international forum - 2001. AIP, 2001. http://dx.doi.org/10.1063/1.1357989.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

de Melo Menezes, Breno Augusto, Diego de Siqueira Braga, Bernd Hellingrath, and Fernando Buarque de Lima Neto. "An evaluation of forecasting methods for anticipating spare parts demand." In 2015 Latin America Congress on Computational Intelligence (LA-CCI). IEEE, 2015. http://dx.doi.org/10.1109/la-cci.2015.7435980.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Song, Hui, Cheng Zhang, Guangyu Liu, and Wukui Zhao. "Equipment spare parts demand forecasting model based on grey neural network." In 2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE). IEEE, 2012. http://dx.doi.org/10.1109/icqr2mse.2012.6246453.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Pawar, Nikita, and Bhavana Tiple. "Demand Forecasting of Anti-Aircraft Missile Spare Parts Using Neural Network." In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2019. http://dx.doi.org/10.1109/iceca.2019.8821903.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Lee, Hanjun, and Jaedong Kim. "A Predictive Model for Forecasting Spare Parts Demand in Military Logistics." In 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2018. http://dx.doi.org/10.1109/ieem.2018.8607801.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Spares Demand Forecasting' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography