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Relevant bibliographies by topics / Spares Demand Forecasting

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Academic literature on the topic 'Spares Demand Forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Spares Demand Forecasting"

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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.

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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 (2019): 53–68. http://dx.doi.org/10.1108/jqme-07-2018-0062.

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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 (lifet

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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 (2021): 604. http://dx.doi.org/10.3926/jiem.3446.

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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

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

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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

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

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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 (2018): 107. http://dx.doi.org/10.28989/angkasa.v10i2.362.

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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

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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.

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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.

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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.

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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.

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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 (2011): 1194–209. http://dx.doi.org/10.1016/j.tre.2011.04.008.

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Dissertations / Theses on the topic "Spares Demand Forecasting"

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

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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 forecasti

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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.

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

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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, know

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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.

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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 cos

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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.

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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 Plan

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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.

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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 availabi

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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.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>107<br>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

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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.

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碩士<br>國立臺灣大學<br>商學研究所<br>105<br>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

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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.

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碩士<br>國立清華大學<br>統計學研究所<br>107<br>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

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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.

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碩士<br>國立交通大學<br>交通運輸研究所<br>98<br>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 Bac

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Books on the topic "Spares Demand Forecasting"

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

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

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

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

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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.

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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 signifi

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Book chapters on the topic "Spares Demand Forecasting"

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Özbay, Elif, Banu Hacialioğlu, Büşra İlayda Dokuyucu, et al. "Developing a Spare Parts Demand Forecasting System." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31343-2_58.

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

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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.

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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. Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54030-5_15.

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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. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34522-7_32.

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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. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30443-0_16.

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

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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.

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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.

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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. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7152-0.ch013.

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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.

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

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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 ...

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Conference papers on the topic "Spares Demand Forecasting"

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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.

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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.

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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 analys

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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. ACM Press, 2019. http://dx.doi.org/10.1145/3335550.3335573.

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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.

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"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.

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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.

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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.

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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.

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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.

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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.

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