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Journal articles on the topic 'Spares Demand Forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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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.

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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.

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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 (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.

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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.

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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 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.

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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.

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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 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.

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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.

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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.

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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 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.

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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.

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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.

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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.

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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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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.

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11

Van der Auweraer, Sarah, and Robert Boute. "Forecasting spare part demand using service maintenance information." International Journal of Production Economics 213 (July 2019): 138–49. http://dx.doi.org/10.1016/j.ijpe.2019.03.015.

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12

Liu, Hao, Jian Min Zhao, Jin Song Zhao, and Hong Zhi Teng. "Analysis of Spare Parts Demand Forecasting Considering Preventive Maintenance." Applied Mechanics and Materials 401-403 (September 2013): 2199–204. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.2199.

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Considering the importance of PM (preventive maintenance) in reliability engineering, the formula is given to calculate spare demand rate for the policies of age replacement policy, minimal maintenance policy and block replacement policy. And average spare demand rate was analyzed for age replacement policy, and an approximate empirical formula with PM interval and parameters of Weibull distribution was given compared to CM(corrective maintenance) and PM. Otherwise, compared to minimal maintenance policy and block replacement policy, the demand rate was analyzed in order to better forecast the spare parts demand.

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13

Gao, Jin Dong, Yong Zhang, Fang Jun Zhou, and Hong Long Mao. "Research on Control Model for Spare Parts Inventory Based on the Optimal Replenishment Cycle." Applied Mechanics and Materials 519-520 (February 2014): 1390–94. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.1390.

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Control model for spare parts inventory is established based on the optimal replenishment cycle. The replenishment cycle impact on the spare parts inventory control is analyzed as well as the demand forecasting. First, the optimal replenishment cycle is given by using the method of the lowest cost of inventory. Then according to the demand characteristic of the spare parts, the safety stock can be calculated. Finally on the basis of the demand forecasting, the calculation method of spare parts replenishment quantity is given. A numerical example is presented to verify the validity and practicability of the model.

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14

Choi, Boram, and Jong Hwan Suh. "Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea." Sustainability 12, no. 15 (July 28, 2020): 6045. http://dx.doi.org/10.3390/su12156045.

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In a weapon system, the accurate forecasting of the spare parts demand can help avoid the excess inventory, leading to the efficient use of budget. It can also help develop the combat readiness of the weapon system by improving weapon system utilization. Moreover, as performance-based logistics (PBL) projects have recently emerged, the accurate demand forecasting of spare parts has become an important issue for the PBL contractors as well. However, for the demand forecasting of spare parts, the time series methods, typically used in the military sector, have low prediction accuracies and the PBL contractors are mostly based on the judgment of practitioners. Meanwhile, most of the previous studies in the military sector have not considered the managerial characteristics of spare parts (e.g., reparability and the irregularity of maintenance). No previous work has considered any such features, which can indicate the reliability of spare parts (e.g., mean time between failures (MTBF)), although they can affect the spare parts demand. Therefore, to develop a more accurate forecasting of the spare parts demand of military aircraft, we designed and examined a systematic approach that uses data mining techniques. To fill up the research gaps of related works, our approach also considered the managerial characteristics of spare parts and included the new features that represent the reliability of spare parts. Consequently, given the case of South Korea and the full feature set, we found random forest gave better results than the other data mining techniques and the conventional time series methods. Using the best technique Random Forest, we identified the contribution of each managerial feature set to improving the prediction accuracy, and we found the reliability and operation environment are valuable feature sets in a significant way, so they should be collected, managed more carefully, and included for better prediction of spare parts demand of military aircraft.

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15

Hasni, M., M. S. Aguir, M. Z. Babai, and Z. Jemai. "Spare parts demand forecasting: a review on bootstrapping methods." International Journal of Production Research 57, no. 15-16 (January 31, 2018): 4791–804. http://dx.doi.org/10.1080/00207543.2018.1424375.

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Milojevic, Ivan, and Rade Guberinic. "Deterministic and heuristic models of forecasting spare parts demand." Vojnotehni?ki glasnik 60, no. 2 (2012): 235–44. http://dx.doi.org/10.5937/vojtehg1202235m.

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17

Yoon, Hyunmin, and Suhwan Kim. "Naval Vessel Spare Parts Demand Forecasting Using Data Mining." Journal of Society of Korea Industrial and Systems Engineering 40, no. 4 (December 31, 2017): 253–59. http://dx.doi.org/10.11627/jkise.2017.40.4.253.

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18

Pinçe, Çerağ, Laura Turrini, and Joern Meissner. "Intermittent demand forecasting for spare parts: A Critical review." Omega 105 (December 2021): 102513. http://dx.doi.org/10.1016/j.omega.2021.102513.

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19

Amirkolaii, K. Nemati, A. Baboli, M. K. Shahzad, and R. Tonadre. "Demand Forecasting for Irregular Demands in Business Aircraft Spare Parts Supply Chains by using Artificial Intelligence (AI)." IFAC-PapersOnLine 50, no. 1 (July 2017): 15221–26. http://dx.doi.org/10.1016/j.ifacol.2017.08.2371.

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20

Zhu, Ya Hong, Ji Ping Cao, Wen Xia Sun, Yang Tao Fan, and Zhi Hui Zhao. "Demand Forecasting Model Based on Equipment Maintenance Resources in Virtual Warehousing." Applied Mechanics and Materials 556-562 (May 2014): 5442–49. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5442.

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Based on the theory of virtual warehousing, the optimization system for equipment maintenance resources in virtual warehousing is established for the security task of equipment maintenance resources. According to the prediction problems on the spare parts requirements for equipment maintenance in this system, the demand forecasting model, based on the combination of rough sets and grey prediction, is adopted. The results of simulation experiment show that this method applied in equipment maintenance spare resources prediction is reliable and with accurate information. While, the relative error and absolute error of the predictive value and practical value are very small, which shows the prediction model is of high precision for the accurate effect prediction. As a result, this model and algorithum is proved to be effective to provide theoretical and practical support for equipment maintenance spare resources in information warfare.

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21

Vitthal Bhosale, Sagar. "New developed method of demand normalization improves spare part forecasting." Journal of Management Research and Analysis 6, no. 2 (July 15, 2019): 101–5. http://dx.doi.org/10.18231/j.jmra.2019.019.

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22

Van der Auweraer, Sarah, Robert N. Boute, and Aris A. Syntetos. "Forecasting spare part demand with installed base information: A review." International Journal of Forecasting 35, no. 1 (January 2019): 181–96. http://dx.doi.org/10.1016/j.ijforecast.2018.09.002.

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23

Ogcu Kaya, Gamze, and Omer Fahrettin Demirel. "Parameter optimization of intermittent demand forecasting by using spreadsheet." Kybernetes 44, no. 4 (April 7, 2015): 576–87. http://dx.doi.org/10.1108/k-03-2015-0062.

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Purpose – Accurate forecasting of intermittent demand is very important since parts with intermittent demand characteristics are very common. The purpose of this paper is to bring an easier way of handling the hard work of intermittent demand forecasting by using commonly used Excel spreadsheet and also performing parameter optimization. Design/methodology/approach – Smoothing parameters of the forecasting methods are optimized dynamically by Excel Solver in order to achieve the best performance. Application is done on real data of Turkish Airlines’ spare parts comprising 262 weekly periods from January 2009 to December 2013. The data set are composed of 500 stock-keeping units, so there are 131,000 data points in total. Findings – From the results of implementation, it is shown that using the optimum parameter values yields better performance for each of the methods. Research limitations/implications – Although it is an intensive study, this research has some limitations. Since only real data are considered, this research is limited to the aviation industry. Practical implications – This study guides market players by explaining the features of intermittent demand. With the help of the study, decision makers dealing with intermittent demand are capable of applying specialized intermittent demand forecasting methods. Originality/value – The study brings simplicity to intermittent demand forecasting work by using commonly used spreadsheet software. The study is valuable for giving insights to market players dealing with items having intermittent demand characteristics, and it is one of the first study which is optimizing the smoothing parameters of the forecasting methods by using spreadsheet in the area of intermittent demand forecasting.

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24

David, Engmir, Irwan Budiman, and Jusra Tampubolon. "Decreasing Total Inventory Cost by Controlling Inventory in Motorcycle Dealer." Jurnal Sistem Teknik Industri 22, no. 2 (July 10, 2020): 41–49. http://dx.doi.org/10.32734/jsti.v22i2.3930.

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This research was conducted at one of the motorcycle dealers in Indonesia. Besides selling motorcycles, this dealer also provides services to repair motorcycles and sells genuine motorcycle parts. Inventory management which the company carried out is still not good enough because there are still demand for spare parts from consumers that cannot be fulfilled by the company. The purpose of this study is to draw up a plan to control spare parts by paying attention to the spare parts that need to be considered, estimating the exact number of spare parts demand, knowing the smallest total inventory cost, knowing the amount of safety stock needed, and knowing when to reorder. In preparing the spare parts control, the methods used are ABC analysis, demand forecasting method, and EOQ method. The results of this study are plans to control the inventory of Tire, Rr. such as the forecasting sales of Tire, Rr. as many as 17338, economic order quantity of Tire Rr are 2158 units, the number of safety stocks of Tire, Rr. needed in 2020 are 1738 units, and the reorder point in 2020 is 8 times with the total inventory cost for Tire, Rr. in 2020 is Rp. 30,009,005.

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25

Cao, Yu Kun, and Yun Feng Li. "Applying ARIMA and Fuzzy Logic to Predict the Electricity Spare Parts Demand." Advanced Materials Research 734-737 (August 2013): 1728–33. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.1728.

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With the development of power industry and the growth of high-voltage power equipments in electric power company, the supply and management of spare parts are becoming more complexity and onerous. This investigation proposed a hybrid method to effectively predict the requirements of electric spare parts utilizing fuzzy logic and ARIMA so as to provide as a reference of spare parts control. The forecasting methods are tested in an empirical, comparative study for an electric power company of China. The results show that the approach is one of the most accurate methods.

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26

Reza, AKM Selim, Sayma Suraiya, and M. Babul Hasan. "A Procedure for Scheduling Inventory of an Industry by Merging Forecasting and Linear Programming." Dhaka University Journal of Science 68, no. 1 (January 30, 2020): 65–70. http://dx.doi.org/10.3329/dujs.v68i1.54598.

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In this paper, we develop a mathematical model combining forecasting and linear programming for a business organization of Bangladesh to calculate optimum order quantity and inventory cost. We test the model using raw data of the demand for the raw materials and spare inventory for the industry and find out minimum total inventory cost along with ordering cost and inventory holding cost. The developed model make a match between the forecasted demand of raw materials and spare inventory and the minimum total cost of inventory. Finally comparing minimum cost, we observe that our estimated appropriate forecasting method gives optimal inventory cost. Dhaka Univ. J. Sci. 68(1): 65-70, 2020 (January)

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27

Kim, Jaedong, and Hanjun Lee. "A Study on Forecasting Spare Parts Demand based on Data-Mining." Journal of Internet Computing and Services 18, no. 1 (February 28, 2017): 121–29. http://dx.doi.org/10.7472/jksii.2017.18.1.121.

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28

Dombi, József, Tamás Jónás, and Zsuzsanna Eszter Tóth. "Modeling and long-term forecasting demand in spare parts logistics businesses." International Journal of Production Economics 201 (July 2018): 1–17. http://dx.doi.org/10.1016/j.ijpe.2018.04.015.

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29

Kim, Thai Young, Rommert Dekker, and Christiaan Heij. "Spare part demand forecasting for consumer goods using installed base information." Computers & Industrial Engineering 103 (January 2017): 201–15. http://dx.doi.org/10.1016/j.cie.2016.11.014.

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Gamberini, Rita, Francesco Lolli, Bianca Rimini, and Fabio Sgarbossa. "Forecasting of Sporadic Demand Patterns with Seasonality and Trend Components: An Empirical Comparison between Holt-Winters and (S)ARIMA Methods." Mathematical Problems in Engineering 2010 (2010): 1–14. http://dx.doi.org/10.1155/2010/579010.

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Items with irregular and sporadic demand profiles are frequently tackled by companies, given the necessity of proposing wider and wider mix, along with characteristics of specific market fields (i.e., when spare parts are manufactured and sold). Furthermore, a new company entering into the market is featured by irregular customers' orders. Hence, consistent efforts are spent with the aim of correctly forecasting and managing irregular and sporadic products demand. In this paper, the problem of correctly forecasting customers' orders is analyzed by empirically comparing existing forecasting techniques. The case of items with irregular demand profiles, coupled with seasonality and trend components, is investigated. Specifically, forecasting methods (i.e., Holt-Winters approach and (S)ARIMA) available for items with seasonality and trend components are empirically analyzed and tested in the case of data coming from the industrial field and characterized by intermittence. Hence, in the conclusions section, well-performing approaches are addressed.

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31

Song, Zhi Jie, Zan Fu, Han Wang, and Gui Bin Hou. "Demand Forecasting Model of Port Critical Spare Parts Based on PSO-LSSVM." Applied Mechanics and Materials 433-435 (October 2013): 545–49. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.545.

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Demand forecasting for port critical spare parts (CSP) is notoriously difficult as it is expensive, lumpy and intermittent with high variability. In this paper, some influential factors which have an effect on CSP consumption were proposed according to port CSP characteristics and historical data. Combined with the influential factors, a least squares support vector machines (LS-SVM) model optimized by particle swarm optimization (PSO) was developed to forecast the demand. And the effectiveness of the model is demonstrated through a real case study, which shows that the proposed model can forecast the demand of port CSP more accurately, and effectively reduce inventory backlog.

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32

Ghobbar, A. A. "Forecasting Intermittent Demand for Aircraft Spare Parts: A Comparative Evaluation of Methods." Journal of Aircraft 41, no. 3 (May 2004): 665–73. http://dx.doi.org/10.2514/1.851.

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Liu, Yang, Qi Zhang, Zhi-Ping Fan, Tian-Hui You, and Lu-Xin Wang. "Maintenance Spare Parts Demand Forecasting for Automobile 4S Shop Considering Weather Data." IEEE Transactions on Fuzzy Systems 27, no. 5 (May 2019): 943–55. http://dx.doi.org/10.1109/tfuzz.2018.2831637.

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Rego, José Roberto do, and Marco Aurélio de Mesquita. "Demand forecasting and inventory control: A simulation study on automotive spare parts." International Journal of Production Economics 161 (March 2015): 1–16. http://dx.doi.org/10.1016/j.ijpe.2014.11.009.

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35

Zhu, Sha, Rommert Dekker, Willem van Jaarsveld, Rex Wang Renjie, and Alex J. Koning. "An improved method for forecasting spare parts demand using extreme value theory." European Journal of Operational Research 261, no. 1 (August 2017): 169–81. http://dx.doi.org/10.1016/j.ejor.2017.01.053.

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36

Kolokolov, Yury, and Anna Monovskaya. "A Practice-Oriented Bifurcation Analysis for Pulse Energy Converters. Part 4: Emergency Forecasting." International Journal of Bifurcation and Chaos 28, no. 12 (November 2018): 1850152. http://dx.doi.org/10.1142/s0218127418501523.

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One of the most illustrative examples of promising applications of the practice-oriented bifurcation analysis concerns forecasting. We focus on emergency forecasting for pulse energy conversion systems (PEC-systems). In this case it becomes necessary to integrate regularities from both phase and parametrical spaces and demands on the operating stability and performance, taking into account the critical demand to uninterrupted analytics during steady states and transients. Thus so-called conflict-of-units between the notions used to understand natural evolution (for example, the evolution connected with the operating process) and the notions used to describe desirable artificial regimes (for example, the operating regime) should be resolved. With this purpose, we attract a specific integrating analytics (bifurcation-fractal analytics), the basis of which is provided by modified bifurcation diagrams and fractal regularities. Here the fractal regularities mirror geometrical similarities between shapes of limit cycles and mirror regular dimensional modifications of these shapes with parametrical variation. Modified bifurcation diagrams provide the conciliation of the practicing and scientific notions without distortions and losses of the useful information from parametrical and phase spaces. Then multi-D conflict-free correspondence between causes (degradation of the operating process stability) and effects (changes of the operating regime characteristics) is established, and empirical recommendations on the operating performance can be substituted by clear nonlinear regularities. Fractal methods of real-time forecasting during transients are included in the discussion and their adaptation to the emergency forecasting is proposed. It opens a novel way on how to forecast the operating stability and performance on the common basis of nonlinear regularities which indicate the operating changes towards emergencies. The discussion is illustrated by computer-based and experimental examples. We believe that the results seem to be interesting to researchers in the field of the practice-oriented bifurcation analysis.

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Wu, Qi, Rob Law, and Xin Xu. "A sparse Gaussian process regression model for tourism demand forecasting in Hong Kong." Expert Systems with Applications 39, no. 5 (April 2012): 4769–74. http://dx.doi.org/10.1016/j.eswa.2011.09.159.

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Armenzoni, Mattia, Roberto Montanari, Giuseppe Vignali, Eleonora Bottani, Gino Ferretti, Federico Solari, and Marta Rinaldi. "An integrated approach for demand forecasting and inventory management optimisation of spare parts." International Journal of Simulation and Process Modelling 10, no. 3 (2015): 233. http://dx.doi.org/10.1504/ijspm.2015.071375.

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Lee, Sangwook, and Chunghun Ha. "Long-Term Demand Forecasting Using Agent-Based Model : Application on Automotive Spare Parts." Journal of Society of Korea Industrial and Systems Engineering 38, no. 1 (March 31, 2015): 110–17. http://dx.doi.org/10.11627/jkise.2014.38.1.110.

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Lee, Min Ye, Ki Woo Sung, and Sung Won Han. "Transfer Learning with Seasonal Adjustment for Automotive Spare Part Long-term Demand Forecasting." Journal of the Korean Institute of Industrial Engineers 47, no. 3 (June 30, 2021): 302–14. http://dx.doi.org/10.7232/jkiie.2021.47.3.302.

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Drechny, Marcin. "Non-Negative K-SVD as an element of the forecasting electricity demand system." E3S Web of Conferences 84 (2019): 01003. http://dx.doi.org/10.1051/e3sconf/20198401003.

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The article describes the NN-K-SVD method based on the use of sparse coding and the singular value decomposition to specific values. An example of using the method is the compression of load profiles. The experiment of compression of 125022 power load profiles has been carried out with the use of registered profiles in households and small offices. Two matrices: patterns (atoms) and scaling factors are the result of the discussed algorithm. Features of the created matrices, which can be used in the creation of fast power demand forecasting systems, have been characterized.

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Bratina, Danijel, and Armand Faganel. "Forecasting the Primary Demand for a Beer Brand Using Time Series Analysis." Organizacija 41, no. 3 (May 1, 2008): 116–24. http://dx.doi.org/10.2478/v10051-008-0013-7.

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Forecasting the Primary Demand for a Beer Brand Using Time Series AnalysisMarket research often uses data (i.e. marketing mix variables) that is equally spaced over time. Time series theory is perfectly suited to study this phenomena's dependency on time. It is used for forecasting and causality analysis, but their greatest strength is in studying the impact of a discrete event in time, which makes it a powerful tool for marketers. This article introduces the basic concepts behind time series theory and illustrates its current application in marketing research. We use time series analysis to forecast the demand for beer on the Slovenian market using scanner data from two major retail stores. Before our analysis, only broader time spans have been used to perform time series analysis (weekly, monthly, quarterly or yearly data). In our study we analyse daily data, which is supposed to carry a lot of ‘noise’. We show that - even with noise carrying data - a better model can be computed using time series forecasting, explaining much more variance compared to regular regression. Our analysis also confirms the effect of short term sales promotions on beer demand, which is in conformity with other studies in this field.

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Ridwan Harimansyah, Faikar, and Tukhas Shilul Imaroh. "AIRCRAFT SPARE PARTS INVENTORY MANAGEMENT ANALYSIS ON AIRFRAME PRODUCT USING CONTINUOUS REVIEW METHODS." Dinasti International Journal of Management Science 2, no. 1 (September 23, 2020): 81–90. http://dx.doi.org/10.31933/dijms.v2i1.528.

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The research aims to find the factors that cause high inventory value, increase the value of forecasting precision, service level and cost efficiency with fishbone diagrams and proposed methods. The research sample is 9 spare parts included in classification A in the ABC analysis and maintenance list 2018. Forecasting methods use Moving Average, Single Exponential Smoothing and Syntetos-Boylan Approximation as well as Mean Square Error calculation, deterministic inventory calculation and Continuous Review Method. The results of this study are an increase in logistics costs by $ 808.71 in the inventory management proposal. An increase in service level from 95% to 99% and the error value in the calculation of the proposal becomes smaller using the proposed method. This study also found that the factor causing the high inventory value was due to inaccurate planning methods so that other comparative methods were needed that could increase the precision of demand forecasting.

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Romeijnders, Ward, Ruud Teunter, and Willem van Jaarsveld. "A two-step method for forecasting spare parts demand using information on component repairs." European Journal of Operational Research 220, no. 2 (July 2012): 386–93. http://dx.doi.org/10.1016/j.ejor.2012.01.019.

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Achetoui, Zineb, Charif Mabrouki, and Ahmed Mousrij. "A review of spare parts supply chain management." Jurnal Sistem dan Manajemen Industri 3, no. 2 (November 18, 2019): 67. http://dx.doi.org/10.30656/jsmi.v3i2.1524.

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The particular characteristics of spare parts have prompted several authors to provide substantial results for effective spare parts supply chain management. In this context, the purpose of this paper is to present the significant contributions that researchers have proposed, over time, for the management of spare parts supply chain. The literature has shown that the particular characteristics of spare parts have a significant impact on inventory performance and customer demand fulfillment. For this reason, most of the contributions were focused on spare parts classification methods, forecasting methods and inventory optimization. The focus of researchers on some areas of spare parts management allowed us to identify some promising perspectives that were not developed in literature, such as the development of performance measurement frameworks for spare parts supply chain and the measurement of organizational maturity.

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Wang, Mingxin, Yingnan Zheng, Binbin Wang, and Zhuofu Deng. "Household Electricity Load Forecasting Based on Multitask Convolutional Neural Network with Profile Encoding." Mathematical Problems in Engineering 2021 (March 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/6661798.

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Household load forecasting provides great challenges as a result of high uncertainty in individual consumption of load profile. Traditional models based on machine learning tried to explore uncertainty depending on clustering, spectral analysis, and sparse coding with hand craft features. Recently, deep learning skills like recurrent neural network attempt to learn the uncertainty with one-hot encoding which is too simple and not efficient. In this paper, for the first time, we proposed a multitask deep convolutional neural network for household load forecasting. The baseline of one branch is built on multiscale dilated convolutions for load forecasting. The other branch based on deep convolutional autoencoder is responsible for household profile encoding. In addition, an efficient encoding strategy for household profile is designed that serves a novel feature fusion mechanism integrated into forecasting branch. Our proposed network serves an end-to-end manner in training and inference process. Sufficient ablation studies were conducted to demonstrate effectiveness of innovations and great generalization in point and probabilistic load forecasting at household level, which provides a promising prospect in demand response.

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Moon, Seongmin. "Predicting the Performance of Forecasting Strategies for Naval Spare Parts Demand: A Machine Learning Approach." Management Science and Financial Engineering 19, no. 1 (May 31, 2013): 1–10. http://dx.doi.org/10.7737/msfe.2013.19.1.001.

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Hua, Z. S., B. Zhang, J. Yang, and D. S. Tan. "A new approach of forecasting intermittent demand for spare parts inventories in the process industries." Journal of the Operational Research Society 58, no. 1 (January 2007): 52–61. http://dx.doi.org/10.1057/palgrave.jors.2602119.

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Srisaeng, Panarat, Glenn S. Baxter, and Graham Wild. "FORECASTING DEMAND FOR LOW COST CARRIERS IN AUSTRALIA USING AN ARTIFICIAL NEURAL NETWORK APPROACH." Aviation 19, no. 2 (June 24, 2015): 90–103. http://dx.doi.org/10.3846/16487788.2015.1054157.

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This study focuses on predicting Australia‘s low cost carrier passenger demand and revenue passenger kilometres performed (RPKs) using traditional econometric and artificial neural network (ANN) modelling methods. For model development, Australia‘s real GDP, real GDP per capita, air fares, Australia‘s population and unemployment, tourism (bed spaces) and 4 dummy variables, utilizing quarterly data obtained between 2002 and 2012, were selected as model parameters. The neural network used multi-layer perceptron (MLP) architecture that compromised a multi-layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. The ANN was applied during training, testing and validation and had 11 inputs, 9 neurons in the hidden layers and 1 neuron in the output layer. When comparing the predictive accuracy of the two techniques, the ANNs provided the best prediction and showed that the performance of the ANN model was better than that of the traditional multiple linear regression (MLR) approach. The highest R-value for the enplaned passengers ANN was around 0.996 and for the RPKs ANN was round 0.998, respectively.

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Babai, M. Z., A. Tsadiras, and C. Papadopoulos. "On the empirical performance of some new neural network methods for forecasting intermittent demand." IMA Journal of Management Mathematics 31, no. 3 (April 29, 2020): 281–305. http://dx.doi.org/10.1093/imaman/dpaa003.

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Abstract In this paper, new neural network (NN) methods are proposed to forecast intermittent demand and we empirically study their performance as compared to parametric and non-parametric forecasting methods proposed in the literature. The empirical investigation uses demand data for 5,135 spare parts for the fleet of aircrafts of an airline company. Three parametric benchmark methods are examined: single exponential smoothing (SES), Croston’s method and Syntetos–Boylan approximation, along with two bootstrapping methods: Willemain’s method and Zhou and Viswanathan’s method. The benchmark NN method considered in this paper is that proposed by Gutierrez et al. (2008) The paper shows the outperformance of SES and the NN methods for (a) their forecast accuracy and (b) their inventory efficiency (trade-off between holding volumes and backordering volumes) when compared to the other methods. Moreover, among the NN methods, a new proposed method is shown to be better than that proposed by Gutierrez et al. in terms of forecast accuracy and inventory efficiency.

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