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Journal articles on the topic 'Demand Forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 July 2024

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1

Jones, Rod. "Forecasting demand." British Journal of Healthcare Management 16, no. 8 (August 2010): 392–93. http://dx.doi.org/10.12968/bjhc.2010.16.8.77654.

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Elkarmi, Fawwaz, and Nazih Abu Shikhah. "Electricity Demand Forecasting." International Journal of Productivity Management and Assessment Technologies 2, no. 1 (January 2014): 1–19. http://dx.doi.org/10.4018/ijpmat.2014010101.

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Forecasting is the backbone of any planning process in all fields of interest. It has a great impact on future decisions. It is also of great importance to the operation and control of business, which is reflected as profits or losses to the entity. This paper aims to provide the planner with sufficient knowledge and background of the different scopes of forecasting methods, in general, and when applied to power system field, in particular. Various load and energy forecasting models, and theoretical techniques are discussed from different perspectives, time frames, and levels. The paper presents the attributes and importance of forecasting through several cases of research conducted by the author for the Jordanian power system. In all cases the methodologies selected cover short, medium and long term forecasting periods and the results are accurate.
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3

Greenidge, Kevin. "Forecasting tourism demand." Annals of Tourism Research 28, no. 1 (January 2001): 98–112. http://dx.doi.org/10.1016/s0160-7383(00)00010-4.

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4

O, Nepochatenko Olena. "Forecasting Investment Demand of Ukrainian Agrarian Enterprises." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 359–69. http://dx.doi.org/10.5373/jardcs/v12sp7/20202117.

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5

Kandananond, Karin. "Applying Kalman Filter for Correlated Demand Forecasting." Applied Mechanics and Materials 619 (August 2014): 381–84. http://dx.doi.org/10.4028/www.scientific.net/amm.619.381.

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Product demands are known to be serially correlated. In this research, a first order autoregressive model, AR (1), is utilized to simulate product demand processes whose behavior are stationary. Since demand forecasting is important to the efficiency improvement of product supply chain system, different forecasting techniques are utilized to predict product demand. In this research, Kalman filter is deployed to forecast demand simulated by AR (1) model. Product demands are simulated at the different degrees of autoregressive coefficients. After the application of Kalman filter to the designated data, the forecasting errors are calculated and the results indicate that Kalman filter is an efficient technique to predict demands in the future.
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Bernard Trustrum, Leslie, F. Robert Blore, and William James Paskins. "Using Demand Forecasting Models." Marketing Intelligence & Planning 5, no. 3 (March 1987): 5–15. http://dx.doi.org/10.1108/eb045750.

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7

Martin, Christine A., and Stephen F. Witt. "Tourism demand forecasting models." Tourism Management 8, no. 3 (September 1987): 233–46. http://dx.doi.org/10.1016/0261-5177(87)90055-0.

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8

Chambers, Marcus J. "Forecasting with demand systems." Journal of Econometrics 44, no. 3 (June 1990): 363–76. http://dx.doi.org/10.1016/0304-4076(90)90064-z.

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9

Wong, James M. W., Albert P. C. Chan, and Y. H. Chiang. "Construction manpower demand forecasting." Engineering, Construction and Architectural Management 18, no. 1 (January 11, 2011): 7–29. http://dx.doi.org/10.1108/09699981111098667.

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Guinel, Ipek. "Forecasting system energy demand." Journal of Forecasting 6, no. 2 (1987): 137–56. http://dx.doi.org/10.1002/for.3980060207.

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11

Bonde, Hans, and Hans-Henrik Hvolby. "The demand planning process." Journal on Chain and Network Science 5, no. 2 (December 1, 2005): 73–84. http://dx.doi.org/10.3920/jcns2005.x057.

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In this paper, demand planning is discussed from a process point of view. Demand planning is not only forecasting but goes beyond, as it combines quantitative forecasting with a causal forecasting approach to plan demand by changing factors within pricing, marketing or selling. A four-phase demand planning process model is introduced, which consists of modeling, forecasting, demand planning and supply planning. The core of the process is a demand planning tool, which allows the combination of quantitative, causal and judgmental forecasting. Finally, some thoughts are given on how SMEs can develop their current forecasting practice when implementing demand planning as a strategic and tactical tool.
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Wang, Yawen. "Overview of Logistics Demand Forecasting Methods." Frontiers in Business, Economics and Management 9, no. 2 (June 12, 2023): 251–55. http://dx.doi.org/10.54097/fbem.v9i2.9293.

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Accurate forecasting of logistics demand is of great theoretical significance and practical application value for the formulation of national policies and the satisfaction of actual demand in the logistics industry. From the modeling form, the existing logistics demand forecasting methods are divided into four categories: single traditional forecasting method, single intelligent forecasting method, combined forecasting method and mixed forecasting method. Among them, single traditional forecasting methods mainly include simple time series method, regression analysis, mathematical and statistical methods, etc.; single intelligent forecasting methods mainly involve gray forecasting method, neural network, support vector machine and their improved forms; combined forecasting methods are mainly summarized into three combined forms: linear combination of single forecasting results, nonlinear combination of single forecasting results, modified single forecasting results; mixed forecasting methods are mainly summarized into three hybrid forms: hybrid intelligent optimization algorithms with single prediction methods, hybrid data dimensionality reduction techniques with intelligent prediction methods, and hybrid data mining techniques with intelligent prediction methods. The four major types of forecasting methods are reviewed, and each forecasting model in the four major types of methods is evaluated in terms of modeling principles, advantages and disadvantages, and applicability, in order to find forecasting methods suitable for different logistics demand forecasting tasks for logistics demand researchers.
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Alasali, Feras, Husam Foudeh, Esraa Mousa Ali, Khaled Nusair, and William Holderbaum. "Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources." Energies 14, no. 8 (April 12, 2021): 2151. http://dx.doi.org/10.3390/en14082151.

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More and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Previous studies have focused mostly on forecasting LV level demand without considering renewable energy sources, disaggregation demand and the weather conditions at the LV level. In this study, we provide detailed LV demand analysis and a variety of forecasting methods in terms of a probabilistic, new optimization learning algorithm called the Golden Ratio Optimization Method (GROM) for an Artificial Neural Network (ANN) model for rolling and point forecasting. Short-term forecasting models have been designed and developed to generate future scenarios for different disaggregation demand levels from households, small cities, net demands and PV system output. The results show that the volatile behavior of LV networks connected to the PV system creates substantial forecasting challenges. The mean absolute percentage error (MAPE) for the ANN-GROM model improved by 41.2% for household demand forecast compared to the traditional ANN model.
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14

Miao, Xin, and Bao Xi. "AGILE FORECASTING OF DYNAMIC LOGISTICS DEMAND." TRANSPORT 23, no. 1 (March 31, 2008): 26–30. http://dx.doi.org/10.3846/1648-4142.2008.23.26-30.

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The objective of this paper is to study the quantitative forecasting method for agile forecasting of logistics demand in dynamic supply chain environment. Characteristics of dynamic logistics demand and relative forecasting methods are analyzed. In order to enhance the forecasting efficiency and precision, extended Kalman Filter is applied to training artificial neural network, which serves as the agile forecasting algorithm. Some dynamic influencing factors are taken into consideration and further quantified in agile forecasting. Swarm simulation is used to demonstrate the forecasting results. Comparison analysis shows that the forecasting method has better reliability for agile forecasting of dynamic logistics demand.
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15

Uyeno, Dean, Craig Galbraith, and David Buchan. "Forecasting the Demand for Maternity Services." Healthcare Management Forum 7, no. 4 (December 1994): 51–53. http://dx.doi.org/10.1016/s0840-4704(10)61078-2.

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Faced with demands on maternity services exceeding design capacity, one British Columbia hospital commissioned forecasting studies to determine trends in demand and if accurate forecasts could be obtained. In addition to describing the forecasting method employed, the data used and the results, the authors look at what literature is available on obstetrics forecasting.
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16

Jeffee Jenson, A., and S. Sowkarthikax. "Electricity Demand Forecasting using LSTMs." Journal of Electrical Engineering and Automation 5, no. 2 (June 2023): 228–37. http://dx.doi.org/10.36548/jeea.2023.2.006.

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Electricity demand forecasting is an essential task in the energy industry, enabling utilities and energy suppliers to optimize the generation, transmission, and distribution of electricity. In recent years, deep learning techniques such as Long Short -Term Memory (LSTM) neural networks have shown great potential in improving the accuracy and efficiency of time-series forecasting tasks, including electricity demand forecasting. This research proposes an LSTM-based neural network architecture for short-term electricity demand forecasting. The proposed model is evaluated on real-world electricity demand data, and the results demonstrate its effectiveness in predicting future demand patterns. The model's performance is evaluated using the Mean Squared Error loss function and the Root Mean Squared Error metric. The proposed model shows promising results compared to traditional time-series forecasting models. The results suggest that LSTM-based neural networks can be a powerful tool for electricity demand forecasting, providing more accurate and efficient forecasting models that can help improve energy system planning and decision making.
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17

Bogdanov, Andrey. "USE OF ANALYTIC HIERARCHY PROCESS IN LOGISTICS DEMAND FORECASTING." Journal Scientific and Applied Research 9, no. 1 (February 5, 2016): 12–16. http://dx.doi.org/10.46687/jsar.v9i1.184.

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18

Mir, Aneeque A., Mohammed Alghassab, Kafait Ullah, Zafar A. Khan, Yuehong Lu, and Muhammad Imran. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons." Sustainability 12, no. 15 (July 23, 2020): 5931. http://dx.doi.org/10.3390/su12155931.

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With the globally increasing electricity demand, its related uncertainties are on the rise as well. Therefore, a deeper insight of load forecasting techniques for projecting future electricity demands becomes imperative for business entities and policy makers. The electricity demand is governed by a set of different variables or “electricity demand determinants”. These demand determinants depend on forecasting horizons (long term, medium term, and short term), the load aggregation level, climate, and socio-economic activities. In this paper, a review of different electricity demand forecasting methodologies is provided in the context of a group of low and middle income countries. The article presents a comprehensive literature review by tabulating the different demand determinants used in different countries and forecasting the trends and techniques used in these countries. A comparative review of these forecasting methodologies over different time horizons reveals that the time series modeling approach has been extensively used while forecasting for long and medium terms. For short term forecasts, artificial intelligence-based techniques remain prevalent in the literature. Furthermore, a comparative analysis of the demand determinants in these countries indicates a frequent use of determinants like the population, GDP, weather, and load data over different time horizons. Following the analysis, potential research gaps are identified, and recommendations are provided, accordingly.
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19

Trubchanin, V. V. "Forecasting Product Demand in Terms of Demand Volatility." Bulletin of Ural Federal University. Series Economics and Management 16, no. 2 (2017): 191–207. http://dx.doi.org/10.15826/vestnik.2017.16.2.010.

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20

Dalimunthe, Sri Baginda, Rosnani Ginting, and Sukaria Sinulingga. "The Implementation Of Machine Learning In Demand Forecasting : A Review Of Method Used In Demand Forecasting With Machine Learning." Jurnal Sistem Teknik Industri 25, no. 1 (January 30, 2023): 41–49. http://dx.doi.org/10.32734/jsti.v25i1.9290.

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Demand Forecasting is essentials in making production decisions. Demand forecasting accuracy affects supply chain management and can reduce its costs. The development of information technology, especially artificial intelligence, has many benefits in many industrial sectors. The development of artificial intelligence is also applied to demand forecasting. The development of Artificial Intelligence technology in forecasting can produce better accuracy than conventional methods that do not use Artificial Intelligence. The use of machine learning in demand forecasting is in various industrial sectors ranging from small-scale industry to large-scale industry. This article will discuss research on the use of machine learning in demand forecasting for the things discussed, including machine learning models, data processing methods, and research variables. The purpose of this review is to see a comparison of the accuracy of each model, method, and variable used in demand forecasting using machine learning. The results of the review show that the characteristics of different product fluctuations require a different demand forecasting model approach. An appropriate approach can produce higher forecasting accuracy. Mistake in choosing a demand forecasting model can reduce the accuracy of demand forecasting. The demand forecasting model must also need to be updated to improve accuracy.
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21

Painter, Kathleen, Eric Jessup, Marcia Hill Gossard, and Ken Casavant. "Demand Forecasting for Rural Transit." Transportation Research Record: Journal of the Transportation Research Board 1997, no. 1 (January 2007): 35–40. http://dx.doi.org/10.3141/1997-05.

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22

Hou, Kai Wen, Kui Ying Wang, Yun Ming Li, Yong He Hu, and Qi Ying Yang. "Emergency Supplies Demand Forecasting Model." Applied Mechanics and Materials 608-609 (October 2014): 129–33. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.129.

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At 8:02 of April 20, 2013, a strong earthquake of magnitude 7.0 occurred in Lushan County, Ya’an, Sichuan, sp that a large area of houses collapsed, and a great loss of people's lives and property was caused. The timeliness and effectiveness of the post-earthquake relief were directly related to saving lives and properties, while the scientific allocation of emergency supplies is a key element in the post-earthquake relief.
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23

Strotsen, L. "Qualitative methods of forecasting demand." Galic'kij ekonomičnij visnik 54, no. 1 (2018): 113–18. http://dx.doi.org/10.33108/galicianvisnyk_tntu2018.01.113.

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24

Kolars, J. C. "Forecasting physician supply and demand." Medical Education 35, no. 5 (May 13, 2001): 424–25. http://dx.doi.org/10.1046/j.1365-2923.2001.00945.x.

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25

Kalchschmidt, Matteo, Roberto Verganti, and Giulio Zotteri. "Forecasting demand from heterogeneous customers." International Journal of Operations & Production Management 26, no. 6 (June 2006): 619–38. http://dx.doi.org/10.1108/01443570610666975.

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26

Tica, Josip, and Ivan Kožić. "Forecasting Croatian inbound tourism demand." Economic Research-Ekonomska Istraživanja 28, no. 1 (January 2015): 1046–62. http://dx.doi.org/10.1080/1331677x.2015.1100842.

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27

Böse, Joos-Hendrik, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Dustin Lange, David Salinas, Sebastian Schelter, Matthias Seeger, and Yuyang Wang. "Probabilistic demand forecasting at scale." Proceedings of the VLDB Endowment 10, no. 12 (August 2017): 1694–705. http://dx.doi.org/10.14778/3137765.3137775.

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28

Long, Wen, Chang Liu, and Haiyan Song. "Pooling in Tourism Demand Forecasting." Journal of Travel Research 58, no. 7 (October 5, 2018): 1161–74. http://dx.doi.org/10.1177/0047287518800390.

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This study investigates whether pooling can improve the forecasting performance of tourism demand models. The short-term domestic tourism demand forecasts for 341 cities in China using panel data (pooled) models are compared with individual ordinary least squares (OLS) and naïve benchmark models. The pooled OLS model demonstrates much worse forecasting performance than the other models. This indicates the huge heterogeneity of tourism across cities in China. A marked improvement with the inclusion of fixed effects suggests that destination features that stay the same or vary very little over time can explain most of the heterogeneity. Adding spatial effects to the panel data models also increases forecasting accuracy, although the improvement is small. The spatial distribution of spillover effects is drawn on a map and a spatial pattern is recognized. Finally, when both spatial and temporal effects are taken into account, pooling improves forecasting performance.
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ARAS, HAYDAR, and NIL ARAS. "Forecasting Residential Natural Gas Demand." Energy Sources 26, no. 5 (April 2004): 463–72. http://dx.doi.org/10.1080/00908310490429740.

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30

Fildes, Robert, and V. Kumar. "Telecommunications demand forecasting—a review." International Journal of Forecasting 18, no. 4 (October 2002): 489–522. http://dx.doi.org/10.1016/s0169-2070(02)00064-x.

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31

Turner, Lindsay. "Tourism demand modelling and forecasting." Tourism Management 22, no. 5 (October 2001): 578–79. http://dx.doi.org/10.1016/s0261-5177(01)00018-8.

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32

Chen, Dongling, Kenneth W. Clements, E. John Roberts, and E. Juerg Weber. "Forecasting steel demand in China." Resources Policy 17, no. 3 (September 1991): 196–210. http://dx.doi.org/10.1016/0301-4207(91)90003-e.

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33

Harvey, Edward B., and K. S. R. Murthy. "Forecasting manpower demand and supply." International Journal of Forecasting 4, no. 4 (January 1988): 551–62. http://dx.doi.org/10.1016/0169-2070(88)90132-x.

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34

Rajopadhye, Mihir, Mounir Ben Ghalia, Paul P. Wang, Timothy Baker, and Craig V. Eister. "Forecasting uncertain hotel room demand." Information Sciences 132, no. 1-4 (February 2001): 1–11. http://dx.doi.org/10.1016/s0020-0255(00)00082-7.

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35

Syntetos, Aris A., Mohamed Zied Babai, and Shuxin Luo. "Forecasting of compound Erlang demand." Journal of the Operational Research Society 66, no. 12 (December 2015): 2061–74. http://dx.doi.org/10.1057/jors.2015.27.

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36

Joyeux, Roselyne, George Milunovich, and John Rigg. "Forecasting Demand for Australian Passports." Asia Pacific Journal of Tourism Research 17, no. 1 (February 2012): 100–119. http://dx.doi.org/10.1080/10941665.2011.613205.

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37

Vu, Chau Jo, and Lindsay Turner. "Data Disaggregation in Demand Forecasting." Tourism and Hospitality Research 6, no. 1 (November 2005): 38–52. http://dx.doi.org/10.1057/palgrave.thr.6040043.

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It is assumed in tourism demand forecasting that the disaggregation of data is useful in terms of country of origin and also in terms of purpose of travel (Smith and Toms, 1967; Blackwell, 1970; Martin and Witt, 1989a). The primary disaggregation by country is useful for determining regional forecast flows and the disaggregation by purpose of visit has been considered potentially useful for increasing forecasting accuracy given the flows have different characteristics (Turner, Kulendran and Pergat, 1995; Morley and Sutikno, 1991). It is also possible to disaggregate on the basis of age and gender. It has been assumed (because no research has disaggregated by age and gender) in previous research that the gender and age composition of flows is a reflection of the total population and therefore exhibits the same time-series characteristics. This may not be the case, however. This study uses data for tourist arrivals into Korea to test the assumptions that further disaggregation of data on the basis of gender and age is not needed, and to further examine whether disaggregation by purpose of visit is worthwhile, when the purpose is to forecast total country arrivals. Quarterly data from 1994 to 2003 are used with the estimation period 1994–2001 and the post-estimation period 2002–2003. The conclusion from the study is that total arrivals forecasting is not more accurate when the data used is the sum of forecast disaggregated series, as opposed to direct forecasts of total arrivals.
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Wan, Shui Ki, Haiyan Song, and David Ko. "Density forecasting for tourism demand." Annals of Tourism Research 60 (September 2016): 27–30. http://dx.doi.org/10.1016/j.annals.2016.05.012.

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Song, Haiyan, Long Wen, and Chang Liu. "Density tourism demand forecasting revisited." Annals of Tourism Research 75 (March 2019): 379–92. http://dx.doi.org/10.1016/j.annals.2018.12.019.

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40

Yelland, Phillip M. "Bayesian forecasting of parts demand." International Journal of Forecasting 26, no. 2 (April 2010): 374–96. http://dx.doi.org/10.1016/j.ijforecast.2009.11.001.

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Rostami-Tabar, Bahman, M. Zied Babai, Aris Syntetos, and Yves Ducq. "Demand forecasting by temporal aggregation." Naval Research Logistics (NRL) 60, no. 6 (July 31, 2013): 479–98. http://dx.doi.org/10.1002/nav.21546.

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Mirowski, Piotr, Sining Chen, Tin Kam Ho, and Chun-Nam Yu. "Demand Forecasting in Smart Grids." Bell Labs Technical Journal 18, no. 4 (February 26, 2014): 135–58. http://dx.doi.org/10.1002/bltj.21650.

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Ferbar Tratar, Liljana. "Forecasting method for noisy demand." International Journal of Production Economics 161 (March 2015): 64–73. http://dx.doi.org/10.1016/j.ijpe.2014.11.019.

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Zadachyn, Viktor, and Oleg Frolov. "Urban daily water demand forecasting." Bulletin of Kharkov National Automobile and Highway University, no. 100 (April 7, 2023): 30. http://dx.doi.org/10.30977/bul.2219-5548.2023.100.0.30.

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Abstract. Problem. The problem of building models and methods of forecasting the daily need for urban water is considered. Much more attention needs to be given to forecasting methods if utilities are to make decisions that reflect the level of uncertainty precisely in future daily demand forecasts. Daily water consumption, unlike annual and monthly water consumption, is much more highly dependent on chance. Goal. The main goal of this paper is to obtain enough accurate forecasts of daily urban water consumption. Method. An algorithm for calculating the urban daily water demand forecast based on the concept of same-type days of water demand for previous years has been suggested. Scientific novelty. The originality of the method lies in the fact that it does not use neural network models, but still makes it possible to obtain enough accurate forecasts of daily urban water needs. Results. The presented algorithm for calculating the urban daily water demand forecast has been implemented in the form of a software package and has been tested for many years in real-life conditions. The average absolute percentage error of the daily forecast of urban water demand for one month does not exceed 5%. Practical significance. The practical value of this work lies in the fact that the presented software complex for calculating the forecast of the city's daily water demand can be used in the information services of city utility companies to make operational and tactical decisions regarding the provision of water supply services to the population.
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KURTULAY, Zeynep. "Forecasting Turkiye's Foreign Tourism Demand." International Journal of Social Sciences 7, no. 32 (December 20, 2023): 370–93. http://dx.doi.org/10.52096/usbd.7.32.24.

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The structure of tourist demand is sensitive since it is very easily affected by the consequences of economic, political, and social crises. Since the limited ability to increase tourism supply, it is crucial to analyze the demand structure and develop suitable strategies. This outcome can only be achieved by an accurate and effective demand prediction. There is no singular approach that ensures success in demand forecasting. Hence, in order to estimate demand accurately, it is advisable to create many models and choose the one with the lowest error rate. This study aimed to develop the best-performing prediction model by using monthly data of international visitors who visited Turkiye from January 2002 toAugust 2023 and stayed in Tourism Ministry-certified accommodations. Within this framework, the data was first analyzed to identify the trend and seasonal component. Afterwards, various models were employed including Naive III, simple moving average, double moving average, seasonal exponential smoothing, and artificial neural networks. The data generated by these models has been analyzed by comparing it with the actual data from the last 24 months, using MAPE and RMSE results. According to the research findings, it has been determined that artificial neural networks produce the most accurate results. Keywords: Tourism Demand, Demand Forecast, Time Series, Foreign Tourism, Forecast.
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Tsai, Yihjia, Kuan-Wu Chang, Giou-Teng Yiang, and Hwei-Jen Lin. "Demand Forecast and Multi-Objective Ambulance Allocation." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 07 (March 14, 2018): 1859011. http://dx.doi.org/10.1142/s0218001418590115.

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This study considers the two-fold dynamic ambulance allocation problem, which includes forecasting the distribution of Emergency Medical Service (EMS) requesters and allocating ambulances dynamically according to the predicted distribution of requesters. EMSs demand distribution forecasting is based on on-record historical demands. Subsequently, a multi-objective ambulance allocation model (MOAAM) is solved by a mechanism called Jumping Particle Swarm Optimization (JPSO) according to the forecasted distribution of demands. Experiments were conducted using recorded historical data for EMS requesters in New Taipei City, Taiwan, for the years 2014 and 2015. EMS demand distribution for 2015 is forecasted according to the on-record historical demand of 2014. Ambulance allocation for 2015 is determined according to the anticipated demand distribution. The predicted demand distribution and ambulance allocation solved by JPSO are compared with historic data of 2015. The comparisons verify that the proposed methods yield lower forecasting error rates and better ambulance allocation than the actual one.
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47

Westcott, R. "A scenario approach to demand forecasting." Water Supply 4, no. 3 (June 1, 2004): 45–56. http://dx.doi.org/10.2166/ws.2004.0042.

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The Environment Agency's 2001 national water resources strategy, Water resources for the future, provides a secure framework for the management of water that protects the long-term future of the water environment while encouraging sustainable development. Underpinning the strategy are a suite of scenario based forecasts developed to explore the impact of key drivers of demand within different sectors of water use across England and Wales. This paper explains the approach used to produce these forecasts, summarises how the individual components of demand were considered and highlights opportunities for future application and development of this approach. Using the premise that total water demand can mask conflicting trends between sectors, it is essential to consider each sector and its micro-components independently to understand the specific drivers of demand and consequently determine how these might best be managed. Four scenarios reflecting different possible futures of socio-economic and governmental structure were created to test “how”, “why” and “where” these water demands may change by 2025. Such an approach provides an opportunity to test the implications of macro drivers of demand, such as, economic growth and regulatory reform, on the micro-components of water use, linking disparate sectors to a common set of assumptions about the future.
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48

Kalev, Krasimir. "Application of statistical methods in forecasting for spare elements demand." Journal scientific and applied research 2, no. 1 (March 3, 2012): 34–38. http://dx.doi.org/10.46687/jsar.v2i1.40.

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The problem of predicting the demand for spare elements is extremely important for continuous operation of machines. It is necessary to know when and how much to order. To calculate and ensure the availability of spare elements, appropriate mathematical models should be applied. The statistical method for predicting spare elements demand is considered in this paper. The study shows how to use a demand forecasting technique for determining the expected number of spare elements. Some of the results are given by engineering software.
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49

Xiao, Chuncai, and Yumei Liao. "Transformer Order Demand Forecasting Based on Grey Forecasting Model." IOP Conference Series: Earth and Environmental Science 831, no. 1 (August 1, 2021): 012004. http://dx.doi.org/10.1088/1755-1315/831/1/012004.

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50

Jugović, Alen, Svjetlana Hess, and Tanja Poletan Jugović. "Traffic Demand Forecasting for Port Services." PROMET - Traffic&Transportation 23, no. 1 (January 26, 2012): 59–69. http://dx.doi.org/10.7307/ptt.v23i1.149.

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Successful management of any sea port depends primarily on the harmonisation of transport supply and demand, whereas their incompatibility leads to a number of problems. The port, i.e. its management, through its operation and part of port policy may affect the planning of the construction or modernization of its port facilities. In doing so, the specified planning requires forecasting and quantification of the needs for infrastructural services of specified port, i.e. assessment of traffic demand. Accordingly, the basic problem of research in this paper is forecasting of traffic demand for the port services by applying the appropriate forecasting methods. In order to find ways of solving concrete problems in the port operations the methods for traffic demand forecasting are set by applying the methodology and the presentation of the application of economic forecasting methods. The selected methods of demand forecasting for port services in demand are illustrated and tested on the example of the Croatian largest cargo port, the Port of Rijeka. KEY WORDS: planning, forecasting, traffic demand, commodity flows, port capacity
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