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Relevant bibliographies by topics / Supply and demand – Forecasting

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Academic literature on the topic 'Supply and demand – Forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Supply and demand – Forecasting"

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

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

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Kiseleva, Irina Anatolievna. "Simulations of Supply and Demand Forecasting in A Market Economy." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 1669–84. http://dx.doi.org/10.47059/revistageintec.v11i4.2218.

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Drackley, Adam, K. Bruce Newbold, Antonio Paez, and Nancy Heddle. "Forecasting Ontario's blood supply and demand." Transfusion 52, no. 2 (2011): 366–74. http://dx.doi.org/10.1111/j.1537-2995.2011.03280.x.

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Reddy, A. Srinivasa, P. CHAKRADHAR, PAVAN KUMAR P, and Teja Santosh. "Demand Forecasting and Demand Supply Management of Vegetables in India: A Review and Prospect." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 17, no. 1 (2018): 7170–78. http://dx.doi.org/10.24297/ijct.v17i1.7305.

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Vegetable quantity arrival to market varies every day with which its prices also changes rapidly. This paper analyses the factors that affect the rapid change in prices of vegetables such as demand forecasting, demand supply management, erroneous statistics of vegetables, storage facilities, and supply chain system, etc. Government of India has no control over the production of horticultural and agricultural crops, sometimes under produced and sometimes over produced, which makes demand supply management hard-won. This paper mainly focuses on advantages of demand forecasting and demand supply

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Wagner. "Forecasting Daily Demand in Cash Supply Chains." American Journal of Economics and Business Administration 2, no. 4 (2010): 377–83. http://dx.doi.org/10.3844/ajebasp.2010.377.383.

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Murray, Paul W., Bruno Agard, and Marco A. Barajas. "Forecasting Supply Chain Demand by Clustering Customers." IFAC-PapersOnLine 48, no. 3 (2015): 1834–39. http://dx.doi.org/10.1016/j.ifacol.2015.06.353.

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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 designate

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Iftikhar, Rehan, and Mohammad Saud Khan. "Social Media Big Data Analytics for Demand Forecasting." Journal of Global Information Management 28, no. 1 (2020): 103–20. http://dx.doi.org/10.4018/jgim.2020010106.

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Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is conc

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Bonde, Hans, and Hans-Henrik Hvolby. "The demand planning process." Journal on Chain and Network Science 5, no. 2 (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 deve

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Dissertations / Theses on the topic "Supply and demand – Forecasting"

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Guerrero, Gomez Gricel Celenne. "Lumpy demand characterization and forecasting performance using self-adaptive forecasting models and Kalman filter." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Jeon, Gyoo Jeong. "Innovative methods for long-term mineral forecasting." Diss., The University of Arizona, 1989. http://hdl.handle.net/10150/184653.

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This study presents improved methods for long-term forecasting of mineral demands. Intensity of use, both in its simple, original form and as described by richer economic relations is one such method, particularly when intensity of use is estimated using rigorous statistical methods. Additionally, this dissertation explores the implications of the learning curve for long term forecasting of mineral demands. This study begins by considering the empirical evidence which applies when a learning curve is present. Then, if a learning pattern is present, the learning model is used to examine an econ

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Homsi, Kristopher L. (Kristopher Leon). "Information flow and demand forecasting issues in a complex supply chain." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11600.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1995, and Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1995.<br>Includes bibliographical references (leaves 97-98).<br>by Kristopher L. Homsi.<br>M.S.

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Liu, Chau-wing. "Forecasting the demand of public international telecommunication originating in Hong Kong." Click to view the E-thesis via HKUTO, 1989. http://sunzi.lib.hku.hk/hkuto/record/B31976013.

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Antunes, André Filipe Martins. "Energetic analysis of water supply systems: demand forecasting using artificial intelligence techniques." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23359.

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Mestrado em Engenharia Mecânica<br>In current days, a large number of water utilities manage their operation on the instant water demand of the network, meaning the use of the equipment is conditioned by the immediate water necessity. The water reservoirs of the networks are filled using pumps that start working when the water level reaches a specified minimum, stopping when it reaches a maximum level. Shifting the focus to management based on future demand allows to use the equipment when energy is cheaper, taking advantage of the electricity tariff in action, thus bringing significant financ

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Watkins, Arica. "Successful Demand Forecasting Modeling Strategies for Increasing Small Retail Medical Supply Profitability." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7576.

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The lack of effective demand forecasting strategies can result in imprecise inventory replenishment, inventory overstock, and unused inventory. The purpose of this single case study was to explore successful demand forecasting strategies that leaders of a small, retail, medical supply business used to increase profitability. The conceptual framework for this study was Winters's forecasting demand approach. Data were collected from semistructured, face-to-face interviews with 8 business leaders of a private, small, retail, medical supply business in the southeastern United States and the review

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Hartley, Joseph Alan. "A neural network and rule based system application in water demand forecasting." Thesis, Brunel University, 1995. http://bura.brunel.ac.uk/handle/2438/7867.

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This thesis describes a short term water demand forecasting application that is based upon a combination of a neural network forecast generator and a rule based system that modifies the resulting forecasts. Conventionally, short term forecasting of both water consumption and electrical load demand has been based upon mathematical models that aim to either extract the mathematical properties displayed by a time series of historical data, or represent the causal relationships between the level of demand and the key factors that determine that demand. These conventional approaches have been able

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Teo, William W. J. "A natural language processing approach to improve demand forecasting in long supply chains." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127104.

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Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, May, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 74-80).<br>Information sharing is one of the established approaches to improve demand forecasting and reduce the bullwhip effect, but it is infeasible to do so effectively in a long supply chain. Using the polystyrene industry as a case study, this thesis explores the usage of modern natural language processing (NLP) techniques in a deep learning model, known as NEMO, to forecast

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Liu, Chau-wing, and 廖秋榮. "Forecasting the demand of public international telecommunication originating in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1989. http://hub.hku.hk/bib/B31976013.

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Kecoglu, Onur, and Melih Sua. "Analyzing Total Demand for Specified Destinations : A Total Demand Analysis for an Airline Company." Thesis, KTH, Industriell ekonomi och organisation (Avd.), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-104733.

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The airline industry is a highly competitive market. Particularly after the major liberalization in the industry, the competition carried a step forward for all the concerned companies. Airline industry is a very dynamic market also; the expenses of a single operation may change substantially in a very short time period, which could be due to the fickle regulations or the fluctuation of oil prices. At any time, for a certain destination, a new airline company can start its operations and become a competitor, which can result in a market share loss for current operators. In such an environment,

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Books on the topic "Supply and demand – Forecasting"

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Billings, R. Bruce. Forecasting urban water demand. 2nd ed. American Water Works Association, Science and Technology, 2007.

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Vaughan, Jones Clive, ed. Forecasting urban water demand. American Water Works Association, 1996.

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Jones, Andy. Mathematical models for forecasting passport demand. Home Office Research and Planning Unit, 1994.

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Harvey, Edward B. Engineering supply and demand, 1988-1998. Technical Service Council, 1988.

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Hydro, Ontario. Ontario Hydro's demand/supply plan. Province of Ontario, 1990.

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Dyson, Tim. World food demand and supply prospects. Fertiliser Society, 1995.

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Hill, Alexandra. Alaska teacher supply and demand: 2005 update. University of Alaska Anchorage, Institute of Social and Economic Research, 2006.

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Board, Canada National Energy. Canadian energy, supply and demand 1990-2010. NEB, 1991.

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Division, Ontario Hydro System Planning. Demand/supply options study: The options. Ontario Hydro, System Planning Division, 1986.

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Howard, Bill. Oils & oilseeds to 1996: The new patterns of supply and demand. Economist Intelligence Unit, 1993.

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Book chapters on the topic "Supply and demand – Forecasting"

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Mauergauz, Yuri. "Demand Forecasting." In Advanced Planning and Scheduling in Manufacturing and Supply Chains. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27523-9_6.

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Shao, Juping, Yanan Sun, and Bernd Noche. "Demand Forecasting Dynamic Equation Model." In Optimization of Integrated Supply Chain Planning under Multiple Uncertainty. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47250-7_3.

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Solis, Adriano O. "Forecasting Lumpy Demand: Statistical Accuracy and Inventory Control Performance." In Supply Management Research. Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-08809-5_4.

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Öztayşi, Başar, and Eda Bolturk. "Fuzzy Methods for Demand Forecasting in Supply Chain Management." In Supply Chain Management Under Fuzziness. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53939-8_11.

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Trapero, Juan R., and Diego J. Pedregal. "Supply Chain Demand Forecasting: Towards an Integrated Approach." In Industrial Engineering: Innovative Networks. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2321-7_31.

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Hart, Martin, Pavel Taraba, and Jiří Konečný. "Sustainable Manufacturing Systems Based on Demand Forecasting—Supply Chain Sustainable Growth." In Sustainable Design and Manufacturing 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32098-4_17.

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Rivera-Castro, Rodrigo, Ivan Nazarov, Yuke Xiang, Alexander Pletneev, Ivan Maksimov, and Evgeny Burnaev. "Demand Forecasting Techniques for Build-to-Order Lean Manufacturing Supply Chains." In Advances in Neural Networks – ISNN 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22796-8_23.

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Zou, An-Quan, and Ren-Cun Huang. "The Inventory Demand Forecasting Model of the Regional Logistics Network in Supply Chain." In International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012) Proceedings. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38445-5_134.

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Zou, An-Quan, and Ren-Cun Huang. "The Inventory Demand Forecasting Model of the Regional Logistics Network in Supply Chain." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44674-4_7.

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Vairagade, Navneet, Doina Logofatu, Florin Leon, and Fitore Muharemi. "Demand Forecasting Using Random Forest and Artificial Neural Network for Supply Chain Management." In Computational Collective Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28377-3_27.

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Conference papers on the topic "Supply and demand – Forecasting"

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Kale, Rajesh V., and Sanjay D. Pohekar. "Long-range forecasting of electricity demand and supply for Maharashtra." In 2013 International Conference on Renewable Energy and Sustainable Energy (ICRESE). IEEE, 2013. http://dx.doi.org/10.1109/icrese.2013.6927797.

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Minhas, Daud Mustafa, Raja Rehan Khalid, and Georg Frey. "Load control for supply-demand balancing under Renewable Energy forecasting." In 2017 IEEE Second International Conference on DC Microgrids (ICDCM). IEEE, 2017. http://dx.doi.org/10.1109/icdcm.2017.8001071.

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Ayoub, Nasser, Farayi Musharavati, Shaligram Pokharel, and Hossam A. Gabbar. "ANN Model for Energy Demand and Supply Forecasting in a Hybrid Energy Supply System." In 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE). IEEE, 2018. http://dx.doi.org/10.1109/sege.2018.8499514.

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Bhowte, Yogesh W. "Forecasting the load of demand and supply of Electricity in India." In 2016 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC). IEEE, 2016. http://dx.doi.org/10.1109/iccpeic.2016.7557308.

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Hu, Hui, Guangyu Zhu, Yanjun Bo, and Jinsheng Shen. "Study on a Combined Demand Forecasting Model of the Supply Chain." In 2006 International Conference on Service Systems and Service Management. IEEE, 2006. http://dx.doi.org/10.1109/icsssm.2006.320621.

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Sarhani, Malek, and Abdellatif El Afia. "Intelligent system based support vector regression for supply chain demand forecasting." In 2014 Second World Conference on Complex Systems (WCCS). IEEE, 2014. http://dx.doi.org/10.1109/icocs.2014.7060941.

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Sun, Li, Kai Sun, Hongxiao Zhang, and Jinchu Zhou. "Forecasting on China’s Labor Supply and Demand Gap during 2018-2035." In Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019). Atlantis Press, 2019. http://dx.doi.org/10.2991/msbda-19.2019.56.

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Yang, Kai, and Lei Hou. "Using Machine Learning Tools for Forecasting Natural Gas Market Demand." In ASME 2020 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/pvp2020-21378.

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Abstract Providing reliable and accurate forecasts of natural gas consumption can keep supply and demand of natural gas pipelines in balance, which can increase profits and reduce supply risks. In order to accurately predict the short-term load demand of different gas nodes in the natural gas pipeline network, a hybrid optimization strategy of integrated genetic optimization algorithm and support vector machine are proposed. Factors such as holidays, date types and weather were taken into account to build a natural gas daily load prediction model based on GA-SVM was established. A natural gas

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Burney, S. M. Aqil, and Syed Mubashir Ali. "Sales Forecasting for Supply Chain Demand Management - A Novel Fuzzy Time Series Approach." In 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). IEEE, 2019. http://dx.doi.org/10.1109/macs48846.2019.9024810.

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Zou, A. Q., G. Huang, J. L. Chen, and Y. Q. Qu. "The inventory demand forecasting model of the regional logistics network in supply chain." In EM 2011). IEEE, 2011. http://dx.doi.org/10.1109/icieem.2011.6035458.

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Reports on the topic "Supply and demand – Forecasting"

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Kiflu, Mordocai, and Carlos Lopez. Demand Forecasting: DLA'S Aviation Supply Chain High Value Products. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada626751.

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Farmer, Roger E. Aggregate Demand and Supply. National Bureau of Economic Research, 2007. http://dx.doi.org/10.3386/w13406.

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Goodwill, Jay. Forecasting Paratransit Services Demand – Review and Recommendations. University of South Florida, 2013. http://dx.doi.org/10.5038/cutr-nctr-rr-2011-06.

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Pathak, Parag, and Peng Shi. Demand Modeling, Forecasting, and Counterfactuals, Part I. National Bureau of Economic Research, 2014. http://dx.doi.org/10.3386/w19859.

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Research Institute (IFPRI), International Food Policy. Seed demand and supply responses. International Food Policy Research Institute, 2018. http://dx.doi.org/10.2499/9780896292833_04.

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Asquith, Brian J., Evan Mast, and Davin Reed. Supply Shock versus Demand Shock. W.E. Upjohn Institute for Employment Research, 2020. http://dx.doi.org/10.17848/pb2020-19.

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Hamilton, James. Supply, Demand, and Specialized Production. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w28888.

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Wilbanks, T. J. (Energy and electricity supply and demand). Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6461786.

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Mullen, G. M. Choppers Grounded: The Supply-Demand Problem. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada264258.

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Langstroth, IV, and Theodore A. Forecasting Demand for KC-135 Sorties: Deploy to Dwell Impacts. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada587364.

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