Relevant bibliographies by topics / Sales forecasting

Contents

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

Academic literature on the topic 'Sales forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Sales forecasting"

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Addison, Ed. "Sales forecasting." Business & Management Collection 2024, no. 8 (2024): e1006330. http://dx.doi.org/10.69645/upug7683.

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Rodrigues, Aaron. "Food Sales Forecasting Using Machine Learning Techniques: A Survey." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 869–72. http://dx.doi.org/10.22214/ijraset.2021.38069.

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Abstract: Food sales forecasting is concerned with predicting future sales of food-related businesses such as supermarkets, grocery stores, restaurants, bakeries, and patisseries. Companies can reduce stocked and expired products within stores while also avoiding missing revenues by using accurate short-term sales forecasting. This research examines current machine learning algorithms for predicting food purchases. It goes over key design considerations for a data analyst working on food sales forecasting’s, such as the temporal granularity of sales data, the input variables to employ for fore
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West, Douglas C. "Managing Sales Forecasting." Management Research News 20, no. 4 (1997): 1–10. http://dx.doi.org/10.1108/eb028556.

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Shahabuddin, Syed. "Forecasting automobile sales." Management Research News 32, no. 7 (2009): 670–82. http://dx.doi.org/10.1108/01409170910965260.

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Money, Arthur. "Practical sales forecasting." Long Range Planning 23, no. 4 (1990): 119. http://dx.doi.org/10.1016/0024-6301(90)90161-v.

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Dalrymple, Douglas J. "Sales forecasting practices." International Journal of Forecasting 3, no. 3-4 (1987): 379–91. http://dx.doi.org/10.1016/0169-2070(87)90031-8.

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Speece, Mark W. "Practical sales forecasting." International Journal of Forecasting 7, no. 1 (1991): 106–8. http://dx.doi.org/10.1016/0169-2070(91)90037-v.

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Mentzer, John T., Carol C. Bienstock, and Kenneth B. Kahn. "Benchmarking sales forecasting management." Business Horizons 42, no. 3 (1999): 48–56. http://dx.doi.org/10.1016/s0007-6813(99)80021-4.

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Fildes, Robert, Stuart Bretschneider, Fred Collopy, et al. "Researching Sales Forecasting Practice." International Journal of Forecasting 19, no. 1 (2003): 27–42. http://dx.doi.org/10.1016/s0169-2070(02)00033-x.

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Alon, Ilan, Min Qi, and Robert J. Sadowski. "Forecasting aggregate retail sales:." Journal of Retailing and Consumer Services 8, no. 3 (2001): 147–56. http://dx.doi.org/10.1016/s0969-6989(00)00011-4.

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Dissertations / Theses on the topic "Sales forecasting"

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SESKAUSKIS, ZYGIMANTAS, and ROKAS NARKEVICIUS. "Sales forecasting management." Thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-10685.

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The purpose of this research is to investigate current company business process from sales forecasting perspective and provide potential improvements of how to deal with unstable market demand and increase overall precision of forecasting. The problem which company face is an unstable market demand and not enough precision in sales forecasting process. Therefore the research questions are:  How current forecasting process can be improved?  What methods, can be implemented in order to increase the precision of forecasting? Study can be described as an action research using an abductive approa
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Laxmidhar, Mohammad, and Dnyanesh Sarang. "Exploratory Investigation of Sales Forecasting Process and Sales Forecasting System : Case Study of Three Companies." Thesis, Jönköping University, JIBS, Business Administration, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-718.

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<p>The future has always caught the attention of the human being. The thirst of exploring the future and to know the unknown has driven the human being toward innovativeness.</p><p>Companies are expanding their operations worldwide since the past few decades. Profit growth coupled with an effective strategy has become the primary need of global companies. Research in this area has given rise to optimization of the supply chain for higher profitability. Considering the overall strategy the company needs to plan production well in advance. The operational planning comes in picture at this moment
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Besharat, Pour Shiva. "Hierarchical sales forecasting using Recurrent Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290892.

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Sales forecasting equips businesses with the essential basis for planning future investments, controlling costs, and production. This research is in cooperation with a property development company for the purpose of improving the accuracy of manual sales forecasting. The objective is to investigate the effects of using the underlying factors that affect the individual sales of the company in forecasting the company’s income. One approach uses an aggregation of the estimates of the individual sales to approximate the company’s income. This approach uses the underlying hierarchical factors of th
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Fredén, Daniel, and Hampus Larsson. "Forecasting Daily Supermarkets Sales with Machine Learning." Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276483.

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Improved sales forecasts for individual products in retail stores can have a positive effect both environmentally and economically. Historically these forecasts have been done through a combination of statistical measurements and experience. However, with the increased computational power available in modern computers, there has been an interest in applying machine learning for this problem. The aim of this thesis was to utilize two years of sales data, yearly calendar events, and weather data to investigate which machine learning method could forecast sales the best. The investigated methods
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Zbib, Imad J. (Imad Jamil). "Sales Forecasting Accuracy Over Time: An Empirical Investigation." Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc332526/.

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This study investigated forecasting accuracy over time. Several quantitative and qualitative forecasting models were tested and a number of combinational methods was investigated. Six time series methods, one causal model, and one subjective technique were compared in this study. Six combinational forecasts were generated and compared to individual forecasts. A combining technique was developed. Thirty data sets, obtained from a market leader in the cosmetics industry, were used to forecast sales. All series represent monthly sales from January 1985 to December 1989. Gross sales forecasts from
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Renner, Nancy A. (Nancy Ann). "Forecasting Quarterly Sales Tax Revenues: A Comparative Study." Thesis, North Texas State University, 1986. https://digital.library.unt.edu/ark:/67531/metadc501220/.

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The purpose of this study is to determine which of three forecasting methods provides the most accurate short-term forecasts, in terms of absolute and mean absolute percentage error, for a unique set of data. The study applies three forecasting techniques--the Box-Jenkins or ARIMA method, cycle regression analysis, and multiple regression analysis--to quarterly sales tax revenue data. The final results show that, with varying success, each model identifies the direction of change in the future, but does not closely identify the period to period fluctuations. Indeed, each model overestimated re
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Jessen, Andreas, and Carina Kellner. "Forecasting Management." Thesis, University of Kalmar, Baltic Business School, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hik:diva-1868.

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<p>In a world that is moving faster and faster, a company’s ability to align to market changes is becoming a major competitive factor. Forecasting enables companies to predict what lies ahead, e.g. trend shifts or market turns, and makes it possible to plan for it. But looking into the future is never an easy task.</p><p>“Prediction is very difficult, especially if it’s about the future.” (Niels Bohr, 1885-1962)</p><p>However, progress in the field of forecasting has shown that it is possible for companies to improve on forecasting practices. This master thesis looks at the sales forecasting p
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Postiglioni, Renato. "Sales forecasting within a cosmetic organisation : a managerial approach." Thesis, Stellenbosch : Stellenbosch University, 2006. http://hdl.handle.net/10019.1/21980.

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Thesis (MBA)--Stellenbosch University, 2006.<br>Although most businesses require accurate sales forecasts in order to survive and to be successful, very little attention has been devoted to examine how sales forecasting processes should be managed, and the behavioural factors associated with the management of forecasting. Sales forecasting activities and research have by and large concentrated on the techniques or on the systems used, rather than on the forecasting management philosophy, which considers the organisational, procedural, and personnel aspects of the process. Both forecast
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Jedeikin, Jonathan. "An adaptive agent architecture for exogenous data sales forecasting." Master's thesis, University of Cape Town, 2006. http://hdl.handle.net/11427/6403.

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Includes bibliographical references (leaves 108-111).<br>In a world of unpredictability and complexity, sales forecasting is becoming recognised as essential to operations planning in business and industry. With increased globalisation and higher competition, more products are being developed at more locations, but with shorter product lifecycles. As technology improves, more sophisticated sales forecasting systems are developed which require increasing complexity. We tum to adaptive agent architectures to consider an alternative approach for modelling complex sales forecasting systems. This r
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Hood, Nicholas Andrew. "Evaluating alternative methods for forecasting convenience grocery store sales." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/16281/.

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Convenience grocery stores have become more commonplace in grocery retailing in Great Britain since the 1990s, with a substantial increase in the proportion of stores operated by the largest grocery retailers in Great Britain that can be defined as convenience grocery stores. Geographically, the convenience networks operated by the largest retailers are more spatially concentrated than their overall grocery networks bringing them into direct competition with retailers more traditionally associated with convenience retailing in some, but not all areas of Great Britain. As convenience stores hav
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Books on the topic "Sales forecasting"

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D, Lawrence Kenneth, and Guerard John, eds. Forecasting sales. JAI Press, 1994.

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Holmes, Peter. Sales forecasting. Centre for Statistical Education, 1987.

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Davis, John. Practical sales forecasting. McGraw-Hill, 1988.

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Mentzer, John T. Sales forecasting mangagement. SAGE, 1998.

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C, Bienstock Carol, ed. Sales forecasting management. Sage Publications, 1998.

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Accountants, National Association of, ed. Sales forecasting systems. National Association of Accountants, 1986.

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Bolt, Gordon J. Market and sales forecasting. Watts, 1988.

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Carlberg, Conrad George. Excel sales forecasting for dummies. Wiley Pub., 2005.

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C, Bienstock Carol, ed. Sales forecasting management: Understanding the techniques, systems, and management of the sales forecasting process. Sage Publications, 1998.

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Lancaster, Geoffrey A., and Robert A. Lomas. Forecasting for Sales and Materials Management. Macmillan Education UK, 1985. http://dx.doi.org/10.1007/978-1-349-17851-3.

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Book chapters on the topic "Sales forecasting"

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Petrochilos, George A. "Sales Forecasting." In Managerial Economics. Macmillan Education UK, 2004. http://dx.doi.org/10.1007/978-1-137-10771-8_5.

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Meldrum, Mike, and Malcolm McDonald. "Forecasting Future Sales." In Key Marketing Concepts. Macmillan Education UK, 1995. http://dx.doi.org/10.1007/978-1-349-13877-7_41.

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Sajtos, Laszlo. "Sales planning and forecasting." In Sales Management. Macmillan Education UK, 2011. http://dx.doi.org/10.1007/978-1-137-28574-4_8.

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Donaldson, Bill. "Sales Forecasting and Budgeting." In Sales Management. Macmillan Education UK, 1998. http://dx.doi.org/10.1007/978-1-349-26354-7_7.

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West, Alan. "Sales Budgets and Forecasting." In Modern Sales Management. Macmillan Education UK, 1987. http://dx.doi.org/10.1007/978-1-349-18570-2_10.

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Chandrika, P. V., and Hema Doreswamy. "Forecasting Sales and Price." In Marketing Analytics. Apple Academic Press, 2022. http://dx.doi.org/10.1201/9781003300632-11.

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Majumdar, Parijata, and Sanjoy Mitra. "Big Mart Sales Forecasting." In Internet of Things and Big Data Analytics for a Green Environment. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781032656830-5.

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Ingram, Thomas N., Raymond W. LaForge, Ramon A. Avila, Charles H. Schwepker, and Michael R. Williams. "Sales Organization Structure, Salesforce Deployment, and Forecasting." In Sales Management, 11th ed. Routledge, 2023. http://dx.doi.org/10.4324/9781003363583-6.

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Kolassa, Stephan, Bahman Rostami-Tabar, and Enno Siemsen. "Sales and operations planning." In Demand Forecasting for Executives and Professionals. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003399599-20.

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Thiesing, Frank M., and Oliver Vornberger. "Forecasting sales using neural networks." In Computational Intelligence Theory and Applications. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62868-1_125.

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Conference papers on the topic "Sales forecasting"

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Sood, Gourav. "Utilizing Machine Learning for Predictive Sales Forecasting." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725596.

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Benghalia, Abderaouf, Hadjer Benkenzoua, Chaima Hamraras, and Chaouki Boufenar. "Long-Term Forecasting of Hydrocarbon Products Sales." In 2024 1st International Conference on Electrical, Computer, Telecommunication and Energy Technologies (ECTE-Tech). IEEE, 2024. https://doi.org/10.1109/ecte-tech62477.2024.10851084.

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Jain, Ashish, Pankaj Gupta, Hemlatha Kaur Saran, Devendra Singh Parmar, Jai Prakash Bhati, and Daksh Rawat. "Forecasting Future Sales Using Linear Regression Approach." In 2024 International Conference on Cybernation and Computation (CYBERCOM). IEEE, 2024. https://doi.org/10.1109/cybercom63683.2024.10803255.

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Dwivedi, Deeptanshu, Abhay Saxena, Viraj Sanadhya, and Shoma Awasthi. "Optimization of Sales Forecasting Using Machine Learning." In 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, 2025. https://doi.org/10.1109/icaect63952.2025.10958885.

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P, Kumar, Vijayaraj A, Senthil Pandi S, and Venkatesh Prabhu Balakrishnan. "Sales and Inventory Demand Forecasting Using Machine Learning." In 2024 2nd International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS). IEEE, 2024. https://doi.org/10.1109/icetems64039.2024.10964960.

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Xie, Jinyi, Zhongming Cai, Jiawei Xia, and Haoyuan Xue. "New Energy Vehicle Sales Forecasting Based on MICMAC Algorithm and Gray Forecasting Model." In 2024 International Conference on Telecommunications and Power Electronics (TELEPE). IEEE, 2024. http://dx.doi.org/10.1109/telepe64216.2024.00035.

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Ganguly, Priyam, and Isha Mukherjee. "Enhancing Retail Sales Forecasting with Optimized Machine Learning Models." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10762950.

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Rajaram, Tharanimathi, and M. Udhayamoorthi. "Machine Learning for Strategic Sales Forecasting in Retail Chains." In 2024 International Conference on Computing and Intelligent Reality Technologies (ICCIRT). IEEE, 2024. https://doi.org/10.1109/iccirt59484.2024.10921891.

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Avasthi, Sandhya, Suman Lata Tripathi, Tanushree Sanwal, and Vimal Dwivedi. "Multi-Channel Sales Forecasting in a Kenyan Retail Environment." In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). IEEE, 2025. https://doi.org/10.1109/cictn64563.2025.10932551.

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J, Sujithra, Nithya Shree S, and Sathyabama R. "Predictive Analytics for Sales Demand Forecasting Using HML Model." In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG). IEEE, 2024. https://doi.org/10.1109/icstsdg61998.2024.11026743.

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Reports on the topic "Sales forecasting"

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LaRaine Ingram, Keisha. Applied Sales Predictive Analytics for Business Development. Vilnius Business College, 2024. http://dx.doi.org/10.57005/ab.2024.1.2.

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In the dynamic business environment, leveraging predictive analytics for sales optimization and business development has become crucial for achieving sustained growth. As the e-commerce landscape continues to evolve, many e-businesses must harness the power of predictive analytics to anticipate sales trends and optimize business development strategies. This paper explores the application of sales predictive analytics, focusing on its role in forecasting sales, optimizing resource allocation, and enhancing customer relationship management. The application of predictive analytics in sales foreca
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Fisher, Andmorgan, Taylor Hodgdon, and Michael Lewis. Time-series forecasting methods : a review. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49450.

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Time-series forecasting techniques are of fundamental importance for predicting future values by analyzing past trends. The techniques assume that future trends will be similar to historical trends. Forecasting involves using models fit on historical data to predict future values. Time-series models have wide-ranging applications, from weather forecasting to sales forecasting, and are among the most effective methods of forecasting, especially when making decisions that involve uncertainty about the future. To evaluate forecast accuracy and to compare among models fitted to a time series, thre
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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3241.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business
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Disch, Wendy. ESRI Nowcast (February 2023 Estimate). ESRI, 2023. http://dx.doi.org/10.26504/ir7.

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Using data available through 17 April 2023, we provide an update to our monthly estimate of modified domestic demand (MDD) using the ESRI Nowcasting model currently employed to support the forecasting exercise in the Quarterly Economic Commentary. MDD in February is estimated to be 3.7 per cent above its level in February 2022. Continued resilience in the labour market as well as growth in industrial production, retail sales and total tax revenue contributed to growth in economic activity in February. While economic sentiment remains below its level in February 2022, it has generally improved
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Semerikov, Serhiy, Hanna Kucherova, Vita Los, and Dmytro Ocheretin. Neural Network Analytics and Forecasting the Country's Business Climate in Conditions of the Coronavirus Disease (COVID-19). CEUR Workshop Proceedings, 2021. http://dx.doi.org/10.31812//123456789/4364.

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The prospects for doing business in countries are also determined by the business confidence index. The purpose of the article is to model trends in indicators that determine the state of the business climate of countries, in particular, the period of influence of the consequences of COVID-19 is of scientific interest. The approach is based on the preliminary results of substantiating a set of indicators and applying the taxonomy method to substantiate an alternative indicator of the business climate, the advantage of which is its advanced nature. The most significant factors influencing the b
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Wendy, Disch. ESRI Nowcast October 2022. ESRI, 2022. http://dx.doi.org/10.26504/ir1.

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Using the ESRI Nowcasting model currently employed to support the forecasting exercise in the Quarterly Economic Commentary, we will now update our forecast of modified domestic demand (MDD) on a monthly basis. Our nowcast estimates that MDD is expected to grow by 4.1 per cent in Q3 2022 on an annual basis, indicating some moderation from its growth of 10.2 per cent per annum in Q2 2022. On a monthly basis, MDD is estimated to be 4.0 per cent above its level from August 2021. Strong tax receipts, continued strength in the labour market and growth in industrial production are all contributing t
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