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Journal articles on the topic 'Sales forecasting Business forecasting'

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Published: 4 June 2021
Last updated: 2 February 2022

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1

West, Douglas C. "Managing Sales Forecasting." Management Research News 20, no. 4 (April 1997): 1–10. http://dx.doi.org/10.1108/eb028556.

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2

Rodrigues, Aaron. "Food Sales Forecasting Using Machine Learning Techniques: A Survey." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 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 forecasting sales, and the representation of the sales output variable. It also examines machine learning algorithms that have been used to anticipate food sales and the proper metrics for assessing their performance. Finally, it goes over the major problems and prospects for applied machine learning in the field of food sales forecasting. Keywords: Food, Demand forecasting, Machine learning, Regression, Timeseries forecasting, Sales prediction
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Lu, Chi-Jie, and Chi-Chang Chang. "A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/624017.

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Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.
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Murdick, Kent. "Applications Short-Term Sales Forecasting." Mathematics Teacher 89, no. 1 (January 1996): 48–52. http://dx.doi.org/10.5951/mt.89.1.0048.

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A medical-supply company asked for help in solving a warehouse-inventory problem. They wanted a computer program to track the inventory of more than one hundred medical items, such as cases of bandages and syringes, and to predict the sales for the next business period. Thus, when the company needed to order a particular item, the quantity could be calculated automatically by the program. Specifically, the problem concerned the short-term prediction of future sales.
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Bakri, Rizal, Umar Data, and Niken Probondani Astuti. "Aplikasi Auto Sales Forecasting Berbasis Computational Intelligence Website untuk Mengoptimalisasi Manajemen Strategi Pemasaran Produk." JURNAL SISTEM INFORMASI BISNIS 9, no. 2 (December 27, 2019): 244. http://dx.doi.org/10.21456/vol9iss2pp244-251.

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Business analytics plays an important role in optimizing the management of product marketing strategies. One of the most popular analytical tools in business analytics is sales forecasting. Businesses need to conduct sales forecasting to optimize marketing management in the form of product availability predictions, predictions of capital adequacy, consumer interest, and product price governance. However, the problem that is often encountered in forecasting is the number of forecasting methods available so that it makes it difficult for business people to choose the best forecasting method. The aims of this research is to develop a forecasting software tha can be accessed online based on computational intelligence, which is a software that can make forececasting with various methods and then intelligently choose the best forecasting method. The software development method used in this study is the SDLC with waterfall model. The result of this research is the Auto sales forecasting software was developed using the R programming language by combining various package and can be accessed online through the page Http://bakrizal.com/AutoSalesForecasting. This software can be used to conduct forecast analysis with various methods such as Simple Moving Average, Robust Exponential Smoothing, Auto ARIMA, Artificial Neural Network, Holt-Winters, and Hybrid Forecast. This software contains intelligence computing to choose the best forecasting method based on the smallest RMSE value. After testing the sales transaction data at the Futry Bakery & Cake Shop in Makassar, the results show that the Robust Exponantial Smoothing method is the best forecasting method with an RMSE value of 0.829
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6

Lauer, Joachim, and Terrence O'Brien. "SALES FORECASTING USING CYCLICAL ANALYSIS." Journal of Business & Industrial Marketing 3, no. 1 (January 1988): 25–35. http://dx.doi.org/10.1108/eb006048.

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7

Stormi, Kati, Teemu Laine, Petri Suomala, and Tapio Elomaa. "Forecasting sales in industrial services." Journal of Service Management 29, no. 2 (March 12, 2018): 277–300. http://dx.doi.org/10.1108/josm-09-2016-0250.

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Purpose The purpose of this paper is to examine how installed base information could help servitizing original equipment manufacturers (OEMs) forecast and support their industrial service sales, and thus increase OEMs’ understanding regarding the dynamics of their customers lifetime values (CLVs). Design/methodology/approach This work constitutes a constructive research aiming to arrive at a practically relevant, yet scientific model. It involves a case study that employs statistical methods to analyze real-life quantitative data about sales and the global installed base. Findings The study introduces a forecasting model for industrial service sales, which considers the characteristics of the installed base and predicts the number of active customers and their yearly volume. The forecasting model performs well compared to other approaches (Croston’s method) suitable for similar data. However, reliable results require comprehensive, up-to-date information about the installed base. Research limitations/implications The study contributes to the servitization literature by introducing a new method for utilizing installed base information and, thus, a novel approach for improving business profitability. Practical implications OEMs can use the forecasting model to predict the demand for – and measure the performance of – their industrial services. To-the-point predictions can help OEMs organize field services and service production effectively and identify potential customers, thus managing their CLV accordingly. At the same time, the findings imply new requirements for managing the installed base information among the OEMs, to understand and realize the industrial service business potential. However, the results have their limitations concerning the design and use of the statistical model in comparison with alternative approaches. Originality/value The study presents a unique method for employing installed base information to manage the CLV and supplement the servitization literature.
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8

Tavakkoli, Amirmohammad, Jalal Rezaeenour, and Esmaeil Hadavandi. "A Novel Forecasting Model Based on Support Vector Regression and Bat Meta-Heuristic (Bat–SVR): Case Study in Printed Circuit Board Industry." International Journal of Information Technology & Decision Making 14, no. 01 (January 2015): 195–215. http://dx.doi.org/10.1142/s0219622014500849.

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Sales forecasting is very beneficial to most businesses. A successful business needs accurate sales forecasting to understand the market and sales trends. This paper presents a novel sales forecasting model by integrating support vector regression (SVR) and bat algorithm (BA). Since the accuracy of SVR forecasting mainly depends on SVR parameters, we use BA for tuning these parameters because Bat is a newly introduced algorithm and has many parameters. In order to find the best set of BA parameters Taguchi method was utilized. We validated our model on four known UCI datasets. Then we applied our model in printed circuit board (PCB) sales forecasting case study. We compared the accuracy of the proposed model with Genetic algorithm (GA)–SVR, particle swarm optimization (PSO)–SVR, and classic-SVR. The experimental results show that the proposed model outperforms the others. To ensure the robustness of our proposed model, sensitivity analysis was also done using our model to find out the effects of dependent variables values on sales time series.
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9

Frees, Edward W., and Thomas W. Miller. "Sales forecasting using longitudinal data models." International Journal of Forecasting 20, no. 1 (January 2004): 99–114. http://dx.doi.org/10.1016/s0169-2070(03)00005-0.

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10

Geurts, Michael D., and J. Patrick Kelly. "Forecasting retail sales using alternative models." International Journal of Forecasting 2, no. 3 (January 1986): 261–72. http://dx.doi.org/10.1016/0169-2070(86)90046-4.

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11

Davis, Donna F., and John T. Mentzer. "Organizational factors in sales forecasting management." International Journal of Forecasting 23, no. 3 (July 2007): 475–95. http://dx.doi.org/10.1016/j.ijforecast.2007.02.005.

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12

Lackman, Conway. "Forecasting sales for regional telephone services." Services Marketing Quarterly 9, no. 1 (1993): 183–87. http://dx.doi.org/10.1080/15332969.1993.9985083.

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13

Omar, Hani, Van Hai Hoang, and Duen-Ren Liu. "A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles." Computational Intelligence and Neuroscience 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/9656453.

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Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words.
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Sa'diyah, Khanifatus, and Narto Narto. "IMPLEMENTASI PERAMALAN PENJUALAN IKAN LAUT UNTUK OPTIMASI PERSEDIAAN BAHAN BAKU (Studi Kasus di UD Harum Bungah Gresik)." JURNAL REKAYASA SISTEM INDUSTRI 6, no. 2 (May 30, 2021): 59. http://dx.doi.org/10.33884/jrsi.v6i2.2643.

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Indonesian marine waters have high marine resource resources. One of Indonesia's seafood commodities is fish. With proper management and utilization, marine products become one of the promising business opportunities for the community, so that fisheries become one of the supporting sectors of national economic development. UD Harum is one of the businesses engaged in the fisheries sector as a supplier of marine fish raw material needs to meet the needs of the manufacturing industry. To optimize production planning to meet industry demand, forecasting of sea fish sales data forecasting in the previous period is needed to anticipate a shortage of raw materials. The purpose of this forecasting is to implement forecasting using the Single Moving Average (SMA), Weighted Moving Average (WMA) and Centered Moving Average (CMA) methods in forecasting sea fish sales at UD Harum and to find out the best forecasting results to increase sea fish sales at UD Harum. Forecasting results show forecasting using the Single Moving Average (3-monthly) and (5-monthly) methods respectively 8107.67 kg and 8399.4 kg. For the Weighted Moving Average (3-monthly) and (5-monthly) methods, the results of forecasting are 7268,963 kg and 7443,452, respectively. As for the Centered Moving Average (3-monthly) method with forecast results of 8107.67 kg. The forecasting method chosen to optimize sales is the Centered Moving Average method with a forecast value of 8107.67 kg and has the smallest forecasting error compared to other forecasting methods with a MAPE value of 0.30875 and MPE of -0.1720.
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15

EDWARDS, D. J., J. NICHOLAS, and R. SHARP. "Forecasting UK construction plant sales." Engineering, Construction and Architectural Management 8, no. 3 (March 2001): 171–76. http://dx.doi.org/10.1108/eb021179.

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16

Pataropura, Amesanggeng, Riki Riki, and Ariadi Saputra. "Sales Analysis Using the Forecasting Method." bit-Tech 1, no. 3 (May 25, 2019): 146–49. http://dx.doi.org/10.32877/bt.v1i3.79.

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Sales Analysis Using Forecasting Method aims to improve effectiveness and efficiency that facilitates companies in business transaction processes, improve the delivery of information quickly, accurately, and transaction data well and minimize errors. The method used in the presentation of this scientific work is by using a forecasting method which helps determine the approximate stock of goods to come. With 3 forecasting modules are: Moving Average, Weighted Moving Average, Trend Projection is used to perform the forecasting process of the upcoming stock of goods. Can solve problems that exist in the current system so that it can help in improving its services by calculating the stock and helping by determining the average data that has been linked to the forecasting module whose results can be concluded through reports per period. It can be concluded that the results of implementing this new system can help companies in recording each transaction that occurs becomes more efficient and effective, so that it can overcome the problems that exist in the current system. With this we can predict the current flow of goods that have been calculated based on 3 (three) modules that have connections with the system
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17

Wright, David J. "Decision support oriented sales forecasting methods." Journal of the Academy of Marketing Science 16, no. 3-4 (September 1988): 71–78. http://dx.doi.org/10.1007/bf02723362.

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18

Wan, Yongquan, Yizhou Chen, Cairong Yan, and Bofeng Zhang. "Similarity-based sales forecasting using improved ConvLSTM and prophet." Intelligent Data Analysis 25, no. 2 (March 4, 2021): 383–96. http://dx.doi.org/10.3233/ida-205103.

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Sales forecasting is an important part of e-commerce and is critical to smart business decisions. The traditional forecasting methods mainly focus on building a forecasting model, training the model through historical data, and then using it to forecast future sales. Such methods are feasible and effective for the products with rich historical data while they are not performing as well for the newly listed products with little or no historical data. In this paper, with the idea of collaborative filtering, a similarity-based sales forecasting (S-SF) method is proposed. The implementation framework of S-SF includes three modules in order. The similarity module is responsible for generating top-k similar products of a given new product. We calculate the similarity based on two data types: time series data of sales and text data such as product attributes. In the learning module, we propose an attention-based ConvLSTM model which we called AttConvLSTM, and optimize its loss function with the convex function information entropy. Then AttConvLSTM is integrated with Facebook Prophet model to forecast top-k similar products sales based on their historical data. The prediction results of all top-k similar products will be fused in the forecasting module through operations of alignment and scaling to forecast the target products sales. The experimental results show that the proposed S-SF method can simultaneously adapt to the sales forecasting of mature products and new products, which shows excellent diversity, and the forecasting idea based on similar products improves the accuracy of sales forecasting.
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Forst, Frank G. "Forecasting Restaurant Sales Using Multiple Regression And Box-Jenkins Analysis." Journal of Applied Business Research (JABR) 8, no. 2 (October 11, 2011): 15. http://dx.doi.org/10.19030/jabr.v8i2.6157.

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Several regression and Box-Jenkins models were used to forecast weekly sales at a small campus restaurant for Years 1 and 2. Forecasted sales were compared with actual sales to select the three most promising forecasting models. These three models were then used to forecast sales for the first 44 weeks of Year 3, and compared against actual sales. The results indicate that a multiple regression model with two predictors, a dummy variable and sales lagged one week, was the best forecasting model considered.
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20

Wilson, J. Holton, Rebecca Dingus, and Jeffrey Hoyle. "Women count: Perceptions of forecasting in sales." Business Horizons 63, no. 5 (September 2020): 637–46. http://dx.doi.org/10.1016/j.bushor.2020.06.001.

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Bonett, Douglas G. "New Product Sales Forecasting Using A Growth Curve Model." Journal of Applied Business Research (JABR) 3, no. 2 (October 31, 2011): 119. http://dx.doi.org/10.19030/jabr.v3i2.6540.

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A logistic growth curve model for new product sales forecasting is proposed as an alternative to the traditional subjective forecasting methods. The parameters of the logistic growth curve model are redefined to represent meaningful growth characteristics that may more easily be specified by expert panels.
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Kaltenbacher, Judith, and Reinhold Decker. "New Product Sales Forecasting: An Approach for the Insurance Business." World Journal of Management 5, no. 1 (March 2014): 36–53. http://dx.doi.org/10.21102/wjm.2014.03.51.03.

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Kim, Juyoung. "Franchise Business Sales Forecasting by comparison of Neural Network models." JOURNAL OF KOREAN MARKETING ASSOCIATION 33, no. 3 (August 31, 2018): 73–90. http://dx.doi.org/10.15830/kmr.2018.33.3.73.

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Sianturi, Charles Jhony Mantho, Elsi Ardini, and Nita Sari Br Sembiring. "SALES FORECASTING INFORMATION SYSTEM USING THE LEAST SQUARE METHOD IN WINDI MEBEL." Jurnal Inovasi Penelitian 1, no. 2 (June 30, 2020): 75–82. http://dx.doi.org/10.47492/jip.v1i2.52.

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Windi Mebel is a business engaged in sales that sell goods and services. This home-based business was established a long time ago, but sales do not get maximum results because consumer interest has also begun to diminish due to competitors selling the same product. Therefore, with the increasingly sophisticated technology at this time it can be utilized to use a system that can forecast sales in the next few years so that the calculations generated when sales forecasting are more accurate, effective and efficient. Sales prediction system or sales forecasting can be used to estimate how much demand or demand for consumers and the market for the products produced. The more requests, the increase in sales results is also greater and as expected. To calculate the prediction of sales, a Least Square Method is applied using sales data a few years ago as a benchmark in predicting sales in the next few years. Based on these problems, the authors carry out a problem solving strategy by creating a system that uses the Least Square method to predict how much demand for furniture products the market wants in the future.
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25

Dawes, John, Rachel Kennedy, Kesten Green, and Byron Sharp. "Forecasting advertising and media effects on sales: Econometrics and alternatives." International Journal of Market Research 60, no. 6 (July 5, 2018): 611–20. http://dx.doi.org/10.1177/1470785318782871.

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The contribution of regression analysis (econometrics) to advertising and media decision-making is questioned and found wanting. Econometrics cannot be expected to estimate valid and reliable forecasting models unless it is based on extensive experimental data on important variables, across varied conditions. This article canvasses alternative, evidence-based methods that have been shown to be useful for forecasting problems. These methods are described with the hope that they are more widely used for marketing forecasting. The approaches include media and copy experiments, analyses of individual level single source data, and structured expert judgment.
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Ubaid, Ayesha, Farookh Hussain, and Muhammad Saqib. "Container Shipment Demand Forecasting in the Australian Shipping Industry: A Case Study of Asia–Oceania Trade Lane." Journal of Marine Science and Engineering 9, no. 9 (September 6, 2021): 968. http://dx.doi.org/10.3390/jmse9090968.

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Demand forecasting has a pivotal role in making informed business decisions by predicting future sales using historical data. Traditionally, demand forecasting has been widely used in the management of production, staffing and warehousing for sales and marketing data. However, the use of demand forecasting has little been studied in the container shipping industry. Improved visibility into the demand for container shipments has been a long-held objective of industry stakeholders. This paper addresses the shortcomings of both short-term and long-term shipment demand forecasting for the Australian container shipping industry. In this study, we compare three forecasting models, namely, the seasonal auto-regressive integrated moving average (SARIMA), Holt–Winters’ seasonal method and Facebook’s Prophet, to find the best fitting model for short-term and long-term import demand forecasting in the Australian shipping industry. Demand data from three years, i.e., 2016–2018, is used for the Asia–Oceania trade lane. The mean absolute percentage error (MAPE), root mean squared error (RMSE) and 2-fold walk-forward cross-validation are used for the model evaluation. The experiment results observed from the selected metrics suggest that Prophet outperforms the other models in its comparison for container shipment demand forecasting.
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Mady, M. Tawfik. "Sales forecasting practices of Egyptian public enterprises: survey evidence." International Journal of Forecasting 16, no. 3 (July 2000): 359–68. http://dx.doi.org/10.1016/s0169-2070(00)00033-9.

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28

Lawrence, Michael, and Marcus O’Connor. "Sales forecasting updates: how good are they in practice?" International Journal of Forecasting 16, no. 3 (July 2000): 369–82. http://dx.doi.org/10.1016/s0169-2070(00)00059-5.

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Bails, Dale. "Sales forecasting: Timesaving and profit-making strategies that work." International Journal of Forecasting 2, no. 2 (January 1986): 250–51. http://dx.doi.org/10.1016/0169-2070(86)90125-1.

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Berry, Lindsay R., Paul Helman, and Mike West. "Probabilistic forecasting of heterogeneous consumer transaction–sales time series." International Journal of Forecasting 36, no. 2 (April 2020): 552–69. http://dx.doi.org/10.1016/j.ijforecast.2019.07.007.

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31

Utami, Ruli, and Suryo Atmojo. "Perbandingan Metode Holt Eksponential Smoothing dan Winter Eksponential Smoothing Untuk Peramalan Penjualan Souvenir." Jurnal Ilmiah Teknologi Informasi Asia 11, no. 2 (August 1, 2017): 123. http://dx.doi.org/10.32815/jitika.v11i2.191.

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UD. Fajar Jaya is a trading business unit engaged in the supply of souvenirs. But in the management of the business there are some problems of which are UD. Fajar Jaya can not predict how the optimal number of souvenirs that must be provided to customers on every item souvenirs are sold. This causes the service to consumers less than the maximum, especially at certain moments sales of souvenirs (example: glass souvenirs) jumped dramatically from the number of average sales. To overcome the above, the authors propose to forecast the level of sales of souvenirs using Holt and Winter methods that exist in the development of Exponential Smoothing (ES) method. From the application of the two methods, then will make comparison of effectiveness of method which measured through actual data accuracy and forecasting result by knowing forecast error level. From the research results obtained forecasting results for Holt Double Exponential Smoothing method in July of 2017 is amounted to 599 items that may be sold with MAD forecasting error rate of 10.54 and MAPE of 3.70%. As for forecasting using Winter Exponential Smoothing method in July of 2017 is 549.6 items that may be sold with MAD 0.02 and MAPE error rate of 2.55%. The conclusion that can be drawn from the research that has been done on sales data souvenirs on UD. Fajar Jaya is that the Winter Exponential Smoothing method is more suitable to be applied in case study of souvenir sales in UD. Fajar Jaya is more than Holt Double Exponential Smoothing method.
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Kolkova, Andrea. "The Application of Forecasting Sales of Services to Increase Business Competitiveness." Journal of Competitiveness 12, no. 2 (June 30, 2020): 90–105. http://dx.doi.org/10.7441/joc.2020.02.06.

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Wu, L. S. Y., N. Ravishanker, and J. R. M. Hosking. "Forecasting for business planning: A case study of IBM product sales." Journal of Forecasting 10, no. 6 (November 1991): 579–95. http://dx.doi.org/10.1002/for.3980100604.

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Fader, Peter S., Bruce G. S. Hardie, and Chun-Yao Huang. "A Dynamic Changepoint Model for New Product Sales Forecasting." Marketing Science 23, no. 1 (February 2004): 50–65. http://dx.doi.org/10.1287/mksc.1030.0046.

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35

Infosino, William J. "Forecasting New Product Sales from Likelihood of Purchase Ratings." Marketing Science 5, no. 4 (November 1986): 372–84. http://dx.doi.org/10.1287/mksc.5.4.372.

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36

Nugraha, Muhammad Agung, Farizal Farizal, and Djoko Sihono Gabriel. "Peramalan Penjualan Kendaraan Mobil Segmen B2B dengan Metode Regresi Linear Berganda, Jaringan Saraf Tiruan dan Jaringan Saraf Tiruan – Algoritma Genetika." EIGEN MATHEMATICS JOURNAL 3, no. 2 (December 30, 2020): 83. http://dx.doi.org/10.29303/emj.v3i2.80.

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This study aims to create an effective forecasting model in predicting sales of car products in the B2B segment (Business to Business) to obtain estimates of product sales in the future. This research uses multiple linear regression and artificial neural networks that are optimized by genetic algorithms. Forecasting factors for car sales are generally issued by total national car sales, the Consumer Price Index, the Consumer Confidence Index, the Inflation Rate, Gross Domestic Product (GDP), and Fuel Oil Price. The author has also gotten the factors that play a role in the sale of B2B segment by diverting the survey to 106 DMU (Decision Making Unit) who decide to purchase cars in their company. Then we evaluate the results of the questionnaire in training data and simulations on the Artificial Neural Network. Optimized Artificial Neural Networks with Genetic Algorithms can improve B2B segment car sales' accuracy when comparing error values in the ordinary Artificial Neural Network and Multiple Linear Regression.
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ARAS, Serkan, İpek DEVECİ KOCAKOÇ, and Cigdem POLAT. "COMPARATIVE STUDY ON RETAIL SALES FORECASTING BETWEEN SINGLE AND COMBINATION METHODS." Journal of Business Economics and Management 18, no. 5 (October 27, 2017): 803–32. http://dx.doi.org/10.3846/16111699.2017.1367324.

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In today’s competitive global economy, businesses must adjust themselves constantly to ever-changing markets. Therefore, predicting future events in the marketplace is crucial to the maintenance of successful business activities. In this study, sales forecasts for a global furniture retailer operating in Turkey were made using state space models, ARIMA and ARFIMA models, neural networks, and Adaptive Network-based Fuzzy Inference System (ANFIS). Also, the forecasting performances of some widely used combining methods were evaluated by comparison with the weekly sales data for ten products. According to the best of our knowledge, this study is the first time that the recently developed state space models, also called ETS (Error-Trend-Seasonal) models, and the ANFIS model have been tested within combining methods for forecasting retail sales. Analysis of the results of the single models in isolation indicated that none of them outperformed all the others across all the time series investigated. However, the empirical results suggested that most of the combined forecasts examined could achieve statistically significant increases in forecasting accuracy compared with individual models and with the forecasts generated by the company’s current system.
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Fauziah, Fauziah, Yulia Istia Ningsih, and Eva Setiarini. "Analisis Peramalan (Forecasting) Penjualan Jasa Pada Warnet Bulian City di Muara Bulian." Eksis: Jurnal Ilmiah Ekonomi dan Bisnis 10, no. 1 (August 8, 2019): 61. http://dx.doi.org/10.33087/eksis.v10i1.160.

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In the business world Forecasting is one of the most important factors that must be applied in a business. Forecasting is a method for estimating a value in the future by using past data effectively and efficiently. This research was conducted at Warnet Bulian City In this study, the author discusses the analysis of forecasting (Forecasting) sales of services at the Bulian City internet cafe in Muara Bulian. Forecasting is done using three methods namely the MOVA (Moving Average) method, the WMA (Weight Moving Average) method and the Exponential Smoothing Method by comparing the smallest error rate Forecasting using the MA (Moving Average) method for 3 periods is predicted the level of profit to be gained by Bulian City Warnet in August amounted to. 11,117,833 with MAD 1,487,370. Forecasting using the Weigh Moving Averages (WMA) 3 method is forecasted at 12,287,300 with MAD Error 3,016,016 while the forecast using the double exponential smoothing method is 13,522,572 with MAD 5513617,364 then the forecasting method chosen is the Single Exponential Smoothing method with the Forecast value in August 2018 9,581.69 for the The Forecast Error is MAD of 1,378,375 which is the method with the smallest error rate.
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Noone, Breffni M., and Tess Hultberg. "Profiting through Teamwork." Cornell Hospitality Quarterly 52, no. 4 (October 14, 2011): 407–20. http://dx.doi.org/10.1177/1938965511419843.

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Revenue management and sales staffs collaborate substantially in making decisions regarding rate setting, accepting group business, and forecasting. However, according to a survey of 82 sales and revenue management executives at three hotel chains (47 revenue managers and 35 sales executives), hotels could foster even better coordination between revenue management and sales by educating each group regarding the other group’s responsibilities. This might reduce sales staff frustrations about the way revenue managers make rate recommendations, and it might help revenue managers understand the importance that sales executives place on maintaining a relationship with a group, even when room rates do not meet targets. Forecasting is a major function for revenue managers, who take numerous factors into account, and some sales executives also are responsible for forecasting, primarily using one data source. Thus, the two groups focus on the data in different ways. Respondents suggest several ways to strengthen the relationship, including on-the-job training and education. For both groups, implementing performance assessments that involve several measures would allow the two groups to have some measures in common. Useful measures might include a group’s total revenue contribution, which is not commonly applied among these respondents. Another measure, the hotel’s total revenue or contribution, is a worthwhile consideration for both revenue managers and sales executives.
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Pasca Riani, Lilia, and Muhammad Roestam Afandi. "Forecasting Demand Produk Batik Di Tengah Pandemi Covid-19 Studi Pada Usaha Batik Fendy, Klaten." JURNAL NUSANTARA APLIKASI MANAJEMEN BISNIS 5, no. 2 (October 23, 2020): 122–32. http://dx.doi.org/10.29407/nusamba.v5i2.14441.

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The objectives of this study are forecast the demand for batik cloth and batik clothing in May 2020 and analyze the accuracy of forecasting demand for batik cloth and batik clothing. This research is a descriptive study with a quantitative approach. Using data by interviews with Batik Fendy Business Managers and actual sales data from November 2019 to April 2020. There are two stages of data analysis that is calculating demand forecasting of batik cloth and batik clothing for May 2020 with the Linear Exponential Smoothing method uses a combination of α 0,8 / β 0,1 and α 0,9 / β 0,2 constant. While the second stage is to analyze the accuracy of the method of demand forecasting using the MAPE Technique. The results of this study are for batik cloth products, predicted sales demand for May 2020 is 316 pieces with 30% MAPE. As for the batik clothing forecasting demand for May 2020 is 432 pieces with a MAPE of 19,8%.
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ÖZEROĞLU, Ali İhsan. "Personal Loan Sales Forecasting Through Time Series Analysis." PRIZREN SOCIAL SCIENCE JOURNAL 5, no. 1 (April 29, 2021): 44–51. http://dx.doi.org/10.32936/pssj.v5i1.216.

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Almost all state enterprises and private sector companies try to foresee future expectations. From the viewpoint of economic, productive, and efficient business management, this is highly important. By making rational decisions, all enterprises aim to rich maximum profitability by taking sales, cost, human resource needs, profits into account. For this reason, enterprises have to make reliable and reasonable forecasts to take the right decisions. Such forecasts might be used in budgeting, cost, and profit analysis. Forecasted scenarios might come true in the future with a great likelihood. The researcher utilizing time series analysis assumes that all findings that come out will be almost the same happened in the past. Analyzing the time series consist of four aims such as defining, modeling forecasting, and controlling. To define a series, it is needed to compute definitional statistics and to draw its graphic. The second purpose of analyzing the time series is to find the appropriate model of the time series. With that work called “Time series and application to sale data”, it is tried to make a suitable guess model by analyzing the data of personal loans of a bank 2004-2010 sale data based on unit. During the stagnation stage of the sequence correlogram and root, analyses are performed. The sequence is analyzed with the help of the Eviews 5,1 program. At the end of the survey, it is seen that natural logarithmic personal loan sale sequences are at their level and in the first gap it is not constant and it is also seen that when the second gap is taken, the constant is obtained. The sequence of which the second gap is taken is shown based on time-way graphs and correlogram. When the constant is provided, the guessed model is formed by taking the second gap. The suitability of the model is observed by the correlogram, Akaike information criteria (AIC), and Schwarz information criteria (SIC) merits.
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Caselles-Moncho, Antonio. "An empirical comparison of cross-impact models for forecasting sales." International Journal of Forecasting 2, no. 3 (January 1986): 295–303. http://dx.doi.org/10.1016/0169-2070(86)90049-x.

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Xi, Jia, and Ping Ba Sha. "Research on Optimization of Inventory Management Based on Demand Forecasting." Applied Mechanics and Materials 687-691 (November 2014): 4828–31. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4828.

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Demand forecasting is the basis of the inventory management. Aiming at the problem of subjective forecasting method, we use quadratic exponential smoothing method to establish the mathematical model, to forecast sales volume of product A in every month in 2013. And based on demand forecasting, we put forward ABC classification management method to solve the inventory management issues. The research result of this paper has important implications in improving the inventory management level for many enterprises.Demand Forecasting and Inventory ManagementInventory management is an important part of enterprise management, and it directly affects the business situation of enterprises. A reasonable inventory can significantly enhance the comprehensive competitiveness of enterprises; too much or too little inventory settings would have a bad impact on the business, and some company even bog down because of inventory problems companies bogged down because of inventory problems [1-2]. To do inventory management, what should we do in the first step. The answer is demand forecast. When business scale reaches a certain level, it would need strict, systematic demand forecasting. The more accurate the demand forecasting is, the more accurate inventory planning would be, and more favorable for business enterprises.Few companies are able to be completely in accordance with the order production, and the vast majority of businesses are not waiting for orders after arrival, then determine how much raw material and manpower needed, and how to arrange production. Because it often takes a long production cycle, and no one is willing to wait a month to buy a bag of washing powder. Successful companies always make accurate predictions for product demand, and then put into production according to forecasting [3]. Due to their more accurate predictions, they can often carry out a reasonable plan and inventory management. Inventory forecasting, its essence is demand forecasting [4]. Demand forecasting provides important information for inventory management such as inventory amount, lead time, inventory turns. Demand forecasting is based on research and statistics, to make a scientific and reasonable inference for product demand. Product demand generally is within a certain period, certain market range, the number of consumers’ demand for a product. Demand forecasting results can help companies determine the amount of raw material inventory and products, and provide enterprise continuous production of raw materials needed, save liquidity and reduce inventory costs, improving the comprehensive competitiveness of enterprises.Product Demand Forecasting Model
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Mahmoud, Essam, Gillian Rice, and Naresh Malhotra. "Emerging issues in sales forecasting and decision support systems." Journal of the Academy of Marketing Science 16, no. 3-4 (September 1988): 47–61. http://dx.doi.org/10.1007/bf02723360.

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Winklhofer, H., and A. Diamantopoulos. "First insights into export sales forecasting practice: a qualitative study." International Marketing Review 13, no. 4 (August 1996): 52–81. http://dx.doi.org/10.1108/02651339610127257.

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46

Setiawan, Setiawan, Donny Arif, Siti Mahmudah, Heni Agustina, and Varid Martah. "The effect of supply chain management on multi-channel retaining and business performance." Uncertain Supply Chain Management 9, no. 4 (2021): 823–30. http://dx.doi.org/10.5267/j.uscm.2021.8.007.

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Pandemic covid has changed the business view to be more dynamic to business performance. The policy of restricting activity also undermines business difficulties that occur, so it is necessary to find how businesses can survive wholesale. This research was conducted to determine the effect of supply chain management (SCM) on multi-channel retailing and business performance in the era of pandemic covid-19 and restrictions on community mobility. Using analysis of this research path is divided into two criteria of direct and indirect influence. This study was conducted on several wholesale shops in Indonesia with 99 respondents. The main finding of this study is that SCM can affect business performance through multi-channel retailing with three main indicators: inventory investment, inventory efficiency, and forecasting accuracy. The added value gained from this research is from the test results obtained that a good inventory management scheme and forecasting and support from many supplies and sales channels will drive business performance for the better.
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Hofer, Peter, Christoph Eisl, and Albert Mayr. "Forecasting in Austrian companies." Journal of Applied Accounting Research 16, no. 3 (November 9, 2015): 359–82. http://dx.doi.org/10.1108/jaar-10-2014-0113.

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Purpose – The purpose of this paper is a comparison of forecasting behaviour of small and large Austrian firms, analysing their forecast practices in a volatile business environment. Design/methodology/approach – The empirical analysis of the paper, deductive by nature, was conducted by means of a quantitative online-survey (199 data sets). The relationship of perceived volatility and forecast predictability was evaluated by correlation analysis. t-Test and analysis of variances were used to examine significant differences in the forecast characteristics between small and large Austrian companies and different industries. Findings – The study provides evidence that the surveyed companies have been hit by volatility, showing that Austrian SMEs are significantly more severely affected than large companies. The increasing volatility correlates with a reduced forecast predictability of sales quantities and commodity prices. Large Austrian companies primarily use a broad spectrum of qualitative forecasting methods. In contrast, Austrian SMEs utilize simple quantitative and qualitative forecast techniques, like the forward projection of historical data. Research limitations/implications – Relevant for the forecasting of small and large companies. Practical implications – Although management requests a broad spectrum of forecast qualities, the current usage of less sophisticated methods reveals a gap between intention and reality. Companies that supplement their qualitative techniques by sophisticated quantitative ones should expect less forecast bias. Originality/value – This paper initially compares forecast methods in large and small Austrian firms and additionally provides the impact of volatility on the forecast predictability.
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Lalou, Panagiota, Stavros T. Ponis, and Orestis K. Efthymiou. "Demand Forecasting of Retail Sales Using Data Analytics and Statistical Programming." Management & Marketing. Challenges for the Knowledge Society 15, no. 2 (June 1, 2020): 186–202. http://dx.doi.org/10.2478/mmcks-2020-0012.

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AbstractForecasting the demand of network of retail sales is a rather challenging task, especially nowadays where integration of online and physical store orders creates an abundance of data that has to be efficiently stored, analyzed, understood and finally, become ready to be acted upon in a very short time frame. The challenge becomes even bigger for added-value third party logistics (3PL) operators, since in most cases and demand forecasting aside, they are also responsible for receiving, storing and breaking inbound quantities from suppliers, consolidating and picking retail orders and finally plan and organize shipments on a daily basis. This paper argues that data analytics can play a critical role in contemporary logistics and especially in demand data management and forecasting of retail distribution networks. The main objective of the research presented in this paper is to showcase how data analytics can support the 3PL decision making process on replenishing the network stores, thus improving inventory management in both Distribution Centre (DC) and retail outlet levels and the workload planning of human resources and DC automations. To do so, this paper presents the case of a Greek 3PL provider fulfilling physical store and online orders on behalf of a large sporting goods importer operating a network of 129 stores in five different countries. The authors utilize the power of ‘R’, a statistical programming language, which is well-equipped with a multitude of libraries for this purpose, to compare demand forecasting methods and identify the one producing the smallest forecast error.
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Chen, Jianping, Nadine Tournois, and Qiming Fu. "Price and its forecasting of Chinese cross-border E-commerce." Journal of Business & Industrial Marketing 35, no. 10 (April 10, 2020): 1605–18. http://dx.doi.org/10.1108/jbim-01-2019-0017.

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Purpose Cross-border e-commerce in China has been booming in recent years. This paper aims to study pricing in Chinese cross-border e-commerce companies and focuses on the baby food market, which is simply examined as a case study to highlight broader implications. In this intensely competitive sector, the biggest challenge faced by such companies is ensuring that they are in a position to be able set prices in the short-term to maximize their competitive advantage and profitability. The study of pricing will help management to make correct operational decisions. Design/methodology/approach This study utilizes transaction data, which were obtained from the Taobao e-commerce platform. Taobao is the largest e-commerce retail platform in the world. We analyzed factors, including business models, homogeneity, reputation ratings and sales volumes, which may affect pricing. Findings This study found that consumers in the baby food sector of Chinese cross-border e-commerce are not price-sensitive. Consumers are reputation-rating-sensitive. The reputation ratings of sellers affect the price dispersion in e-commerce markets. The Core Price Dispersion Rate Model not only considers the prices but also takes sales volumes into account in the calculations. Finally, based on Gaussian processes, a model was developed for price forecasting in the area of cross-border e-commerce. The experimental results show that the proposed method is highly valuable for price forecasting. Originality/value This study provides a novel understanding of the baby food sector in the Chinese cross-border e-commerce market by examining the business model, price dispersion, reputation rating and correlation between the reputation of sellers, prices and sales volume. Furthermore, a model for price forecasting is proposed.
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Muqodas, Avicienna Ulhaq, and Gede Putra Kusuma. "Promotion Scenario Based Sales Prediction on E-Retail Groceries Using Data Mining." International Journal of Emerging Technology and Advanced Engineering 11, no. 6 (June 6, 2021): 9–18. http://dx.doi.org/10.46338/ijetae0621_02.

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Mostly in many business cases, sales prediction plays an important role. Production planning is a good example. One aspect which affecting sales forecasting is promotion schedule. Since using promotion is commonly done nowadays, especially in internet business, it is hardly seen a day without promotion in Indonesian e-commerce. Thus, this study discusses about forecasting future sales based on promotion scenario data with main objective is to discover the best machine learning algorithm and model to forecast future sales. Promotion mechanism which employed in this study are price cut, buy 1-get 1, and product bundling. We use 577 data from January 2018 to July 2019 as dataset. We compare kNN, GLM, and SVR as the model predictor to forecast number of transactions in a day. From the experiment k-NN yielded the highest performance ability with squared correlation of 0.938. the worst model predictor for this case is GLM with squared correlation of 0.507. We also determine the best parameter input for each parameter using grid optimization method. We discover 2 is the best k value of kNN and Manhattan distance is the best distance calculation for this case
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