Future Sales Research Articles

Forecasts of Video Game Sales based on the ARIMA Model

Abstract. With the rapid development of the digital age, video games have become an important component of the cultural industry, and their sales forecasting has become a focus of industry attention. This article aims to predict the sales of video games based on the ARIMA model and explore effective methods to improve prediction accuracy. Through in-depth research, this article found that the ARIMA model performs well in capturing the time series characteristics of sales data and can accurately predict future sales trends. The ARIMA model provides a systematic and scientific method for predicting the future market performance of video games by comprehensively considering the trends, seasonality, and random changes in historical sales data. This research achievement not only provides strong decision support for game developers, publishers, and platform operators but also promotes the optimization of resource allocation and market risk avoidance in the video games industry, which is of great significance for promoting the healthy and sustainable development of the industry.

Spectators’ opinions about purchasing tickets from a smoke/vape-free stadium in Ireland: a qualitative study

This study sought to qualitatively explore spectators’ opinions of purchasing tickets in the future if Croke Park (a large stadium in Ireland) were to implement a smoke/vape-fee policy. During 2022 semi-final matches, a total of 107 spectators who were smoking/vaping at Croke Park’s designated areas were briefly interviewed. Transcript data was qualitatively analyzed, showing that most (80%) participants would continue to purchase tickets because of two common reasons: supporting their county at Croke Park was more important than smoking/vaping, and they would find a way to smoke/vape regardless of the rules. Reasons for not purchasing future tickets included: needing to smoke/vape, smoking/vaping is a human right, and smoking/vaping is part of the experience. The implications from the findings, and prior literature, suggest that a smoke/vape-free policy might not impact future ticket sales, given the rich sports heritage of Croke Park.

Hybrid LSTM-GBRT based machine learning technique implementation for electric vehicle sales prediction analysis

ABSTRACT The investigation of machine learning models in Europe and India for predicting future sales of electric cars (EVs) worldwide has been the focus of present research. In this study, Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), Linear Regression, hybrid long- and short-term memory neural network model and gradient boosted regression trees model (LSTM-GBRT) models based machine learning techniques are compared for performance analysis to predict EV sales. Moreover, in this study, a hybrid LSTM-GBRT technique was developed, which forecasts EV sales, particularly for Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) over the next five years. During the prediction of EV sales, it has been validated that hybrid LSTM-GBRT technique has better predictive power. In addition, the present study attempts to provide estimated sales statistics for the designated locations. The results indicate that EV industry is evolving, with India showing a strong growth rate in BEV sales and establishing itself as a major participant in the global EV scenario by showing a notable increase in its share of worldwide EV sales volume by 2027. Conversely, the performance of EV sales in Europe has been analysed and validated through the proposed LSTM-GBRT model.

The Environmental Impacts of Future Global Sales of Hydrogen Fuel Cell Vehicles

During the last decade, developing more sustainable transportation modes has become a primary objective for car manufacturers and governments around the world to mitigate environmental issues, such as climate change, the continuous increase in greenhouse gas (GHG) emissions, and energy depletion. The use of hydrogen fuel cell technology as a source of energy in electric vehicles is considered an emerging and promising technology that could contribute significantly to addressing these environmental issues. In this study, the effects of Hydrogen Fuel Cell Battery Electric Vehicles (HFCBEVs) on global GHG emissions compared to other technologies, such as BEVs, were determined based on different relevant factors, such as predicted sales for 2050 (the result of the developed prediction model), estimated daily traveling distance, estimated future average global electricity emission factors, future average Battery Electric Vehicle (BEV) emission factors, future global hydrogen production emission factors, and future average HFCBEV emission factors. As a result, the annual GHG emissions produced by passenger cars that are expected to be sold in 2050 were determined by considering BEV sales in the first scenario and HFCBEV replacement in the second scenario. The results indicate that the environmental benefits of HFCBEVs are expected to increase over time compared to those of BEVs, due to the eco-friendly methods that are expected to be used in hydrogen production in the future. For instance, in 2021, HFCBEVs could produce more GHG emissions than BEVs by 54.9% per km of travel, whereas in 2050, BEVs could produce more GHG emissions than HFCBEVs by 225% per km of travel.

Research on accurate prediction of vegetable commodity pricing strategy and dynamic replenishment quantity based on optimization model

Based on the characteristics of timeliness, fast price change and easy loss, this paper deeply studies the automatic pricing and replenishment strategy of fresh suppliers, in order to maximize the profit of the supermarket. We propose and solve the number of related problems, and fit the linear and nonlinear models, and the results have limited effect. Therefore, this study translated the replenishment problem into sales volume prediction, using LSTM and predicting future sales by random forest model. At the same time, based on the periodicity of vegetable commodity sales, this study proposes the single product replenishment volume and pricing strategy. Finally, in view of the poor fitting effect of a single factor in this study, the improvement  method  of  collecting  data  from  multiple  dimensions  is  proposed  to  deeply  explore  the relationship  between  replenishment  and  pricing,  so  as  to  improve  the  accuracy  of  prediction   and supermarket revenue.

Optimization of frozen goods distribution logistics network based on k-means algorithm and priority classification.

Maintaining the quality and integrity of frozen goods throughout the supply chain necessitates a robust and efficient cold chain logistics network. This research proposes a machine learning-based method for optimizing such networks, resulting in significant cost reduction and resource utilization improvement. The method employs a three-phase approach. First, K-means clustering groups sellers based on their geographical proximity, simplifying the problem and enabling more accurate demand prediction. During the second phase of the proposed method, Gaussian Process Regression models predict future sales volume for each seller cluster, leveraging historical sales data. Finally, the Capuchin Search Algorithm simultaneously optimizes distributor location and resource allocation for each cluster, minimizing both transportation and holding costs. This multi-objective approach achieved a 34.76% reduction in costs and a 15.6% reduction in resource wastage compared to the existing system. This novel method offers a valuable tool for frozen goods distribution networks, with advantages such as considering multiple goals for optimization, focusing on demand prediction, potential for reduced complexity, and focusing on managerial insights over compared methods.

Implementasi Data Mining dengan Metode Regresi Linear untuk Prediksi Hasil Penjualan di PT Awitama Cyndo Wahana

The increase in the number of new entrepreneurs in Indonesia is influenced by technological developments, easier access to information, and government support. This increases the need for legal services such as making business permits and business agreements. Lack of understanding of the legal aspects of business can hinder business growth. PT Awitama Cyndo Wahana, a legality management service provider, has great potential to support new entrepreneurs. However, this company has not utilized sales data to predict future sales. This research uses sales data from PT Awitama Cyndo Wahana to predict sales using the linear regression method. The test results show that the Root Mean Squared Error (RMSE) is 458618.289, the Absolute Error is 183084.456, and the Relative Error is 0.98%. These results indicate that the prediction model has an adequate level of accuracy, although there are some prediction errors. The practical implication of this research is that PT Awitama Cyndo Wahana can use these sales predictions to plan more effective marketing strategies and make data-based business decisions, so that they can better support the growth of new entrepreneurs.

Stock exchange operations in an anti-crisis management system of the enterprise

Today, in the process of building an effective system of anti-crisis management of the enterprise, more attention should be paid to such less common, but highly effective tools as stock transactions. Anti-crisis management is a certain set of strategic measures and tactical actions aimed at identifying, preventing and overcoming crisis phenomena in the company's activities, as well as impulses that potentially threaten the development of crisis phenomena in the future. The main directions of action are the following: development of the general anti-crisis strategy of the enterprise, ensuring the financial stability of the enterprise, implementation of the risk management system, restructuring of the enterprise and its business processes, personnel and communications management, increasing the efficiency of the marketing system, etc. Speaking about the place of stock exchange operations in the system of anti-crisis management of the enterprise, it should be noted that they can be considered as a tool in the middle of some of the above measures, namely optimization of business processes, improvement of the level of communications and activation of marketing. In all these aspects, the use of exchange operations can be effective. In our opinion, transactions with goods and derivative financial instruments require the main attention. Speaking about the optimization of business processes, the following advantages of cooperation with stock exchange institutions can be distinguished: guaranteed replenishment of production stocks with raw materials and materials of the required quality and quantity due to the conclusion of term exchange agreements for their supply, the forecast level of prices for raw materials and materials, which is fixed during the conclusion of exchange agreements , clearly defined conditions of supply. Access to the stock market also has a positive effect on the level of business communications, because it is the stock exchange that maximally concentrates all interested parties regarding specific types of goods, which contributes to the intensification of business activity. Exchange operations can also be considered as a highly effective tool for activating the marketing activities of the enterprise. In addition to the purchase of raw materials on the stock exchange for your activity, you can use the stock market for the sale of goods manufactured by the enterprise. In this way, the company can guarantee itself future sales in a certain forecasted price range, and accordingly, reasonable planning of the company's performance indicators, cash flows is possible, therefore, it becomes possible to manage insolvency, which is the main crisis impulse for the company.

Time Series Analysis for Tractor Sales using SARIMAX and Deep Learning Models

Time series forecasting is known for playing vital role in many industries to make important decisions and strategies. This study concentrates on providing accurate insights that can help manufactures and stakeholders of agriculture machinery industry on future sales of tractors by applying both traditional and deep learning models like SARIMAX which is extension of SARIMA and deep learning models. Research starts by observing history data which include years of tractor sales then preprocess the data to find its quality and stationarity further applying SARIMAX model to find trends and seasons and cycles in the data and this model is evaluated by famous metrics like Root Mean Squared Error (RMSE).Deep learning models like Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, CNN LSTM Encoder Decoder, Convolutional Neural networks (CNN). They can help in enhancing the forecasting accuracy by handling all the non-linear relationships and their dependencies in the timeseries and this study will provide comparative analysis of deep learning models and SARIMAX model. Where SARIMAX outperformed the deep learning models with RMSE score 0.01 and provide forecast of next year’s tractor sales using SARIMAX model from the study and use q-q plot, residual plots and ACF and PACF graphs to make sure forecast was done accurately.

A Comprehensive Study of Walmart Sales Predictions Using Time Series Analysis

This article presents a comprehensive study of sales predictions using time series analysis, focusing on a case study of Walmart sales data. The aim of this study is to evaluate the effectiveness of various time series forecasting techniques in predicting weekly sales data for Walmart stores. Leveraging a dataset from Kaggle comprising weekly sales data from various Walmart stores around the United States, this study explores the effectiveness of time series analysis in forecasting future sales trends. Various time series analysis techniques, including Auto Regressive Integrated Moving Average (ARIMA), Seasonal Auto Regressive Integrated Moving Average (SARIMA), Prophet, Exponential Smoothing, and Gaussian Processes, are applied to model and forecast Walmart sales data. By comparing the performance of these models, the study seeks to identify the most accurate and reliable methods for forecasting retail sales, thereby providing valuable insights for improving sales predictions in the retail sector. The study includes an extensive exploratory data analysis (EDA) phase to preprocess the data, detect outliers, and visualize sales trends over time. Additionally, the article discusses the partitioning of data into training and testing sets for model evaluation. Performance metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are utilized to compare the accuracy of different time series models. The results indicate that Gaussian Processes outperform other models in terms of accuracy, with an RMSE of 34,116.09 and an MAE of 25,495.72, significantly lower than the other models evaluated. For comparison, ARIMA and SARIMA models both yielded an RMSE of 555,502.2 and an MAE of 462,767.3, while the Prophet model showed an RMSE of 567,509.2 and an MAE of 474,990.8. Exponential Smoothing also performed well with an RMSE of 555,081.7 and an MAE of 464,110.5. These findings suggest the potential of Gaussian Processes for accurate sales forecasting. However, the study also highlights the strengths and weaknesses of each forecasting methodology, emphasizing the need for further research to refine existing techniques and explore novel modeling approaches. Overall, this study contributes to the understanding of time series analysis in retail sales forecasting and provides insights for improving future forecasting endeavors.

Data-driven sales optimization with regression and chaotic pattern search.

Lead generation is the process of gaining potential customers' interest to increase future sales, and it is an essential part of many businesses' (amusement parks, theme parks, clubs, etc.) sales processes as their membership is more expensive. The main objective of these businesses is to increase the count of customers. By generating sales leads, a club/park can find leads who have already expressed interest in its products and services and access their audience potential, allowing them to focus on future marketing and sales efforts on those leads that are more likely to convert. The current work focuses on how to convert a lead to a customer in optimum number of days. We collect two kinds of data: customer data and lead generation data. The customer data consists of all the leads who have taken the membership, and the lead generation data consists of all current leads. The details of those converted from a lead into a customer in the last 60 days are filtered out from the customer data. Using this data, patterns are generated, which are used to predict the following activity (step) for qualified leads, along with the optimal number of days required to complete that activity. This optimal number of days is found using the Hybrid Chaotic Pattern Search Algorithm (HCPSA). This novel approach here helps in boosting sales by prioritizing leads who have expressed interest and identifying the optimal window for converting them into paying customers. This strategy holds significant potential to benefit businesses across various industries.

Application of Mathematical Modelling in Sales Forecast Using Time Series

Sales forecasting is a crucial aspect of business management, which involves predicting future sales based on historical data and market trends. Accurate sales forecasts are essential for effective decision-making, such as inventory management, production planning, and resource allocation. This study explores the application of mathematical modelling in sales forecasting. A case study approach was used to demonstrate how mathematical modelling can be deployed to develop accurate sales forecasts. Specifically, historical sales data and market trends were used to develop mathematical models, including regression analysis, and time series analysis, to predict future sales.

Pre-harvest treatments: A different insight into preservation of strawberries

Strawberry is one of the most favored consumed fresh fruits worldwide. However, the major constraint limiting future sustainable production and sales of strawberries is post-harvest decay. Thus, taking a deep look into the effective preservation measures including pre- and post-harvest techniques to retain freshness and inhibit the pathological spoilage of strawberries in the supply chain is of great significance and necessity. At present, relying solely on post-harvest technology is not enough to fix the aim of strawberry fruit preservation and longer shelf life. On the contrary, pre-harvest treatments could be extend shelf life of strawberry fruit and adapt to the diverse post-harvest environments. This review give the latest pre-harvest treatments, their effects on post-harvest quality and storability of strawberries, and develop the expectation of strawberries pre-harvest methods applicated in the future.

Legal Risks in Future Housing Purchase and Sale Contracts: A Case Research in Vietnam

A future housing purchase contract stipulates a predetermined sale price between a home buyer and seller, with the transaction set for a future date. This type of contract offers sellers the advantage of price security and continued property use until the sale, mitigating risks associated with market fluctuations and buyer scarcity. Nevertheless, these contracts also present several challenges, particularly the need for clear, transparent terms and careful legal risk management to safeguard the interests of all parties involved. This article uses the method pf analyzing the written law, the method of analyzing the situation trough the researching on secondary sources to analyze anticipated legal challenges in future housing purchase contracts and reviews relevant Vietnamese legal frameworks. It proposes essential contract terms and preventive measures aimed at minimizing risks and protecting the rights of the parties, focusing particularly on the buyer. These insights are intended to guide stakeholders in crafting more secure and equitable future housing purchase agreements.

Regression Learner Application Model-Based Short-Term Load Forecasting for Mascouche (Quebec, Canada)

Load forecasting is crucial for power systems optimal operation and allows power utilities to overcome technical and economic issues. Some forecasting techniques are currently being deployed on a large scale to meet the requirements of increased energy demand while balancing it with the production to achieve socio-economic benefits for sustainable development. In this paper, we are diving into the forecasting using the regression method. We are focusing on short-term load forecasting and how it can give businesses valuable insights into future sales, labor needs, and more. Power utilities use short-term load forecasting technology to make reasonable power systems. A forecasting model with low prediction errors helps reduce operating costs and risks for the operators leading to models’ optimization. To make things real, we are using actual load and weather data from the Hydro-Quebec database. We will be exploring the capabilities, advantages, and limitations of this method, all while keeping an eye on the changing landscape of electricity supply and demand. Our study is centered around the Mascouche region in Quebec, Canada, where the load fluctuates between 60 to 140 megawatts.

Can crowdsourcing improve prediction accuracy in fashion retail buying?

Fashion retailers’ buyers must decide how much to buy of merchandise well before a selling season. This order quantity decision is always challenging due to limited historical data and high demand unpredictability arising from the novelty of fashion merchandise. Despite many attempts to solve this longstanding problem, industry reports consistently show that fashion buyers’ predictions of product salability and future sales are frequently inaccurate, leading to loss of profits for retailers. In this research, the authors take an “Empirics-First” approach to explore an alternative solution, crowdsourced forecasts from ordinary customers, and investigate whether crowdsourced forecasts would be more accurate than those of expert fashion buyers and if so, how should a crowd be formed in terms of size and composition? After conducting an online experiment, finding that forecasts by a “crowd” of ordinary customers are significantly more accurate than those of expert fashion buyers, the authors test a contingency framework in a second empirical study examining how crowd size and composition impact forecasting accuracy for products of varying fashionability. The results revealed that heterogeneity in a crowd is a key factor in prediction accuracy. Specifically, crowds with more variation in income and shopping frequency made notably accurate predictions. Another key finding of the study pertains to the required crowd size; increasing the size of a crowd at first sharply decreased the crowd's prediction error. However, after a certain point, there were diminishing returns in prediction accuracy. Given the interesting results, the paper concludes with guidelines for implementing crowdsourced forecasting by fashion retailers and directions for future research.

Predictive Analysis for Big Mart Sales Using Machine Learning Algorithms

Abstract: Supermarkets and retail giants like Big Mart rely heavily on tracking individual item sales data to anticipate consumer demand and optimize inventory management. By mining data warehouses, anomalies, and trends can be identified, providing valuable insights for forecasting future sales volumes. Leveraging advanced machine learning techniques, such as Decision Tree Regression, predictive models can be developed to forecast sales accurately. Through the implementation of such a model, it has been observed that it surpasses the performance of existing forecasting models. This innovative approach not only enhances decision-making processes but also enables businesses like Big Mart to stay ahead in a competitive market landscape.

Forecasting Dairy Sales Estimates: A Comparative Analysis of Logistic Regression and Random Forest Algorithm

This study examines the effectiveness of logistic regression and random forest algorithms in forecasting sales estimates for dairy products. Using a comprehensive data set that includes factors such as product features, pricing dynamics, promotional efforts and consumer demographics, the models are trained to accurately predict future sales figures. Logistic regression provides a transparent framework for estimating probabilities, while random forest uses ensemble learning to capture complex relationships between variables. Through careful evaluation and comparison, the research aims to identify the strengths and weaknesses of each algorithm in producing reliable sales forecasts for dairy products. The results show that while logistic regression offers interpretability and simplicity, random forest excels in handling non-linear relationships and achieving higher prediction accuracy. Insights gathered from this analysis can help dairy industry stakeholders make informed decisions, optimize resource allocation and improve sales forecasting strategies.

Who Benefits From Supplier Encroachment in the Presence of Manufacturing Cost Learning?

Manufacturing cost plays a crucial role in suppliers’ encroachment decisions. A high manufacturing cost impedes suppliers’ capacity to encroach. However, cost learning may reduce this cost sufficiently enough to make encroachment profitable for the supplier at a later point in time. Accordingly, he may have an incentive to boost production so as to promote cost learning. Thus, he may drop the wholesale price to induce the retailer to buy more. On the one hand, cost learning may enable encroachment, which may be detrimental to the retailer. On the other hand, cost learning results in a lower manufacturing cost which may translate into a lower future wholesale price, benefiting the retailer. Therefore, the retailer faces a dilemma: should she increase her order quantity to advance cost learning or not? As the retailer may order fewer units in the initial period to limit future direct channel sales, the supplier faces a challenge: should he, instead of dropping his initial wholesale price, raise it to signal his intention of not encroaching so as to induce the retailer to sell a higher quantity in the first period? We model the supplier–retailer interaction as a two-period Stackelberg game to address the retailer’s dilemma and to identify the optimal supplier response. We uncover a new outcome, which arises in the presence of cost learning, where the supplier encroaches but decides not to sell anything through the direct channel. In addition, we find that supplier encroachment may reduce or eliminate the retailer’s incentive to advance cost learning. This results in lower sales by the retailer, which impedes cost learning, leading to a higher future manufacturing cost (compared to the no encroachment setting). As a result, encroachment, which is typically viewed as advantageous for the supplier, may become detrimental to him. Surprisingly, the supplier continues to encroach and sell directly unless he can credibly assure the retailer that he will not encroach in the future.

Research on Pricing and Replenishment of Vegetables Based on Grey Relational Model and Seasonal Index Prediction Model

This article mainly studies the problem of automatic pricing and replenishment decisions for vegetable products in supermarkets. A grey correlation model and ARIMA time series model are comprehensively established, and based on clustering analysis and regression analysis methods, the distribution patterns of sales volume for each vegetable category and item over time, as well as the relationship with cost plus pricing and mutual correlation, are analyzed to make reasonable predictions for future sales volume. In order to make decisions on product pricing, sales combination, and replenishment, and maximize supermarket profits, the data is preprocessed through normal test and quartile method. Through screening and sorting analysis, the distribution patterns of sales volume for each vegetable category and item over time are obtained; In order to analyze the relationship between different categories of vegetables and between different single products, a grey correlation model is established to determine the analysis sequence, and the correlation coefficients between each category and each single product are calculated to analyze the mutual relationship. And combined with the hierarchical clustering analysis method, the vegetable categories were divided into three sales types: high, medium, and low, expanding the relationship between categories. At the same time, a periodic chart was drawn to determine the periodicity of sales volume distribution in each category. Then, statistical analysis was conducted on the data to determine the linear correlation between sales volume and cost plus pricing. Based on this, a linear regression model was established to analyze the linear relationship between sales volume and cost plus pricing for the six categories; In order to provide the total daily replenishment amount and pricing strategy for the next week, this article establishes a seasonal index prediction model for sales forecasting. Combining the seasonal distribution of each category, the seasonal index is used to predict the sales volume for the next week. Considering the goal of maximizing profits, the target function is listed to comprehensively analyze the replenishment and pricing strategy for the next week.