Sales Forecasts Research Articles

Effects Influence of Social Media Constructs on Shopping: An Empirical Study on the Prediction of Retail Clothing Sales

AbstractIn a market characterized by frequent new product placement and their rapid exit, accurate forecasting of the sales is an important goal to guarantee the profitability and long-term survival of companies. This paper follows this line of research and explores the sales data of an Italian clothing company. The objective of this research is to improve sales forecasts.The data under study were extracted from the corporate datalake of the clothing company analyzed as well as from the company’s official Facebook page using Facebook’s Graph API. The sales forecasting should try to consider all the possible demand influencing factors and also explanatory variables. In this way, the time series perspective is combined with qualitative indicators on the demand side. The approach taken confirms the influence of sales from exogenous variables: there is a delay of 15 days between social communication and the sale of clothing. Social communication, as well as some regular Italian holiday, negatively affects sales. On the other hand, school holidays and some regular Italian holidays have a positive impact on sales. As a result, the advance knowledge of revenue forecasts allows managers to choose suitable marketing strategies.

Data Analytics for Decision-Making in Evaluating the Top-Performing Product and Developing Sales Forecasting Model in an Oil Service Company

This study addresses the strategic challenges faced by a company specialising in the manufacture of oil and gas equipment. Following organisational restructuring, which involved the dissolution of one business unit and the creation of another, the company is navigating complexities in product focus and manpower allocation within the Asia-Pacific region. The research problem centres on identifying the top-performing product, determining potential countries for establishing a support base facility based on sales performance, and developing a method for forecasting future sales. The research involved retrieving and pre-processing historical sales data, then performing a thorough descriptive and predictive analysis. The data was partitioned into training and testing sets to facilitate predictive analytics. Several predictive models were developed and tested, including neural networks, linear regression, gradient-boosted trees, random forests, and ARIMA methods. Tableau Public was utilised for descriptive analytics, whereas RapidMiner Studio was employed for predictive analytics. The study’s results, derived through both descriptive and predictive analytic methods, reveal critical insights. The Blowout Preventer (BOP) emerged as the top-performing product in the Asia-Pacific region. In terms of establishing support base facilities, Malaysia was identified as the ideal location for the BOP, while Indonesia was found suitable for the Manifold product group. Furthermore, the Random Forest model was determined to be the most effective for forecasting future sales. These findings provide strategic guidance for the company in product focus, regional expansion, and resource allocation, contributing significantly to the company’s decision-making process in a competitive industry.

Application of Computer Vision on E-Commerce Platforms and Its Impact on Sales Forecasting

In today's digital age, the e-commerce industry continues to grow and flourish. The widespread application of computer vision technology has brought revolutionary changes to e-commerce platforms. Extracting image features from e-commerce platforms using deep learning techniques is of paramount importance for predicting product sales. Deep learning-based computer vision models can automatically learn image features without the need for manual feature extractors. By employing deep learning techniques, key features such as color, shape, and texture can be effectively extracted from product images, providing more representative and diverse data for sales prediction models. This study proposes the use of ResNet-101 as an image feature extractor, enabling the automatic learning of rich visual features to provide high-quality image representations for subsequent analysis. Furthermore, a bidirectional attention mechanism is introduced to dynamically capture correlations between different modalities, facilitating the fusion of multimodal features.

Research on Automated Pricing and Supplementation Decision-making of Vegetable Products

In fresh supermarkets, the shelf life of vegetable products is usually very short, so it is crucial to replenish inventory daily based on historical sales data and product line requirements. In order to strengthen decision-making and inventory management, this article first selects representative supermarket sales data and conducts appropriate preprocessing, and then adopts a series of effective analysis methods. Firstly, using Kappa consistency testing to explore the correlation between different vegetable categories can help determine which products may be correlated in sales. In addition, in order to better formulate pricing and replenishment strategies for vegetable products, this article introduces a cost-plus pricing model. When determining the markup coefficient, multiple linear regression was used to ensure that the price-setting process is reasonable and consistent with cost and demand. In order to further confirm the accuracy and feasibility of this method, the article calculated the R-squared value of multiple linear regression, which reached 0.975, providing strong validation for the effectiveness of this method. When formulating replenishment strategies, in order to obtain sales forecasts for vegetable categories within the next 7 days, this article also uses a seasonal ARIMA model, which helps supermarkets plan inventory and supply chain more effectively. These strengthening measures have jointly improved the decision-making ability of vegetable product management in supermarkets, promoted more effective data-driven strategies, ensured the freshness and quality of vegetable products, and maximized the economic benefits of supermarkets.

Heavy Moving Average Distances in Sales Forecasting

This paper presents a new aggregation operator technique that uses the ordered weighted average (OWA), heavy aggregation operators, Hamming distance, and moving averages. This approach is called heavy ordered weighted moving average distance (HOWMAD). The main advantage of this operator is that it can use the characteristics of the HOWMA operator to under-or overestimate the results according to the expectations and the knowledge of the future scenarios, analyze the historical data of the moving average, and compare the different alternatives with the ideal results of the distance measures. Some of the main families and specific cases using generalized and quasi-arithmetic means are presented, such as the generalized heavy moving average distance and a generalized HOWMAD. This study develops an application of this operator in forecasting the sales growth rate for a commercial company. We find that it is possible to determine whether the company's objectives can be achieved or must be reevaluated in response to the actual situation and future expectations of the enterprise.

Performance of Chinas Macroeconomic Indicators in a Regression Model-Based Sales Forecasting

The impact of macroeconomic indicators on sales forecasts is a topic worthy of corporate consideration in China. However, previous research on this topic is rarely based on the Chinese context and lacks discussion of specific industry scenarios. In this study, 5 macroeconomic indicators that the Chinese government and people are concerned about are selected and put into the regression model and the AIC model selection criterion to study their performance in sales forecasts of 4 industries in China. It is concluded that under Chinas national conditions, Disposable Income per Capita and National Fixed Asset Investment are two macroeconomic indicators that deserve attention and perform better than other indicators in sales forecasts, and they have hardly been mentioned in previous studies. At the same time, the most appropriate model for sales forecasting in each industry contains different combinations of macroeconomic indicators. It is believed that this result is valuable for companies in various industries, and will be inspiring for subsequent research in the field of sales forecasts.

Прогнозирование банковских продаж на примере ПАО «Сбербанк»

Introduction. This scientific study highlights the relevance of modeling and forecasting sales of Sberbank in terms of effective business management. Sales forecast is an important tool for predicting the demand for goods and services, as well as determining the adequate strategies and tactics to achieve the company’s goals. The research is distinguished by its reference to artificial intelligence methods in the field of marketing. Forecasting methods applied to a proprietary data sample of Sberbank’s daily sales give novel results, which reliably supports the development of adequate strategies and tactics for successful business management. The key hypothesis of the study is to check the prognostic potential of machine learning methods against the traditional econometric approaches to modeling Sberbank’s sales. The purpose of the study is to develop sales forecasting models for multifunctional products and their practical instruments for Sberbank’s Sales Network Block. Materials and Methods. The study relies on the methods of system-oriented analysis, statistical and economic mathematical methods of data analysis and their processing. Collected and pre-processed sales data for Sberbank’s phantom products reflecting the dynamics of bank sales were used for computational experiments to build a few forecasting models and justify the choice of the best model among those built. Results. Random Forest and Gradient Busting (XGBRegressor) Models used training and test samples to give the forecasts with the accuracy significantly higher than the accuracy of forecasts by ARIMA-model and linear regression. Conclusions. The results of the analysis reliably confirm that machine learning methods are currently promising methods for forecasting bank sales and can be the subject of further research in this area. Machine learning techniques introduced into banking practices have the potential to significantly improve the effectiveness of existing sales and risk management.

The Role of Predictive Analytics in Sales Forecasting

The Role of Predictive Analytics in Sales Forecasting

Online sales prediction approach using methodology of CRISP-DM

This article studies the sales forecasting problem in the field of e-commerce. Based on the CRISP-DM methodology, innovative data mining technology is used to construct a variety of forecasting models, and is compared and optimized. This article improves the quality and quantity of sales forecasts and provides enterprises with more accurate and effective decision support. In terms of modeling optimization in this article, data mining models such as random forest, support vector machine, and neural network are used for comprehensive prediction, and comparative analysis is conducted with the classic multiple linear regression model. Through model evaluation and optimization, this paper achieved better prediction performance and accuracy. This research has certain theoretical significance and practical value, and provides new ideas and methods for the marketing decisions and business development of e-commerce enterprises.

Research on Vegetable Cost Pricing and Sales Forecast Based on PSO Optimization and Forecast Model

Research on Vegetable Cost Pricing and Sales Forecast Based on PSO Optimization and Forecast Model

Advancing Retail Predictions: Integrating Diverse Machine Learning Models for Accurate Walmart Sales Forecasting

Advancing Retail Predictions: Integrating Diverse Machine Learning Models for Accurate Walmart Sales Forecasting

Sales Forecasting of Overrated Products: Fine Tuning of Customer’s Rating by Integrating Sentiment Analysis

Sales Forecasting of Overrated Products: Fine Tuning of Customer’s Rating by Integrating Sentiment Analysis

Sales Forecasting Method for Inventory Replenishment Systems of Vehicle Energy Stations Without Stockouts

Sales Forecasting Method for Inventory Replenishment Systems of Vehicle Energy Stations Without Stockouts

A Study of Impact and Applications of Predictive Analytics in Sales Forecasting

Abstract: Predictive analysis has become an efficient tool and procedures now a day for many industries, organization, firms and service sectors. In sales, predictive analytics refers to the software and/or procedures that evaluate past and present sales data in order to forecast sales results and enhance efficiency. A vast amount of first-party and third-party customer/prospect data is available these days to sellers and marketers. Indeed, sellers and marketers frequently feel overpowered by the sheer amount of data when deciding how to leverage it to get desired outcomes. That issue is resolved by predictive analytics in sales, which uses artificial intelligence systems to assist sales representatives in understanding customer and sales pipeline information. Advances in data analytics are being used by an ever-increasing number of organizations to identify patterns and trends, increase productivity, and make more informed business decisions. In this paper, we will discuss and analyze the role and impact of the predictive analytics in sales.

Inventory Control for Eyeglass Supply Using the P Model Based on Sales Products Sales Forecasting (Case Study: Merry Optic Bandung)

Inventory is a resource owned by the company to be used in the production process to meet consumer demand. Companies must be able to control inventory appropriately in order to avoid excess or shortage of inventory by using inventory control. Inventory control is a necessary part of a company that requires an appropriate inventory policy to meet uncertain needs. Based on this background, this study discusses the single item inventory model in the form of photochromic glasses at Merry Optik to find the optimal total inventory cost. In meeting the uncertain needs of the company, the Additive Decomposition forecasting method is used in order to find out the forecast sales data pattern in the future. Uncertain demand causes the inventory system to be probabilistic, so it is necessary to carry out probabilistic inventory control. The P model of the case of back orders was chosen because the range of ordering periods is fixed and the company can buy inventory when it runs out before the time the inventory order is made so that buyers can wait until the inventory arrives. By using Model P for the case of back orders, the company can obtain the period between orders, the total cost of inventory, and the optimal level of service. Based on the results of this study, a pattern of sales forecast data is obtained which repeats every 12 months. Companies must order glasses within a period of 32 days between orders so that it is optimal and able to provide a reduction in the total inventory cost of IDR 21,828,771 with a service level of 95%. Companies can save on inventory costs if they use shorter periods between orders. The total cost of inventory can be more optimal if the company reduces the cost of storing inventory in the warehouse.

Application of Big Data Analysis in Sales Forecasting

As a matter of fact, with the rapid development of the computation ability and data delivering, the big data analysis becomes a common tool to realize the predictions. With this in mind, this paper focuses on the impact of big data analysis on sales forecasting, starting with an introduction to the present-day context and significance of sales forecasting and some of the forecasting scenarios implemented by previous generations based on machine learning. After introducing these backgrounds, we start to introduce big data analysis and its impact on sales forecasting. According to the analysis and the summaty of the previous experience, we introduce the commonly used models, the parameters of the models, and the evaluation of the models as well as the application scenarios, and the specific application results. Finally, we demonstrate the limitations as well as future perspectives of big data analysis and sales forecasting. Overall, these results shed light on guiding further exploratio of bigdata analysis in sales prediction.

Intelligent Design Suggestion and Sales Forecasting for New Products in the Apparel Industry

Abstract This study demonstrates how algorithms can assist humans in decision-making in the apparel industry. A two-stage method including suggestions and intelligent forecasting was proposed. In the first stage, a web crawler was used to browse a B2C apparel website to identify popular products. In the second stage, machine learning methods were used to predict the sales demand for new products. Additionally, we used Google Trends to collect external information indices to adjust the demand forecasting. Our numerical study shows that the intelligent forecasting approach can effectively reduce the Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by at least 45.79, 26.35, and 26.34 %, respectively.

ANALISIS FORECASTING PENJUALAN GUNA MEMINIMALISIR KERUGIAN AKIBAT KEKURANGAN STOK MENGGUNAKAN METODE MOVA DAN WMA (STUDI KASUS PADA TOTAL BUAH SEGAR CABANG CIKARANG PENJUALAN JERUK WOKAM)

In Indonesia, citrus fruits occupy the second level, which is the most consumed fruit with an average of 12.57 grams/capita/day. Lately the type of orange that is popular in Indonesia is wokam orange. Wokam oranges are citrus fruits imported from China. This type of orange can only be found in large fruit stores or supermarkets. Total Buah Segar is a large fruit shop that has several branches, one of which is Total Buah Segar Cikarang branch. Total Buah Segar Cikarang branch sells a variety of quality fruits, one of which is the popular wokam orange. Of course, its popularity can also cause problems in terms of stock inventory, namely there is often a shortage of wokam orange stock in Total Buah Segar Cikarang branch. Therefore, the management of Total Buah Segar Cikarang branch needs a sales forecast in order to determine the supply of wokam oranges in order to meet consumer demand. The method used in this study is a quantitative descriptive method. The calculation of forecasting the sales of wokam oranges uses the MOVA and WMA methods. Precise forecasting results are obtained, namely the WMA method from determining the smallest MAD, MSE, and MAPE values. So that Total Buah Segar Cikarang branch can consider the results of forecasting to make decisions regarding the supply of wokam oranges in the future.

IMPLEMENTATION OF MSME INVENTORY MANAGEMENT IN CIPULIR VILLAGE – JAKARTA SELATAN

Community Service Activities (CSA) were carried out in the Cipulir sub-district. The activity participants are MSMEs who live in Cipulir sub-district. The people at the location generally work as traders and small industries. MSMEs often face problems with shortages or excess supplies which have the impact of hampering business activities. The causes of shortages or excess inventory include: a) MSME actors do not forecast sales, so MSME actors only order inventory according to previous order history. This causes ambiguity between the production plan and the raw material requirements plan, which is the reference for purchasing inventory, b) the absence of safety stock, which is useful for guarding against the possibility of stock outs, c) the absence of clear inventory management, with no there is evidence of inventory recording, as well as inventory control. Based on an analysis of the situation faced by partners, the Community Service Team intends to provide solutions to partner problems by increasing knowledge and understanding regarding inventory management for MSMEs. The method used in this activity is sharing sessions and discussions, which are then given training related to inventory management. Then the activity continued with questions and answers and case study simulations. The results of the evaluation showed that most participants understood the material presented. The evaluation results also illustrate that all participants felt they had benefited from this activity, and were satisfied with the implementation of this activity.

Pelatihan Teknik Proyeksi Bisnis Pada Kelompok Usaha Batu Bata Di Kelurahan Limbung Kecamatan Bajeng Kabupaten Gowa

Community Service Partners (PKM) is a micro and small brick business. Knowledge of business projections among business owners in Batu Bata is still very limited. The aim of this service is to empower traditional business owners with the knowledge and skills to produce accurate business projections to assist in developing their business. The implementation method uses lecture, exercise, and discussion/question and answer methods. The participants were brick business owners from the Limbung village, Bajeng District, Gowa Regency. The technique for implementing this activity includes three stages starting from the preparation stage, implementation stage and activity evaluation stage. The results of the training show that traditional business owners experience difficulties in applying business forecasting techniques to their businesses because sales records are sometimes incomplete or non-existent. After carrying out the simulation, participants can understand the basis of sales forecasts for future sales forecasts. Additionally, brick-and-mortar business owners can know the exact estimates of their business. Knowledge of business forecasting is expected to improve business performance and competitiveness. The limitation of this training is that the implementation method does not use instructional techniques so that knowledge about business forecasting can be implemented in business activities.