Commodity Sales Research Articles

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.

Commodity Dynamic Pricing and Replenishment Decision Model Based on Cosine Annealing

The purpose of this paper is to analyze the relationship between commodity sales volume, commodity types, sales time, etc., and propose a decision model to predict future sales data through historical sales data to determine a reasonable pricing strategy. First, the collected data is preprocessed. Through correlation analysis, the most important influencing factors of commodity sales are obtained. Through the construction of XGBoost model to model the influence factors obtained, the wholesale prices of chili products (yuan/kg) from July 1 to 7, 2023 are 3.63, 6.68, 6.90, 5.44, 6.62, 6.90, 5.44, respectively. In order to further develop a reasonable pricing strategy, this paper constructs a model based on cosine annealing algorithm combined with dynamic pricing algorithm. Through this model, it is calculated that from July 1 to 7, 2023, the restocking quantity (kg) of chili products is 234.20, 95.85, 110.04, 179.77, 176.60, 110.04, respectively. 179.77. Profit (Yuan) is 148.81, 298.96, 347.82, 370.75, 287.54, 356.33, 383.00. By comparing the model with the actual data, the error of the model is small and the robustness is high, which provides an effective decision-making model for supermarkets to formulate commodity sales strategy.

Research on Automatic Pricing and Replenishment Decision of Vegetable Products Based on Machine Learning

In fresh food supermarkets, due to the short shelf life of vegetable products and the deterioration of products over time, unsold products on the same day cannot be sold the next day. Therefore, supermarkets need to make automatic pricing and replenishment decisions on a daily basis based on historical sales and demand. All product data for this study were provided by the 2023 Higher Education Society Cup National College Student Mathematical Modeling Competition.This study aims to explore the relationship between commodity sales and wholesale prices and pricing using five machine learning methods: neural networks, linear regression, decision tree regression, random forest regression, and XGBOOST regression, in order to predict sales and wholesale prices for the next week. By comparing the performance of five machine learning methods, it was found that the XGBOOST regression prediction model is the most suitable for predicting the daily sales of goods. The linear regression prediction model is the most accurate for predicting the wholesale prices of "chili", "flower and leaf", "edible fungi", "cauliflower", and "eggplant" goods, while the random forest regression prediction model is the most accurate for predicting the wholesale prices of "aquatic rhizome" goods. By constructing a dynamic programming model and using genetic algorithms to solve, automatic pricing and replenishment decisions for various goods within the next week can be obtained.

Evaluating economic efficiency of the national high-tech industrial development districts in the Yangtze River Delta by stochastic frontier analysis

The High-tech Industrial Development Districts (HIDDs) are technological engines for the regional economy in China. The Chinese government implemented the Torch Initiative to accelerate industrial agglomeration and innovation development by administratively upgrading the local HIDDs into a national list since 1989. The policy intervention emphasizes the labor and capital inputs on science and technology. The study adopts the Stochastic Frontier Analysis (SFA) to understand the economic efficiency of the enlisted national HIDDs in the Yangtze River Delta. The results suggest that the average efficiency trends of the all-in-total sales, product sales and commodity sales are decreasing while that of technology sales is increasing from 2007 to 2019. In the total sales efficiency evaluation, most early-enrolled HIDDs are in the high-efficiency group whereas the newcomers are in the low-efficiency group. The Nantong HIDD has the highest efficiency, followed by Wuxi, Taizhou, Suzhou, Nanjing, Shanghai ZJ, Xiaoshan, Ningbo, Suzhou IP and Jiangyin HIDDs. Huainan, Huaian, Tongling, Shanghai ZZ, Lianyungang and Suqian HIDDs rank very low in the total sales evaluation. Besides, Hangzhou, Nantong and Ningbo HIDDs rank first in the technology, product and commodity sales efficiency evaluation correspondingly. In addition, the increase of S&T labor inputs would decrease the efficiency of the product, commodity and total sales while the increase of the S&T capital inputs is as expected a facilitator to the efficiency of product, commodity and total sales. This study contributes to the discussion on the role of political intervention in technological innovation by breaking down the economic efficiency into three major parts, the efficiency of technology, product and commodity sales. The findings could help policymakers strike a balance between the human and capital inputs in regional innovation capability.

Research on Profit Maximization Model for Automatic Pricing and Replenishment Decision of Vegetable and Plant Commodities

The strategy of automatic pricing and replenishment of fresh commodity sales statistics is a long-standing challenge. Accurate pricing is of great significance for commodity sales, loss reduction and economic benefit improvement. The automatic pricing and replenishment strategy for fresh product sales statistics is a long-standing challenge, and accurate pricing is of great significance for product sales, reducing losses, and improving economic benefits. In order to investigate the pricing relationship between the sales of vegetables within a certain time range and the cost of individual products or categories, as well as the replenishment plan and pricing strategy for the next week, based on the Adaptive Differential Evolution Algorithm theory, the analysis is conducted with individual products and categories as units to achieve the goal function of maximum revenue, and a dynamic programming model is constructed, Obtain the specific replenishment total amount and pricing strategy for each vegetable category in the next week and per day. The results have been tested to have good applicability.

A study on the correlation of cyclical fluctuations in vegetable commodity sales based on wavelet analysis modeling

As the sales volume and time of vegetable commodities tend to have a certain correlation, in order to explore the distribution law of the sales volume of each category and single product of vegetables, this paper firstly handles the data to get the monthly sales volume of each vegetable category and single product of time series data, then uses Matlab's Wavelet Toolbox to carry out wavelet analysis,drawing wavelet variance diagrams, the main cycle trend graph to judge the cyclical pattern of each vegetable category and single product,and then use Origin to make wavelet coefficients real part contour graph to verify the law, and finally get the distribution law. This study can help superstores better grasp the sales situation and development trend of each vegetable category and single product.

Performacnes comparison of CNN-based models for fine-grained image classification

Fine-grained visual classification is regarded as a more refined classification that can identify specific types of objects. It has been widely used in commodity sales, vehicle recognition and person recognition, etc., and has played a great value in many fields. For example, it requires an algorithm to identify different species of birds or dogs to facilitate more practical applications. This task is difficult since the objects has similar appearance and there exist obvious intra-class variance and limited inter-class differences, where different kinds of birds could share very similar appearance. Deep learning techniques has been applied to image recognition, natural language processing, and many other fields. Several approaches tackling the fine-grained classification problem are proposed. To further demonstrate the different designs of these solutions, in this paper, fine-grained identification methods are compared and analyzed, among which WS-DAN achieves better results and it is preferred to be an effective method, which is expected to be more widely used in this field.

Commodity storage and the cost of capital: Evidence from Illinois grain farms

AbstractCommodity inventories are the key state variable determining the magnitude of commodity price responses to supply and demand shocks. Many firms in commodity supply chains use storage, but we know little about which firms and why. The economic theory of storage asserts that firms in a competitive market for inventories will store based on current and expected market prices and the per‐unit cost of storing. We empirically test at the firm‐level the importance of one major cost of storage: the opportunity cost of capital used to value foregone revenue from deferred commodity sales. To do so, we use panel data from thousands of Illinois farms who hold inventories of corn and soybeans. Although interest rates as a measure of capital costs are unlikely to vary widely across firms in this context, we exploit variation in the weighted average cost of capital due to cross‐firm differences in capital structure. Using two‐way fixed effects regressions, we find a statistically significant average effect of capital costs on inventory that masks notable heterogeneity across firms. Panel quantile regressions reveal two groups of firms: one whose inventory holdings are responsive to changes in the opportunity cost of storage and another whose are not. Our results suggest some farms behave like the profit‐maximizing ones from theory but substantial inframarginal commodity inventories are held by farms for other reasons.

Research on Strategies to Enhance the International Communication of Chinese Culture ——Taking Advertisement Translation as an Example

Driven by global development strategies, both domestic and foreign enterprises tend to utilize international brand advertisements to enhance their visibility and expand their global businesses. Since its accession to the WTO, China has experienced rapid development. Therefore, for Chinese enterprises, the appropriate translation of their product advertisements directly impacts commodity sales and consumer’s brand awareness. Building upon intercultural communication theory, this thesis will further analyze the challenges in intercultural communication as perceived by professional translators and explore their specific solutions through a case study. Subsequently, this essay will provide comprehensive insights into strategies and techniques for translating contemporary Chinese local brand advertisements in the context of intercultural communication.

Contrasting Univariate and Multivariate Time Series Forecasting Methods for Sales: A Comparative Analysis

In commodity-based industries, accurate sales forecast is very important for effective inventory management and decision-making. Univariate and multivariate time series forecasting methods have been widely used to predict commodity sales. The purpose of this study is to make a comprehensive comparative analysis of these two methods under the background of commodity sales forecast. Firstly, the concept of time series prediction and its significance in the field of commodity sales are introduced. It emphasizes the challenges related to sales forecast, including demand fluctuation, seasonality and external factors affecting sales model. The forecasting methods of univariate time series, such as ARIMA, are discussed in detail, focusing on their ability to capture the time correlation in a single sales variable. In contrast, the multivariate time series prediction method considers the relationship between multiple variables. Vector autoregressive and multivariate extension of ARIMA. These techniques combine the interaction of various factors, including external influences, to improve the accuracy of prediction. In order to make a comprehensive comparative analysis, a data set including historical sales data of specific commodities is used. Both univariate and multivariate models are suitable for forecasting future sales, and their performance indicators are evaluated by MASE. The results show that although univariate models are easier to implement and explain, they often fail to capture the complex interdependence between different factors that affect sales. On the other hand, the multivariate model shows excellent prediction accuracy by integrating related variables and their dynamic relationships. However, they need more additional data and more complex modeling techniques. Finally, the research gives the practical significance and suggestions of choosing an appropriate forecasting method based on the characteristics of commodity sales data and the specific business environment. It emphasizes the importance of considering the accuracy and interpretability of the prediction model in practical application.

CISG의 한계와 UNIDROIT원칙의 적용가능성

It is necessary to present that the UNIDROIT principle can be applied to international commodity sales contracts through arbitral award on the limits of application of CISG, and to examine how it is applied. In the following, we will examine the applicability of the UNIDROIT principle as a supplement for defects in Article 7 of the CISG. The CISG is a hard law as a convention, but the biggest feature of the UNIDROIT principle is that it is not a convention like the CISG, so it does not need to be domesticized through ratification procedures in each country, and it is non-binding and soft law. The CISG can be legalized domestically through ratification by the state, but the UNIDROIT principle cannot be legalized domestically through ratification. This characteristic allows the UNIDROIT principle to define matters wider than CISG. In addition, the UNIDROIT principle is based on universality and equity in domestic transactions, so it is highly applicable. Through a comparative legal review of the CISG’s defect supplementation regulations, we intend to clarify the defect regulations to promote predictable application of the law to international transactions with high uncertainty. The principles of CISG and UNIDROIT can be seen as a representative unified law applied to international commodity sales contracts. To this end, a comparative analysis was conducted with the UNIDROIT principle’s defect supplementation regulations, and as discussed in the attitude of the arbitral award, this attempt can play a role in finding a solution to the extent that the UNIDROIT principle has not been resolved by CISG. Therefore, in order to adapt the Convention to new needs and to develop each arbitration case according to the practical situation through analysis, the interpretation and defect supplementation of the Convention will be an important means of UNIDROIT.

Machine Learning in the Prediction of Commodity Sales

Machine Learning in the Prediction of Commodity Sales

A New Feature Based Deep Attention Sales Forecasting Model for Enterprise Sustainable Development

In recent years, with the rise of the Internet, e-commerce has become an important field of commodity sales. However, e-commerce is affected by many factors, and the wrong judgment of supply and marketing relationships will bring huge losses to operators. Therefore, it is of great significance to establish a model that can effectively achieve high precision sales prediction for ensuring the sustainable development of e-commerce enterprises. In this paper, we propose an e-commerce sales forecasting model that considers the features of many aspects of correlation. In the first layer of the model, the temporal convolutional network (TCN) is used to extract the deep temporal characteristics of univariate sales historical data, which ensures the integrity of temporal information of sales characteristics. In the second layer, the feature selection method based on reinforcement learning is used to filter the effective correlation feature set and combine it with the temporal feature after processing, which not only improves the amount of effective information input by the model, but also avoids the high feature dimension. The third layer of the reformer model learns all the features and pays different attention to the features with different degrees of importance, ensuring the stability of the sales forecast. In the experimental part, we compare the proposed model with the current advanced sales forecasting model, and we can find that the proposed model has higher stability and accuracy.

Procurement Volume Prediction of Cross-Border E-Commerce Platform Based on BP-NN

For logistics management, predictive analysis has never been more crucial than it is now, thanks to the vast number of deal data generated every second by electronic commerce. In an effort to enhance customer service and supply control, e-commerce businesses are progressively utilizing machine learning technologies to enhance projections. The back-propagation neural network (BP-NN) model is used to develop a C-A-BP forecasting model that considers commodity sales characteristics and the data series’ trend. In order to predict each cluster, a C-BP-NN model is first created, adding sales information as influencing elements into the C-BP-NN model. An A-BP-NN model is used in combination with the ARIMA that is employed for the linear component. These two forecasting models are merged to provide the final results. By comparing the results of the ARIMA, BP-NN, A-BP-NN, and C-BP-NN using the information provided by Jollychic’s cross-border platform, the A-C-BPNN was shown to be the best.

Aspect sentiment analysis with heterogeneous graph neural networks

Aspect-based sentiment analysis technologies may be a very practical methodology for securities trading, commodity sales, movie rating websites, etc. Most recent studies adopt the recurrent neural network or attention-based neural network methods to infer aspect sentiment using opinion context terms and sentence dependency trees. However, due to a sentence often having multiple aspects sentiment representation, these models are hard to achieve satisfactory classification results. In this paper, we discuss these problems by encoding sentence syntax tree, words relations and opinion dictionary information in a unified framework. We called this method heterogeneous graph neural networks (Hete_GNNs). Firstly, we adopt the interactive aspect words and contexts to encode the sentence sequence information for parameter sharing. Then, we utilized a novel heterogeneous graph neural network for encoding these sentences’ syntax dependency tree, prior sentiment dictionary, and some part-of-speech tagging information for sentiment prediction. We perform the Hete_GNNs sentiment judgment and report the experiments on five domain datasets, and the results confirm that the heterogeneous context information can be better captured with heterogeneous graph neural networks. The improvement of the proposed method is demonstrated by aspect sentiment classification task comparison.

Some approaches to increasing the turnover of a small commercial enterprise

The article highlights the measures aimed at increasing the turnover of a commercial enterprise. The whole set of measures that contribute to the increase in the turnover of a trading enterprise can be combined into two groups: internal and external. Internal events are aimed at increasing the conversion of sales and the average check, while external ones are aimed at increasing customer flow. Tools for internal measures to increase turnover include, firstly, measures aimed at improving the efficiency of retail processes, improving the technology of commodity display and sales of goods, and, secondly, tools for rationalizing the use of the existing material and technical base and increasing inventory turnover. As part of external events, an increase in client flow can be ensured through active marketing and promotional activities of the organization. The purpose of these events is to activate the already existing customer coverage area, as well as its expansion by attracting potential buyers from more remote areas.

Fast clustering algorithm of commodity association big data sparse network

How to dig out the business perspectives and market rules behind commodity transaction data, explore the relationship between commodities, so as to more scientifically and rationally classify and promote commodity categories and improve commodity sales performance for e-commerce companies has become a recent research hotspot. To this end, this paper proposes to use clustering algorithm to explore the hidden laws of commodity-related big data. This article first consults a large amount of information through the literature survey method, systematically summarizes the relevant theoretical knowledge of the association rule method and clustering algorithm and gives a detailed introduction to its application in the commodity association big data mining. The research in this area has laid a sufficient theoretical foundation; after that, the Apriori algorithm in the association rules and the K-means algorithm in the clustering algorithm were used to carry out the fast clustering algorithm experiment of the commodity-related big data sparse network and the commodity transaction data was introduced in detail. The process of association analysis and cluster analysis; then taking China’s well-known e-commerce platform Jingdong Mall as an example, by investigating the commodity transaction records of Jingdong Mall in the 4th week of July, the association and cluster analysis of its commodity transaction data were found. Among them, mobile phones and Bluetooth earphone, laptops and Bluetooth earphone, laptops and hard disks have the highest correlation and their confidence thresholds have reached 25%, 35 and 40% respectively. Finally, when the clustering results were tested, they were also found in the store. Strengthening the push and shopping guide of highly relevant product combinations on the website pages will increase the sales of products.

МОНІТОРИНГ РОЗВИТКУ АГРОПРОМИСЛОВОГО КОМПЛЕКСУ УКРАЇНИ

The purpose of the article is to monitor the development of the agro-industrial complex of Ukraine, to identify problems related to the further development of the industry. The indicators of the development of the agro-industrial complex in Ukraine were analyzed, namely: the sown area and the gross harvest of agricultural crops; the number of farm animals and the production of the main types of animal husbandry products; commodity structure of foreign trade of Ukraine; indicators of the development of the food industry of Ukraine; dynamics and structure of export of finished food products; indicators of export and import of ready-made food products. The conclusions were drawn that: according to many indicators, cultivated areas in Ukraine are increasing, but there are also indicators that are decreasing (TOP-3 regions: Poltava, Ternopil and Vinnytsia); there is also a positive trend in terms of increasing the gross collection of agricultural crops in Ukraine in 2021 (Top-5 leaders: Poltava, Kharkiv, Chernihiv, Vinnytsia and Sumy regions); animal husbandry in Ukraine has serious prospects for development in view of the shortage of milk and meat, but it needs the support of the state, since it directly affects the food security of the state; the number of dairy cows has decreased, which leads to an increase in the price of whole milk and a shortage of raw materials for processors; the largest share of exports is crop production; agricultural producers of Ukraine need additional financing; recently, in the structure of Ukraine's exports, the share of goods with a high added value, primarily readymade food products, is decreasing, while their share in imports is increasing, forming a growing negative balance. It is noted that the export and production of goods with a low degree of processing leads to a crisis, that is, a decrease in income from each unit of commodity sales, as enterprises that produce products with a high added value prevail. It can be concluded that deindustrialization dictates the conditions for the transition of Ukraine, as an agrarian country, to a country with a technological process of deep processing and production of innovative products. A complete reorientation and changes in the strategic planning of the country's economic development are necessary. The state policy of the country should be aimed at promoting the development of agriculture, namely the production of goods with high added value (high-tech and innovative products).

Организационный механизм трансграничной торговли сырьевыми товарами

The article analyzes the basic strategies and business models of international commodity trade. The success factors of the organization of an effective system of commodity sales are highlighted: 1) a reliable network of global communication, which is provided by highly qualified personnel; 2) the ability to attract resources in international financial markets; 3) control over the objects of the basic logistics infrastructure of cross-border trade; 4) timely digital transformation of business. The article concludes that the current organizational mechanism for cross-border commodity trade is based on the following key success factors: an effective network of global business contacts, access to Bank financing and risk hedging tools, qualified personnel, and effective digitalization of business processes. The intersection of the competencies that lie in these planes allows us to obtain a stable competitive advantage in the most important commodity markets for the world economy. From a practical point of view, the greatest synergy of the key success factors of cross-border trade is achieved in the main hubs, which is important to take into account when implementing projects to build organizations that are competitive in foreign markets.

Study on convolutional neural network and its application in data mining and sales forecasting for E-commerce

In recent years, the rapid development of e-commerce has brought great convenience to people. Compared with traditional business environment, e-commerce is more dynamic and complex, which brings many challenges. Data mining technology can help people better deal with these challenges. Traditional data mining technology cannot effectively use the massive data in the electricity supplier, it relies on the time-consuming and labour-consuming characteristic engineering, and the obtained model is not scalable. Convolutional neural network can effectively use a large amount of data, and can automatically extract effective features from the original data, with higher availability. In this paper, convolutional neural network is used to mine e-commerce data to achieve the prediction of commodity sales. First, this article combines the inherent nature of the relevant merchandise information with the original cargo log data that can be converted into a specific “data frame” format. Raw log data includes items sold over a long period of time, price, quantity view, browse, search, search, times collected, number of items added to cart, and many other metrics. Then, convolutional neural network is applied to extract effective features on the data frame. Finally, the final layer of the convolutional neural network uses these features to predict sales of goods. This method can automatically extract effective features from the original structured time series data by convolutional neural network, and further use these features to achieve sales forecast. The validity of the proposed algorithm is verified on the real e-commerce data set.