Vegetable Sales Research Articles

Implementation of a UK supermarket intervention to increase purchasing of fresh fruit and vegetables: process evaluation of the WRAPPED natural experiment.

Placement interventions, characterised by greater availability and more prominent positioning of healthy food products in supermarkets and other food stores, are associated with healthier patterns of purchasing and diet. The WRAPPED intervention study is a natural experiment that aims to evaluate a supermarket placement intervention to improve fruit and vegetable sales, household purchasing and the dietary quality of women and their children. Process evaluation, alongside the evaluation of outcomes, is essential to understand how interventions are implemented, under what circumstances they are effective, and their mechanisms of impact. This study aimed to assess the implementation of the WRAPPED placement intervention. The study adopted a convergent mixed-methods design. Quantitative data extracted from study store planograms (visual representation of stores and product placement) before and after intervention implementation were used to assess the positioning of fresh fruit and vegetables in the first aisle from the front entrance (intervention dose). The availability of fresh fruit and vegetables in each study store was examined from stock-keeping unit (SKU) figures before and after intervention implementation. An intervention implementation survey (IIS) completed with store managers and senior supervisors before and 1- and 6-months post-intervention implementation enabled examination of the context across study stores. Semi-structured interviews with store managers and senior supervisors provided qualitative data about store staff experiences and perceptions of the intervention between 6-months post-intervention implementation. The placement intervention was implemented with close adherence to the study protocol. There were marked differences, post-intervention implementation, in the positioning of fresh fruit and vegetables in intervention stores compared with control stores: median distance in intervention stores was 8.0m (IQR 5.0 to 10.0) compared with 23.8m (IQR 21.0 to 30.0) in control stores (P < 0.0001). The availability of varieties of fresh fruit and vegetables increased in intervention stores post-intervention compared with control stores: median (IQR) among intervention stores was 72 (51, 84) compared with 56.5 (50, 62) in control stores (P = 0.03). The mean change from baseline to post-implementation in number of different fruit and vegetables available in intervention stores was 15.3 (SD 16.7) (P = 0.01). IIS and interview data demonstrated little difference between intervention and store contexts over time. Reinforcing factors for intervention implementation included: head-office leadership, store staff views and attitudes and increased awareness of the importance of offering healthy food in prominent locations within stores. This study demonstrated that placement interventions which promote fresh fruit and vegetables to customers in discount supermarkets can be implemented effectively. These findings are encouraging for the implementation of national food policies which modify retail environments to improve population purchasing and dietary patterns. NCT03573973; Pre-results.

Application of Linear Programming in Automatic Pricing and Replenishment Strategies for Vegetable Commodities

Vegetable commodities are indispensable parts of the current consumer market. Vegetables possess different characteristics such as seasonality, perishability and so on. Thus, it is critical to formulate reasonable automatic pricing and replenishment strategies for vegetable commodities. However, previous kinds of literature have either ignored some of the attributes of vegetables or restricted their application regions to some specific situations. This review compares the pros and cons of diverse linear programming methods and models in previous research, synthesizes the core method, and finally concludes with two relatively reasonable algorithms for pricing and replenishment strategies. It also gives an evaluation of these two algorithms and proposes suggestions for improvement. The purpose of this review is to provide guidelines for subsequent research to optimize existing methods and models to work out more objective and accurate optimal strategies. Ultimately, vegetable sales and supply can better meet the market demand, which can help rejuvenate economic vitality and bring in higher profits at the same time.

Research on Vegetable Sales and Pricing Based on ARIMA Model and Regression Analysis

Research on Vegetable Sales and Pricing Based on ARIMA Model and Regression Analysis

Automated pricing and replenishment forecasting for vegetable items

Firstly, the thesis extensively analysed and processed the vegetable sales data of a fresh food superstore. It integrated the product information and sales data of each vegetable category, and carried out data cleaning and analysis through Excel tools, including sorting out the outliers, listing the sales volume of each of the six categories, using pivot tables for statistical analysis, and establishing line graphs, heat maps, scatter plots, and other visual displays of the data characteristics and correlations. Secondly, the thesis investigated the relationship between sales volume and pricing, fitted the correlation between total sales volume and pricing using a random forest regression model, and predicted the daily replenishment volume for the coming week using an LSTM time series model. It also fitted quantitative fitting formulas for metrics for each vegetable category, classified profits into normal sales volume profits and discounted sales volume profits, and ultimately maximised profits through dynamic programming models and particle swarm optimisation algorithms. Finally, the thesis considered the impact of other factors such as weather, holidays, seasons and market environment on sales volume with specific data collection and analyses that help guide better pricing strategies. The findings will contribute to a better understanding of vegetable sales behaviour and optimise superstore operational strategies.

Research on the social functions of trade to support the development of marketability of farm products

In recent years, there has been a problem of reducing the marketability of agricultural products of farms for many types of goods. Among the key food products supplied by farmers, the negative dynamics of sales was noted for the sale of potatoes and vegetables. This situation is largely related to the annual fluctuations in agricultural production, which is especially evident in the cultivation of fruit and vegetable crops. Using econometric modeling based on the construction of regressions based on panel data, the article shows the negative impact of changes in production volumes on the percentage of marketability of farm products. Farmers, like other small-scale enterprises, do not have a developed marketing infrastructure, unlike agricultural organizations. Therefore, farms are particularly sensitive to fluctuations in production volumes, which create for them the problem of lack or excess of resources for sales. An important way to solve this problem is the activity of trade organizations aimed at implementing social functions to support the sale of farm products. In Russia, such social practices are being implemented by the largest representatives of the retail industry in a pilot mode in certain regions of the country. In particular, in some areas, agricultural aggregators are operating, launched by the “X5 Group” and “Magnit” retail chains, which perform technological and marketing functions to ensure the sale of farm goods. There are other social projects of trade organizations, for example, “Agrocontract” and “Farmer›s Island”, initiated by “X5 Group”, which involve the conclusion of direct contracts with farmers in order to reduce the purchase prices of goods and provide for the purchase of various unique goods from farmers. The expansion of the social functions of trade organizations will significantly overcome the existing problems of marketing farm products and provide such farms with sufficient income and profit for further self-development of activities.

Research on Vegetable Sales Strategies Based on Multi-objective

Research on Vegetable Sales Strategies Based on Multi-objective

A Study on Vegetable Sales Forecasting Based on Seasonal Indices and LSTM Model

A Study on Vegetable Sales Forecasting Based on Seasonal Indices and LSTM Model

The Level of Closed Relationship of Innovation with Hydroponic Vegetable Sales in Bogor City

The Level of Closed Relationship of Innovation with Hydroponic Vegetable Sales in Bogor City

Statistical Forecasting of Fresh Vegetable Sales Trends: Application of Correlation Analysis and ARIMA Model

Statistical Forecasting of Fresh Vegetable Sales Trends: Application of Correlation Analysis and ARIMA Model

Optimization Of Vegetable Sales Strategies and Daily Replenishment in Supermarkets

Optimization Of Vegetable Sales Strategies and Daily Replenishment in Supermarkets

Research on Forecasting Vegetable Sales and Prices Based on Time Series Models

Research on Forecasting Vegetable Sales and Prices Based on Time Series Models

Fusion Of SSA Mahjong Search and LSTM Model for Vegetable Sales and Pricing Research

Fusion Of SSA Mahjong Search and LSTM Model for Vegetable Sales and Pricing Research

An Investigation into the Pricing and Replenishment Strategy of Vegetable Commodities in Fresh Food Shops

Fresh supermarkets face challenges in marketing and inventory management due to the short shelf life and high loss rate of fresh vegetables. This necessitates the immediate development of a practical replenishment plan and pricing strategy. The paper commences by analyzing the sales distribution patterns of various vegetable categories, identifying leaves and flowers as having notably higher sales compared to other categories. Subsequent examination of the sales trend over time reveals variations in the sales of different categories or individual vegetables across months or quarterly periods, indicating a degree of seasonality. This study calculates the Spearman correlation coefficient for each category and individual product sales, presenting the results in a heat map. Findings indicate a significant positive correlation among all vegetable categories except for solanaceae, implying bundled sales or a consumer tendency to purchase specific vegetable categories concurrently. Consequently, when formulating replenishment and pricing strategies, considering a rational sales mix is crucial. The paper constructs a linear regression model, solving the equation using the least squares approach, and identifies a negative association between sales volume and cost-plus pricing. To accommodate seasonal patterns, the Prophet time series model forecasts wholesale prices for various vegetable categories during July 1-7, 2023. The objective is to maximize profit by accurately predicting vegetable costs. A nonlinear programming model is formulated to achieve this objective, determining the maximum revenue from days 1 to 7 as ￥(108953.65 - K), where K represents the fixed indirect cost. The model optimizes replenishment and pricing strategies for different categories.

Research on Dynamic Pricing Strategy of Fresh Supermarket Based on ARIMA and Integer Programming Model

The study of automatic pricing and replenishment decision-making of vegetable products is a key measure for modern fresh food merchants to pursue high efficiency, accuracy and competitiveness. In this paper, the time series ARIMA(p,d,q) model and 0-1 integer programming model were used to realize the intelligent replenishment and pricing decision of fresh supermarkets, so as to ensure the reasonable pricing and sufficient inventory of vegetable products and meet the changing market demand. This paper obtains the replenishment strategy in the next week and the maximum profit of vegetable sales in Supermarket on July 1 is 3831.2 RMB.

Research on the correlation and distribution law of commodities

In the fresh supermarket, the supermarket will generally make replenishment plans according to the historical sales and demand of each commodity. There is a certain correlation between the sales volume of different commodities. It is crucial to deeply understand the distribution law and correlation relationship of commodity sales to optimize the replenishment strategy. In this paper, Spearman correlation coefficient analysis and entropy weight method-TOPSIS model are constructed to analyze the distribution law and correlation relationship of vegetable products and single product sales volume. First of all, this paper aggregates the sales volume of different vegetable categories and single items by month to get the average monthly sales volume. The average monthly sales data of each vegetable category was visualized, and the distribution pattern of each vegetable category was analyzed. The relation of vegetable categories was analyzed using Spearman's correlation coefficient. Then, for vegetable items, the TOPSIS model based on entropy weight method was constructed, and the 8 most representative items were found among 251 vegetable items. Finally, the average monthly sales data of the selected vegetable products was visualized, the distribution pattern of the sales of each vegetable product was analyzed and the correlation coefficient of Spearman was used to analyze the sales of different vegetables.

Replenishment Pricing of Vegetable Commodities in a Supermarket as an Example in China

In this paper, for the automatic pricing and replenishment decision-making problem of vegetable commodities, it is found that there are certain seasonal characteristics in the sales of vegetables in each category, and wavelet analysis is carried out on the daily sales data of single products to get the sales cycle of each category. For the vegetable single product with large amount of data, all single products are divided into four categories of common household vegetables, packaged vegetables, seasonal special vegetables and special vegetables by systematic cluster analysis, and the time distribution characteristics of single products are described by the sales time series of typical members in each cluster. Based on the market demand theory, the cost-plus pricing and sales volume data of the categories were fitted, and the relationship was obtained to be negatively correlated. Taking the purchase quantity and pricing of each category as the decision variables, maximizing revenue as the objective function, and taking the historical maximum purchase quantity of each category and the historical maximum purchase quantity of the superstore as the constraints, a nonlinear programming model was established to formulate the pricing and replenishment strategies for vegetable products. By solving the model, the optimal strategy of replenishment volume and pricing for the future seven-day category is derived, corresponding to the maximum profit of ￥3124.75.

The Relationship Between Vegetable Sales Volume and Cost-Plus Pricing Strategies: A Multi-Method Analysis and Forecast

In today's highly competitive supermarket industry, optimizing sales strategies for perishable goods such as vegetables is crucial for maintaining profitability and market share. This study provides an analytical framework for optimizing vegetable sales strategies in supermarkets, focusing on perishability and consumer behavior. Through regression analysis, it explores the sales volume and pricing relationship, identifying the most effective model with a Mean Squared Error (MSE) criterion. Time series models forecast future needs, reflecting on historical sales' cyclical and seasonal trends. A Particle Swarm Optimization (PSO) algorithm, tailored with constraints, offers predictions on sales volumes and pricing, aiming to enhance profitability. Iterative refinements reveal that the model achieves stability and accuracy in predicting profits. The findings emphasize the critical role of data-driven strategies in the competitive supermarket sector, advocating for adaptive approaches in response to evolving market conditions.

Study on the pricing and replenishment of vegetable commodities based on the optimization model

Based on the problems of short shelf life, inventory control and pricing management, marketing, economics, operation research, and the control optimal theory, this paper establishes the regression prediction analysis model; establish the optimization model, formulate the total amount of daily replenishment; analyze the deficiencies of the model. Finally, the combination of the research results gives the replenishment and pricing decision to improve the revenue of the supermarket. In this study, the correlation degree between the two variables of each vegetable category and the single product sales volume was measured, and the time series analysis was used to identify the sales trend. Then according to the correlation analysis of descriptive statistics, using cosine similarity formula, analyze the data units, the total sales dimension normalization, using the method of standardize the correlation between item and category, using pearson correlation coefficient and Spearman correlation coefficient to study the relationship between category and sales, establish Spearman correlation coefficient to determine the final correlation coefficient, cluster analysis of each category, analysis of the correlation. By processing the relevant data, establish the ARIMA time series model, forecast on July 1,2023-July 7, the relevant data, using the adftest function time series stability test, fit the ARIMA time series model, using differential sequence to time series analysis, calculate vegetable sales and sales price of elastic demand function, finally find the optimal solution through the particle swarm algorithm.

Vegetable Merchandise Analysis from a Quarterly Sales Data Perspective

In the field of fresh vegetable retailing, the category diversity and time-sensitive characteristics of commodities pose unique challenges to inventory management and pricing decisions. Comprehensively analyzing the distribution pattern of sales volume of different categories and individual items of vegetable commodities and their interrelationships is of great significance to retailers' scientific replenishment and pricing decisions. By categorizing vegetable sales every quarter, a comprehensive analysis is carried out on a category and individual item basis. Visualization was used to show the distribution pattern of sales volume by category and individual item. Subsequently, Kendall correlation analysis was utilized to explore the relationship between the sales volume of different vegetable categories. In addition, the interrelationships between the sales volumes of different vegetable individual items were investigated in conjunction with waterfall diagrams. Through in-depth analysis of sales data, understanding the sales relationship between different vegetable categories can help retailers develop a more refined pricing strategy, make pricing adjustments based on the relative sales of goods, and improve sales revenue and profit levels. Such scientific inventory management and pricing strategies not only help meet customer demand and enhance customer experience, but also strengthen customer loyalty and promote long-term stable business development.

Инновационное развитие овощеводства

The article provides an overview of innovations in the technological process of production and sale of vegetables - seed preparation, fertilization, weed control. Innovation is a decisive factor in the technological, economic and social development of an economic entity. In dynamics, the innovative activity of organizations in agriculture is increasing, although lower than in the economy as a whole. It was concluded that the state should become the coordinator of innovative processes in the industry.