Sales Of Items Research Articles

A IMPORTÂNCIA DO PLANO DE NEGÓCIO PARA O MICROEMPREENDEDOR INDIVIDUAL NA ABERTURA DE LOJA DE COSMÉTICOS

Objective: Emphasize the importance of the business plan for Individual Microentrepreneurs when opening a cosmetics store. Methods: This is bibliographical research, where data was collected by searching for articles in the Acervo Mais and Google Scholar databases, applying the filter of works in Portuguese, published in the last 5 years. For analysis of the results and discussion, five complete works were selected. Results: When you prepare a business plan you have a clearer and more organized view of the enterprise, making it easier to study your objectives and measure your results and put your ideas into practice. It was possible to identify that the perfumery and cosmetics sector is a great opportunity for Individual Microentrepreneurs and the business becomes more attractive and profitable with the sale of perfumes, makeup items and hair products. Final Considerations: It is viable to open a cosmetics store, due to the great customer demand and wide variety of products, even with competition, but it is essential to develop a business plan for this venture.

Smart agriculture assurance: IoT and blockchain for trusted sustainable produce

Current conventional agricultural practices have exacerbated the depletion of soil, loss of biodiversity, and intensified the substantial challenges of food security including monoculture dependence, resource intensiveness, access and distribution issues. This has raised consumers awareness towards monitoring the resources of their food consumption. Nevertheless, greenwashing and deceptive food labels are prevalent leading to sales of items with misaligned labels, underpinning the need to authenticate, trace, and track the agricultural supply chain from the farm to shop. Consumers should be able to select fresh produce and verify its origin through a transparent, trustworthy, accountable, and reliable process. This paper introduces a blockchain-based solution that leverages intrinsic features of this innovative and disruptive technology to harness transparency, reliability, authenticity, and accountability within an agricultural supply chain. Additionally, our solution eliminates misleading food labels on fresh produce by enabling consumers to reliably trace and verify the origin and authenticity of the food source in a transparent manner. Through the integration of the Internet of Things (IoT), the well-being of the soil, air, and crops is consistently monitored. The sustainable solutions employed are transparent, aligning with the standards set by various sustainable food certifications and agencies, including organic, non-Genetically Modified Organisms (GMO), and Good Agricultural Practices (GAP). The paper presents detailed algorithms that represent the smart contract code, sequence diagrams along with testing results and security analysis. The smart contract code is made publicly available on GitHub.

Effect of Covid-19 on Female-Headed Farming Household Food Security in Northern Bahr El Ghazal State, South Sudan

At the beginning of 2020, global food security was interrupted by the unprecedented occurrence of the COVID-19 pandemic and its associated restrictions to contain it. In South Sudan, women were among the most vulnerable groups due to restrictions and standard operating procedures (SoPs). This study was conducted to determine the effect of COVID-19 on the food security of female-headed households from three counties in Northern Bahr el Ghazal state, South Sudan. The analysis of the food security status was based on the Consolidated Approach for Reporting Indicators of Food Security (CARI) and food security was viewed in terms of availability of, and accessibility to food among households. Findings revealed that only 15.5% of the female-headed households sampled were food secure while 84.5% of them were food insecure on the 2-level scale. There was a significant reduction in average household income, size of land under agricultural production and crop harvest. Households lost access to produce markets, supermarkets, agro-input markets and produce stores, lowering their food production potential and farm output. Coping strategies included sale of household items, finding other work to supplement their agricultural activities, revision of household food consumption tendencies, new sales channels, while some of the households did nothing. These strategies are indicators of poverty, hunger and food insecurity which can lead to malnutrition. The study concluded that food security among female-headed households is low post-COVID, pushing women into life-threatening coping strategies in South Sudan. Development efforts in the country should be directed towards supporting recovery and resilience systems among these households in the country

Looking at the bid picture: A framework for identifying reverse auctions in ecological systems.

Biological market theory can be used to explain intraspecific cooperation, interspecific mutualism, and sexual selection through models of game theory. These models describe the interactions between organisms as two classes of traders (buyers/sellers) exchanging commodities in the form of goods (e.g. food, shelter, matings) and services (e.g. warning calls, protection). Here, we expand biological market theory to include auction theory where bidding serves to match buyers and sellers. In a reverse auction, the seller increases the value of the item or decreases the cost until a buyer steps forward. We provide several examples of ecological systems that may have reverse auctions as underlying mechanisms to form mutualistic relationships. We focus on the yellow baboon (Papio cynocephalus) mating system as a case study to propose how the mechanisms of a reverse auction, which have the unintended but emergent consequence of producing a mutually beneficial outcome that improves collective reproductive benefits of the troop in this multi-female multi-male polygynandrous social system. For the yellow baboon, we posit that the "seller" is the reproductively cycling female, and the "buyer" is a male looking to mate with a cycling female. To the male, the "item for the sale" is the opportunity to sire an offspring, the price is providing safety and foraging time (via consortship) to the female. The "increasing value of the item for sale" is the chance of conception, which increases with each cycle since a female has resumed cycling post-partum. The female's sexual swelling is an honest indicator of that cycle's probability of conception, and since resident males can track a female's cycle since resumption, there is transparency. The males presumably know the chance of conception when choosing to bid by offering consortship. Across nature, this reverse auction game likely exists in other inter- and intraspecific social relationships. Considering an ecological system as a reverse auction broadens our view of social evolution and adaptations through the lens of human economic structures.

Bigmart Sales Prediction Based on Voting Classifier Algorithm and Linear Regression

Machine Learning is a technology that allows machines to become more accurate in predicting outcomes without being explicitly programmed for it. The basic premise of machine learning is to build models and deploy algorithms that can receive input data and use statistical analysis to predict an output while modifying outputs as the new data becomes available. These models can be used in different areas and trained to match the expectations so that accurate steps can be taken to achieve the organization’s target. In this paper, the case of Big Mart Shopping Centre has been discussed to predict the sales of different types of items and for understanding the effects of different factors on the sales of different items. Taking various features of a dataset collected for Big Mart, and the methodology followed for building a predictive model, results with high levels of accuracy are generated, and these observations can be used to take decisions to improve sales. Key words : Machine Learning, Sales Prediction, Big Mart, Voting classifier algorithm, Linear Regression.

Research on the Optimization of Pricing and the Replenishment Decision-Making Problem Based on LightGBM and Dynamic Programming

Vegetables have a short period of freshness, and therefore, the purchase of vegetables has to be carefully matched with sales, especially in the “small production and big market” setting prevalent in China. Therefore, it is worthwhile to develop a systematic and comprehensive mathematical model of replenishment plans and pricing strategies for each category of vegetables and individual products. In this paper, we analyze the following three questions: Question One: What is the distribution law and relationship between the sales volume of vegetable categories and single products? Question Two: What is the relationship between total sales volume and cost-plus pricing of vegetable categories? And is it possible to provide the daily total replenishment and pricing strategy of each vegetable category for the following week to maximize supermarket profit? Question Three: How can we incorporate the market demand for single vegetable products into a profit-maximizing program for supermarkets? Is it possible to further formulate the replenishment plan requirements for single products? To answer the first question, we created pivot tables to analyze occupancy. We found that mosaic leaves, peppers, and edible mushrooms accounted for a larger proportion of occupacy, while cauliflowers, aquatic rhizomes, and eggplants accounted for a smaller proportion. For the single items, lettuce, cabbage, green pepper, screw pepper, enoki mushroom, and shiitake mushroom accounted for a large proportion of their respective categories. We used the Pearson correlation coefficient and the Mfuzz package based on fuzzy c-means (FCM) algorithm to analyze the correlation between vegetable categories and single products. We found that there was a strong correlation between vegetable categories. Moreover, the sale of vegetable items belonging to the same category exhibited the same patterns of change over time. In order to address the second question, we established the LightGBM sales forecasting model. Combined with previous sales data, we forecasted and planned an efficient daily replenishment volume for each vegetable category in the coming week. In addition, we developed a pricing strategy for vegetable categories to maximize supermarket profits. For the third question, we built a dynamic programming model combining an optimal replenishment volume with a product pricing strategy for single items, which let the supermarket maximize its expected profits.

Non-excludable Bilateral Trade between Groups

Bilateral trade is one of the most natural and important forms of economic interaction: A seller has a single, indivisible item for sale, and a buyer is potentially interested. The two parties typically have different, privately known valuations for the item, and ideally, they would like to trade if the buyer values the item more than the seller. The celebrated impossibility result by Myerson and Satterthwaite shows that any mechanism for this setting must violate at least one important desideratum. In this paper, we investigate a richer paradigm of bilateral trade, with many self-interested buyers and sellers on both sides of a single trade who cannot be excluded from the trade. We show that this allows for more positive results. In fact, we establish a dichotomy in the possibility of trading efficiently. If in expectation, the buyers value the item more, we can achieve efficiency in the limit. If this is not the case, then efficiency cannot be achieved in general. En route, we characterize trading mechanisms that encourage truth-telling, which may be of independent interest. We also evaluate our trading mechanisms experimentally, and the experiments align with our theoretical results.

How Can Price Promotions Make Consumers More Interested? An Empirical Study from a Chinese Supermarket

Price promotions are commonly employed to enhance supermarket performance and the sustainable development of the retail industry, yet their effectiveness may vary among similar supermarket chains. In contrast to Western countries, Chinese supermarkets are typically community-centered, allowing consumers to make frequent visits due to lower transaction costs. This multiple-visit pattern discourages substantial one-time purchases based on promotions. This study aims to investigate how pricing promotions can attract consumers more effectively and which product categories are most suitable for this purpose. Utilizing scanner data from Chinese chain supermarkets, we empirically assess the impact of promotion depth, breadth, and duration on consumer purchasing behavior using fixed effects models, IV, and GMM methods. Furthermore, we identify product category characteristics that are more appealing to consumers based on the relationships between different product category promotions and consumer behavior. Results demonstrate that each of the three price-promotion features has a positive effect on Chinese supermarket performance, with varying degrees of significance. Different promotion methods not only benefit promoted products but also stimulate sales of non-promotional items. At the product level, the impact of supermarket promotions on performance differs across categories. The most attractive category in terms of consumer purchases influenced by discounts is special paper, while small kitchen appliances have the least impact. Promoting categories with lower average prices, higher average sales volumes, fewer products, and better storage durability is conducive to attracting consumer shopping. These empirical findings have implications for academic research on price promotion theory and supermarket managers’ pricing strategy decisions, as well as the sustainable development of the offline retail industry.

E-Coin-Based Priced Oblivious Transfer with a Fast Item Retrieval

Priced oblivious transfer (POT) is a cryptographic protocol designed for privacy-preserving e-commerce of digital content. It involves two parties: the merchant, who provides a set of priced items as input, and a customer, who acquires one of them. After the protocol has run, the customer obtains the item they chose, while the merchant cannot determine which one. Moreover, the protocol guarantees that the customer gets the content only if they have paid the price established by the merchant. In a recent paper, the authors proposed a POT system where the payments employed e-coin transactions. The strong point of the proposal was the absence of zero-knowledge proofs required in preceding systems to guarantee the correctness of payments. In this paper, we propose a novel e-coin-based POT system with a fast item retrieval procedure whose running time does not depend on the number of items for sale. This is an improvement over the aforementioned existing proposal whose execution time becomes prohibitively long when the catalog is extensive. The use of zero-knowledge proofs is neither required.

TREND THRIFTING SEBAGAI ALTERNATIF OUTFIT KULIAH MAHASISWA FAKULTAS ILMU SOSIAL UIN SUMATERA UTARA

Thrifting, or purchasing second-hand items, has become a popular cultural phenomenon in Indonesian society, particularly among budget-constrained students. This research employs a qualitative descriptive approach using in-depth interviews, observations, and literature studies to comprehend the impact and motivations of students adopting the thrifting lifestyle. The findings reveal that thrifting not only serves as an economic solution for students but also fosters unique and creative fashion styles. Additionally, this activity positively influences the environment by reducing waste and supporting charitable causes through the sale of second-hand items by non-profit organizations. Thrifting emerges as an alternative that allows students to stay up-to-date with fashion trends without compromising their budget balance. In conclusion, thrifting is more than a shopping activity; it is a lifestyle that reflects creativity, social responsibility, and the need to adapt to changing fashion trends.

Application analysis of data mining in shopping APP

Dingdong Buying Vegetable is a cohort of emerging entities that have swiftly gained prominence within the business domain in recent times. The data mining function of its data platform APP is undeniably linked to the underlying factor contributing to its commercial success. This article examines the fundamental principles, practical manifestations, and pertinent research instances of data mining. It specifically centers on the primary interface of the Dingdong Buying Vegetable APP, scrutinizing its design and distinctive attributes tailored to specific customers. Furthermore, it conducts an in-depth analysis of the correlation between the platforms commercial success and its data mining functionality. The primary aspect in which the data mining function of the Dingdong Buying Vegetable APP is expected to be manifested is through extensive data mining. The process of mining client data and conducting a full comparison and analysis of sales data for all products sold is a highly intricate and exhaustive endeavor. The use of these data mining functions serves multiple purposes. Firstly, it efficiently identifies customers genuine requirements on the client side, enabling the recommendation of suitable products and fostering customer reliance on the application. Additionally, it facilitates accurate decision-making in marketing products on the product side by comparing and analyzing diverse data pertaining to the sold products. This approach helps prevent the sale of unsought items and effectively minimizes company expenditure.

Increasing the Sale of Long Tail Items on E-Commerce Websites

The advent of e-commerce has transformed the retail industry, offering a platform for retailers to reach a vast audience, enabling consumers to shop from anywhere in the world at any time. However, most e-commerce websites are dominated by sales of popular or mainstream items, referred to as the head of the distribution. The long-tail items, which make up the tail of the distribution, are often overlooked, leading to reduced sales and low revenue for online retailers. This research paper aims to explore strategies that online retailers can use to increase the sale of long-tail items on their websites. It also includes a review of existing literature, a qualitative analysis of consumer behavior, and a quantitative analysis of sales data. The findings indicate that online retailers can increase the sale of long-tail items by optimizing their website design, improving search functionality, using data-driven pricing strategies, and implementing targeted marketing campaigns. These strategies have the potential to improve the visibility of long-tail items, increase consumer engagement, and boost revenue for online retailers. A study has also been conducted on the Retail Rocket e-commerce dataset from Kaggle which involved a meticulous examination of various aspects of user interactions within the online retail platform. The dataset provided a rich source of information, including events such as page views, add-to-cart actions, and completed purchases. The analysis aimed to uncover specific patterns and trends related to these events, shedding light on how users engage with both popular and long-tail items.

PU-Detector: A PU Learning-based Framework for Real Money Trading Detection in MMORPG

Massive multiplayer online role-playing games (MMORPG) have been becoming one of the most popular and exciting online games. In recent years, a cheating phenomenon called real money trading (RMT) has arisen and damaged the fantasy world in many ways. RMT is the sale of in-game items, currency, or even characters to earn real money, breaking the balance of the game economy ecosystem and damaging the game experience. Therefore, some studies have emerged to address the problem of RMT detection. However, they cannot well handle the label uncertainty problem in practice, where there are only labeled RMT samples (positive samples) and unlabeled samples, which could either be RMT samples or normal transactions (negative samples). Meanwhile, the trading relationship between RMTers is modeled in a simple way, leading to some normal transactions being falsely classified as RMT. In this article, we propose PU-Detector, a novel framework based on PU learning (learning from positive and unlabeled data) for RMT detection, considering the fact that there are only labeled RMT samples and other unlabeled transactions. We first automatically estimate the likelihood of one transaction being RMT by developing an improved PU learning method and proposing an assessment rule. Sequentially, we use the estimated likelihood as edge weight to construct a trading graph to learn trader representation. Then, with the trader representations and basic trading features, we detect RMT samples by the improved PU learning method. PU-Detector is evaluated on a large-scale real world dataset consisting of 33,809,956 transaction logs generated by 43,217 unique players. Compared with other approaches, it achieves the state-of-the-art performance and demonstrates its advantages in detecting underlying RMT samples.

Waqf Law is Relevant in Responding to the Challenges of the Times regarding the Law of Selling Waqf Goods

This research aims to provide a deeper understanding and offer solutions to the statements in the book. The research method used is qualitative research with a normative legal approach or literature research. Contextual analysis is carried out to understand and analyze in depth the contents of the book "Fathul Mu'in". The results of the review showed that the sale of waqf items in the book was not allowed, even if the items had been damaged. This finding confirms the importance of understanding the views of texts in various literatures and includes postulates and opinions of other scholars to provide solutions to problems that often arise in today's society. Shaykh Ahmad Zainuddin Al-Malibari is based on the law of the Ash-Shafi'i madhhab and prioritizes opinions that are in line with it in waqf law. However, the proposal of fiqh states that every matter must be adapted to its purpose. This approach is in line with the hadith which states that every deed depends on its intention. Some scholars also argue that the important thing is to preserve the good old and take the new better. Problem solving in religion basically aims for the benefit of religion and people in general.

Optimization of replenishment and sales strategy of vegetable products based on ARIMA time series model and combination sales

Due to the short shelf life of vegetable items, supermarkets usually restock daily based on the historical sales and demand of each item. In order to develop an optimal strategy for replenishment and sales of vegetable items, this study firstly predicted the total daily replenishment in the coming week using ARIMA time series model, and then this study took the hypermarket revenue as the objective function and considered the correlation between vegetable categories in the sales data, and adopted a combination sales strategy to increase the sales volume. This study establishes the combination sales formula, and uses the third-party library function combined with the genetic algorithm to obtain the optimal coefficient combination to maximize the superstore's revenue, and finally constructs the optimization model of pricing strategy for each category of vegetables, and solves the optimal pricing strategy, and ultimately obtains the daily replenishment of total amount of various categories of vegetables in the coming week.

Penerapan Sistem Informasi Geografis Dalam Pemetaan Toko Oleh - Oleh Khas Lombok

This research aims to produce a geographic information system with spatial data visualization containing the location of East Lombok souvenir shops, shop information and directions that will be presented to visitors or users. The research targets were carried out in several sub-districts in the East Lombok Regency area which have souvenir shops. This is an effort to introduce craft products and food and drinks that are characteristic of the East Lombok Regency area. The data used in this research is by collecting map data of the East Lombok Regency area using Google Map API and Google Map Direction Service, interviews with shop owners, data collection on coordinate points and several other supporting data. The result of this research is that a web-based geographic information system for East Lombok souvenir shops has been built using the Google Maps API which makes it easy for web users to find out the position of East Lombok souvenir shops, where there is shop information and shop position. , items for sale and directions to the desired store.

Importing Clothing Made from Recycled Textile Materials? A Study of Retailers’ Sourcing Strategies in Five European Countries

This study examined retailers’ sourcing strategies for clothing made from recycled textile materials in the five largest European clothing retail markets (i.e., the UK, Italy, France, Germany, and Spain), given the increasing availability of such products. The multivariate analysis of variance (MANOVA) was conducted based on the detailed country of origin and product assortment information of thousands of such clothing items for sale in these five EU countries from January 2021 to May 2023. The results show that EU retailers sourced clothing using recycled textile materials diversely from over 40 developing and developed countries across Asia, America, Europe, and Africa. The geographic location of supplying countries statistically affects the detailed assortment of recycled clothing that EU retailers import. However, apart from assortment diversity, no statistical evidence indicates that developing countries had advantages over developed ones regarding product sophistication, replenishment frequency, and pricing in the five EU markets. The study’s findings offer new insights into the business aspects of recycled clothing and reveal promising global sourcing opportunities for such products. The results also imply a potentially significant shift in global apparel trade patterns as retailers embrace fashion circularity and expand sourcing clothing made from recycled textile materials.

The Profit Pattern and Financial Impact of NetEase Cloud Music's Marketing Strategy

With the rise of short video platforms and the onset of the post-copyright era, the digital music industry is undergoing significant transformations. The competition within the industry has intensified, while user growth and conversion to paying users have encountered obstacles. As a leader in China's digital music industry, it is worth exploring how to overcome these challenges and identify new areas for growth. This paper employs literature research, case analysis, data analysis, and other research methods. Firstly, it analyzes NetEase Cloud Music's profit model based on subscription payments, advertising revenue, and sales of virtual items outlined in its prospectus. Secondly, it utilizes authoritative industry research reports to analyze NetEase Cloud Music's user positioning, consumer profile, and partnerships, simultaneously examining their marketing strategy through a case study involving Hangzhou Metro's collaboration with NetEase Cloud Music, known as "Reviews Train." By analyzing structural changes in financial data disclosed in annual performance announcements, this paper examines the impact of marketing strategies on revenue growth, cost control measures, and profitability, as well as explores how adopting diversified market strategies can enhance risk management capabilities and improve financial stability. Finally, this paper presents corresponding countermeasures and suggestions based on identified issues from previous analyses along with future prospects.

Retail demand for emergency contraception in United States following New Year holiday: time series study.

To estimate the increase in sales of emergency contraception following the New Year's Eve/New Year's Day holiday. Time series analysis using autoregressive integrated moving average (ARIMA) model. Traditional (that is, "bricks and mortar") retail outlets-grocery stores, drug stores, mass merchandisers, club stores, dollar stores, and military outlets-in the United States from 2016 to 2022. Marketing data on weekly aggregated sales of items classified as emergency contraception gathered between 2016 and 2022 (n=362). On the basis of dates, weeks were classified as following the New Year holiday (n=6) or not (n=356). Weekly sales of levonorgestrel emergency contraception per 1000 women of reproductive age in the US population. Sales of levonorgestrel emergency contraception significantly increased after the New Year holiday (0.63 (95% confidence interval 0.58 to 0.69) unit increase per 1000 women aged 15-44). Holidays that share some aspects of the elevated risks of unprotected sexual intercourse with the New Year holiday (Valentine's Day, St Patrick's Day, US Independence Day) were associated with increased sales, albeit to a lesser degree, with respective sales increases per 1000 women aged 15-44 of 0.31 (0.25 to 0.38), 0.14 (0.06 to 0.23), and 0.20 (0.11 to 0.29). Holidays without these expectations (Easter, Mother's Day, Father's Day) were not significantly associated with sales of levonorgestrel emergency contraception. Increased sales of emergency contraception following the New Year's holiday suggest that this period is associated with increased risks of unprotected vaginal intercourse compared with other holidays. Targeting behavioral risks, prevention strategies to mitigate sexual violence, and improving access to contraception around holidays may limit the risks associated with unprotected vaginal intercourse.

Developing a Policy for Healthy Schools Food: A Plan of Action

Background and Objectives: Childhood obesity is a pressing global health concern, particularly in low- and middle-income countries like Palestine. This study aims to address the prevalence of childhood obesity in Palestine and its association with the school food environment. The objective is to propose and advocate for the implementation of a "healthy schools' food policy" to promote the consumption of nutritious food while prohibiting the sale of unhealthy items. Methods: A comprehensive analysis of the sociocultural, economic, political, ethical, and legal contexts surrounding childhood obesity in Palestine was conducted. This included a review of existing studies, stakeholder analyses, and an evaluation of policy alternatives. A legislative strategy was developed to guide the implementation of the proposed policy. Results: The study identified the widespread prevalence of childhood obesity in Palestine, with contributing factors rooted in sociocultural, economic, and political contexts. The analysis of policy alternatives revealed that establishing and enforcing a written protocol for "Food and Beverage Services" in schools emerged as the most effective option. This policy approach received the highest score in terms of effectiveness, access and fairness, cost-effectiveness, and administrative and political feasibility. Conclusion: The findings highlight the urgent need for policy interventions to address childhood obesity in Palestine. The proposed "healthy schools' food policy" offers a promising avenue to promote nutritious food consumption while curbing the availability of unhealthy options. Collaborative efforts involving stakeholders, including schools, the Ministry of Education, healthcare professionals, parents, and the community, are crucial for successful policy implementation. Regular evaluations and community feedback will be essential to monitor the policy's impact and make necessary adjustments. This study underscores the significance of prioritizing children's health within the broader context of societal well-being.