UK manufacturers'sales decline 10%in Q12024

UK manufacturers experienced a challenging start to 2024,with sales in the first quarter(Q1)down 10 per cent on the previous quarter,according to a report by Unleashed.However,year-on-year growth showed a modest increase of 2 per cent,reflecting the Bank of England’s assessment of weak growth in the manufacturing sector.

A Study on the Factors Influencing Consumer Purchase Decision Under the Live-Streaming Sales Model

In recent years,with the rapid development and popularization of Internet information technology,many new media platforms have risen rapidly,and major e-commerce companies have begun to explore the mode of livestreaming.Especially during the COVID-19 pandemic,due to the lockdown,live-streaming has become an important means of economic development in many places.Owing to its remarkable characteristics of timeliness,entertainment,and interactivity,it has become the latest and trendiest sales mode of e-commerce channels,reflecting huge economic potential and commercial value.This article analyzes two models and their characteristics of live-streaming sales from a practical perspective.Based on this,it outlines consumer purchasing decisions and the factors that affect consumer purchasing decisions under the live-streaming sales model.Finally,it discusses targeted suggestions for using the live-streaming sales model to expand the consumer market,hoping to promote the healthy and steady development of the live-streaming sales industry.

The Impact of The Three-Dimensional Cultivation Model on the Development of the Suzhou Tea Industry:Estimating Value of Output Per Acre and Diversified Sales Models

This article explores the impact of the three-dimensional cultivation mode on the development of the Suzhou tea industry,focusing on the diversified estimation of the value of output per acre and sales mode.It introduces the history and traditional cultivation practices of tea in Suzhou,as well as the current challenges and problems faced by the industry.An in-depth analysis was conducted on the overview and improvement plans of the three-dimensional cultivation mode,covering relevant technical methods.Based on this analysis,the impact of the three-dimensional cultivation on the value of output per acre was studied and predicted.Its potential and advantages were explored and compared with the effectiveness of traditional cultivation models.Additionally,the impact of the three-dimensional cultivation mode on sales was analyzed,examining its market adaptability and competitiveness,as well as its advantages in expanding sales channels and market coverage.The study also focused on the promoting effect of diversified sales models on the Suzhou tea industry,including direct consumption market development,tea processing product development and promotion,and the integration of tea culture and the tourism industry.To ensure sustainable development,the article evaluates the environmental impact,economic feasibility,social benefits,and farmer benefits of the three-dimensional cultivation model.Finally,the prospects for the development of the Suzhou tea industry were discussed,and the positioning and response strategies of the threedimensional cultivation model were proposed.

Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning

Reliable sales forecasts are important to the garment industry. In recent years, the global climate is warming, the weather changes frequently, and clothing sales are affected by weather fluctuations. The purpose of this study is to investigate whether weather data can improve the accuracy of product sales and to establish a corresponding clothing sales forecasting model. This model uses the basic attributes of clothing product data, historical sales data, and weather data. It is based on a random forest, XGB, and GBDT adopting a stacking strategy. We found that weather information is not useful for basic clothing sales forecasts, but it did improve the accuracy of seasonal clothing sales forecasts. The MSE of the dresses, down jackets, and shirts are reduced by 86.03%, 80.14%, and 41.49% on average. In addition, we found that the stacking strategy model outperformed the voting strategy model, with an average MSE reduction of 49.28%. Clothing managers can use this model to forecast their sales when they make sales plans based on weather information.

Product Specification Analysis for Modular Product Design Using Big Sales Data

Big data on product sales are an emerging resource for supporting modular product design to meet diversified customers’requirements of product specification combinations.To better facilitate decision-making of modular product design,correlations among specifications and components originated from customers’conscious and subconscious preferences can be investigated by using big data on product sales.This study proposes a framework and the associated methods for supporting modular product design decisions based on correlation analysis of product specifications and components using big sales data.The correlations of the product specifications are determined by analyzing the collected product sales data.By building the relations between the product components and specifications,a matrix for measuring the correlation among product components is formed for component clustering.Six rules for supporting the decision making of modular product design are proposed based on the frequency analysis of the specification values per component cluster.A case study of electric vehicles illustrates the application of the proposed method.

Optimization of Electricity Purchase and Sales Strategies of Electricity Retailers under the Condition of Limited Clean Energy Consumption

In the process of my country’s energy transition,the clean energy of hydropower,wind power and photovoltaic power generation has ushered in great development,but due to the randomness and volatility of its output,it has caused a certain waste of clean energy power generation resources.Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption,this paper establishes a quantitative model of clean energy restricted electricity fromthe perspective of power system supply and demand balance.Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid-to long-term electricity market and the day-ahead market.Through the multi-scenario analysis method,the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene,and the typical scene is obtained by using the hierarchical clustering algorithm.This paper establishes a electricity retailer’s risk decisionmodel for purchasing and selling electricity in themid-and long-term market and reduce-abandonment market,and takes the maximum profit expectation of the electricity retailer frompurchasing and selling electricity as the objective function.At the same time,in themediumand longterm electricity market and the day-ahead market,the electricity retailer’s purchase cost,electricity sales income,deviation assessment cost and electricity purchase and sale risk are considered.The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies,on the premise of balancing profits and risks.

Comparison of Sales Prediction in Conventional Insights and Machine Learning Perspective

With the integration of global economy development and the rapid growth of science knowledge and technology,the needs of people’s consumption are increasingly personalized and diversified.Such a market background makes sales forecasting become an indispensable part of enterprise management and development.The definition of the sales forecasting is that based on the past few years’sales situation,the enterprises through systematic sales forecasting models estimate of the quantity and amount of all or some specific sales products and services in a specific time in the future.Accurate sales forecasting can promote enterprises to do better in future revenue,and can also encourage enterprises to set and keep an efficient sales management team.This paper will analyze traditional sales forecasting methods and sales forecasting methods based on big data models related to the perspective of machine learning,and then compare them.The research shows that the two sales forecasting methods have their own advantages and disadvantages.In the future,enterprises can adopt the two sales forecasting methods in parallel to maximize the utilization advantage of sales forecasting for enterprises.

Did weekly economic index and volatility index impact US food sales during the first year of the pandemic?

We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United States from January 2020 to January 2021.We use the US weekly economic index(WEI)to measure economic dislocations and the Chicago Board Options Exchange volatility index(VIX)to capture the broader stock market dislocations.We validate the NARDL model by testing a battery of models using the autoregressive distributed lags(ARDL)methodology(ARDL,NARDL,and QARDL specifications).Our study postulates that an increase in WEI has a significant negative long-term effect on food sales,whereas a decrease in WEI has no statistically significant(long-run)effect.Thus,policy responses that ignore asymmetric effects and hidden cointegration may fail to promote food security during pandemics.

Production and sales growth of China's knitting industry slows down,export hits a record high

In 2022,the international economic and trade environment is in turmoil,singed by the pandemic,while domestic sales are weak.In the face of complex and severe situation at home and abroad,China textile industry has shown strong resilience.In 2022,36,000 enterprises in China's textile industry achieved more than 5,200 billion yuan in main business revenue and 200 billion yuan in profit,with operating margin of 3.9 percent.In 2022,China's textile and apparel exports reached a new record high of 340.95 bilion US dollars,up 2.5 percent year-on-year.

NEV Sales Surge In April

Retail sales of new energy vehicles(NEVs)in China jumped 85.6 percent year on year in April,data from the China Passenger Car Association(CPCA)showed on 9 May.A total of 527,000 NEVs were sold in China in April,down 3.6 percent from March,according to the CPCA.NEV sales of major domestic brands accounted for 70.5 percent of the total NEV sales in the country,the data revealed.

Research on the Effect of R&D Investment Intensity and Sales Expense on the Performance of Biomedical Enterprises

Objective To explore the influence of new drug R&D investment and sales expense on the performance of biomedical enterprises.Methods The financial statements of 76 listed biomedical enterprises for 5 consecutive years were selected,and the data were modeled to study the effect of R&D investment and sales expense on the performance of biomedical enterprises by using financial indicators as tools and statistical methods of multiple linear regression.Results and Conclusion Under the premise that the weak related factors such as enterprise scale,life cycle and asset-liability ratio are set as unrelated variables,the R&D investment intensity of biomedical enterprises is negatively correlated with the current performance,which also shows that the R&D of biomedical enterprises has the characteristics of high risk.Besides,the influence of early R&D investment is delayed.However,the sales expense of leading biomedical enterprises with large scales have higher proportion.Meanwhile the greater sales expense of the same enterprise in different periods,the better the enterprise performance is.Biomedical enterprises should consider their own development stage to develop more patented drugs.Besides,they must formulate plans for allocating reasonable sales personnel and cost expense to ensure that enterprises can obtain better benefits.

The Contribution Margin due to a Limiting Factor in the Presence of Several Sales Options: Actuality Is Not Always As It Appears at the Beginning of the Analysis

The analysis of company data useful for economic decisions,if not interpreted in an overall view of the company situation,can lead to wrong conclusions.This is the case when a company has to choose between several sales options for one or more products in the presence of a limiting factor.The continuation of the investigation often denies the initial analysis.Not everything is as it appears,therefore,at the beginning of the deepening of the data useful for economic decisions.As it is well known,the choices of profitability concerning the planning of the sale of company products take place,at least in the majority of cases,through the determination of the contribution margin,i.e.the profitability margin connected to the individual goods/services sold by the companies(selling price net of variable costs).The contribution margin can be determined with four objectives:(1)Determination of the yield of the single product,net of variable costs only.In this case,the margin defines unitary,from net product yield to unitary contribution margin.(2)Determination of the return on total sales of an individual product,net of variable costs.In this hypothesis,reference is made to the first level(or gross)contribution margin.(3)Determination of the ability of the individual product to contribute to the coverage of fixed costs common to the company.This margin is determined net of special product variable and fixed costs.This aggregate is defined as a Level II(or semi-gross)margin.(4)Determination of the useful value in the planning choices in case of presence of scarce productive factors.In this case,it must identify the so-called unitary margin for low factor.Here we will only deal with the problem of the use of the contribution margin in the presence of rare factors.To complete the analysis,below are some very brief considerations regarding,respectively,the unitary,level I,and level II contribution margin in order to better understand where the problem of the most convenient choice of income is located in the event of the presence of rare production factors,especially in an environment characterized by a plurality of sales options.

企业数字化转型与产品市场竞争地位

数字经济促进企业生态系统的构建,大数据为企业开展多元化经营、获取和沉淀客户资源赋能,研究企业数字商业模式的形成和扩大如何影响公司在产品市场中的竞争地位已成为深入了解企业数字化转型的重要议题。受资源积累和动态能力的影响,企业数字化转型提高产品市场竞争地位主要是通过增强企业创新能力和扩大企业客户网络影响来实现。当企业的融资约束较大、管理层权力较小和所在地区的文化更加强调业绩或未来导向时,上述结果更加显著。进一步的研究发现,企业数字化转型将提高公司采用差异化竞争战略的概率,降低公司采用成本领先战略的倾向,补充证明了数字技术驱动产品市场竞争力提升主要表现为企业销售优势的重构和增强。

油气企业销售业务和加油站转型路径设计

“双碳”目标背景下,国内能源结构加速调整,新能源汽车开始发力,对传统油气企业业务影响深远,尤其是给终端销售环节带来的冲击和影响巨大,油气企业的销售业务与加油站经营亟待转型。文章分析光伏发电、充电、换电、加氢四大赛道无法承载销售业务转型的原因;并探讨油气企业打造电氢新能源产业链的设想,销售业务以补能为中心纵向延伸、横向拓展构建培育“车—能—路—云”融合发展新生态服务模式。销售业务转型需要以加油站和站外站为依托,站点通过分步、分类、分层、有序转型推动销售业务转型。提出了智慧化建设、投资策略、考核引领、机构设施、人才培养以及跟踪研究等方面的建议。

消费行为数据采集平台的安全保障与预测模型研究

依据用户浏览记录等信息进行兴趣爱好的预测并进行合理推荐,已成为诸多销售平台优化用户体验的常用手段,而用户信息安全问题自然也成了各大平台面临的一大挑战.提出一种基于内生安全的消费行为数据采集与分析平台,通过采集用户数据,使用基于长短时记忆网络的预测模型,精准预测未来销售流量数据.在数据安全性方面,平台使用基于内生安全的拟态云WAF,通过动态选择算法、异构执行体和裁决算法3种核心技术为整个数据平台提供了自主可控的安全保障,并利用基于Sketch的网络测量技术对异常流量进行了检测.此外,平台融合了数据备份和恢复、加密存储、数据传输加密技术,并对重要的数据采取分类存储、访问控制等措施.多项对比实验验证表明,用于中烟销售流量的预测平台相较于目前提出的多种技术在预测准确度和数据安全方面都有显著提升,可为企业销量预测提供一种合理可行的解决方案.

高速铁路预售期旅客购票量分布预测

在预售前(相隔31 d)预测高速铁路预售期旅客购票量分布是铁路企业精准进行收益管理的前提。基于高速铁路预售模式和旅客售票数据,分析预售期内各预售日旅客购票量的相关性,探究预售期旅客购票量分布的影响因素。综合考虑出发日特征以及旅客购票量分布时序特征的影响,构建了考虑多输出间关联性的最小二乘支持向量回归-卷积长短期记忆网络(MLSSVR-ConvLSTM)模型。以京沪高铁线路中上海虹桥站至北京南站、上海虹桥站至徐州东站、上海虹桥站至无锡东站这3种不同距离OD旅客为例,进行预售期旅客购票量分布预测实例分析。研究结果显示:MLSSVR-ConvLSTM模型预测结果较好地反映了真实的预售期旅客购票量分布的变化趋势,平均绝对百分比误差为6.7%~11.0%,预测效果优于多元线性回归(MLR)、K近邻回归(KN)、极致梯度提升算法(XGBoost)、支持向量回归机(SVM)、多输出最小二乘支持向量回归(MLSSVR)和卷积长短期记忆网络(ConvLSTM)等模型,验证了所提出模型的合理性和有效性。进而表明,在构建预售期旅客购票量分布预测模型时,考虑预售期旅客购票量分布整体性以及各类因素的综合影响可有效地提高模型预测精度。所提出的预售期旅客购票量分布预测模型可以为铁路企业制定动态票额分配和浮动票价等政策提供理论支撑。

1+X证书背景下药品经营人才培养研究

目的探讨“1+X”证书背景下药品经营人才培养路径。方法以“药品购销”职业技能等级标准为指南,以“1+X”药品购销员考试内容为依据进行改革。结果修订和完善人才培养方案、课程标准,同时进行课程“三教”改革。结论“1+X”证书制度背景下进行药学专业(群)教育改革必要可行。

基于卷积神经网络的电力市场短期售电量预测方法

电力市场短期售电量预测的精度对优化用电结构以及提高供电可靠性具有重要意义,传统短期售电量预测方法没有考虑偏差电量考核影响、用电行为差异导致电预测精度低,提出基于卷积神经网络的电力市场短期售电量预测方法,首先根据用户的用电负荷率进行分类,获取不同行业的用电特征和需求模式,然后考虑正负偏差电量的影响,设计基于CNN-ResNet的短期售电量预测方法,通过实验分析表明,该方法能够有效提高多因素影响下售电量预测的准确率。

数字平台信息生产与新创企业股权融资研究——基于多源数据方法

以2014—2020年在京东众筹平台发起预售众筹的新创企业为样本,考察预售众筹融资表现是否对融资企业后续获得风险资本产生影响。实证发现,较好的融资表现可以彰显企业家才能和揭示市场需求,从而产生信号效应,提高新创企业后续获得风险资本的可能性,这一效应主要受最近一次众筹经历影响,且不受众筹之前早期风险资本的介入影响。进一步研究发现,预售众筹的信号效应弱于股权众筹的信号效应,但两者可以形成互补。新创企业应重视预售众筹的信息生产作用,利用良好的融资表现发送高质量信号。

经济制裁对两岸价值链关联的影响研究

文章运用亚洲开发银行的多区域投入产出表数据,测算经济体价值链关联、经济体生产距离和销售距离,在此基础上实证检验了经济制裁对价值链关联的影响,并基于“台湾地区—内地—美国”三角贸易,考察了美国经济制裁对三角贸易和两岸价值链关联的影响。结果表明,两岸价值链关联不断深化,经济体在GVC活动中的生产和销售并未出现明显的“近岸”特征;经济制裁对经济体价值链关联具有显著的负向作用;聚焦于三角贸易,美国经济制裁显著降低了美国与中国内地和台湾地区的价值链关联,并且促进了两岸价值链融合。最后,文章从创新驱动机制提升两岸价值链关联、以两岸携手嵌入内地内循环应对经济制裁、两岸携手开拓外部市场塑造新型贸易关系等方面提出了相应政策建议。