浅析数控曲轴连杆轴颈车床夹具设计的有效改进

数控曲轴连杆颈车床用来加工一般四拐曲轴的连杆颈,该夹具已使用多年,但随着近年来新产品开发数量的增多,已远不能满足使用要求,本文就CF-702数控曲轴连杆轴颈车床的夹具设计,进行有效的改进,具有一定的参考价值。     

信息分享对原始设备制造商与其合同制造商竞争的影响

研究了原始设备制造商的预测信息分享对一个原始设备制造商和一个与其同时有合作和竞争的合同制造商组成的供应链系统的影响,建立制造商间信息分享的模型,该模型包括一个原始设备制造商和一个合同制造商。研究发现,原始设备制造商关于市场潜在需求预测信息的分享对其预期利润是不利的,同时需求信息预测的精度对原始设备制造商信息分享的决策也有影响,原始设备制造商没有动机与其供应链成员进行信息分享,但信息分享使得供应链整体利润增加。最后,建立一个信息分享补偿机制分享供应链利润的增加量,以期通过信息分享补偿机制促使原始设备制造商有动机进行信息分享,从而实现其与合同制造商的“双赢”。     

The problem of employees' network centrality and supervisors' error in performance appraisal: A multilevel theory

This article reveals an unexplored paradox for HR managers: the centrality of an employee in the social network benefits performance but hampers performance appraisal because it affects supervisors' rating errors. Central employees can be erroneously rated high on performance even when they are not high performers because supervisors tend to overappraise their performance. A distinction is made between rating precision, which depends on supervisors' uncertainty regarding employees' performance, and rating accuracy, which depends on supervisors' bias in favor of employees. Employee centrality is posited to be beneficial to precision but deleterious to accuracy because it regulates the diffusion of positive information, status, and power, all of which distort supervisors' capacity and motivation to accurately appraise performance. It is then argued that rating errors caused by network centrality affect aggregate perceptions of justice in organizations. When employees are highly connected to each other in a dense network, organizations have a strong and positive justice climate. Yet when some employees are more central than others in a centralized network, organizations have a negative and weak justice climate. The article contributes to the literature because it identifies an unexplored dark side of network centrality and offers recommendations for HR managers to cope with its deleterious consequences and for scholars to study them.     

The M4 Competition: Results,findings, conclusion and way forward

The M4 competition is the continuation of three previous competitions started more than 45 years ago whose purpose was to learn how to improve forecasting accuracy, and how such learning can be applied to advance the theory and practice of forecasting. The purpose of M4 was to replicate the results of the previous ones and extend them into three directions: First significantly increase the number of series, second include Machine Learning (ML) forecasting methods, and third evaluate both point forecasts and prediction intervals. The five major findings of the M4 Competitions are: 1. Out Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches. 2. The biggest surprise was a “hybrid” approach that utilized both statistical and ML features. This method’s average sMAPE was close to 10% more accurate than the combination benchmark used to compare the submitted methods. 3. The second most accurate method was a combination of seven statistical methods and one ML one, with the weights for the averaging being calculated by a ML algorithm that was trained to minimize the forecasting. 4. The two most accurate methods also achieved an amazing success in specifying the 95% prediction intervals correctly. 5. The six pure ML methods performed poorly, with none of them being more accurate than the combination benchmark and only one being more accurate than Naïve2. This paper presents some initial results of M4, its major findings and a logical conclusion. Finally, it outlines what the authors consider to be the way forward for the field of forecasting.     

THE IMPACT OF POINT‐OF‐SALE DATA INACCURACY AND INVENTORY RECORD DATA ERRORS

The presence or absence of error in point‐of‐sale (POS) data and inventory system records directly affects retailer performance. This study identifies various error sources in retail supply chains and studies the influence of inventory and POS (demand) errors in a simulated retail outlet according to fill rate and average inventory. Other things being equal, we find that inventory record error reduces fill rate more than demand error. This study adds further evidence to other studies that suggest the costs caused by errors in POS systems may be overstated.     

模拟环境件的定位和检测设计

针对内外饰零件的结构匹配特点以及目前零件在检具上的尺寸检测结果与实车装配效果存在差异的现象,本文通过实例简要介绍了带卡扣、卡钩、筋条的这几类零件模拟环境件进行定位设计和检测设计的方法,并提出了一种新式的、组合环境件进行定位和检测的设计方案。     

Does the financial analysts' usage of non-financial information influence the analysts' forecast accuracy? Some evidence from the Belgian sell-side financial analyst

This paper examines whether the use of non-financial information by sell-side financial analysts influences the accuracy of analysts’ forecasts. The research findings, based on a survey of Belgian financial analysts, suggest that financial analysts who use more forward-looking information and more internal-structure information offer more accurate forecasts. Furthermore, the listed Belgian firms examined in this study have improved their non-financial information reporting over time. However, neither the frequency nor the quantity of non-financial information mentioned by financial analysts in their reports appears to have increased over time.     

Using Leading Indicators to Forecast U.S. Home Sales in a Bayesian Vector Autoregressive Framework

This article uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting U.S. home sales. The benchmark Bayesian model includes home sales, price of homes, mortgage rate, real personal disposable income, and unemployment rate. We evaluate the forecasting performance of six alternative leading indicators by adding each, in turn, to the benchmark model. Out-of-sample forecast performance over three periods shows that the model that includes building permits authorized consistently produces the most accurate forecasts. Thus, the intention to build in the future provides good information with which to predict U.S. home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators.     

提高电磁流量计测量准确度的方法

针对在注水中电磁流量计测量准确度普遍低于理论值的现状，试验小组根据电磁流量计原理与实际应用工况条件，查找影响电磁流量计测量准确度的主要因素，并制定对策措施进行反复比较实验和效果检查，最终通过5项有效措施的实施，完成电磁流量计测量准确度达到并超过理论值的目标。     

Comparing the accuracy of several network-based COVID-19 prediction algorithms

Researchers from various scientific disciplines have attempted to forecast the spread of coronavirus disease 2019 (COVID-19). The proposed epidemic prediction methods range from basic curve fitting methods and traffic interaction models to machine-learning approaches. If we combine all these approaches, we obtain the Network Inference-based Prediction Algorithm (NIPA). In this paper, we analyse a diverse set of COVID-19 forecast algorithms, including several modifications of NIPA. Among the algorithms that we evaluated, the original NIPA performed best at forecasting the spread of COVID-19 in Hubei, China and in the Netherlands. In particular, we show that network-based forecasting is superior to any other forecasting algorithm.