Exploring the factors affecting global manufacturing performance*

ABSTRACT

A decision tree is used to explore the effects of Information and Communication Technologies (ICTs) and financial factors on global manufacturing industry sales performance. Existing research provides an unclear picture of how ICTs affect sales performance. Earlier studies showed little evidence that ICTs improve performance, more recent studies do. Our investigation found that between 2006 and 2009, companies with high ICT scores performed well, but their performance declined substantially between 2010 and 2014. ICTs do not significantly influence manufacturing sales growth performance, but financial factors do. Supplier credit as a source of financing leads to negative sales growth, while low loan collateral improves sales performance. Taking advantage of bank financing improves sales, but using equity or stocks to fund investments negatively affects them. These findings are used to develop seven research hypotheses for future studies.     

Sales and Use Taxation in E-Commerce: Where We Are and What Needs to be Done

ABSTRACT

As sales through the Internet have increased, states have sought to force remote sellers to collect and remit sales/use taxes. In response to the U.S. Supreme Court's Constitutional obstacles, the Streamlined Sales Tax Project (SSTP) was organized in 2000. SSTP is a voluntary effort by a number of states to address the Constitutional problems and reduce the burden of sales tax administration for all sellers in all types of commerce. This article examines the current state of the law and the major problems surrounding the SSTP.     

Optimal production lot size and reorder point of a two-stage supply chain while random demand is sensitive with sales teams' initiatives

The paper develops a production-inventory model of a two-stage supply chain consisting of one manufacturer and one retailer to study production lot size/order quantity, reorder point sales teams’ initiatives where demand of the end customers is dependent on random variable and sales teams’ initiatives simultaneously. The manufacturer produces the order quantity of the retailer at one lot in which the procurement cost per unit quantity follows a realistic convex function of production lot size. In the chain, the cost of sales team's initiatives/promotion efforts and wholesale price of the manufacturer are negotiated at the points such that their optimum profits reached nearer to their target profits. This study suggests to the management of firms to determine the optimal order quantity/production quantity, reorder point and sales teams’ initiatives/promotional effort in order to achieve their maximum profits. An analytical method is applied to determine the optimal values of the decision variables. Finally, numerical examples with its graphical presentation and sensitivity analysis of the key parameters are presented to illustrate more insights of the model.     

城市道路交通数据可视分析综述

目的 交通是困扰现代大都市的世界性难题.近年来,可视分析技术在分析和利用交通大数据中扮演了越来越重要的角色,成为一项重要的智能交通技术.本文将全面回顾自信息可视化和可视分析兴起以来城市交通数据可视分析领域的研究现状.方法 从道路交通流量分析和其他交通问题分析两个方面,按照数据的类型及问题的分类探讨交通领域的可视化技术和可视分析系统,简单回顾近年来出现的研究新趋势.结果 早期研究注重对道路流量的可视化展示方案,主要方法有箭头图、马赛克图和轨迹墙等.随着可视分析手段的丰富,对城市道路交通流量的分析层次上升到交通事件层面,但是交通事件的定义仅局限于交通拥堵.应用可视分析的其他交通问题领域包括公共交通、交通事故和人群出行行为等.近年出现了挖掘和利用交通轨迹或交通事件的社会属性或称环境上下文信息的研究新趋势.结论 从对交通流量的可视化到交通事件的可视分析,从面向道路交通状况到与交通相关的其他社会性问题,从单纯反映路况的交通数据到富含社会性语义的多源数据,从传统的PC端可视化和交互范式到新型的可视化展示介质,交通数据可视化领域的研究在深度和广度上都得到大大拓展,未来该领域的研究趋势也体现于其中.     

Improving Cyber Security Awareness and Training Programs with Data Analytics

ABSTRACT

This article it is a modest attempt to explore the issues facing cyber security awareness and training programs and potential benefits of using learning analytics, an emerging field in data analytics, for combining existing data sources to provide additional value to these programs. This article was written under the assumption that awareness and training are valid preventive controls and therefore the pros and cons of implementing such programs are not being discussed.     

Tracking precursors of learning analytics over serious game team performance ranking

ABSTRACT

The use of serious games to improve collaborative skill transfer and retention has received considerable attention from scholars, web marketing practitioners and business consultants. Team rankings and learning progress in game learning analytics, however, have yet to be empirically examined. Using fuzzy-set qualitative comparative analysis to study the performance of competing teams in a web marketing serious game (Simbound), we highlight a combination of causal conditions (engagement, reach and profitability) affecting team rankings. This paper proposes a conceptual architecture of the forces that influence learning progress within a collaborative learning environment. This learning environment is studied for web marketing boot camps powered by Simbound at three European universities: Grenoble Alpes University (France), University of Milano-Bicocca (Italy) and Dunarea de Jos University of Galati (Romania). Gaining knowledge of cases through game learning analytics is valuable for two reasons: It emphasises the instructor’s role in mobilising players’ engagement, and it tests variability across cases, offering precursors of team performance rankings. This approach to collective skill retention highlights the moderating factors of team performance rankings, whilst purposely calibrating a gameable learning environment. This paper enriches our knowledge of how active experimentation in learning analytics metrics can develop skills for real business competition.     

The value of postponing online fulfillment decisions in multi-channel retail/e-tail organizations

Many retail/e-tail organizations assign responsibilities for online sales immediately and to the closest fulfillment location that has available stock. Unfortunately there is little research on the value of using such policies in retail/e-tail companies. To fill this gap, this paper examines two aspects of the online fulfillment assignment decision that differ from current practice. We propose that online sales should be accumulated before they are assigned to a fulfillment site and that more inventory position information should be leveraged into the fulfillment decision. Specifically, we develop and evaluate a “quasi-dynamic” allocation policy that assigns accumulated online sales to fulfillment locations based on expected inventory, shipping, and customer wait costs. Computational results show that our policy can reduce costs (i.e., holding, backorder, transportation cost) at the fulfillment locations by as much as 23% on average over a commonly used transportation cost policy. In addition, postponing the allocation decision and allowing sales to accumulate can reduce inventory costs at the fulfillment sites by 14% over common practice of instantaneously assigning online sales responsibilities. The magnitude of the benefit depends critically on the number of allocations made each period and the fraction of total sales coming from the online channel. Although postponement delays receipt of online sales, our findings suggest that explicitly incorporating customer service in the allocation decision can improve product availability at little or no additional cost.     

Using online pickup site inclusion policies to manage demand in retail/E-tail organizations

A major development in online retailing is the significant increase in the number of traditional “offline” retailers extending their brands online. Many of these retail/e-tail firms are attempting to leverage channel synergies by allowing customers to purchase products over the internet and then pick their orders up at one of the firm's local stores. This paper proposes that the firm presents only a subset of its stores to online customers as available pickup locations, rather than simply listing all local stores with inventory. By doing so, the firm can protect stores with critically low inventory levels and thereby reduce backorder costs. Specifically, we develop and evaluate a dynamic pickup site inclusion policy that incorporates real-time information to specify which of the firm's e-fulfillment locations should be presented at online checkout. Computational results indicate that managing in-store demand via such policies can decrease total cost (holding, backorder, and lost or redirected pickup sale costs) by as much as 18% over allowing customers to pick online orders up from any site with available inventory. The percentage of pickup sales and customers' sensitivity to travel are critical in determining the magnitude of the benefit.     

Mapping the study of learning analytics in higher education

ABSTRACT

In recent years, the application of technological innovation in higher education has become more and more widely spread, and technological innovation has been improving the level of education. In the research of higher education with innovation technology, one of the main focuses is on the dynamic data which can lay a foundation for the analysis of educational activities by learning analytics. The dynamic data created by technological innovation will become the key basis for analytical research and development in higher education. The methods and analysis results of learning analytics will directly affect decision-making and strategy about higher education. In this paper, we use bibliometric and visualisation methods to review the literature, in order to highlight the development of learning analytics in higher education. Using bibliometric analysis, our study depicts the development process of the main methods used in learning analytics, and summarises the current situation in this field, which increases the level of understanding provided by those studies. Finally, we summarise the research hotspots and study trends, which will be useful for future study in this field.     

A bibliometric perspective of learning analytics research landscape

ABSTRACT

Learning analytics is an emerging field of research, motivated by the wide spectrum of the available educational information that can be analysed to provide a data-driven decision about various learning problems. This study intends to examine the research landscape of learning analytics to deliver a comprehensive understanding of the research activities in this multidisciplinary field, using scientific literature from the Scopus database. An array of state-of-the-art bibliometric indices is deployed on 2811 procured publication datasets: publication counts, citation counts, co-authorship patterns, citation networks and term co-occurrence. The results indicate that the field of learning analytics appears to have been instantiated around 2011; thus, before this time period no significant research activity can be observed. The temporal evolution indicates that the terms ‘students’, ‘teachers’, ‘higher education institutions’ and ‘learning process’ appear to be the major components of the field. More recent trends in the field are the tools that tap into Big Data analytics and data mining techniques for more rational data-driven decision-making services. A future direction research depicts a need to integrate learning analytics research with multidisciplinary smart education and smart library services. The vision towards smart city research requires a meta-level of smart learning analytics value integration and policy-making.     

基于多维灰色模型及神经网络的销售预测

在时尚销售领域,如服饰、手袋、钱包等,准确的销售预测对企业非常重要.然而由于客户的需求受诸多因素的影响,要做到准确的销售预测一直是一个富有挑战性的问题.基于改进的多维灰色模型（GM（1,N））和神经网络（ANN）提出一种混合模型来预测销量,其中多维灰色模型对销售数据建模,神经网络对误差进行校正.该混合模型的优点是考虑了影响客户需求的因素与销量之间的关系.通过对阿里天猫销售数据来评估混合模型的表现,实验结果表明,所提出的混合模型的预测结果要优于其他几种销售预测模型.     

Key based data analytics across data centers considering bi-level resource provision in cloud computing

Due to the distribution characteristic of the data source, such as astronomy and sales, or the legal prohibition, it is not always practical to store the world-wide data in only one data center (DC). Hadoop is a commonly accepted framework for big data analytics. But it can only deal with data within one DC. The distribution of data necessitates the study of Hadoop across DCs. In this situation, though, we can place mappers in the local DCs, where to place reducers is a great challenge, since each reducer needs to process almost all map output across all involved DCs. In this paper, a novel architecture and a key based scheme are proposed which can respect the locality principle of traditional Hadoop as much as possible while realizing deployment of reducers with lower costs. Considering both the DC level and the server level resource provision, bi-level programming is used to formalize the problem and it is solved by a tailored two level group genetic algorithm (TLGGA). The final results, which may be dispersed in several DCs, can be aggregated to a designative DC or the DC with the minimum transfer and storage cost. Extensive simulations demonstrate the effectiveness of TLGGA. It can outperform both the baseline and the state-of-the-art mechanisms by 49% and 40%, respectively.     

Big Data Initiatives: Minimizing Risk and Maximizing Value with Governance

Abstract

Big Data Analytics can be a fantastic business opportunity for many organizations. Already organizations are using advanced analytics to streamline production processes, optimize back office activities, market more effectively, and better satisfy customer demand. That said, it goes without saying (as recent headlines can attest) that sometimes enhanced analytics capabilities can introduce risks such as erosion of privacy, overly-intrusive knowledge about customers, etc.

Given this dichotomy, making the decision about when, whether, how much, and how to invest in big data analytics initiatives can be a challenge. Invest too soon and you may obviate existing investments or disrupt business activities; invest too late and you may find that competitors gain advantages that make the market landscape asymmetric.

This article outlines how and why applying “tried and true” governance principles can help make this decision easier. For those that have formalized governance structures in place, how they might inform the decision an organization makes in this regard – and for those that don’t have a formalized governance program – how they might co-opt some of those principles to help make this decision more approachable.     

Performance on agile teams: Relating iteration objectives and critical decisions to project management success factors

ContextWhile project management success factors have long been established via the golden triangle, little is known about how project iteration objectives and critical decisions relate to these success factors. It seems logical that teams’ iteration objectives would reflect project management success factors, but this may not always be the case. If not, how are teams’ objectives for iterations differing from the golden triangle of project management success factors?ObjectiveThis study identifies iteration objectives and the critical decisions that relate to the golden triangle of project management success factors in agile software development teams working in two-week iterations.MethodThe author conducted semi-structured interviews with members across three different agile software development teams using a hybrid of XP and Scrum agile methodologies. Iteration Planning and Retrospective meetings were also observed. Interview data was transcribed, coded and reviewed by the researcher and two independently trained research assistants. Data analysis involved organizing the data to identify iteration objectives and critical decisions to identify whether they relate to project management success factors.ResultsAgile teams discussed four categories of iteration objectives: Functionality, Schedule, Quality and Team Satisfaction. Two of these objectives map directly to two aspects of the golden triangle: schedule and quality. The agile teams’ critical decisions were also examined to understand the types of decisions the teams would have made differently to ensure success, which resulted in four categories of such decisions: Quality, Dividing Work, Iteration Amendments and Team Satisfaction.ConclusionThis research has contributed to the software development and project management literature by examining iteration objectives on agile teams and how they relate to the golden triangle of project management success factors to see whether these teams incorporate the golden triangle factors in their objectives and whether they include additional objectives in their iterations. What’s more, this research identified four critical decisions related to the golden triangle. These findings provide important insight to the continuing effort to better assess project management success, particularly for agile teams.     

A new forecasting scheme for evaluating long‐term prediction performances in supply chain management

Supply chain management (SCM) practitioners in inventory sites are often required to predict the future sales of products in order to meet customer demands and reduce inventory costs simultaneously. Although a variety of forecasting methods have been developed, many of them may not be used in practice for various reasons, such as insufficient viable information about sales and oversophisticated methods. In this paper, we provide a new forecasting scheme to evaluate long‐term prediction performances in SCM. Three well‐known forecasting methods for time series data—moving average (MA), autoregressive integrated MA, and smoothing spline—are considered. We also focus on two representative sales patterns, each of which is with and without a growth pattern, respectively. By applying the proposed scheme to various simulated and real datasets, this research aims to provide SCM practitioners with a general guideline for time series sales forecasting, so that they can easily understand what prediction performance measures and which forecasting method can be considered.     

Big data analytics enhanced healthcare systems: a review

There is increased interest in deploying big data technology in the healthcare industry to manage massive collections of heterogeneous health datasets such as electronic health records and sensor data, which are increasing in volume and variety due to the commoditization of digital devices such as mobile phones and wireless sensors. The modern healthcare system requires an overhaul of traditional healthcare software/hardware paradigms, which are ill-equipped to cope with the volume and diversity of the modern health data and must be augmented with new “big data” computing and analysis capabilities. For researchers, there is an opportunity in healthcare data analytics to study this vast amount of data, find patterns and trends within data and provide a solution for improving healthcare, thereby reducing costs, democratizing health access, and saving valuable human lives. In this paper, we present a comprehensive survey of different big data analytics integrated healthcare systems and describe the various applicable healthcare data analytics algorithms, techniques, and tools that may be deployed in wireless, cloud, Internet of Things settings. Finally, the contribution is given in formation of a convergence point of all these platforms in form of SmartHealth that could result in contributing to unified standard learning healthcare system for future.

     

Performance evaluation of a two‐echelon supply chain with stochastic demand,lost sales,and Coxian‐2 phase replenishment times

This paper deals with a two‐echelon supply chain comprising a retailer and manufacturer. The retailer faces Poisson demand and follows a (S, s) continuous review inventory policy. The manufacturer produces and ships the retailer's orders with random delay that follows the Coxian‐2 distribution. Assuming lost sales at the retailer and infinite capacity at the manufacturer, we try to explore the performance of the supply chain system. The system is modeled as a continuous‐time Markov process with discrete space. The structure of the transition matrices of these specific systems is categorized as block‐partitioned, and a computational algorithm generates the matrices for different values of system characteristics. The proposed algorithm allows the calculation of performance measures—fill rate, cycle times, average inventory (work in progress [WIP])—from the derivation of the steady‐state probabilities. Moreover, expressions for the holding costs and shortage costs are derived.     

煤炭销售运输调度信息系统集成研究

目前煤炭销售运输调度过程存在调度计划编制的粗放性、缺乏系统性等问题。针对这类情况,为了提高国内煤炭销售运输调度计划编制效率、增强月计划完成率与降低调度运输成本,首先,提出了运输调度五过程模型并构建了运输调度数学模型,从而为优化煤炭销售运输调度业务流程提供理论基础。然后,调度业务流程重构后,为了使调度计划编制实现自动化,计算机信息系统化,摆脱编制过程只靠人工的传统方式,研究了基于面向服务的体系结构（ Service-Oriented Ar-chitecture,SOA）思想的煤炭销售运输调度信息系统的集成方案。