大数据分析在物流及供应链管理中的应用研究进展

随着数字时代的到来,Web 2.0以及Industry 4.0、物联网(Internet)等数字技术相继出现,物流及供应链管理领域中的大数据呈爆发式增长,大数据分析在物流及供应链管理中的应用研究也面临很大的挑战。文章运用文献综述的方式对于以往关于大数据分析在物流及SCM中应用的相关研究进行了总结,分别从大数据采集、储存、分析、应用、增值几个方面开展,并对大数据分析在应用中的问题以及未来的发展趋势提出了一些有效建议。     

大数据对零售业采购与供应链管理的影响

随着信息技术的不断发展,企业在信息化过程中积累了大量的结构化和非结构化数据。目前,大数据正在逐渐为各行业创造价值,其所积蓄的价值将驱动经营和决策的管理变革、商业模式变革等;与此同时,大数据也对零售业的采购与供应链管理产生着重大影响,引发着深刻地变革。本文通过案例分析大数据对当前零售企业的采购与供应链管理发展变化产生的影响,得出大数据分析方法与技术的应用能够推进零售企业的采购与供应链的转型、能够优化其采购与供应链管理中的成本结构、能够有效配置供应链上有限的资源。     

大数据统计分析方法在经济管理领域中的应用

随着我国社会经济的迅速发展,经济管理和统计学之间得到了进一步的融合。 大数据技术能够从数据中找出相应的规律,在对宏观经济进行分析的过程中起到了关键作用,同时也在企业管理中得到了十分广泛的应用。 在这样的情况下,对大数据统计分析方法在经济管理领域中的应用进行分析有着十分重要的意义。 文章首先对于统计分析方法的概念和种类进行阐述,同时对大数据统计所发挥的作用进行分析,从而对于经济管理领域中大数据统计分析方法的具体应用进行探讨,最后对于大数据统计分析方法在经济管理领域中应用的完善措施进行研究。 希望通过文章,能够为经济管理中大数据分析方法的合理应用提供一些参考和帮助。     

浅谈聚类分析在大数据分析中的应用

大数据指的是所涉及的数据量规模巨大到无法通过人工，在合理时间内达到截取、管理、处理、并整理成为人类所能解读的信息。传统的数据分析方法无法对大数据进行分析。本文针对大数据的特性，总结了聚类分析方法再大数据分析中的应用以及对分析结果的评测方法。     

大数据在生鲜电商供应链中的应用研究

生鲜作为电商领域的最后一片蓝海被业界所看好,然而遭遇的诸多"痛点"使得众多商家对其"触网"之路望而却步。生鲜产品因其易逝性的特点,供应链管理被视为生鲜电商制胜的关键。本文从供应链的视角,简述了生鲜电商供应链模式。从大数据的角度阐释了大数据思想在生鲜电商供应链管理中的应用,强调了大数据对于升级生鲜电商供应链的重要性,并对其未来发展趋势做一展望。     

数据分析在广告投放中的应用

现如今大数据时代背景下,用户的数据信息变得更加详细、透明。数据分析在广告投放中的应用,将摆脱以往传统媒介投放模式的不足,通过更为有效、准确的方式赢得用户的青睐,满足用户的需求,促使市场营销切实以用户为本代替过去以商品为中心的传统。文章通过阐述数据分析在广告投放中的价值,分析大数据环境下的广告投放,对数据分析在广告投放中的应用展开探讨研究,旨在为相关人员基于数据分析在广告投放中的价值、大数据环境下的广告投放的数据分析在广告投放中的应用研究适用提供一些思路。     

大数据背景下数据统计分析理念探讨

以计算机、网络为主体的现代信息技术的发展,催化了大数据时代的到来,也对统计科学与数据分析带来了深刻影响。毫无疑问,数据统计分析理念是统计科学理论研究、应用研究的指导思想,面对大规模、复杂数据结构的日益增长,在数据统计分析中,数据的获取、管理、处理等方法也要跟着变革,以顺应大数据统计学发展需要。本文分析了当前大数据统计分析的特点,对统计研究工作过程及数据分析理念进行梳理,提出转变策略和建议。     

大数据在企业财务管理的应用分析

随着科技技术的快速发展,大数据的概念逐渐在社会中进行普及,此种发展背景下,大部分企业都在想尽各种方法来提高企业内部运转效率的方式。经过相关研究发现,利用数据分析可以更快速理解用户需求和企业运营,这使得相关企业纷纷开始借助大数据的优势,不断的提升企业综合竞争能力,而在大数据技术的进一步发展下,企业风险管理的手段也愈来愈深入。对此,本文主要对大数据在企业财务管理中的应用进行了相关分析。     

企业经营分析管理工作中财务大数据的应用分析

随着现代化科学技术的不断发展,在这种背景下我国企业得到了快速的发展,在企业的经营和发展中,为了保障企业的经济效益不受到影响,因此需要对企业发展中的财务管理数据进行专门的分析,这样才能通过分析,及时的处理好企业发展的效益提升。鉴于此,本文针对企业经营分析管理工作中财务大数据分析应用进行了分析,首先进行了财务大数据应用分析的必要性分析,其次进行了企业经营分析财务大数据应用的形式分析,最后就企业经营分析管理中应用财务大数据的方法和策略展开了探索,希望在本文的研究帮助下,能够有效地提升我国企业经营和发展中财务大数据的应用能力。     

大数据应用对制造业企业供应链的影响——以M公司为例

文章通过对M公司应用大数据前后供应链管理系统变化的对比分析,发现M公司在供应链管理实施大数据上存有把供应商纳入自身数据仓库、建立基于大数据技术供应链管理新架构、学习先进数据处理技术等优点,但也存在对大数据重视投入不够、专业人员匮乏、软件融合困难等问题,基于此文章提出建立专门化大数据处理团队、建立以信息聚合为依据的供应链管理系统等建议,对未来企业发展大数据具有一定的借鉴意义.     

Leveraging Big Data to Develop Supply Chain Management Theory: The Case of Panel Data

Increased data availability is poised to shape both business practice and supply chain management (SCM) research. This article addresses an issue that can arise when trying to use big data to answer academic research questions. This issue is that distilled data often have a panel structure whereby repeated measurements are available on one or more variables for a substantial number of subjects. Thus, to fully leverage the richness of big data for academic research, SCM scholars need an understanding regarding the different types of research questions answerable with panel data. In this article, we devise a framework detailing different types of research questions SCM scholars can answer with panel data. This framework provides a basis to categorize how SCM scholars have examined the services supply chain setting of health care with public data regarding hospital‐level patient satisfaction. We extend prior research by testing a series of three questions not yet examined in this area by fitting a series of structured latent curve models to seven years of hospital‐level patient satisfaction for nearly 4,000 hospitals. The discussion highlights theoretical and methodological challenges SCM scholars are likely to encounter as they use the panel data in their research.     

Thirsty in an Ocean of Data? Pitfalls and Practical Strategies When Partnering With Industry on Big Data Supply Chain Research

Increased volume, velocity, and variety of data provides new opportunities for businesses to take advantage of data science techniques, predictive analytics, and big data. However, firms are struggling to make use of their disjointed and unintegrated data streams. Despite this, academics with the analytic tools and training to pursue such research often face difficulty gaining access to corporate data. We explore the divergent goals of practitioners and academics and how the gap that exists between the communities can be overcome to derive mutual value from big data. We describe a practical roadmap for collaboration between academics and practitioners pursuing big data research. Then we detail a case example of how, by following this roadmap, researchers can provide insight to a firm on a specific supply chain problem while developing a replicable template for effective analysis of big data. In our case study, we demonstrate the value of effectively pairing management theory with big data exploration, describe unique challenges involved in big data research, and develop a novel and replicable hierarchical regression‐based process for analyzing big data.     

Data Science,Predictive Analytics,and Big Data: A Revolution That Will Transform Supply Chain Design and Management

We illuminate the myriad of opportunities for research where supply chain management (SCM) intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of an SCM data scientist. Such knowledge is crucial to develop future supply chain leaders. We propose definitions of data science and predictive analytics as applied to SCM. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of SCM and DPB.     

Sustainable Supply Chains in the Age of AI and Digitization: Research Challenges and Opportunities

Sustainability has become a global corporate mandate with implementation impacted by two key trends. The first is recognition that global supply chains have a profound impact on sustainability which requires “greening” the entire supply chain. The second is technology—digitization, artificial intelligence (AI), and “big data”—which have become ubiquitous. These technologies are impacting every aspect of how companies organize and manage their supply chains and have a powerful impact on sustainability. In this essay, we synthesize current dominant themes in research on sustainable supply chains in the age of digitization. We also highlight potential new research opportunities and challenges and showcase the papers in our STF.     

Click Here for a Data Scientist: Big Data,Predictive Analytics,and Theory Development in the Era of a Maker Movement Supply Chain

Predictive analytics is impacting many diverse areas, ranging from baseball and epidemiology to forecasting and customer relationship management. Manufacturers, retailers, software companies, and consultants are creatively discovering new applications of big data using predictive analytics in supply chain management and logistics. In practice, predictive analytics is generally atheoretical; however, we develop a 2 × 2 model to explain the role of predictive analytics in the theory development process. This 2 × 2 model shows that in our discipline we have traditionally taken one path to theory development, but that predictive analytics can be a salient component of a comprehensive theory development process. The model points to a number of research questions that need to be addressed by our research community. These questions are not just highly relevant to the academic community but also in urgent need of answers to help practitioners execute the right strategies with greater precision and efficiency. We also discuss how one disruptive trend, the maker movement, changes the nature of who the producers are in the supply chain, making big data even more valuable. As we engage in higher levels of dialogue we will be able to make meaningful progress addressing these vital research topics.     

Data Science,Predictive Analytics,and Big Data in Supply Chain Management: Current State and Future Potential

While data science, predictive analytics, and big data have been frequently used buzzwords, rigorous academic investigations into these areas are just emerging. In this forward thinking article, we discuss the results of a recent large‐scale survey on these topics among supply chain management (SCM) professionals, complemented with our experiences in developing, implementing, and administering one of the first master's degree programs in predictive analytics. As such, we effectively provide an assessment of the current state of the field via a large‐scale survey, and offer insight into its future potential via the discussion of how a research university is training next‐generation data scientists. Specifically, we report on the current use of predictive analytics in SCM and the underlying motivations, as well as perceived benefits and barriers. In addition, we highlight skills desired for successful data scientists, and provide illustrations of how predictive analytics can be implemented in the curriculum. Relying on one of the largest data sets of predictive analytics users in SCM collected to date and our experiences with one of the first master's degree programs in predictive analytics, it is our intent to provide a timely assessment of the field, illustrate its future potential, and motivate additional research and pedagogical advancements in this domain.     

大数据、人工智能与厂商竞争路径

经济学对市场竞争路径的学理性分析，主要集中在价格确定、产量确定、规模经济、产业组织等方面，而对科技进步引发市场竞争路径的变化并没有足够的关注。其实，市场竞争路径变化的底蕴是科技进步，只是经济学家在分析市场竞争路径时偏好于将科技因素作为外生变量处理。大数据和人工智能等的发展可谓是一场史无前例的科技革命，它对人类经济活动产生广泛而深刻的影响主要表现为：大数据及其运用怎样影响厂商投资经营，大数据与机器学习等人工智能手段相融合会在哪些方面改变厂商竞争路径，厂商如何提高数据智能化和实现网络协同化，在什么样的条件下会出现行业垄断，等等。文章的基本分析观点是：厂商竞争路径变化是贯穿于大数据、互联网和人工智能等相互融合过程的一种现象，这种现象对应于新科技进步和运用的不同层级；微观经济分析需要将新科技因素作为内生变量，通过分析大数据、机器学习与厂商竞争路径之间的关联，揭示厂商竞争路径变化机理以及由此引致的产业组织等问题。     

A Note on the Use of Survey Research Firms to Enable Empirical Data Collection

This forward thinking article examines the risks and rewards of using survey research firms to enable empirical data collection, and issues a cautionary note about its application. An exposition and discussion of this form of data collection in supply chain management is relevant today, due to the “survey‐fatigue” among the population of business professionals from whom we seek a response. While this approach has some history in other disciplines, it is still relatively new among supply chain management researchers. To help supply chain management scholars assess the appropriateness of this type of data collection method, this forward thinking article provides invaluable guidance as derived from the authors' recent experiences with the approach. As such, we share our observations and lessons learned. The conclusion is that the use of survey research firms for empirical data collection can be a viable, alternative approach to self‐administered surveys. However, care should be taken in its application.     

海关大数据知识图谱构建技术及应用

海关业务改革的不断深化对海关管理提出了新的要求。本文结合信息爆炸时代海关管理面临的挑战及业界知识图谱的应用情况,全面分析了在海关管理中引入知识图谱的必要性,提出了海关大数据知识图谱的构建技术流程及应用场景,旨在为智慧海关建设提供参考。     

The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources

Big data analytics have transformed research in many fields, including the business areas of marketing, accounting and finance, and supply chain management. Yet, the discussion surrounding big data analytics in human resource management has primarily focused on job candidate screenings. In this article, we consider how significant strategic human capital questions can be addressed with big data analytics, enabling HR to enhance overall firm performance. We also examine how new data sources that help assess workforce performance in real time can assist in the identification and development of the knowledge stars that contribute to firm performance disproportionately as well as help reinforce firm capabilities. But in order for big data analytics to be successful in the HR field, regulatory and ethical challenges must also be addressed; these include privacy concerns and, in Europe, the General Data Protection Regulation (GDPR). We conclude by discussing how big data analytics can facilitate strategic change within HR and the organization as a whole.