供应链中大数据分析应用研究综述

近年来,供应链管理实践中产生的数据量呈指数增长,大数据分析在供应链中存在巨大的发展空间,然而,当前对于供应链中大数据分析应用还缺乏深入研究。通过相关文献梳理,对国外供应链中大数据应用进行深入探析,结合国内外研究成果回顾了不同行业供应链中的大数据应用及其商业价值,鉴于已有研究,对未来该领域研究进行大胆展望。文章将丰富国内供应链中的大数据分析应用理论,为学术界和实务界在供应链管理各个方面实施大数据分析应用提供指导。     

Integrating Analytics Through the Big Data Information Chain: A Case From Supply Chain Management

The benefits of good decision making by a distributor often have broad leverage across a supply chain, and data science provides a comprehensive framework for making this possible. We present a case study of an ongoing partnership between the authors and corporate managers at a distributor of heating, ventilating, and air‐conditioning products. We describe in detail the “vertical integration” of our analytical tools through a long chain of data scientific activities, backward to raw data, and forward to visually appealing output, in an organization with legacy information technology infrastructure. The models are applied to a large‐scale data set, and spreadsheet‐based decision support tools that include useful visualization capabilities for the firm are illustrated. We also offer this case as a blueprint for building a collaborative research relationship between academia and industry.     

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.     

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.     

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.     

大数据统计分析方法在经济管理领域中的实践探析

随着我国经济的快速发展,经济学和管理学不断融合加深,基于大数据统计分析法可以获得经济的发展走向和规律,该方法被广泛运用到经济管理学领域,并能更好提升工作效率。依据大数据进行数据统计、分析的方式对于企业产品销售有着重要的意义,有效的数据统计分析不仅能及时反馈产品的运营情况,还能反馈出市场对产品的需求量,有助于企业产品在生产和运营环节根据市场需求及时调整。基于此,本文对该方法在经济管理领域中发挥的作用进行了全面分析与研究。     

浅谈大数据时代下的会计专业学生的自主学习

大数据时代的到来为会计学习提供了丰富的数据资源,也给传统的会计学习模式带来了挑战。在大数据时代背景下,面对快速发展的网络科技,会计院校的学生缺乏自主学习意识和对新知识的认知感,对数据的收集、分析处理的敏感度弱。在大数据背景下增强会计专业学生的自主学习能力,需要教师创新教学方式和内容,充分利用网络会计资源和学生数据,开展多角度、全方位的教学实践,培养学生自主学习意识和能力;学生应借助大数据,更好地了解自己的学习状况,有针对性开展自主学习。     

用CAS理论分析供应链管理系统

供应链组织的核心纽带不是资金,而是以管理、技术、市场、信息和理念等企业无形资产为核心而结成的一个虚拟性组织,供应链的形成过程是各个企业成员在以上述核心纽带为基础优势组合时按照“适者生存”原则所进行的自然选择的结果,最终达成了一条企业认为的“最佳”组合(即系统),是目前乃至将来的经济组织发展的主流。因此,如何使供应链系统更加有效的运行,以及研究供应链管理复杂系统的性质、内部模式和规律将是21世纪最具有竞争力、最具有前途的研究领域之一。     

大数据时代下企业人力资源管理模式的创新研究

社会快速发展,经济稳步提升,网络技术的出现在一定程度上给人们的工作和生活带来了便利。企业人力资源管理者必须转变观念,随着时代的发展而做出相应地改变,积极使用网络信息技术在庞大的信息中筛选有用数据,同时根据企业自身的运营情况有针对性地制定人才管理方式,以此提升企业的市场竞争力,为其持续发展提供保障。对此,本文进行了细致的分析,以供参考。     

The Post-Promotion Dip Puzzle: What do the Data Have to Say?

One of the puzzles of store-level scanner data is the lack of a dip in quantity sold in the weeks following a promotion. Such a dip is predicted by a consumer inventory model. During a promotion consumers buy more, not only for current consumption, but stockpile for future consumption. The predictions of such a model have been confirmed by household-level data yet seem harder to find in aggregate brand- or category-level data. We re-examine this puzzle and reach two conclusions. First, the effects at the household-level are present, but are much smaller than previously found. Our estimates are different because we control for household heterogeneity in a more general way than most previous work. This suggests that since the effects are small they might be harder to spot in aggregate data. Second, we show that the dip is present in the aggregate data, once we control for additional promotional activity, like feature and display. The latter has an opposing dynamic effect that masks the existence of the post-promotion dip.     

Finding the True Voice of the Customer in CPG Supply Chains: Shopper‐Centric Supply Chain Management

Demand and supply integration is the subject of increasing scholarly attention. The theoretical emphasis on combining market and supply chain data as the basis for strategic and operational decision making is particularly relevant in the context of Consumer Packaged Goods (CPG) supply chains, and offers the basis for advancing our understanding and knowledge in this field. Point‐of‐sale (POS) data are commonly used as the demand signal in CPG supply chains. Using empirical data, this research demonstrates that POS data can be influenced by nondemand factors. We present a number of issues raised by this finding.     

The Evolution of Resilience in Supply Chain Management: A Retrospective on Ensuring Supply Chain Resilience

We opened our 2010 paper in the Journal of Business Logistics with a 6th century quote by Heraclitus – “The only constant is change.” This immutable law certainly holds in today's volatile business world, especially for supply chain management, and has been the driving factor behind the interest in resilience. Since the seminal works of the mid‐to‐late 1990s and early 2000s, the supply chain discipline has progressed toward a common understanding of resilience. But we're not there yet. This paper reflects on the impact of our 2010 paper, and envisions future opportunities for advances in resilience that will improve both day‐to‐day business continuity and long‐term sustainability. We are honored to be selected for this 40th Anniversary Issue of the Journal of Business Logistics, and proud that our paper has been recognized as the most cited of the decade.     

When worlds collide: The implications of panel data-based choice models for consumer behavior

Panel data, both diary and scanner, have been analyzed by marketing scientists for over thirty years. One of the important uses of panel data is to better understand consumer behavior by developing and testing hypotheses using the revealed preference data rather than experimental data that uses only self-reported behavior or behavior in a simulated choice environment. The purpose of this paper is to suggest areas of research where panel data can be used to better understand the underlying behavior of the panel members.     

Threats to the Validity of Logistics and Supply Chain Management Research

To increase the relevance of logistics and supply chain academic research, this paper recommends the development and testing of middle‐range theory and practice‐level theory. Yet, there are a number of research issues that arise when academic researchers test middle‐range and practice‐level theory, both in measuring constructs and in testing theoretical relationships between constructs. Concerning the measurement of constructs, this paper recommends that academic researchers pay more attention to content validity and undertake rigorous processes to ensure content validity. In addition, academic researchers need to more explicitly define constructs as either reflective or formative. If the construct is defined as formative, then the traditional statistical approaches to validate these measurement scales are not recommended. The appropriate use of employing single‐item measures for concrete constructs is discussed. In regard to conducting hypothesis tests, research issues associated with multicollinearity and omitted variable bias are discussed. Relative weight analysis is ideal for testing theoretical models and research hypotheses when survey data are obtained, multicollinearity is present, and there are a large number of independent variables predicting a dependent variable. Thus, relative weight analysis is ideal for testing research hypotheses in logistics and supply chain management.     

A Multivariate Time‐Series Examination of Motor Carrier Safety Behaviors

Motor carriers’ operational safety affects multiple stakeholders including truck drivers, motor carriers, insurance companies, shippers, and the general public. In this article, I devise and test theory regarding motor carriers’ longitudinal performance for three classes of safety behaviors linked to carriers’ accident rates—Unsafe Driving, Hours‐of‐Service Compliance, and Vehicle Maintenance—tracked by the Federal Motor Carrier Safety Administration as part of the Compliance, Safety, and Accountability (CSA) program. Specifically, I draw on core concepts from sociological agency theory and resource dependency theory to devise middle‐range theory that generates never‐before‐tested hypotheses regarding carriers’ longitudinal safety performance for these classes of safety behaviors after the start of the CSA program. The hypothesized predictions are tested by fitting a series of multivariate latent curve models to four years of panel data for a random sample of 484 large, for‐hire motor carriers operating in the United States. The empirical findings corroborate the theoretical predictions and remain after robustness testing. These findings have important implications for scholars, motor carrier managers, procurers of motor carrier transportation services, and public policy makers.     

Toward Greater Validation of Supply Chain Management Theory and Concepts: The Roles of Research Replication and Meta‐Analysis

Although researchers have been working for over three decades to identify, construct, and test the critical concepts and theories that comprise the supply chain management (SCM) discipline, the field’s theoretical basis has yet to be thoroughly vetted through repetitive, rigorous study. Emerging theories in SCM offer great explanatory potential, but if they are to stand the tests of time, place, and reason, research findings must be accumulated and synthesized. For the field to prosper, we advocate the execution of multiple studies that replicate findings across contexts while simultaneously encompassing relevant theoretical extensions. Furthermore, as SCM research streams mature, scholars should then employ meta‐analytic techniques to gain consensus across studies, such that findings can be more confidently extolled to both academic and practitioner constituencies. In this article, we evaluate the progress of replication and meta‐analysis to date within the SCM field and provide some prospects for thematic research that could perpetuate accelerated validation.     

Fit,Bias, and Enacted Sensemaking in Data Visualization: Frameworks for Continuous Development in Operations and Supply Chain Management Analytics

Data visualization has a critical role in the advancement of modern data analytics. Visualization lends assurances to data validity and completeness, as well as to the effectiveness of cleaning and aggregation tactics. It provides the means by which to explore and discover relationships otherwise hidden from default assumptions in statistical modeling. Strong visualization is also fundamental to end‐result conveyance and audience interpretation. But how can one ensure that strength? How can one avoid developing representations that are marginal in value, or worse misleading? In this paper, I will discuss theory, evidence, and practical approaches to managing data visualization development, viewing data visualization not simply as an outcome but as a continuous process and facet of organizational culture.     

The Relationship Between Strategic Supply Chain Integration and Performance: A Meta‐Analytic Evaluation and Implications for Supply Chain Management Research

Although research evaluating the impact of supply chain integration on performance has advanced substantially in the last decade, inconsistency and considerable variability of empirical findings leave unanswered questions for both research and practice. Using a meta‐analysis, we examine empirical studies to clarify the actual relationship, suggest new directions, and ultimately contribute toward the development of supply chain management theory. We focus on “strategic” supply chain integration rather than on functional or operational/tactical studies, which would weaken the practical value of the analysis and findings. To ascertain focus and homogeneity of the sample, we adopt a rigorous search protocol and sample construction. We find that integration–performance relationships are complex and nuanced such that integration should not be universally viewed as improving performance. We identify relationships that are more generalizable and also those that need additional scrutiny. Finally, we discuss the implications of our findings and provide directions for future research.     

新冠肺炎疫情对供应链弹性管理的启示

新冠肺炎疫情将对中国产业供应链的持续竞争力形成挑战,使企业面临人力短缺、成本增加、现金流紧张和供应链不确定性增高等问题,在企业供应链的原材料供应、采购管理、生产复工、物流以及市场等方面均会产生一定的负面影响,这对供应链弹性管理提出了新的要求。在政府层面,应建立综合、协同性的产业供应链风险管理和沟通机制,构建基于事件的产业供应链预警体系,保障企业供应链运营的资源体系,推动产业平台和产业集群带动供应链参与者建立供应链弹性体系。在企业层面,应建立基于企业事件的供应链预警体系,根据供应链预警信息和状况,合理规划供应链运营方式,运用数字化能力建设企业供应链弹性体系,稳定供应链运营,提升供应链效率。     

The Gravity Model Analysis of Bangladesh's Trade: A Panel Data Approach

The article applies generalized gravity models to analyze Bangladesh's bilateral trade pattern using the panel data estimation technique. The results reveal that Bangladesh's trade is positively determined by the size of the economies, per capita gross domestic product differential and openness of the trading countries. Bangladesh's exports are positively determined by its income, partner countries' total import demand and openness, but negatively determined by partner countries' income and domestic inflation. Bangladesh's imports are positively determined by income of trading countries and degree of openness of the partner countries and negatively determined by partner countries' inflation. Transportation costs affect Bangladesh's trade negatively.