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.     

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.     

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。     

Mapping the Landscape of Future Research Themes in Supply Chain Management

Supply chain researchers are confronted with a dizzying array of research questions, many of which are not mutually independent. This research was motivated by the need to map the landscape of research themes, identify potential overlapping areas and interactions, and provide guidelines on areas of focus for researchers to pursue. We conducted a three‐phase research study, beginning with an open‐ended collection of opinions on research themes collected from 102 supply chain management (SCM) researchers, followed by an evaluation of a consolidated list of themes by 141 SCM researchers. These results were then reviewed by 10 SCM scholars. Potential interactions and areas of overlap were identified, classified, and integrated into a compelling set of ideas for future research in the field of SCM. We believe these ideas provide a forward‐looking view on those themes that will become important, as well as those that researchers believe should be focused on. While areas of research deemed to become most important include big data and analytics, the most under‐researched areas include efforts that target the “people dimension” of SCM, ethical issues and internal integration. The themes are discussed in the context of current developments that the authors believe will provide a valuable foundation for future research.     

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.     

Does the Starting Point Matter? The Literature‐Driven and the Phenomenon‐Driven Approaches of Using Corporate Archival Data in Academic Research

Despite recent and perhaps myopic criticisms of archival data with regard to supporting causal theoretical claims, it would be folly to disregard the exploratory and grounded theory development potential that these substantial, rich, and timely archives hold. The question then becomes one of how academics might tap into such archives. This paper considers this issue from a pragmatic perspective, drawing on the experiences of various academics with extensive experience in constructing data‐access relationships with industry. With the support of scholars who published their work using corporate archival data in leading academic journals, we suggest a phenomenon‐driven approach paralleled with the traditional literature‐driven approach in academic studies. This paper highlights best practices, pitfalls, and future opportunities, with the aim of serving as a guide for intrepid scholars interested in capitalizing on contemporary big data initiatives supported at many firms.     

SUPPLY CHAIN MANAGEMENT AND ITS RELATIONSHIP TO LOGISTICS,MARKETING, PRODUCTION,AND OPERATIONS MANAGEMENT

The renaming of the Council of Logistics Management (CLM) to the Council of Supply Chain Management Professionals (CSCMP) ushered in some interesting definitional dialogue and debate within the practitioner and academic communities. Inherent in emerging definitions is the notion that SCM encompasses activities traditionally considered aspects of production, logistics, marketing, and operations management. Defining SCM in such a broad scope (i.e., a “within” and “across” functions perspective), while considered by many scholars as the true representation of the essence of SCM, creates confusion regarding the appropriate organizational level within a business that is best suited for managerial decision making regarding the phenomenon. This paper contributes to the emerging SCM dialogue by highlighting the functional spaces (the “within” function perspective), relationships, and conceptual overlaps (the “across” functions perspective) between marketing, logistics, production, operations, and supply chain management. By comparing and contrasting the literature‐based conceptual boundaries of each discipline, a framework is proposed that more clearly captures the essence of the SCM decision making sphere. Managerial insights and future research implications are presented.     

Supply Chain Management at the Crossroads: Divergent Views,Potential Impacts,and Suggested Paths Forward

The academic field of supply chain management (SCM) is comprised of scholars holding four different worldviews based on their academic heritages. This situation presents both threats and opportunities for SCM scholarship and practice. In this brief paper, we explain why we see this occurring, and actions that academics might take in order to form a more cohesive and effective SCM discipline.     

Accessing Online Data: Web‐Crawling and Information‐Scraping Techniques to Automate the Assembly of Research Data

There is a growing interest in leveraging alternate sources of empirical data, with an increasing emphasis being placed on the Internet. This paper serves as a primer for supply chain management (SCM) researchers that may be interested in leveraging Internet‐based sources for their own research, but perhaps not familiar with how to begin. Here, definitions and concepts critical to successful implementation in practice are provided. In addition, concrete, discipline‐relevant examples accompany the discussion, and are aided by a fully detailed online code supplement. Performance enhancements are discussed, as well as associated caveats and limitations. Additionally, insights and guidance are offered on the unique responsibilities for researchers to uphold the ethical spirit of scientific research when continuing along these paths. Pragmatic issues related to the application of these techniques are presented for consideration of individual researchers and the SCM community as a whole.     

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.     

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.     

Opportunities for and Pitfalls of Using Big Data in Advertising Research

This editorial introduces the special section on big data. We define big data by examining how it is, or will be, created in advertising environments. We propose a conceptual framework for understanding the different types of digital advertising touch points that create big data, and use the framework for identifying research opportunities. We discuss the types of research questions that big data can inform, including developing and testing theories, identifying insights, and optimizing the delivery of messages. New methods that advertisers will need to use big data are identified. Recommendations are provided for how to think about and approach big data. Using the framework, we identify specific opportunities for advertising researchers to use big data. We also discuss pitfalls in using big data.     

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.     

Effects of Motor Carriers’ Growth or Contraction on Safety: A Multiyear Panel Analysis

Motor carrier safety remains a highly relevant issue for supply chain managers and scholars because carriers’ safety affects supply chains as well as the welfare of the motoring public. This article enriches understanding regarding this topic by investigating how motor carriers’ growth or contraction since the start of the Compliance, Safety, and Accountability (CSA) program in 2010 affects their safety performance. Drawing on core principles from theories regarding internal adjustment costs from economics and nonscale free capabilities from management, we explain why carriers’ growth or contraction should differentially affect various safety metrics tracked by the CSA program. To test our theory, we assemble a multiyear panel data set for over 1,000 of the largest for‐hire motor carriers operating in the United States by melding together several different governmental data sources. We fit a series of multivariate seemingly unrelated regression models to test our hypothesized effects. Our results corroborate our theorized predictions and are robust to alternative model specifications. We conclude by detailing how this work contributes to extant theory, summarizing managerial and policy implications, highlighting limitations, and suggesting directions for further pursuit.     

Expanding Horizons and Deepening Understanding via the Use of Secondary Data Sources

While primary data analysis has been popular in logistics and supply chain research, secondary data methods have been overlooked. These methods, however, have the potential to generate a variety of important opportunities to expand the horizons of logistics and supply chain research. In this article, we emphasize the use of secondary data analysis and how it can address contemporary challenges in logistics and supply chain research. Our review of the logistics and supply chain literature identifies six important methodologies that can be useful for secondary data generation and analysis. We discuss how these methods can help effectively address various logistics research questions.     

Maximizing the Potential of Contemporary Workplace Monitoring: Techno‐Cultural Developments,Transactive Memory,and Management Planning

Nonintrusive data collection and analysis technologies are increasingly being used to monitor worker behavior in the global workplace. This essay explores the factors that can affect the extent to which management, work teams, and even individuals can benefit from real‐time data monitoring of worker productivity, coordination, and performance. Leveraging organizational information processing and transactive memory systems theories, I develop a theoretical framework for how access to real‐time data can impact team coordination activities and how the implementation of work monitoring technology and analytics might be best approached. Last, I present a set of future research opportunities that supply chain scholars should pursue to examine how the real‐time monitoring of work affects team performance.     

What is the Right Supply Chain for Your Shopper? Exploring the Shopper Service Ecosystem

The emergence of shopper marketing represents a major strategic force in consumer goods branding and retailing, and poses significant challenges to pre existing supply chain thought that most scholars have yet to address. In order to address the marketing and supply chain challenges presented by shopper marketing initiatives, scholars from both disciplines can find common ground by focusing on the shopper instead of consumers or products. The issue is no longer one of matching a supply chain to a product. Instead, marketers and supply chain managers need to ask, “What's the right supply chain for our shopper?” The shopper service ecosystem framework developed in this paper brings together the service dominant logic of marketing with the service ecosystem perspective in supply chain management. Future research frontiers suggest research directions that bridge the marketing and supply chain disciplines to create a multidisciplinary foundation for future research in the shopper marketing realm.     

Customer and Supplier Portfolios: Can Credit Risks be Managed Through Supply Chain Relationships?

While supply chain risk has been the subject of an ever‐increasing amount of research attention, the importance of credit risk has been less studied, at least by supply chain researchers. Yet, there may be risks inherent within a firm's portfolios of supply chain relationships that are manifested in the credit risk of a firm. Moreover, portfolio characteristics may serve as a signal to the external market regarding these risks. While customer and supplier portfolio characteristics may impact the firm's exposure to risks, the specific issue of how portfolio characteristics relate to credit risk has rarely been examined by supply chain scholars. This research bridges extant works in supply chain management and finance to relate supply chain characteristics to a critical reputational outcome, namely credit ratings. In this research, we utilize a sample of firms that recently underwent an initial public offering to empirically examine the theoretical predictions of Resource Dependence Theory regarding the relationships between different supply chain portfolio characteristics and credit risk.     

LOGISTICS AND SUPPLY CHAIN MANAGEMENT APPLICATIONS WITHIN A GLOBAL CONTEXT: AN OVERVIEW

This paper reviews 726 academic research studies covering logistics and supply chain management (L'SCM) applications within a global context. Articles are analyzed according to their publication year and outlet, common themes, and countries or regions investigated. The sample was drawn from the ABI/INFORM Global database, which includes almost 1,800 journal titles. The review offers a starting point for researchers, graduate students, faculty members or practitioners examining international L'SCM applications. The article also draws attention to the wide array of global L'SCM studies conducted, their settings and viewpoints. Additionally, this paper highlights some of the most interesting works and draws attention to lesser‐researched countries and regions of the world. The overview also provides an introduction for practitioners and managers to what academic research has covered in the area of global L'SCM. Practitioners can use this review as a starting point for the identification of relevant and useful references for their particular task.     

A supply chain management framework for services

While services represent the largest sector of the global economy, 86.8% in the United States, most supply chain management (SCM) research is focused on product flows. Executives in manufacturing firms have benefited from frameworks created to implement SCM processes, but this is not the case for their counterparts in service companies. The two most cited SCM frameworks for services have methodological and conceptual deficiencies. Drawing upon the service-dominant logic of marketing and using an empirical research approach, we developed supply chain structure maps for nine service firms. Our findings indicate that the supply chain structure maps of service companies are comparable to those in the product-based literature, which supports the service-dominant logic. Then, we identified the six key processes that constitute a framework to manage service supply chains. The customer relationship management and the supplier relationship management processes form the critical links in the supply chain, and the other four processes are coordinated through this linkage. The SCM framework for services provides direction for executives in service firms who need to increase cross-functional integration within their firms and with other members of the supply chain. For academics, 12 avenues for future research are identified.