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.     

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.     

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.     

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.     

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。     

Developing and Testing a Dynamic Theory of Motor Carrier Safety

Developing an understanding of the longitudinal relationships between different measures of motor carrier safety is important to advance theory and practice regarding this significant supply chain management and public policy issue. In this article, we combine core principles from several theoretical traditions to propose a dynamic theory of motor carrier safety that specifies the longitudinal relationships between three core measures of safety publically reported by the Federal Motor Carrier Safety Administration: unsafe driving, hour‐of‐service compliance, and vehicle maintenance. We test this theory using four years of longitudinal data on motor carrier safety for a random sample of large, for‐hire motor carriers. Results from fitting a vector multivariate autoregressive moving average time‐series model are largely consistent with the theory we propose. We describe the implications of our research for supply chain management theory and practice, summarize limitations, and suggest directions for future research.     

The Role of Big Data and Predictive Analytics in Retailing

The paper examines the opportunities in and possibilities arising from big data in retailing, particularly along five major data dimensions—data pertaining to customers, products, time, (geo-spatial) location and channel. Much of the increase in data quality and application possibilities comes from a mix of new data sources, a smart application of statistical tools and domain knowledge combined with theoretical insights. The importance of theory in guiding any systematic search for answers to retailing questions, as well as for streamlining analysis remains undiminished, even as the role of big data and predictive analytics in retailing is set to rise in importance, aided by newer sources of data and large-scale correlational techniques. The Statistical issues discussed include a particular focus on the relevance and uses of Bayesian analysis techniques (data borrowing, updating, augmentation and hierarchical modeling), predictive analytics using big data and a field experiment, all in a retailing context. Finally, the ethical and privacy issues that may arise from the use of big data in retailing are also highlighted.     

Defining Supply Chain Management: In the Past,Present, and Future

The article titled “Defining Supply Chain Management” published in 2001 in the Journal of Business Logistics has been cited over 4,900 times in the last 17 years. In this paper, we first provide a historical review of how the article originated and the contributions the article made to both the theory and practice of supply chain management (SCM). Next, we highlight the key market and technological changes that have emerged in SCM followed by how the theory proposed in the 2001 article can still be relevant to support SCM research and practice going forward. We also propose ways of configuring a supply chain and partnering across companies to serve customers in an optimal way. We conclude with a call for research on developing new frameworks to better describe, explain, predict, and shed light on the evolving nature of SCM.     

Implementing big data strategies: A managerial perspective

Despite considerable recent advances in big data analytics, there is substantial evidence that many organizations have failed to incorporate them effectively in their own decision-making processes. Advancing the existing understandings, this article lays out the steps necessary to implement big data strategies successfully. To this end, we first explain how the big data analytics cycle can provide useful insights into the characteristics of the environments in which many organizations operate. Next, we review some common challenges faced by many organizations in their uses of big data analytics and offer specific recommendations for mitigating them. Among these recommendations, which are rooted in the findings of strategy implementation research, we emphasize managerial responsibilities in providing continued commitment and support, the effective communication and coordination of efforts, and the development of big data knowledge and expertise. Finally, in order to help managers obtain a fundamental knowledge of big data analytics, we provide an easy-to-understand explanation of important big data algorithms and illustrate their successful applications through a number of real-life examples.     

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.     

Big data and predictive analytics for supply chain and organizational performance

Scholars acknowledge the importance of big data and predictive analytics (BDPA) in achieving business value and firm performance. However, the impact of BDPA assimilation on supply chain (SCP) and organizational performance (OP) has not been thoroughly investigated. To address this gap, this paper draws on resource-based view. It conceptualizes assimilation as a three stage process (acceptance, routinization, and assimilation) and identifies the influence of resources (connectivity and information sharing) under the mediation effect of top management commitment on big data assimilation (capability), SCP and OP. The findings suggest that connectivity and information sharing under the mediation effect of top management commitment are positively related to BDPA acceptance, which is positively related to BDPA assimilation under the mediation effect of BDPA routinization, and positively related to SCP and OP. Limitations and future research directions are provided.     

Implementing Big Data Analytics in Small Firms: A Situated Human Practice Approach

This study identifies and addresses an important gap in the nascent literature on big data analytics, using a longitudinal case study to investigate the implementation and application of big data analytics into a small firm specialized in transport logistics. Our research is rooted in Practice Theory, considering the implementation of new technologies in organizations as a result of multiple social negotiations, interpretations, and interactions. Our findings indicate the importance and centrality of human factors in decision-making and operational implementation, technology representing only a means to a clearly specified and collectively assumed objective. Big data analytics adoption and use in the case-study firm represents a gradual process, with each stage justified by the need to solve the problems caused by heavy and unpredictable road traffic. This approach validates the entrepreneurial effectuation model, which defines a firm's strategy as a fragmented but continuous effort to find and implement effective solutions to the market challenges encountered.     

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.     

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.     

Supply Chain Game Changers—Mega,Nano, and Virtual Trends—And Forces That Impede Supply Chain Design (i.e., Building a Winning Team)

Strategic systems design is essential to structuring and governing a supply chain for competitive advantage. To effectively co‐create value, decision makers must manage the three rights of supply chain design: right players, right roles, and right relationships. Doing this well requires managers discern how the unwritten competitive rules are changing as well as determine firm readiness to compete. As part of this analysis, we briefly explore five emerging “game changers” that represent potential supply chain design inflection points: (1) Big Data and predictive analytics, (2) additive manufacturing, (3) autonomous vehicles, (4) materials science, and (5) borderless supply chains. We also consider four forces that impede transformation to higher levels of value co‐creation: (1) supply chain security, (2) failed change management, (3) lack of trust as a governance mechanism, and (4) poor understanding of the “luxury” nature of corporate social responsibility initiatives. How well managers address sociostructural and sociotechnical issues will determine firm survivability and success.     

Big data in an HR context: Exploring organizational change readiness,employee attitudes and behaviors

This research highlights a contextual application for big data within a HR case study setting. This is achieved through the development of a normative conceptual model that seeks to envelop employee behaviors and attitudes in the context of organizational change readiness. This empirical application considers a data sample from a large public sector organization and through applying Structural Equation Modelling (SEM) identifies salary, job promotion, organizational loyalty and organizational identity influences on employee job satisfaction (suggesting and mediating employee readiness for organizational change). However in considering this specific context, the authors highlight how, where and why such a normative approach to employee factors may be limited and thus, proposes through a framework which brings together big data principles, implementation approaches and management commitment requirements can be applied and harnessed more effectively in order to assess employee attitudes and behaviors as part of wider HR predictive analytics (HRPA) approaches. The researchers conclude with a discussion on these research elements and a set of practical, conceptual and management implications of the findings along with recommendations for future research in the area.     

Business analytics: Why now and what next?

Business analytics is a revolution that is impossible to miss. At its core, business analytics is about leveraging value from data. Instead of being referred to as the ‘sludge of the information age,’ data has recently been deemed ‘the new oil.’ While data can be employed for purposes such as detecting new opportunities, identifying market niches, and developing new products and services, it is also notoriously amorphous and hard to extract value from. In this Guest Editors’ Perspective, we first present a structural framework for deriving value from business analytics. Extracting value from data requires aligning strategy and desirable behaviors to business performance management in conjunction with analytic tasks and capabilities. We then introduce three special articles that provide in-depth insights regarding how business analytics is being employed in the management of healthcare, accounting, and supply chains.