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.     

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.     

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.     

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.     

Costs,culture and life decisions: analysing the factors that influence enrolment in master's level education in Germany

Through evaluation of current literature and survey data, this exploratory study aimed to determine factors that influence enrolment in master's level education and attendant delay of life decisions. A total of 134 master's students at a German university were surveyed. The mean age was 25 years and respondents were nearly equal parts German and non‐German nationalities. Results show how cultural factors motivate enrolment in master's programmes and how enrolment influences the timing of other important life decisions. Respondents expected significant benefits to social status, job placement and income. While this small‐scale study does not include all decision factors and its findings are not fully generalizable, we hope it motivates further research in this area.     

Master's Level Education in Business Marketing: Quo Vadis?

ABSTRACT

Given the paucity of knowledge on the state of business marketing education at the master's level, we conducted an exploratory survey of instructors at selected universities in North America and Europe. We supplemented results from this survey with discussions with our colleagues who teach business marketing at the master's level, and a review of articles on master's level programs.

Through our investigations, we discovered that there is a shortage of management cases that address contemporary issues in business marketing. We learned that nearly half of survey participants do not use a textbook and that the remaining participants use a wide variety of textbooks. We observed that many instructors still teach business marketing courses from a traditional functional perspective. Instead, we believe that business marketing courses must address a host of emerging issues such as value and its assessment, business process reengineering, global marketing, working relationships and business networks, and cross-functional coordination issues, among other things.

We believe that scholars can rejuvenate the discipline by tailoring business marketing courses to the new generation of accelerated master's programs, by creating a network for sharing information on new cases and teaching materials, and by writing more relevant and timely business marketing cases. Finally, we believe that the time is right for a faculty consortium on teaching business marketing.     

The power of prediction: predictive analytics,workplace complements,and business performance

Anecdotes abound suggesting that the use of predictive analytics boosts firm performance. However, large-scale representative data on this phenomenon have been lacking. Working with the Census Bureau, we surveyed over 30,000 American manufacturing establishments on their use of predictive analytics and detailed workplace characteristics. We find that productivity is significantly higher among plants that use predictive analytics—up to $918,000 higher sales compared to similar competitors. Furthermore, both instrumental variables estimates and the timing of gains suggest a causal relationship. However, we find that the productivity pay-off only occurs when predictive analytics are combined with at least one of three workplace complements: significant accumulation of IT capital, educated workers, or workplaces designed for high flow-efficiency production. Our findings support claims that predictive analytics can substantially boost performance, while also explaining why some firms see no benefits at all.

     

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.     

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.     

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.     

Involving consumers in the work of a data linkage research unit

We describe and reflect on the work of a Consumer Panel for a data linkage research unit in Wales, and show how the members are inputting into plans for the future. Our work is centred on conducting health‐related data linkage research using anonymously linked, routinely collected data from across Wales via the Secure Anonymous Information Linkage system. In recognition of the importance of including patients and the public in health‐related research, we have established a Consumer Panel to strengthen this voice in our work, and there are currently 10 members (4 men and 6 women) from across Wales, with a range of health‐related areas of interest. A review of Panel activities was carried out after the first year, and all members were invited to provide their views via a questionnaire survey using structured and free‐text responses. Initial feedback, obtained after the first meeting, was tentatively positive, and the questionnaire survey identified practical measures for improvement and future work. We have found the Consumer Panel to be a valuable addition to our work in the rapidly growing area of data linkage research. The views of Panel members provide a positive outlook and a fresh, and sometimes unexpected, perspective on various issues. The lessons we have learned, and our experience of involving the Panel in various aspects of our work, may be of value to others seeking to work with consumers in data linkage research, to researchers in general and to consumers themselves.     

Curriculum Internationalization at AACSB Schools: Immersive Experiences,Student Placement,and Assessment

This article presents results from a survey of AACSB-accredited business schools’ progress in internationalizing their curricula in view of a recent AACSB report. We present data on the use of immersive experiences, degree of success in student placement in internationally oriented careers, and assessment of internationalization efforts. The results indicate growth of internationalization activities at virtually all schools as expected, but these efforts may not always match AACSB recommendations. For instance, AACSB criticized business programs for not coordinating internationalization activities in a strategic manner to improve courses and develop skills needed by international managers. Our survey finds that many schools do not attempt to tie their international experiences to specific courses, but they report the experiences are used to build skills students need. Most institutions also do not examine job placement as a measure of curriculum internationalization success. We find that many schools do not assess the outcomes of their internationalization efforts in a way that can demonstrate whether or not recent AACSB suggestions are being met.     

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.     

Applying a Danish version of the Learning Transfer System Inventory and testing it for different types of education

The purpose of this study was to answer two research questions. First, will an exploratory factor analysis of a Danish version of the Learning Transfer System Inventory (LTSI) result in a factor structure which is consistent with the original American LTSI factor structure? Second, does the mean score in the factor analysis vary in a statistically significant way across different types of education, suggesting that the LTSI may be more suitable a measure in some educational contexts than others? To answer these questions survey data from 411 students following four different types of formal education – adult vocational training, academy profession programs, diploma programs and master's degree programs – were analysed. Principal component analysis was used to answer research question one. Factorial ANOVA was used to answer question two. The analysis resulted in fewer factors than in the original American LTSI. The study also found that the mean score differs in a statistically significant way between the different types of education. Specifically, LTSI may be more suitable in measuring transfer systems and therefore promoting transfer in relation to short courses offering training in specific skills than in relation to long‐term continuing education.     

An investigation of skill requirements for business and data analytics positions: A content analysis of job advertisements

Presently, analytics degree programs exhibit a growing trend to meet a strong market demand. To explore the skill sets required for analytics positions, the authors examined a sample of online job postings related to professions such as business analyst (BA), business intelligence analyst (BIA), data analyst (DA), and data scientist (DS) using content analysis. They present a ranked list of relevant skills belonging to specific skills categories for the studied positions. Also, they conducted a pairwise comparison between DA and DS as well as BA and BIA. Overall, the authors observed that decision making, organization, communication, and structured data management are key to all job categories. The analysis shows that technical skills like statistics and programming skills are in most demand for DAs. The analysis is useful for creating clear definitions with respect to required skills for job categories in the business and data analytics domain and for designing course curricula for this domain.     

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.     

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.     

How data analytics is transforming agriculture

Two discussions about the interaction between data analytics and competitive analysis have been taking place in the past decade: one focusing on micro-level firm capabilities and the other on macro-level industry competitiveness. We seek to integrate the micro- and macro-level analyses via the lenses of firms in agricultural input markets. Agriculture is undergoing a tremendous transformation in the collection and use of data to inform smarter farming decisions. Precision agriculture has brought a heightened degree of competition for input supply firms, forcing greater interactions among friends and foes.     

Do Retailers Benefit from Deploying Customer Analytics?

Prior research has documented a general positive relationship between the deployment of customer analytics and firm performance. In this research we focus on the retailing industry, an industry characterized by tight margins that lead to careful scrutiny of all business investments. Using survey data from 418 top managers based in the Americas, Europe Middle East and Africa (EMEA) and Asia, we show that of the eight industries in the study, firms in the retail industry have the most to gain from deploying customer analytics. However, we also find that not only do many retailers not perceive this potential gain, they do not invest in customer analytics at an economically appropriate level. Thus we identify a gap between perception and reality concerning the potential for customer analytics in the retail industry that has both theoretical and practical implications.