Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management
TL;DR: In this article, the authors 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.
Abstract: 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.
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TL;DR: In this article, the authors present a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions.
1,267 citations
TL;DR: In this article, the authors classify the literature on the application of big data business analytics (BDBA) on logistics and supply chain management (LSCM) based on the nature of analytics (descriptive, predictive, prescriptive) and the focus of the LSCM (strategy and operations).
938 citations
TL;DR: This paper analyses recent literature and case-studies seeking to bring the discussion further with the help of a conceptual framework for researching the relationships between digitalisation and SC disruptions risks and emerges with an SC risk analytics framework.
Abstract: The impact of digitalisation and Industry 4.0 on the ripple effect and disruption risk control analytics in the supply chain (SC) is studied. The research framework combines the results from two is...
884 citations
TL;DR: The data quality problem in the context of supply chain management (SCM) is introduced and methods for monitoring and controlling data quality are proposed and highlighted.
652 citations
TL;DR: This article reviews the state-of-the-art of existing DSC literature in detail and identifies key limitations and prospects, summarizes prior research and identifies knowledge gaps by providing advantages, weaknesses and limitations of individual methods.
528 citations
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TL;DR: This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A, and introduces and characterized the six articles that comprise this special issue in terms of the proposed BI &A research framework.
Abstract: Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.
4,610 citations
Journal Article•
TL;DR: Big data, the authors write, is far more powerful than the analytics of the past, and executives can measure and therefore manage more precisely than ever before, and make better predictions and smarter decisions.
Abstract: Big data, the authors write, is far more powerful than the analytics of the past. Executives can measure and therefore manage more precisely than ever before. They can make better predictions and smarter decisions. They can target more-effective interventions in areas that so far have been dominated by gut and intuition rather than by data and rigor. The differences between big data and analytics are a matter of volume, velocity, and variety: More data now cross the internet every second than were stored in the entire internet 20 years ago. Nearly real-time information makes it possible for a company to be much more agile than its competitors. And that information can come from social networks, images, sensors, the web, or other unstructured sources. The managerial challenges, however, are very real. Senior decision makers have to learn to ask the right questions and embrace evidence-based decision making. Organizations must hire scientists who can find patterns in very large data sets and translate them into useful business information. IT departments have to work hard to integrate all the relevant internal and external sources of data. The authors offer two success stories to illustrate how companies are using big data: PASSUR Aerospace enables airlines to match their actual and estimated arrival times. Sears Holdings directly analyzes its incoming store data to make promotions much more precise and faster.
3,616 citations
Book•
05 Mar 2013TL;DR: Big data improves health care, advances better education, and helps predict societal change from urban sprawl to the spread of the flu, and is roaring through all sectors of the economy and all areas of life.
Abstract: Amazon Exclusive: Q&A with Kenneth Cukier and Viktor Mayer-Schonberger Q. What did it take to write Big Data? A. Kenn has written about technology and business from Europe, Asia, and the US for The Economist, and is well-connected to the data community. Viktor had researched the information economy as a professor at Harvard and now at Oxford, and his book Delete had been well received. So we thought we had a good basis to make a contribution in the area. As we wrote the book, we had to dig deep to find unheard stories about big data pioneers and interview them. We wanted Big Data to be about a big idea, but also to be full of examples and success stories -- and be engrossing to read. Q. Are you big datas cheerleaders? A. Absolutely not. We are the messengers of big data, not its evangelists. The big data age is happening, and in the book we take a look at the drivers, and big datas likely trajectory: how it will change how we work and live. We emphasize that the fundamental shift is not in the machines that calculate data, but in the data itself and how we use it. Q. In discovering big data applications, what was your biggest surprise? A. It is tempting to say that it was predicting exploding manholes, tracking inflation in real time, or how big data saves the lives of premature babies. But the biggest surprise for us perhaps was the very diversity of the uses of big data, and how it already is changing peoples everyday world. Many people see big data through the lens of the Internet economy, since Google and Facebook have so much data. But that misses the point: big data is everywhere. Q. Is Big Data then primarily a story about economic efficiency? A. Big data improves economic efficiency, but thats only a very small part of the story. We realized when talking to dozens and dozens of big data pioneers that it improves health care, advances better education, and helps predict societal changefrom urban sprawl to the spread of the flu. Big data is roaring through all sectors of the economy and all areas of life. Q. So big data offers only upside? A. Not at all. We are very concerned about what we call in our book the dark side of big data. However the real challenge is that the problem is not necessarily where we initially tend to think it is, such as surveillance and privacy. After looking into the potential misuses of big data, we became much more troubled by propensity -- that is, big data predictions being used to police and punish. And by the fetishization of data that may occur, whereby organizations may blindly defer to what the data says without understanding its limitations. Q. What can we do about this dark side? A. Knowing about it is the first step. We thought hard to suggest concrete steps that can be taken to minimize and mitigate big datas risk, and came up with a few ways to ensure transparency, guarantee human free will, and strike a better balance on privacy and the use of personal information. These are deeply serious issues. If we do not take action soon, it might be too late.
2,556 citations
13 Feb 2013
TL;DR: It is argued that there are good reasons why it has been hard to pin down exactly what is data science, and that to serve business effectively, it is important to understand its relationships to other important related concepts, and to begin to identify the fundamental principles underlying data science.
Abstract: Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-science programs, and publications are touting data science as a hot-even "sexy"-career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles underlying data science.
1,023 citations
Journal Article•
TL;DR: Harvard Business School's Davenport and Greylock's Patil take a deep dive on what organizations need to know about data scientists: where to look for them, how to attract and develop them, and how to spot a great one.
Abstract: No sales force consists entirely of stars; sales staffs are usually made up mainly of solid perfomers, with smaller groups of laggards and rainmakers. Though most compensation plans approach these three groups as if they were the same, research shows that each is motivated by something different. By accounting for those differences in their incentive programs, companies can coax better performance from all their salespeople. As the largest cadre, core performers typically represent the greatest opportunity, but they're often ignored by incentive plans. Contests with prizes that vary in nature and value (and don't all go to stars) will inspire them to ramp up their efforts, and tiered targets will guide them up the performance curve. Laggards need quarterly bonuses to stay on track; when they have only annual bonuses, their revenues will drop 10%, studies show. This group is also motivated by social pressure-especially from new talent on the sales bench. Stars tend to get the most attention in comp plans, but companies often go astray by capping their commissions to control costs. If firms instead remove commission ceilings and pay extra for overachievement, they'll see the sales needle really jump. The key is to treat sales compensation not as an expense to rein in but as a portfolio of investments to manage. Companies that do this will be rewarded with much higher returns.
860 citations