Big Data and Global Supply Chains

After the NPR radio piece I brought up in class this past week, I became increasingly interested in how predictive analytics in the Big Data hullabaloo has been impacting the SCM field. After a brief search, I was able to find a piece from Supply Chain 247 that summarized and commented on a research report issued by the Economist Intelligence Unit (EIU) this past December 2013. EIU did a survey of 395 top performing companies in conjunction with Dun & Bradstreet, Inc. to assess the ways in which the increasingly spread out reach of the globalized market creates both untold opportunities as well as increasingly complex supply chain flows. In particular, the report - entitled "Strategies for Managing Customer and Supplier Risk" - rightly dedicated a section to the promise of predictive analytics of the Big Data craze could assist CIOs and their SC administrators better manage both risk and potentials for new revenue at the same time. What's more, the section proves to be particularly intriguing because it relates very closely to this week's focus on inventory management within the supply chain.

Beginning on page 7 of the report, you will find that the survey results suggest both optimism and caution among the data-drive SC leaders. Many have been using sophisticated data models to manage their flows within the supply chain, and even incorporated many of the newer predictive models brought along with Big Data (BD) practices. However, as large firms become increasingly entangled in an ever-increasing global trading pool, the sources and uses of data are expanded faster than BD specialists can devise predictive models suited for the breadth. As I see it, the caution is a necessary approach that recognizes how difficult analyzing and assessing risk management within the supply chain will be for even the most advanced analytics models. This is so because the sources of variable risk, as well as the sources of data to be compiled, are both increasing beyond the scope of what the BD models have been accustomed to handling.

Industry leaders are not dubious about the potential for this technology to inform largest firms over the coming decade. However, there will be a significant obstacle to hurdle for suppliers to consider as they will need to develop streamlined and uniform systems of data collection. The report suggests, and I here now further expound, that firms such as Caterpillar will need to increase their data collection activities to incorporate the many partners they maintain on the supply chain around the world to develop data management and modeling systems for their entire network. Imagine, for example, that their system is not simply an overseer of their own activities, but also links up with the systems of those who supply them, as well as those who they supply. Herein lies the heaviest lift: outside behemoth multinationals, the high cost of devising these systems is currently so great that it outweighs the justification for the investment. The technology and its potential benefits in risk reduction are not quite outpacing the cost of development and implementation at this time because the expense is too great, but also because the benefits are not quite promising enough to secure the investments.

As was mentioned in passing in the previous class, the use of the cloud for these matters increases the promise in the coming decade.