Tuesday, February 25, 2014

Big Data in the 21st Century for Supply Chain Executives: Opportunity or a Challenge?

 “Big Data” is the buzzword of the day. We cannot deny the fact – it is pervasive! Companies see an opportunity in harnessing big data abilities to drive better Supply Chain Management initiatives, however, they must surpass the hype surrounding “big data” to capitalize on this opportunity.
But what is “Big Data”? I like to define big data as data with a volume greater than a petabyte but also data that is growing at a rapidly increasing pace in terms of both variety and size. At this point, we’d also like to ask, how “big” is a petabyte? Well, for better or worse, twenty petabytes represents the amount of data processed by Google on a daily basis!

The software being used for utilizing the power of big data today deems investments in traditional IT architectures obsolete as these traditional (relational) databases are incapable of handling petabytes of data. So, do we see this as a problem or an opportunity?
Big data offers new methods for a corporation to listen, interpret, understand and respond faster, but only if enterprises change their process methodologies.
Figure 1: Is Big Data an Opportunity or a Problem? While most supply chain executives see big data as an opportunity for their operations, 12 percent of the 123 respondents to the Supply Chain Insights survey characterize the issue as a problem to solve. Of those calling it a problem, more came from the IT function than others.
Going for the Win
In order to be successful in a big data supply chain strategy, an effort must be holistically made that relates not only to data but the overall process to ignite new business opportunities. Seizing this opportunity requires leadership and the initiatives a corporation pursues need to be aligned to business objectives, with a focus on small and iterative projects. Below are five key recommendations for supply chain executives:
  • Build a cross-functional team that focuses end-to-end. Big data offers an opportunity to use new data forms and emerging analytics to build processes outside-in, from the customer to corporate headquarters. This can best be accomplished when there is a team of IT and line-of-business leaders that can work cross-functionally with a focus on end-to-end processes.
  • Side-step religion. The term “supply chain” is ambiguous. Some corporations like to think of it as a limited function within the organization that focuses on inventory, while others believe the term is a much broader concept that encompasses end-to-end processes. Corporations should not get entangled in arguments of supply chain as a function or an end-to-end process. 
  • Start small and iterate. Focus on small wins and learn from the use of analytics to spread to other functions. For example, the use of in-memory reporting from Qlikview and visualization from Spotfire and Tableau are being used by a number of corporations to improve data usage today to win funding for big data initiatives.
  • Provide innovation funding. Allow for trial and error in the process by providing cross-functional teams with money to experiment.
  • Consolidate business intelligence centers of excellence and master data management efforts into big data initiatives with business goals. This is primarily so because some of the new techniques associated with advanced analytics enable data enrichment and data parsing that previously had to be hard-coded into systems. Organizations that are adept at using data will be able to seize big data opportunities and take advantage of these opportunities earlier with the end goal to be able to solve business problems.  The results are achieved only by harnessing of the cross-functional efforts of knowledgeable people, working on teams to solve analytical problems. 

5 Supply Chain Opportunities in Big Data and Predictive Analytics


  • Mobility. Data from mobile devices is real-time. This is a big change for supply chain systems because traditional systems were based on limited sets of near real-time data.  With in-memory processing and the lower computing costs, supply chain leaders can redefine the customer experience by enabling the mobile worker. The goal is to make the process more productive and harness data as it arrives versus put it into batch systems for overnight processing.

  • Internet of things.  Mobility also bolsters the evolution of the internet of things, where sensors with IP addresses collect and communicate data on a wide range of conditions.  It will drive machine sensing and redefine service supply chains.  For example, the health care supply chain is based on efficient sickness.  Today, patients go to the hospital and are tested and are treated.  The supply chain is based on efficient check-in and check-out.  But, how do you know if you are sick? Based on new forms of body sensing, patients will be alerted that they have a problem, they will not have to go to a hospital for testing, and testing and patient care can be done at their home through sensing technologies.
  • Big data.  The traditional supply chain was designed to use structured, clean data.  But it turns out the most important data for the supply chain is often unstructured data. (For example, customer service call center data, Twitter data, warranty and return information, and customer rating).
  • New forms of predictive analytics.  Supply chains are more complex and the needs are greater.  They are ready for new ways to mine text data, and utilize pattern recognition technologies.  Rules-based ontologies, in-memory processing and map reduce technologies offer great promise for the supply chain.

  • The cloud.  Cloud computing offers promise to connect the extended supply chain.  It also offers great promise to enable real-time bench-marking. Traditional bench-marking techniques are difficult because they are static and are the inputs lack common data models and data definitions to enable comparisons. The cloud will make technology deployments easier; but more importantly it will allow real-time sensing on bench-marking data.

How Big Data Will Impact Supply Chain Planning by Tools Group


Albeit, a Few Challenges

Corporations see the power of Big Data to parse out increasingly complex risks within their supply chains. However, most still see deploying predictive analytics as too costly, according to a new report from the Economist Intelligence Unit.

Integrating multiple, disparate sources of external data poses a hindrance for corporations looking to make more use of complex predictive analytics. External sources like company credit ratings, data on a country’s political situation and even weather patterns, could all help executives assess the risk profile of suppliers.

That complexity has so far kept the cost of deploying Big Data too high for many risk managers. This is so because integrating disparate data sources often means more specialized and high-cost staff. And some companies believe the cost-benefit equation of Big Data still doesn’t add up.


Adapting systems to take advantage of new technologies is about more than modernizing supply chains or stuffing new forms of data into existing architectures.  It requires a redesign. It is about improving visibility into business activities, providing better service to customers and improving profitability.  But, then shouldn't this be what the supply chain is really all about?

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