data and discovering an increased demand for Pop-Tarts
in regions threatened by hurricanes. The forecast proved
accurate because the environmental dynamism was low and
the historic data reliable. However, in a setting where the
environment can change dramatically, there is a need for
continuous visibility, which calls for data provision at high
volume, velocity, and variety. The example of ABB (detailed
in the sidebar) shows how valuable continuous visibility
can be: The use of real-time weather forecasts, social media,
and newscasts in combination with internal ERP systems
allowed the company to counteract the impact of the 2011
Thailand floods on its supply chain.
When data collection and visibility needs do not match
well, putting them in a “misfit” quadrant, companies
will incur additional costs. They may, for example, have
needlessly invested in sophisticated information systems,
which will diminish their financial performance. However,
if failing to analyze and respond to environmental changes
would have a significant detrimental effect on the supply
chain or on customer satisfaction, then investments in such
information systems will be both justified and wise.
3. Distribute data across the company and reevaluate
processes. Lastly, the information must be provided to
decision makers in standardized formats, and in a timely
way. Again, for discrete information needs, scheduled
query updates will provide the right amount of visibility,
whereas a need for continuous visibility calls for a “
push-in-formation” flow. Data access should not be limited to the
primary addressees, but should instead be made available
to all possible stakeholders, as human creativity will drive
new applications. Once a well-fitted information system
has been established, periodic evaluations will ensure that
information collection still matches the visibility needs and
that the underlying assumptions continue to hold true.
In conclusion, it is clear that the application of big data
analysis in a supply chain management context provides
magnificent opportunities for improvement, but before
engaging in costly experiments it is paramount to exploit
the data already at hand. Following a structured approach
and taking into account the limitations of both novel and
traditional data sources, companies can achieve an optimal
level of visibility in their supply chains and maximize the
value extracted from their data warehouses. c
Notes:
1. A. Hamzawi, e TURNING internal document (2014).
2. World Economic Forum, Building Resilience in
Supply Chains (2013), http://www.weforum.org/reports/
building-resilience-supply-chains.
3. V. Trost, “Cross-industry comparison of supply chain
visibility—Do complex supply chains have a higher supply
chain visibility?” (master’s thesis, ETH Zurich, 2014).
4. F. J. Ohlhorst, Big Data Analytics: Turning Big Data into
Big Money (Hoboken, N.J.: John Wiley & Sons, 2012).
5. A. McAfee and E. Brynjolfsson, “Big Data: The
Management Revolution,” Harvard Business Review 90, no.
10 (2012): 60–68.
6. O. Ashenfelter, “Predicting the Quality and Prices
of Bordeaux Wine,” The Economic Journal 118, no. 529
(2008): F174–F184.
7. M. A. Waller and S. E. Fawcett, “Click Here for a
Data Scientist: Big Data, Predictive Analytics, and Theory
Development in the Era of a Maker Movement Supply
Chain,” Journal of Business Logistics
34 no. 4 (2013):
249–252.
8. J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C.
Roxburgh, and A. Hung Byers, Big data: The next frontier for
innovation, competition, and productivity, McKinsey Global
Institute (2011).
9. J. Ginsberg, M. H. Mohebbi, R. S. Patel, L. Brammer,
M. S. Smolinski, and L. Brilliant, “Detecting influenza epi-
demics using search engine query data,” Nature 457, no.
7232 (2009): 1012–1014.
10. S. Goel, J. M. Hofman, S. Lahaie, D. M. Pennock,
and D. J. Watts, “Predicting consumer behavior with Web
search,” Proceedings of the National Academy of Sciences 107
no. 41 (2010): 17486–17490.
11. J. W. Adrian, Quartet FS internal document (2014).
12. McAfee and Brynjolfsson (2012).
13. DHL, “Intelligent transport hits the road” (2014)
http://www.dpdhl.com/en/logistics_around_us/from_our_
divisions/ dhl_smarttrucks.html.
14. UPS Inc., “ORION Backgrounder” (2013) http://
www.pressroom.ups.com/Fact+Sheets/ci.ORION+
Backgrounder.print.
15. Capgemini, “Big data—Finding the value” (2013)
http://www.capgemini.com/resources/big-data-finding-the-value.
DENIS HÜBNER AND BORIS ZAREMBA ARE DOCTORAL
CANDIDATES AND RESEARCH ASSISTANTS UNDER THE
CHAIR OF LOGISTICS MANAGEMENT AT ETH ZURICH.
STEPHAN M. WAGNER HOLDS THE CHAIR OF LOGISTICS
MANAGEMENT AND IS ACADEMIC DIRECTOR OF THE
EXECUTIVE MBA IN SUPPLY CHAIN MANAGEMENT AT ETH
ZURICH.
