Using Big Data and Analytics to Manage Risk • 213
have packaged and outsourced supply market intelligence while others have
developed hosted analytics tools and bundled professional services and
analysis across the entire supply chain spectrum.
We’d like to close this section with some comments on analytics in
manufacturing from the vice- president of Invensys Solutions. He talks
about big data for manufacturing by comparing big data for consumers
and the use of Google. He says we start Google maps on our phone and it
immediately knows where we are. We click a box and it shows the trac
from here to the airport. If we’re hungry, it can pull down restaurants and
menus. Now, take this into the supply chain arena. All that information is
available, but instead of restaurants, we’re looking for the best batch, opti-
mal production run, and more. A good analytics manufacturing system
has to show what is out there, display the information that is available,
interpret what it means, how to react to it, and then help predict what is
coming next.
HOW EARLY ADOPTER COMPANIES LEVERAGE BIG DATA
With a broad denition of big data now established, we want to provide
examples of industries that are leveraging big data for competitive advan-
tage. In this section we’ll touch on several industries and dig a bit deeper
into a few name- brand companies that are leveraging this approach for a
competitive advantage.
During our initial evaluation of the big data landscape, we speculated
that there might be an industry that is far and away the leader in leveraging
big data for competitive advantage. With that hypothesis, we attempted to
gather data and prole the use of big data by industry. e hypothesis that
one industry might dominate the landscape is far from reality. e top
four industries within our sample that use big data are consumer pack-
aged goods (CPG)/grocery (16% of rms), electronics (10%), automotive
assembly (10%), and energy (10%). All other industries were 7% or less.
It’s evident that these industries are leading the way toward leveraging
predictive analytics to solve operational problems, followed by additional
industries beginning their use of big data. Overall, we have a long way to
go before the use of big data becomes routinized.
214 • Supply Chain Risk Management: An Emerging Discipline
Consumer Packaged Goods
A large multibillion dollar CPG company with razor- thin margins was
facing highly volatile commodity prices on the supply side of its business
and unforgiving, price- sensitive retailers on the demand side. e compa-
ny’s approach was to develop an integrated Sales & Operations Planning
process and link it to the supply market by integrating supply market
intelligence and purchase price forecasts. e company used this forward-
looking supply intelligence to create robust scenarios, perform additional
analyses, and then optimize all options associated with each scenario.
It attempted to mitigate risk by nding substitute materials, modifying
specications, reconguring its product mix, changing its supply chain
network and delivery methods where possible, hedging on the nancial
side, as well as modifying strategies throughout the planning horizons.
is eort resulted in minimizing millions of dollars of product/ customer
prot erosion and more robust, predictable strategies.
Dell Computers
Most of us know that Dell is a company in transition. Aer dominating
the enterprise PC market for decades, the Texas- based congure- to- order
manufacturer is making a denitive move away from the product side of
the business and toward services and solutions. Unfortunately, over the
past decade, Dell’s strategy, options, and variants in models, soware con-
gurations, memory, screens, and other customizable features has resulted
in over seven septillion possible congurations of Dell’s products! A sep-
tillion is equivalent to 1,000,000,000,000,000,000,000. Obviously, product
portfolio complexity had become a major risk for the company.
To trim its product portfolio Dell began to utilize its abundance of big
data. A Dell team created a new system called optimized conguration.
Dell’s analytics team clustered high- selling congurations from histori-
cal data to create technology roadmaps. e team also created automated
algorithms to identify what congurations Dell should build to order and
what Dell should produce for inventory. e analysis leveraged historical
data and ran cluster analysis to identify the most common congurations
sought by customers.
Clustering around commonality of product ordering allowed Dell
to trim the seven septillion options to several million and provided
the company’s marketing and supply chain teams with agreement on
Using Big Data and Analytics to Manage Risk • 215
precongured products built for inventory and ready to ship. is new
supply chain strategy, driven by data analysis, also supports the company’s
make- to- order strategy.
Still another use of big data at Dell has been inside the company’s online
ordering system. Dell’s business intelligence team ran analytics on click
stream data, tracing every move and path taken by customers. e out-
come showed that customers navigate through more than 40 clicks to
place an order. e team used that information to optimize the site and
reduce the number of clicks to ve.
Western Digital
Western Digital, a global manufacturer of disc drives, is obsessed with
quality. To serve that obsession the company has transformed its manu-
facturing process to allow scanning, recording, testing, and tracking of
every disc drive produced while still on the production line. By running
real- time shop- oor analytics, the company can locate and remove non-
conforming discs before they reach the customer. Even if a disc passes
an initial analytical review, if further analysis reveals a problem, the disc
can be located and pulled from inventory bins. is capability, supported
by big data, has resulted in the lowest warranty return rate in the entire
industry. It has also helped make Western Digital the supplier of choice for
many computer manufacturers.
Harley Davidson
Harley Davidson, the king of the hogs, introduced soware that tracks
even the minutest details on the assembly oor, such as the speed of fans
in the painting booths. When the soware detects that the fan speed,
temperature, or humidity has deviated from the optimal settings, it auto-
matically adjusts the operations. is allows a consistency on the shop
oor by staying within preestablished parameters. e soware has also
been used to identify bottlenecks on the assembly oor. One of Harley’s
goals is to complete a motorcycle every 86 seconds. A recent study using
shop oor data revealed that the rear fender assembly time was taking
longer than planned. e company changed the factory conguration
so the fenders would ow directly to the assembly line rather than being
placed on carts and moved across the oor. is is but one example of how
216 • Supply Chain Risk Management: An Emerging Discipline
Harley Davidson is using big data to streamline its operations and avoid
operational risk.
Raytheon
Raytheon, a household name in the aerospace/ defense manufacturing
arena, is betting on big data to reduce the risk of quality and operational
problems. In its Huntsville, Alabama, missile plant, if a screw is sup-
posed to be turned 13 times aer it is inserted but instead is turned only
12 times, an error message ashes and production of the missile or com-
ponent stops. “Manufacture of a missile is either right or it’s not; there’s no
in between,” says a Raytheon executive. Many manufacturers are install-
ing sophisticated automated systems to gather and analyze shop oor
data, known as manufacturing execution systems (MES). Manufacturers
are looking harder at data partly because of increasing pressure from cus-
tomers to eliminate defects and from shareholders to squeeze out addi-
tional cost and mitigate risk to the brand. ese new capabilities mean
Raytheon is catching more aws as they occur. Raytheon also keeps data
for each missile, including the names of all the machine operators who
worked on any part of it, as well as the humidity, temperature, and more
at each workstation.
e system is designed to prevent any operator from performing a task
for which he or she is not certied. According to Raytheon, leveraging
big data systems is a form of risk mitigation and management. Millions
of dollars have been spent in the past to rework items that did not meet
specications. If Raytheon’s experience is any indicator, cost containment,
real- time event monitoring, and process optimization are but a few of the
key drivers supported by big data. Tracking physical items and people
throughout the supply chain, capturing and acting on streaming data, and
enabling faster reaction to specic problems before they escalate in major
situations is becoming the norm rather than the exception.
European Electrical Utility
A major European electric utility company sought to improve the man-
agement of budgeted versus actual spend for nonfeedstock and indirect
spending. It wanted a single system that separated consumption varia-
tion, within a contract and across contracts, external market pricing
variation, and procurement- led pricing impacts. e company used a
Using Big Data and Analytics to Manage Risk • 217
third- party tool for spend analysis and procurement performance analysis.
ese data were cross- referenced against an external database with thou-
sands of price indexes. is approach of combining internal and external
data and benchmark indexes allowed for fact- based discussions and deci-
sion making for continuous improvement in its cash ow management.
is approach conrmed that analytics works best when integrated
with external information. e utility company concluded that integrat-
ing internal and external data through the use of big data analytics is
an enabler for managing supply risk, supplier risk, regulatory risk, com-
petitive risk, and intellectual property risk. Managing these risks should
include analytical approaches such as scenario planning, Monte Carlo/
probabilistic modeling to quantify the probability of occurrence and
impact, segmenting and visualizing risk using heat maps, and predictive
analytics to manage risk.
Schneider
Schneider National, a $3 billion transportation and logistics company, has
developed a computer model that mimics human decision making, help-
ing the company to assign trucks and drivers in the most cost- eective
way possible. At any given time, Schneider has 10,000 trucks on the road
with over 30,000 trailers waiting to be picked up or delivered. Drivers work
alone or in pairs, and Schneider must get them back home by a certain
date and time. Drivers also need to conform to the government’s hours-
of- service regulations regarding rest periods and breaks.
With the help from several Princeton University researchers, Schneider
developed a simulator utilizing dynamic programming, which takes into
account the presence of uncertainty. e simulator, which took two years
to develop, runs forward in time for three weeks to approximate the value
of having a truck and driver at a certain location at a certain time. e out-
put from the report is a called a rst- pass cost estimate. e tool then runs
backward in time, something called postanalysis, to reconcile the past
results with those that were determined in the future estimate. e simu-
lator then runs forward again for three weeks and then backward as it
seeks to improve the total cost estimates. is forward– backward process
encompasses hundreds of thousands of iterations.
Schneider estimates its big data tool has saved the company tens of
millions of dollars as well as increased revenue by justifying price hikes
to customers with specic service- level constraints. e simulator also
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