scores suggest a problem, companies can then pursue secondary sourcing or work with existing suppliers to identify alternate locations. These scoring
models increasingly incorporate relatively subjective factors, such as perceived economic and political risk. But while supplier risk and resiliency scores
are undeniably useful tools, with few exceptions
they are not yet based on statistical analysis.
Of particular interest to many companies now is
whether critical suppliers that weathered the last
economic downturn will be capable of meeting
increased demand during an upturn. Analytic tools
that incorporate public, third-party data can help
companies assess this risk.
As companies accumulate more experience with
supplier risk, they can begin to create predictive statistical models that are based on actual supplier failures. This would, of course, require tracking and
analyzing a sufficient number of actual supplier
failures to allow them to accurately identify attributes associated with failure.
Interestingly, the current leaders in statistically
assessing supplier risk generally are not the manufacturers but the firms that insure them against
such risk. Because the insurance industry has a
strong actuarial tradition, firms such as Aon and
Marsh have developed statistical models of the likelihood of supply and supplier risks. The key variables considered in these models are the frequency
and severity of those risks.
TAKE ADVANTAGE OF SENSORS
One of the primary drivers of analytics in organizations is the availability of extensive data. As their
use expands, new sensors—in particular, radio frequency identification (RFID)—will make dramatic
amounts of data increasingly available for the next
generation of supply chains.
For more than a decade, supply chain managers
have been bombarded with warnings that RFID
devices and networks will change their lives. Thus
far, however, the high price of RFID technology has
prevented widespread deployment from taking
place. But prices for RFID tags and readers continue to fall, albeit slowly, and the adoption rate is
gradually rising.
At some point in the next several years, most
manufacturers and retailers are expected to deploy
some degree of RFID capability. When that happens, a great deal of RFID-generated data will be
available for analysis. Initial applications using
RFID data will primarily be transactional, but
shortly thereafter organizations will want to monitor and optimize the efficiency and effectiveness
of their RFID networks. This set of applications
will demand the use of sophisticated supply chain
analytics.
Some companies have employed RFID analytics
for several years. For example, Daisy Brand, a dairy
products manufacturer in the United States, began
using RFID analytics in 2007 to track how long it
takes products to reach the store shelf as well as
replenishment rates. Prediction of replenishment
rates is particularly important during promotions.
In addition to RFID data, Daisy Brand also makes
extensive use of Wal-Mart Stores’ Retail Link data,
which provides suppliers with weekly point-of-sale
and inventory information, in its analyses. 1
Sensors for more expensive and substantial supply
chain assets are already in wide use. Some major carriers, for example, are deploying geographic positioning system (GPS)-based telematics devices in
trucks and trains. These devices provide a wide variety of data about driving behavior, speeds under
various conditions, traffic, and fuel consumption.
Companies such as UPS and Schneider have already
employed telematics data to redesign logistical networks in whole or in part. UPS, in fact, is using
telematics data to redesign and optimize its entire
delivery network for only the third time in its more
than 100-year history.
Other types of sensors are likely to lead to a flood
of additional data—and opportunities to analyze it.
RFID and telematics sensors primarily track location, but so-called ILC (identification, location,
condition) sensors can also monitor the condition
of goods in the supply chain. ILC sensors monitor
such variables as light, temperature, tilt angle, gravitational forces, and whether a package has been
opened. They can transfer data in real time via cellular networks. Obviously, the potential to identify
supply chain problems in real time and take immediate corrective action is greatly enhanced with this
technology. We have only begun to consider how
analytics might be used to enhance the value of
ILC-derived data.
IMPROVING ANALYTICAL “LITERACY”
The next-generation approaches to supply chain
analytics involve not only new applications but also
new ways to ensure that analytics are used to make
strategic and tactical decisions. Unfortunately, bet-
