dream of managing their operations without a
variety of analytical models.
Yet supply chain-related analytics activities have
plateaued in many organizations in recent years.
Other than the occasional re-tuning of supply networks that has principally focused on cost management, companies have not taken advantage of
all that supply chain analytics can offer to their
businesses. Further, even when analytical tools are
available to front-line supply chain personnel, the
tools often go unused because of a lack of skills or
understanding.
We believe that there will be a set of new frontiers in supply chain analytics that will lead to dramatically higher levels of performance. If companies are to achieve these rewards, however, they
will have to be more ambitious in their analytical
goals and investments. In this article we describe a
number of relatively new domains for supply
chain analytics as well as the opportunities and
primary obstacles for each. We also describe several ways in which the day-to-day usage of supply
chain analytics will change in the future.
CONNECT DEMAND AND SUPPLY IN
REAL TIME
One of the most important attributes of next-generation supply chain analytics is that they will
address issues beyond the supply chain. To optimize operations, companies need to link their
supply chains with metrics and analytics on the
demand side. For example, at the simplest level,
price changes or promotions for products will
change demand and hence the required supply of
those products. Similarly, changes in the availability of products and components should be reflected in marketing and sales processes.
This integration of supply and demand was pio-
neered in the 1990s by Dell Computer, which was
able to suggest to call-center customers ways to
shorten delivery time or take advantage of excess
inventory. This was mostly dependent on human
decision making: manufacturing supervisors
would track supply levels and notify sales and
marketing managers, who would then promote or
downplay particular items and configurations
based on their availability. But in a real-time,
online business environment, companies will
need to have analytical models in place that will
continuously integrate supply and demand with-
out human intervention. Such models would, for
example, automatically extend offers and promo-
tions to customers based on the availability of
inventory and components. There has been a
shortage of initiatives in this area since Dell’s pio-
neering work, but the direction for future innova-
tions is clear.
ANALYZE SUPPLIER RISK
Many companies recognize that the success of
their operations is highly dependent upon their
suppliers. Yet supplier risk analytics have hardly
moved beyond simple metrics and reports in
most organizations. The most sophisticated
approaches to supplier risk monitoring and management—used by companies that heavily
depend on external suppliers and contract manufacturers, such as Cisco Systems—are only somewhat more analytical.
One example is the creation of a supplier
resiliency score based on several variables. The
variables are based on logic (for instance, reports
of bad weather near suppliers’ manufacturing
locations). If the variables or the overall resiliency
