adapt the routes of its delivery vehicles to real-time traffic
conditions.
13 Another example is UPS, which gathers traffic
data from its delivery vehicles and uses that information for
route optimization in its On-Road Integrated Optimization
and Navigation (ORION) system. When their routes are
optimized to the current traffic flow, the truck drivers save
fuel and time on their daily runs through the city.
14
On the consumer side, mobile apps are reinventing the
taxi market in megalopolises. Patrons can find a cab suited
to their personal preferences and pay with the app. To avoid
supply shortages, the providers dynamically adapt fares
during rush hours or inclement weather. The application of
this process to business logistics would give small and medium-sized enterprises access to transportation services without intermediaries. This would be a sea change from today’s
freight exchanges, which carry big transactional costs and
where the final price of a service is open to negotiation.
As is clear from the examples above, real-time-enabled
supply chains enhance operational efficiency by allowing
companies to make real-time adjustments in response to
demand and capacity fluctuations. The business case, however, must be evaluated for each scenario.
In addition, any efforts to benefit from the analysis and
application of big data will be subject to the same success
factors as any other business endeavor; that is, it is critical
to set clear goals and requirements and to not overestimate
the capabilities of new technologies. Modern technologies,
like in-memory processing (the computation of data with-
out storing it on the hard drive, resulting in a large velocity
benefit) make predictions and searches faster, not better!
Prior to systemwide rollouts, therefore, it is best to prove
the applicability—and especially the profitability—of a
solution through a pilot project and data collection coupled
with utilization scenarios.
THREE STEPS TO VISIBILITY
We have described how the smart use of classic and novel
data sources can help companies reduce costs and adapt to
changing environments. If they seize those opportunities,
they can realize a 26-percent performance improvement
from big data analysis, according to the consulting firm
Capgemini.
15 To extract this value, it is essential to align
data collection and analysis efforts to the visibility requirements, and to not over- or under-engineer these processes.
The following three-step approach will help companies
achieve supply chain visibility in an efficient manner:
1. Set goals and explore visibility needs companywide.
The first step toward alignment consists of a companywide
stocktaking of visibility requirements and information
availability, conducted by a cross-functional team with top
management’s support. The result of this step is a decision
map that breaks down the decisions that usually are made
to achieve corporate goals and sub-goals, along with the
visibility levels they require. The visibility needs for the
goals of reducing inventory levels, increasing sales, and protecting the supply chain from risks, for example, will vary
depending upon their environmental dynamism. Decisions
made in a dynamically changing environment demand
continuous visibility. In contrast, when the environment
remains relatively static within the decision horizon, the
visibility need is discrete.
2. Match data collection to visibility needs. The decision
map created in the initial step identifies each function’s visibility needs, which are deduced from the corporate goals.
The second step calls for action: Data collection must be
aligned with those visibility requirements. As summarized
in Figure 1, data collection fits the visibility needs when
data characteristics meet the analytic requirements. In the
case of discrete visibility needs, the use of historic data
sources is sufficient to provide solid decision support—
recall the example of Wal-Mart analyzing point-of-sale
[FIGURE 1] INFORMATION ALIGNMENT MATRIX
SOURCE: AUTHORS (2014)
