they have at their disposal.
1 The remaining 80 percent
are missing an opportunity: Smart data management is
linked to supply chain visibility—a strategic advantage
that enables companies to outpace competitors in supply chain performance.
Indeed, recent business reports have highlighted the
need for supply chain visibility and noted that the lack
thereof may undermine a business’s general and financial performance.
2 That may explain why over the past
five years, prioritizing visibility within organizations
and across their end-to-end supply chains has moved
to top management’s agenda. Nevertheless, the results
of a recent survey among 111 managers in supply chain
functions in international businesses in Switzerland,
suggest that the real-time visibility of their supply chains
is mediocre, averaging 3. 9 on a 7-point scale.
3 This troubling gap should be a mandate for action.
How can companies achieve the kind of supply
chain visibility they need? We believe that a three-step
approach involving data gathering and analysis, alignment of data sources and visibility requirements, and
information sharing allows companies to leverage the
potential of both the data itself and the information-in-tensive environments of their supply chains.
GATHER AND ANALYZE THE RIGHT DATA
Each year the amount of stored data around the globe
increases by 40 to 60 percent.
4 This huge and steadily
growing amount of information and data (
originating from both internal/company sources and external/
public sources) holds enormous potential benefits for
companies if they tap into it. But to do so effectively,
business leaders have to understand their companies’
true information needs. Utilizing the right data provides
a better information base, which translates into superior
decisions. In fact, companies that employ data-driven
decision-making processes outperform their peers by 5
percent in terms of productivity and 6 percent in regard
to profitability.
5
There are many ways the right data, properly ana-
lyzed, can provide the information companies need
in order to make improvements within their supply
chains. Data from customer service and social media
will enable research and development (R&D) engineers
at a consumer-focused company to craft the kinds of
products consumers really desire. Obtaining data that
identify where drivers are wasting their time is essential
to improving the efficiency of a delivery fleet. Accurate,
real-time, stock-level information will help manag-
ers improve delivery reliability for orders. Machines
equipped with sensors that measure parts wear promote
higher utilization of production equipment by trans-
forming maintenance into an activity that is driven by
demand instead of by schedules.
After evaluating what kind of data will best support
their decision-making processes, business leaders need
to formulate a clear strategy for how to obtain this data
and then make sense of it. Their strategy should include
the standardization of IT platforms and interfaces to
increase companywide availability of data.
FINDING NEW MEANINGS IN EXISTING DATA
As part of the second step of the journey—alignment
of data sources and visibility requirements—companies
must structure and analyze established data sources in
terms of their potential to meet their information needs.
In most cases, statistical algorithms perform that task
better than do human decision makers, who typically
take irrelevant context information into account. This
advantage is especially apparent in low-validity environments characterized by a high degree of uncertainty
and unpredictability. An illustrative example of this
phenomenon is the prediction of future wine prices. The
standard practice is for a circle of skilled experts to rate
fine wine after harvest and then predict which bottles
will become the most valuable. Strikingly, researchers
found that a simple linear regression analysis of three
features of the weather conditions during the growing
season outperformed the experts’ appraisals.
6 For supply chain executives, the implication of this example is
that they should abandon the practice of having people
analyze key performance indicators (KPIs) and develop
action plans based on contextual factors, and instead
rely on decision-making algorithms.
In many cases it is not even necessary to collect new
data to enhance decision-making processes. A fresh look
into a data warehouse can open up many new business
opportunities, because the stored data often contains an
abundance of unused but potentially useful information.
This may happen because individual pieces of data are
