DC Velocity - February 2018

www.dcvelocity.com FEBRUARY 2018 DC VELOCITY 43 mational” stage of adoption and benefits.

That raises the question of what’s holding them back. It turns out, there’s no single answer. When asked what roadblocks they had encountered in trying to implement big data analytics in their supply chains, respondents identified a variety of impediments. Some have to do with technical issues, such as integration with siloed or data warehousing initiatives, deemed a significant or very significant impediment by 47 percent of respondents. Others that were high on respondents’ list of concerns included the need to invest in software and hardware; analytical tools’ level of difficulty for users; and security and other risks (Exhibit 1).

Other impediments deemed to be sig-
nificant or very significant by a large
number of respondents are business
management, as opposed to technical,
issues. One is the acquisition of tal-
ent and expertise ( 41 percent), which
may correlate to comments made by
respondents about the difficulty of using
analytical tools. Another is the level of
management commitment and support
( 44 percent). Almost the same number
(43 percent) named uncertainty about
return on investment or value as a major
impediment. “Usually things like this
are big sweeping initiatives that come
down from the top, but generally [big
data analytics initiatives] originate at
mid-level with people who are actual-
ly using data, and they have to sell it
upward,” says Zac Rogers, assistant pro-
fessor of supply chain management at
Colorado State University. “We’re seeing
in the results the difficulty of upselling
the concept of big data to managers
who don’t understand why they should invest time and
money in it.”
Those that have implemented big data analytics say
they’re encountering some roadblocks to getting signifi-
cant value from those efforts. Just under two-thirds ( 64

percent) agreed to some extent (somewhat agree, agree, or strongly agree) that difficulty overcoming inadequate data-capturing capabilities in their legacy systems was hindering their ability to harness value from supply chain data. “I think they’re expecting value will come out

of this, but they don’t necessarily see a clear way to get there because of their legacy systems and frustration of working with their ERP (enterprise resource planning) system,” says Dale Rogers, ON Semiconductor Professor of Business at Arizona State University.

0 5 10 15 20 25 30 35
Not
Applicable
Not
Significant
Moderately
Insignificant
Neither
Significant Nor
Moderately
Significant
Significant
Very
Significant
Percentage of Respondents