DC Velocity - February 2018

In many cases, these roadblocks stem from the difficulty of integrating data from external suppliers as well as other internal departments, and ensuring the quality and consistency of that data. For example, 67 percent said difficulty breaking through internal data silos to access information that currently is not integrated or shared is hampering their ability to gain value from supply chain analytics, and 71 percent said the same about the difficulty of achieving consistent data quality and integrity.

EARLY WINS

Despite all those challenges, respondents are seeing some payback for their efforts. When asked how much beneficial impact their companies have already realized from big data analytics in their supply chains, their answers varied widely across eight critical areas (Exhibit 2). The big winner was profitability: 89 percent of respondents said big data analytics has already provided at least some positive impact in that area, with 44 percent reporting a significant or very significant beneficial impact on profits. And 6 percent of respondents went so far as to say big data analytics has had a transformative, supply chain-wide impact on their company’s profitability.

Customer service and inventory management also scored well, with 47 percent and 42 percent, respectively, reporting a significant or very significant impact. Areas that have seen less beneficial impact so far include risk and resiliency management, end-to-end supply chain collaboration, and visibility to total cost-to-serve.

The research found that there was a strong correla-
tion between the benefits already achieved and the type
of analytics tools respondents were using. [Editor’s
note: The survey queried respondents about their use of
the five most common types of analytics tools: descrip-
tive (tells you what is happening); prescriptive (tells you
what should/could be done); diagnostic (tells you why
it’s happening); predictive (tells you what will happen);
and cognitive (uses machine learning to tell you what
should be done).]
“What we found is descriptive analytics had negative
associations with almost all benefits—meaning it didn’t
move the dial much,” Zac Rogers says. “Conversely,
prescriptive and diagnostic analytics had positive rela-
tionships with almost all benefits. For example, the
companies using these more advanced applications
report having better supply chain visibility, planning
models, risk management, and customer service.” The
survey also found very strong positive correlations
between predictive analytics and achieved benefits in
end-to-end supply chain communication, supply chain
visibility, risk management, demand planning, and
cost-to-serve visibility.

FUTURE PLANS AND PRIORITIES

To get some insight into what lies ahead, the research also queried respondents on their plans and priorities for the future. For example, the survey asked about their companies’ priorities over the next 12 months in regard to big data analytics projects. For many, getting the fundamentals right will be a high or extremely high/critical priority. That includes improving data accuracy ( 47 percent), data accessibility ( 46 percent), data availability ( 45 percent), and data consistency ( 43 percent).

T ec h no l o g y B IG DA TA

0 5 10 15 20 25 30

No
Impact
Some
Impact
Moderate
Impact
Moderately
Significant
Impact
Significant
Impact
Very
Significant
Impact
Transformative
(Supply Chain-
wide) Impact
Percentage of Respondents