DC Velocity - January 2018

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

chain point solutions. And as with spreadsheets, they
don’t necessarily trust the ERP data—at least not to
manage their supply chains the way they need to.”
When talking about big data analytics, supply
chain organizations typically rely on five basic kinds
of tools:

b Descriptive—tells you what is happening
b Diagnostic—tells you why it’s happening
b Predictive—tells you what will happen
b Prescriptive—tells you what should/could be
done

b Cognitive—uses machine learning to tell you what should be done.

By far the most widely used of these five is descriptive analytics, according to the survey results. Sixty-one percent of respondents report using this type of analytics tool. Furthermore, use of the four other types of analytics tools lags descriptive applications by a significant margin. According to the survey, companies that deploy these tools regularly, frequently, or heavily use them as follows: diagnostic, 42 percent; prescriptive, 36 percent; predictive, 31 percent; and cognitive, 18 percent (Exhibit 3).

Supply chain organizations that limit themselves
to descriptive analytics are unlikely to make much
progress. “Descriptive data tools are absolutely
necessary,” Sharpe says. “But they are only good
for telling you what has already happened. To get
greater insight, companies need to move into the
other types of applications.”
Adoption of these more advanced analytics tools
takes time, however. To that point, how far have compa-
nies come in their use of big data analytics in their supply
chains? How mature are they not just in implementing the
technologies, but in realizing benefits?

The answer is “not very far,” as the survey numbers indicate:

b 28 percent of companies are in the “developing” stage, with one or more big data analytics initiatives under way.

b 24 percent are in the “early” stage, conducting proof-of-concept testing to determine benefits and drawbacks.

b 20 percent have not adopted big data analytics in their supply chain.

b Only 2 percent rank themselves as mature; that is, in the “transformational” stage of adoption and benefits.

One interesting note on the maturity question: Different
industry sectors are at varying stages of not just maturity,
but also plans for adoption. On a maturity model scale of
1 to 6, no industry was a 6; in fact, none reached the top
two tiers—“advanced” or “transformational.” The technol-
ogy sector ranked highest at 3. 7, just short of “somewhat
advanced,” while the lowest was life sciences, at 2. 3 solidly
in the “early” stage. Machinery manufacturers ranked
themselves just slightly ahead of life sciences, and third-par-
ty logistics companies (3PLs) and retailers fell about half-
way between “early” and “developing.” (Other industries
were not represented in significant numbers.)

Commenting on these rankings, Sharpe observes that some industries are more cognizant of the value that can be derived from supply chain data analytics, while some show little interest in moving beyond what they traditionally have done. For example, although life sciences (which also includes healthcare and pharmaceuticals) scored lowest in maturity, respondents in that industry put very high or moderately high priority on investing in big data analytics. “They understand they need to advance quickly, because of how fast their industry is changing, so they’re making these investments,” he says.

To be continued … Look for the second part of our special report on big data analytics in our February issue. In that article, we’ll look at the roadblocks companies encounter when implementing big data analytics in their supply chains, the benefits they’ve realized to date and expect down the road, and plans for future investment in the technology.