DC Velocity - February 2018

Although descriptive analytics has little impact on benefits, more than one-third of respondents plan to make a moderate investment and 23 percent plan to make a large or very large investment in that technology. That’s not a bad thing, says Richard Sharpe, chief executive officer of Competitive Insights, but descriptive analytics only provides “a look in the rearview mirror; the more important question for respondents is, what are we going to do about it?” Many respondents are indeed looking to answer that question: 29 percent plan to make a moderate investment and 22 percent will make a large or very large investment in diagnostic analytics, while 26 percent plan a moderate investment and 25 percent will make a large or very large investment in prescriptive analytics (Exhibit 3).

Regardless of where they planned to invest, the great majority of respondents said they expect to see at least some benefits in all of the areas shown in Exhibit 2 over the next 12 months. While few foresee a transformative, supply chain-wide impact for any of those areas—percentages ranged from a low of 7 percent for profitability to a high of 13 percent for end-to-end supply chain collaboration—respondents clearly believe their data analytics efforts will pay off in the near term. More than half of respondents said they expect fairly significant, significant, or very significant beneficial impacts from applications of supply chain data analytics. In descending order, they included customer service ( 62 percent), profitability ( 60 percent), visibility to cost-to-serve ( 59 percent), inventory management ( 59 percent), risk and resiliency management ( 52 percent), demand planning ( 52 percent), and end-to-end supply chain collaboration ( 51 percent).

OVERCOMING BARRIERS TO SUCCESS

One of the practical takeaways from the survey responses, says Dale Rogers, is that supply chain organizations need to devote more resources to overcoming the technical, organizational, and business impediments to big data analytics. “People want it, but there are a lot of problems and barriers, and they don’t really know how to implement it,” he says.

As noted in the first installment of this report, one
of the biggest barriers is the difficulty of getting consis-
tent, clean, trustworthy, and appropriate data. That’s
a complex problem with many different causes, but
one important aspect, Sharpe says, is to first determine
exactly what data will be needed, and then establish a
process for governing that data, including identifying
the subject-matter experts who can validate that data.

One key to getting support and funding is to clearly understand a big data analytics project’s purpose and objectives. That means supply chain organizations must convincingly demonstrate the business benefits of such initiatives to company leadership, Sharpe says. “You have to show that what you’re ultimately trying to do with supply chain data analytics is to make the enterprise more successful and profitable.”

Want to participate in the study? The research team is looking for more supply chain professionals to participate in future surveys. Information will only be used in the aggregate. For more information, contact Dr. Zac Rogers ( Zac.Rogers@colostate.edu) or Tami Kitajima ( tkitajima@ci-advantage.com).

0 5 10 15 20 25 30 35 40
No
Investment
Very Little
Investment
Small
Investment
No Change
Moderate
Investment
Large
Investment
Very Large
Investment
Percentage of Respondents

; Descriptive analytics—what is happening

; Diagnostic analytics—why it is happening

; Prescriptive analytics—what could/should be done

; Cognitive analytics—machine learning for what should be done Over the next 12 months, to what degree will your company invest resources in the following areas—specifically in regard to the application of big data analytics?