Editor's note: This is the first installment of a special two-part report on how companies are using big data in the supply chain. This month, we'll look at how satisfied businesses are with their data, the technologies they're using for data analysis, and how far along they are in their efforts to leverage the data they collect. Next month, we'll examine some of 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 their plans for future investment in the technology.
Technology is making it possible for supply chain organizations to gather enormous amounts of information from an expanding variety of sources. Billions of data points are pouring in from supply chain network nodes, multiple retail channels, the industrial Internet of Things ... the list goes on and continues to grow. The aim, of course, is to analyze that information to gain visibility into opportunities for innovation and improvement. But few companies are actually deriving sustainable value from the supply chain data they are accumulating. Instead, they are struggling with how to ensure the quality of their data, how to analyze it, and how to make practical use of what they learn from it.
New research conducted by CSCMP's Supply Chain Quarterly; Arizona State University; Colorado State University; Competitive Insights LLC, a provider of advanced supply chain analytics solutions; and the consulting firm lharrington group LLC investigated the current state of supply chain data analytics and the strategies organizations are adopting to harness the power of big data. Over time, this annual survey will track, in the aggregate, companies' progress in using big data analytics in supply chain management.
In this article and a companion piece to be published next month, we outline the study's principal findings. Among the topics we'll discuss are respondents' satisfaction level with their data; what technologies companies are using for data analysis; the challenges and benefits associated with managing growing volumes of supply chain data; and finally, where respondents stand now as well as their priorities for near-term investment in data analytics.
SATISFACTION WITH DATA PROVING ELUSIVE
The survey was conducted in June 2017 via an e-mail invitation to readers of CSCMP's Supply Chain Quarterly and subscribers to a newsletter produced by Competitive Insights. A total of 126 fully completed, usable responses were compiled to obtain the survey results. (For more information about the research, see the sidebar.)
There's no question that most companies are seeing a significant increase in the amount of data they are collecting. When asked to characterize the increase over the past three years in the volume of supply chain data available to them, 36 percent said it was moderately high, while 38 percent said it was high or very high. But, as is often the case, quantity does not necessarily equate to quality.
How satisfied are supply chain managers with the data they currently have to run their supply chains? A majority of survey respondents report being at least moderately satisfied with their supply chain data in terms of availability, usability, integrity, and consistency. However, the combined "favorable" numbers (moderately high, high, or very high level of satisfaction) were not overwhelmingly higher than those for the correlating unfavorable scores (Exhibit 1).
Interestingly, only a very few respondents report being very satisfied in all four data attribute areas: availability of data (3 percent); usability (2 percent); integrity (6 percent); and consistency (4 percent).
"Data is the foundation the 'house' sits on," observes Richard Sharpe, chief executive officer of Competitive Insights. "The survey results clearly show that there are cracks in that foundation—cracks in companies' ability to bring data together, integrate it, have confidence in it, and believe that it is consistent. To take advantage of big data analytics, we have to do better in all four categories."
If companies are only partially satisfied with the data they're getting, the logical question to ask is, exactly what software solutions are they currently using to gather that data? Overwhelmingly, the tools in heaviest use are not advanced analytics or business intelligence solutions. Nor are they operational point applications like warehouse management systems. Despite the availability of an array of sophisticated analytics software, the most widely used tool for managing supply chain data today is still Excel spreadsheets (Exhibit 2).
Despite their dependency on spreadsheets, users aren't necessarily happy with it as a data management tool. "Our survey shows that Excel is very negatively associated with user satisfaction in terms of usability, integrity, and consistency of data," Dale Rogers, ON Semiconductor Professor of Business at Arizona State University, reports. "The problem with Excel is that everyone builds their own spreadsheets, so there's no consistency, no single version of the truth that's shared across departments. That makes it very difficult to trust the data sufficiently to make big decisions across departments."
The survey also found that, not unexpectedly, large companies rely on their enterprise resource planning (ERP) systems to run the financials of the business. But for supply chain professionals, ERP has shortcomings.
"Supply chain folks don't really like ERP that much," notes Zac Rogers, assistant professor of supply chain management at Colorado State University. "Many do not think the data that comes out of ERP systems is very useable for their purposes. They find it too rigid. They also lose the granular operational data they used to get with older supply 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:
- Descriptive—tells you what is happening
- Diagnostic—tells you why it's happening
- Predictive—tells you what will happen
- Prescriptive—tells you what should/could be done
- 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 companies 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:
- 28 percent of companies are in the "developing" stage, with one or more big data analytics initiatives under way.
- 24 percent are in the "early" stage, conducting proof-of-concept testing to determine benefits and drawbacks.
- 20 percent have not adopted big data analytics in their supply chain.
- 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 technology 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-party logistics companies (3PLs) and retailers fell about halfway 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.
