“Now that we’re all connected and responding 24×7 via any number of devices,” writes Jennifer Baljko, “of course there would be a loud corresponding call to have a ‘real-time supply chain,’ too.” [“Betting on Analytics as Supply Chain’s Next Big Thing,” EBN, 3 May 2013] A number of supply chain analysts have noted that the clock speed of business has been accelerating for several decades. That’s why the term “real-time” is finding its way into more supply chain discussions. Baljko asserts that the vision of real-time, “follow-the-sun supply chains” is coming closer to becoming a reality; but, “a necessary foundation for moving efficiently at real-time speed — supply chain analytics — is still very much at the beginning stages of development at many companies, and will take time to build out.”

I found it interesting that Baljko singled out analytics as the foundation of an improved supply chain. When you think about it, however, it makes a great deal of sense because each company faces different challenges and has different concerns. Analytics has the potential to address uniquely each of these challenges and concerns. The Strategic Sourceror website puts it this way:

“Each business faces different challenges; some companies may be trying to expand global reach in new markets while, others are attempting to optimize sourcing and manufacturing networks. … As consumers feel they have more say, other organizations are meeting the challenge of multichannel distribution.” [“Betting on Analytics as Supply Chain’s Next Big Thing,” 8 May 2013]

Even though Baljko began her article by discussing the real-time supply chain (i.e., “there’s evidence of increased interest in real-time supply chain support tools to address things like supply chain traceability, multi-level inventory optimization, demand signal repository, sales and operations planning, and leveraging point of sale data”), she spends a lot of her column talking about forecasting. Citing a recent survey, Baljko writes:

“Forecasting improvement remains top of mind for a sweeping majority of the 318 executives polled [in a recent survey]. Seventy-seven percent of respondents said demand and supply forecasting and planning tools were very important in achieving their company’s 2012 objectives; and 80 percent said forecasting will be important in 2014.”

The Strategic Sourceror adds its voice to Baljko’s in noting “supply chain software may need to adapt further before managers can see the benefits of analytics. Technology upgrades and implementing new systems takes some time, and it can be a large investment that companies are unwilling to risk. … While supply chain executives understand the increased need for analytics as manufacturers and suppliers change, the data capabilities have not caught up to the industry yet. …. Many companies do not currently have accurate data, so new analytics software will not quickly repair supply chain gaps.” Baljko concludes:

“Bill Roberts, a consultant for Bloomberg Businessweek Research Services, Triangle Publishing Services, had this to say, adding insight from a survey-related interview he had with a semiconductor executive:

You need to get the underlining systems right before you can build analytics capabilities on top of them. I’m going to quote him [the semiconductor executive] at length here because it’s interesting: ‘Analytics is the culmination of years of investment in the basic tools. You need the underlying databases; they need to be maintained and structured in a methodical and rigorous way. You need technology that allows basic reporting. These are all key to get value from analytical tools.’ He added that he and his staff for the last year, year and a half, have spent fully half of their time building analytics capabilities on top of their existing supply chain tools. I thought that was an interesting way of [noting] his priorities.

With that in mind, analytics (and, importantly, analytics done well) could be a big leap forward in terms of supply chain innovation.”

Despite all of the challenges and hurdles that face businesses as they try to obtain better analytics, both Baljko and The Strategic Sourceror are optimistic and believe that supply chain analytics could be the next big thing. Robert L. Mitchell offers an interesting example of how predictive analytics can help boost a business. The example is interesting because it comes from sector not normally discussed in supply chain circles — sports. [“Putting predictive analytics to work,” Computerworld, 27 June 2012] Mitchell specifically discusses how the NBA’s Orlando Magic used predictive analytics to improve ticket sales. For his article, Mitchell interviewed Anthony Perez, the franchise’s director of business strategy. He writes:

“Perez’s team began by using analytic models to predict which games would oversell and which would undersell. The box office then took that information and adjusted prices to maximize attendance — and profits. ‘This season we had the largest ticket revenue in the history of our franchise, and we played only 34 games of the 45-game season due to the lockout,’ he says.”

Perez indicated that the team was so pleased with how analytics helped “optimize ticket sales” they also use analytic tools “to help the coaching staff predict the best lineups for each basketball game, and which potential players offer the best value for the money.” Despite impressive results like those achieved by the Magic, an unexpectedly low number of businesses have jumped on the bandwagon. “The use of predictive analytics is common in industries such as telecommunications, financial services and retail,” Gareth Herschel, an analyst at Gartner, told Mitchell. “But overall it’s still a relatively small percentage of organizations that use it — maybe 5%.” One of those elite companies is Procter & Gamble. Mitchell writes:

“P&G uses predictive analytics for everything from projecting the growth of markets and market shares to predicting when manufacturing equipment will fail, and it uses visualization to help executives see which events are normal business variations and which require intervention.”

Guy Peri, director of business intelligence for P&G’s Global Business Services organization, underscored the point that analytics can be used to address a lot of challenges; nevertheless, it’s critical you that you identify exactly what you want your analytics to address. He told Mitchell, “‘Be clear on what the question is, and what action should be taken’ when the results come back. It’s also important to keep the scope focused. Mission creep can destroy your credibility in a hurry, Peri says.” Peri made another critical point, “Analytics is only valuable when you take action on the insight.” Mitchell writes:

“P&G once developed a model designed to provide an ‘early warning’ on how each business was going to perform. ‘It was actually quite accurate, but the warnings were given in such a way that people didn’t understand how to take action on them, and so we didn’t get the proactive decisions we wanted,’ [Peri] says.”

Although people can intellectually accept the fact that analytics are important, Mitchell notes, “People can also feel threatened by analytics.” That fear normally arises because they see analytics as a threat to, rather than a supporting tool for, their job. “Users need to understand that the predictive model serves as a decision support tool,” Mitchell writes. He also stresses that they need to learn “how to use the output in their own decision-making processes.”

There is plenty of empirical evidence to support the notion that big data analytics provides value to businesses and their supply chains. The fact that 95% of companies have yet to adopt significant analytic approaches is the reason that Baljko can reasonably predict that predictive analytics is likely to be the next big thing in supply chain management. There is simply a lot of room for growth.