The Hype Cycle for Supply Chain Planning (SCP) by Gartner has its detractors. Doesn’t everything! Yes, it’s true that it:
- Relies on subjective terms and lacks data or analysis
- Offers no practical advice. For example, should users avoid a specific technology during the dreaded trough or double down?
- Lacks information on how a technology can advance to the next stage
Curmudgeons like to pile on by pointing out that an analysis of Gartner Hype Cycles going back 17 years showed that few technologies journey through something resembling the hype cycle. In fact, some claim that most of the important technologies adopted since 2000 were not detected early.
But the cycle’s scientific shortcomings are part of its charm and precisely why it’s an excellent framework for sparking everything from friendly water-cooler debates to dead-serious strategic planning sessions. Indeed, the recent Supply Chain Planning hype cycle is an excellent springboard for discussing where the market stands today and where it’s headed. It covers several planning solutions, as well as some of the key underlying technologies helping to define what next-generation Supply Chain Planning solutions will look like.
Let’s briefly examine what this hype cycle gets right and what it gets wrong.
Change is Coming? It’s Already Here!
The report states a couple of times that change is imminent.
“SCP is a well-established technology market. But change is coming, fast. The SCP market is close to a tipping point when traditional, batch-oriented solutions will be replaced by highly scalable, near-real-time, automated and self-learning solutions to enable a company’s SCP maturity journey.”
“SCP may seem like a mature technology market; many of the SCP vendors have been around for years, as have their solutions. However, significant change is coming.”
Granted, markets tend to evolve rather than flash-cut, but the SCP space is already in the throes of massive change. Halo users frequently highlight how their traditional SCP solutions could not keep pace with evolving business needs. And that was 3-5 years ago! While its timing might not be impeccable, Gartner clearly gets it right when pointing to key reasons why legacy SCP technologies are getting bulldozed:
- More complex and unpredictable digital business environments
- Global footprints
- New and expanding sources of internal and external data
- Focus on actionable information instead of just data
In one of the more provocative statements in the report, advice comes in the form of a warning. Paraphrasing here: What will it take to drive competitive advantage in Supply Chain Planning? First, if you’re starting today, don’t implement “today’s” solutions. If you do, you’ll always be behind. Adopt a strategy that let’s you bring the future forward.
This suggestion is relevant to the analytics solutions we will look at next.
What’s More Important? Descriptive Analytics or Diagnostic Analytics?
The report highlights two emerging technologies on the left-hand or “promising someday” side of the adoption curve: descriptive analytics and diagnostic analytics.
The author defines descriptive analytics as the application of logic, pattern detection, discovery and business rules to data to understand what is currently happening or has happened. Capabilities span reporting, dashboards, supply chain visibility, visualization and alerts.
Descriptive analytics is the most common analytics discipline in the supply chain because it’s fundamental to all the more advanced analysis techniques. Implementations range from spreadsheets to advanced data discovery and visualization.
Supply chain diagnostic analytics, according to the report, are techniques to identify the possible drivers or reasons behind an event, outcome or trend. Tools include root cause analysis and ad hoc queries. The goal of diagnostic analytics is to explain the reasons behind an event.
In short, descriptive is what happened, diagnostic is why.
So which one is more important? False question. As noted above, it’s impossible to know the cause of an event if you can’t see the event and then break it down. Therefore diagnosis always lags description and moreover depends on the accuracy of the description. As the report puts it, diagnostic analytics requires a clear understanding of the intertwined relationships among supply chain variables to pinpoint the root cause behind observed performance. But that doesn’t make it less important. Which is one reason Halo supports both descriptive and diagnostic analytics, as the report makes clear.
Now let’s not leave the obvious question begging. If you could, with confidence, describe events and outcomes in your supply chain and know the key factors producing those events and outcomes, what would you do next? You would give your users tools to simulate, measure and manage corrections, eliminating actual root causes to altogether avoid problems in the future. You would implement predictive analytics. The natural progression is Descriptive → Diagnostic → Predictive.
Predictive analytics is active, not passive, while diagnostic analytics still requires the user to find the best course of action to correct a problem. We’ll leave predictive analytics for another day. It’s (predictably) a big topic and a core offering at Halo.
One last hat-tip to Gartner — the Supply Chain Planning Hype Cycle Report repeatedly stresses the importance of data governance. Comprehensive, consistent, cleansed data is the foundation for sound decision-making based on analytics platforms like Halo. We couldn’t agree more. Not a glamorous point to make, but a critical one nonetheless.