Visibility was step one. Time to decision is the next level of supply chain optimization
Every so often a new report detailing the 2026 trends for the supply chain comes out. We read through them for you. A suspicious consistency keeps jumping out at us, though. First, McKinsey talks about “nerve centers” for supply chains, and Gartner writes about control towers. “Autonomous agents” are thrown into the mix. Plenty of supply chain companies, from vendors of freight technology to big consulting firms, all have different frameworks for the supply chain. But in the end, they are always measuring the same thing: how much of your network you can see and how quickly you can see it.
So for the majority of the last decade’s ‘supply chain challenges’ (see historical examples here), measuring the ability to ‘see’ a problem had been an appropriate thing to do. All too often, chains had operated ‘in the dark,' with status updates having to be called up on the phone and problems only discovered days later. Reconciling three different spreadsheets to establish where a shipment was had become the norm of everyday operations. As such, it was right that the investment in visibility was what it was. However, now the supply chain can ‘see’ most of what’s going on (as examples of current visibility measurement here), treating ‘visibility’ as the ongoing scorecard for the supply chain in 2026 is to measure something that has largely been solved and to completely ignore the current largest cost to operations of the time lag between problem detection and problem resolution.
Visibility solved the first question. It was never going to solve the second.
The distinction is worth stating plainly, because it's easy to blur in a pitch deck. When reading through trends for the 2026 supply chain, one can notice that many reports measure “visibility” and how it has changed. In reality, what matters most is the time to make a decision. Measuring visibility has been the industry’s main focus over the last decade, and yes, it was a major problem that needed to be solved. The measurement of “visibility” into a supply chain has improved dramatically over the last decade due to the use of control towers and real-time tracking of shipments. However, seeing that there is a problem occurs frequently within supply chains today and does not automatically mean that something is being done about said problem.
The measurement of “visibility” into a supply chain must now shift towards how to effectively use the information that is currently available and translate it into effective decision-making in order to close the current gap between insight and action.
You may notice the alert overload problem has not gone away. Recent research into supply chain technology in 2026 identified an ‘insight-to-action gap’—the growing disconnect between generating supply chain insights and being able to take timely action in response. Although most companies have made good progress solving for visibility, executing the resulting actions is an entirely different problem. Many companies are now under-investing in how to make the decisions that their current supply chain data systems identify.
The industry already has a name for this gap
The insight-to-action gap in supply chain technology has recently become a hot topic within the industry and related trade research. As most serious platforms for managing supply chains already offer a reasonable level of network visibility, “visibility” in itself is no longer sufficient to set companies apart from one another. Instead, the challenge facing organizations in the short- to medium-term is execution and, in particular, the way in which decisions are made within supply chain operations. Since many of the complex problems of supply chain management are of a tactical nature, as have been described in recent years, they depend on decisions being taken within tight time frames and on a vast amount of information. This means that although the supply chain industry has made great progress in recent years in generating vast amounts of data, the challenge now is to get a handle on the processes governing the way in which this data is used in making decisions.
A recent survey conducted by Gartner in 2023 found that 72% of supply chain leaders have had to revisit final approvals for a network of decisions at least once. Each revisit further delays the original decision, which could have been solved quickly had the proper approval processes been in place in the first place. Three out of four organizations re-litigate decisions that their own systems have already flagged for them.
Why "time to decision" is the metric that actually matters
The important metric here, therefore, is time to decision. Time to decision is the time from the moment a system (automatically) detects a genuine exception to the moment the exception is resolved. This is very different from the visibility metrics we mentioned above (i.e., percentage of the network visible, number of different data sources integrated, etc.). To measure time to decision, we have to look at the flow of decisions through a supply chain system.
Rethinking the industry’s archetype. The distinction between asking the right questions and using the right metrics for measurement is critical to our exploration of supply chain technology trends. Earlier we distinguished between two questions: How much of the network can you see? And how fast can you see it? The first question is a great benchmark for how far the industry has progressed in the last decade. The second question has much greater currency today, however, since so much of what is viewed in terms of visibility has already been accomplished. True differentiation must be based upon how the industry’s various solutions help organizations reduce the time between the moment a problem with a shipment is first detected and the moment that problem is fully resolved.
This point is consistent with previous work done by Libera, including, for example, insight into the value delivered by exception-based control towers. The real value of such systems isn’t their ability to view hundreds of active shipments but rather to very quickly reduce that to a handful that actually require action and then change outcomes very quickly once a decision has been made. Thus, for example, a system that surfaces 12 active trips out of hundreds of active shipments is only half doing its job if each of those 12 active trips requires the same manual escalation process as if no system at all were in place to flag that trip in the first place.
Two control towers, same alert
Consider the same flagged exception passing through two differently designed systems: a truck running noticeably behind schedule on a lane with a tight delivery commitment.
Visibility-optimized: The control tower detects the delay instantly and displays it clearly as a red flag on the dashboard, an accurate ETA update, and full location history for the trip. Every stakeholder who checks the dashboard can see exactly what's happening. But acting on it means the ops lead has to manually identify who owns the decision to reroute or expedite, check whether the customer relationship allows for a service recovery gesture, and get sign-off from a manager before committing to either option. By the time that chain completes, the delivery window has already passed. The dashboard was accurate the entire time. It just wasn't connected to anything capable of moving faster than the manual approval process behind it.
Decision-optimized: The same delay is detected at the same instant, but the system already knows the predefined decision rule for this category of exception: delays under 30 minutes on this customer tier trigger an automatic proactive notification with a revised ETA; delays over 30 minutes on a committed window trigger an automatic reroute evaluation and execute the best available option within pre-approved cost parameters, only escalating to a human if the cost exceeds a set threshold. The gap between detection and resolved action is measured in minutes, not in however long it takes to identify an owner and route the decision through a manual chain.
Both systems had identical visibility into the problem. The only difference was whether the architecture behind the dashboard was built to shorten the distance between seeing the issue and resolving it or simply to display it as clearly as possible and stop there.
What actually closes the gap
Closing the insight-to-action gap is a matter of organizational design that technology can support but not replace. Decision rights for exceptions need to be predefined so that the system can automatically take the best action for all categories of exceptions that can be resolved within set limits. In addition, the system that detects a problem needs to be structurally connected to the system that has the authority to take action on that problem, which is the same structural connection between detection and execution that Libera's Freight Transportation Management System was built around, rather than treating visibility and resolution as two separately purchased capabilities. Lastly, the right thing needs to be measured and tracked. While operations teams are celebrating 98% network visibility, the time to resolve exceptions on flagged trips, for example, is slowly but surely increasing for them on a year-over-year basis. That insight into the problem is of little value in improving their operations.
Saying that all needed visibility has been acquired is in no way to diminish the value of past investments in ‘visibility’-oriented supply chain solutions. However, when it comes to competing in 2026, it is no longer sufficient to simply utilize the myriad of ‘supply chain visibility’-oriented solutions currently available across the full spectrum of serious platforms. In terms of both how individual vendors compete against one another as well as how individual supply chain operations themselves are run, a significant gap currently exists between a system’s ability to identify a problem and a system’s ability to take action in response to having identified a problem.