Closing the execution gap: Why supply chain investments still struggle to deliver results

Supply chain organizations have spent the better part of the last decade investing in digital capabilities, ranging from control towers and advanced planning systems to artificial intelligence and automation. But despite those investments, many are still struggling to translate visibility into measurable operational outcomes.

That gap between insight and execution is becoming one of the defining challenges in supply chain transformation. According to Nick Banich, chief revenue officer at Miebach Consulting, the issue is not for a lack of tools or data; it’s the difficulty of turning those inputs into decisions that drive real performance improvements.

“One of the biggest requests we’re seeing is how can we get more tangible results operationally out of the digital investments that we’re putting into place,” he told Supply Chain Management Review in an interview at the Modex 2026 conference in Atlanta.  

From investment to impact

The surge in digital investment has been driven in part by rising complexity. Global supply chains are dealing with tariff volatility, shifting sourcing strategies, and ongoing disruptions across logistics networks.

At the same time, organizations have built out increasingly sophisticated technology stacks to manage that complexity. Data lakes, control towers, and AI-enabled tools are now common across large enterprises. But more data has not necessarily led to better decisions.

Banich noted that many companies have successfully piloted new tools but are struggling to demonstrate clear return on those investment.

“We see a lot of frustration of, ‘we’ve tried this, we put in this proof of concept, but what’s the ROI,’” he said.  

That frustration is particularly evident as organizations move beyond experimentation and begin to evaluate whether digital initiatives are delivering value at scale.

The limits of visibility

One of the core challenges is that visibility alone does not drive execution, Banich noted. Dashboards can aggregate signals from across the network, and AI tools can process those signals faster than ever. But neither can determine what a specific development means for a given business or what action should be taken. In many organizations, that responsibility still falls to individuals who may not have clear ownership of external risk or decision-making authority.

As Banich explained, signals are often identified but not acted upon in a coordinated way. “Signals get picked up in pieces,” he said. “Without focused attention or anyone clearly responsible for deciding what needs escalation.”

The result is delayed responses, missed opportunities, and in some cases, avoidable disruptions.

When  outpaces process

The execution gap is also being driven by a mismatch between technology adoption and process maturity. Many companies have implemented advanced planning systems, warehouse management systems or analytics tools. Often, though, those systems are not always being used to their full potential. In some cases, organizations revert to legacy processes, limiting the value of the new platforms.

“Are you getting the most out of that system?” Banich asked. “Or does it become outdated and then everyone goes back to Microsoft Excel and they’re just using the APS to pass data around?”

Similarly, continuous improvement efforts are often constrained by how data is accessed and analyzed. Rather than focusing on operational changes, teams can become bogged down in manual analysis.

“The continuous improvement people [are not] continuous improvement people, they’re continuous analytics people going to the floor with a stopwatch and a notepad,” Banich noted.

Tools such as process mining are beginning to address that challenge by providing real-time visibility into how processes actually operate. But adoption is still evolving.

AI: Promise vs. reality

Artificial intelligence is adding another layer to the conversation and another source of expectations. While AI is now a standard feature in many supply chain platforms, its impact is still emerging. Banich noted that much of what is currently labeled as AI delivers incremental improvements rather than transformational change.

“I think right now we’re primarily in small productivity—seven, eight percent—I don’t think we’re at the point [where] large-scale systemic agentic AI solves [all] problems,” he said.

One of the limiting factors is data quality and system complexity. Supply chains remain highly fragmented, with multiple systems of record and inconsistent data structures.

“Our data is rough. The number of systems we have is endless. We still have those same issues,” Banich said. As a result, AI initiatives often struggle to move beyond pilot stages or deliver consistent results across the organization.

Bridging the gap

Closing the execution gap requires more than additional technology investment. Instead, it requires a combination of clear ownership of decision-making, alignment between systems and processes, and a focus on operational outcomes rather than tool deployment.

Organizations are increasingly looking to integrate digital capabilities with stronger process discipline and governance. That includes using simulation, process mining, and scenario analysis to better understand how decisions impact performance and to act on those insights more quickly.

Banich emphasized that the goal is not simply faster analysis, but better decision-making.

“Having the IT and tool stack … you can rapidly analyze a number of options to make a comprehensive decision in a shortened decision cycle,” he said.

A shift in focus

As supply chains continue to evolve, the focus is shifting from building visibility to enabling execution. The organizations that succeed will be those that can connect data, systems, and processes in a way that drives consistent action, Banich said.

That may require rethinking how digital investments are structured and how success is measured, but with the data and tech available today, that success is now within reach.

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