Key Takeaways from the 2026 Gartner Supply Chain Symposium/Xpo

The 2026 Gartner Supply Chain Symposium/Xpo made one theme known: manufacturers are still struggling to bridge the gap between planning, scheduling, and execution. Across booth conversations, analyst sessions, and peer discussions, the same themes surfaced repeatedly: disconnected systems, manual workarounds, limited visibility, and growing pressure to operationalize AI in environments that still lack a reliable planning foundation.

Planning is viewed as unreliable

One of the strongest takeaways throughout the event was the decreasing confidence manufacturers have in their planning processes. For many, the plan is assumed to be unreliable before execution even begins.

Manufacturers described environments where plans quickly become outdated due to disruptions, changing priorities, and limited ability to react in real time. In many cases, manufacturers are forced to rely on manual interventions, temporary workarounds, or simply wait to see what happens during a shift because adjusting the plan fast enough is nearly impossible.

After speaking with many manufacturers, here are several root causes that surfaced:

• Legacy systems that are inherently disconnected from upstream and downstream operations
• Limited time to rework plans without risking delayed or missed orders
• Too many disruptions and operational issues to manage manually

The result is a planning process that often lacks trust across the organization.

The gap between planning and scheduling is very real

Detailed scheduling emerged as one of the most discussed topics at the event. Many attendees expressed frustration that while high-level planning systems exist, few solution providers offer meaningful, detailed scheduling capabilities.

A common scenario shared by manufacturers involved organizations progressing through digital transformation initiatives, implementing Integrated Business Planning (IBP), Sales & Operations Planning (S&OP), and Manufacturing Execution Systems (MES), while still relying on spreadsheets to manually schedule work orders on the shop floor.

Throughout the event, many conversations revealed that attendees were unaware that a detailed scheduling solution even existed.

But, when it came to scheduling? This is how it’s being managed today, with the same tools appearing; Excel remained the primary scheduling system, ERP systems were the second most common approach, and manual and homegrown tools ranked third.

This highlighted a significant operational gap between strategic planning systems and the realities of execution.

Why does detailed scheduling matter

Manufacturers consistently identified 4 key benefits that detailed scheduling could provide:

1. Identifying operational gaps ahead of execution
2. Adjusting plans to restore feasibility after disruptions
3. Improving visibility into risks tied to customer orders and delivery dates
4. Better understanding true capacity constraints across machines, labor, and inventory

The ability to move from being reactive toward proactive decision-making resonated strongly with attendees.

Execution visibility remains a major challenge

Execution conversations centered heavily on shop floor data collection and visibility. While many manufacturers are investing in gathering more operational data, a recurring question remains: what should manufacturers actually do with that data once they have it?

Key execution gaps identified

Several execution-related gaps surfaced consistently:

• Overall Equipment Efficiency (OEE) measurements are immature or completely missing in many MES environments
• Overall Labor Efficiency (OLE) was often an unfamiliar topic to attendees until explained, but was then quickly recognized as an important gap
• Limited visibility into plan and schedule adherence was one of the most referenced challenges

Many manufacturers admitted they could not say with certainty what was actually being produced on the shop floor at any given moment. This lack of real-time operational visibility continues to undermine planning accuracy and execution confidence.

The trust in AI remains low

AI-dominated conversations throughout the event, but the tone was more cautious than expected.
From the solution provider side, many companies have aggressively invested in AI capabilities, agents, and automation tools. Nearly every booth showcased some version of AI-enabled planning, decision-making, or operational support.

However, manufacturers themselves appeared far more cautious.

In several discussions involving AI agents and autonomous capabilities, attendees questioned the value of deploying AI when foundational issues around data quality, accuracy, and operational visibility remain unresolved. Many manufacturers admitted they still struggle to understand the reliability of their own data, making fully autonomous decision-making difficult to trust.

We identified 3 trends, which were relatively simple and low risk:

1. Cleaning and organizing ERP data into usable datasets
2. Explaining schedules, plans, or operational exceptions
3. Assisting planners with interpreting information more quickly

Another consistent observation was that AI adoption appeared strongest among batch and process manufacturers rather than discrete manufacturers.

AI differentiation is already becoming difficult

One interesting trend at the event was how similar AI offerings already appear across supply chain software solution providers. Many demonstrations featured nearly identical interfaces, dashboards, and AI-driven use cases. Without company branding, it was often difficult to distinguish one planning platform from another.

At the same time, solution providers are discovering that highly autonomous AI demonstrations are easier to showcase than to operationalize safely in production environments.

Several solution providers appeared to be moving toward a more practical middle ground, ultimately allowing AI agents to perform carefully controlled, lower-risk tasks that still deliver measurable value without introducing significant operational risk.

This balance between automation and governance is likely to become increasingly important as adoption grows.

Strong planning foundations are key

Perhaps the most important insight came from manufacturers already running production-ready AI initiatives.

These companies consistently emphasized that successful AI deployment depends on having:

• A strong planning foundation
• Accurate models of the production environment
• Reliable and timely execution of data
• Near real-time operational feedback loops

Without accurate execution data and planning models that reflect operational reality, AI systems struggle to generate recommendations that planners can trust.

This reinforces a broader theme from the conference: advanced technologies cannot compensate for weak operational foundations.

Centralized planning models are gaining momentum

Another notable discussion point during Gartner-led sessions was the shift toward centralized planning structures.

Manufacturers are increasingly recognizing the limitations of siloed, plant-led decision-making, particularly when facilities share constrained resources such as supplier capacity, labor, or inventory.

More coordinated and collaborative planning approaches are becoming necessary to prevent sites from competing against one another and creating inefficiencies across the broader supply chain network.

Final thoughts

The overarching message from the 2026 Gartner Supply Chain Symposium/Xpo was that manufacturers are searching for ways to simplify complexity and regain operational control.

Planning, scheduling, and execution remain fragmented across many organizations, with manual processes still filling critical gaps. At the same time, AI is creating excitement, but also exposing how important foundational data, visibility, and process maturity truly are.

Overall, what resonated most strongly with attendees and analysts is the need for:

• Ease of use
• Operational clarity
• Real-time visibility
• Connected systems
• Flexible integration across technology stacks
• Tools that reduce disruptions

Manufacturers best positioned for the future will likely be those that first establish reliable operational foundations before layering on advanced AI-driven capabilities.