Supply chain volatility has become persistent. Planning cycles are shorter, and leadership expectations are higher. Teams are expected to absorb noise, evaluate tradeoffs, and maintain service even when execution conditions shift hour to hour. Decision Intelligence provides a structure for operating in this environment by focusing on the decisions that drive performance, and supporting those decisions with models, analytics, and governance built for real-world variability.
The trends below reflect what operational leaders will encounter in 2026 based on our project patterns and the technology investments that are gaining traction.
AI agents advanced rapidly in late 2025. Gartner included multiagent systems in their strategic trends for 2026, and Deloitte highlighted agent orchestration as a high upside development. Early operational deployments show similar movement. Retailers are experimenting with agents that monitor inventory, route orders, and flag emerging issues, as noted in industry reporting.
SimWell sees a practical model emerging.
Teams give an agent a narrow mandate such as protecting a lane, monitoring plant throughput, or preparing scenario branches when demand shifts. The agent retrieves signals from planning and execution systems, prepares alternate operating strategies, and tests them inside a simulation model. Planners receive a ranked set of options with cost, service, and risk implications. The planner still owns the decision. The agent handles the monitoring and preparation that planning teams rarely have time to complete at operational speed.
What this means for supply chain leaders
AI agents will increase analytical capacity without altering headcount. The value will come from the collaboration between human planners and computational agents that can run structured analysis continuously. Simulation provides the safety system that validates each option before execution.
Forecast accuracy remains important, but it no longer defines supply chain performance. Leaders now focus on the quality and speed of decisions. ABI Research reported that ninety four percent of supply chain leaders plan to use AI for decision support. Commentary from Logistics Viewpoints describes Decision Intelligence as a cross functional layer spanning planning, logistics, procurement, and finance.
SimWell sees the same pattern inside client programs.
The question is no longer “How accurate is the forecast?” The question is “What should we do given the probabilistic forecast we have?” Planners want guidance on replenishment policies, line sequences, capacity assignments, and routing strategies. Simulation becomes essential because it can reveal how each policy performs under variability. This is the difference between a predictive model and a decision engine that protects the P&L.
A realistic workflow looks like this:
a. AI updates demand probabilities.
b. A decision system prepares three replenishment strategies.
c. Simulation evaluates each strategy against realistic noise and constraint interactions.
d. Planners select the policy that delivers the right balance between service and capital productivity.
What this means for supply chain leaders
Improvements will come from policy design and validation rather than from incremental gains in forecast accuracy. Simulation sits at the center of this shift because it verifies decisions under uncertainty. Decision Intelligence aligns forecasting, policy selection, and scenario validation into one workflow.
Digital twins regained momentum in 2025, driven by concern over tariffs, geopolitical exposure, supplier instability, and transportation variability. CFO Dive described how AI informs tariff disruption modeling and supplier exposure analysis in their review. Academic work highlighted the importance of digital twins as structured environments for disruption rehearsal rather than branded dashboards. Even the World Economic Forum linked autonomous orchestration to continuous disruption management. The value no longer stops at producing a model; it’s in maintaining a living environment where teams rehearse responses before reality forces them.
SimWell encounters this shift across manufacturing, consumer goods, and transportation networks.
Executives want a controlled space to answer questions such as:
What this means for supply chain leaders
Digital twins will deliver the most value when they become rehearsal environments for volatility. Scenario assets will travel with the business across planning cycles rather than existing as isolated studies. Simulation provides the experimental space where disruption responses can be evaluated before reality forces them.
Visibility across ERP, WMS, TMS, and execution systems has improved significantly. Performance gains require more than visibility. Gains arrive when live signals feed structured decision workflows that evaluate options in minutes rather than hours.
Independent analyses summarizing McKinsey and Deloitte findings show that organizations using connected intelligence experience fewer disruptions and faster order to delivery cycles when predictive insights inform routing, labor allocation, and inventory positioning. Similar patterns appear across logistics and warehousing commentary, including recent industry work describing AI driven adjustments to daily operating conditions.
SimWell often finds that teams do not struggle with visibility. They struggle with the latency between seeing a problem and correcting it. Decision latency has emerged as a critical metric. Simulation reduces that latency by proving which actions are viable under realistic constraints. The combination of real time signals, structured decision rules, and simulation driven validation supports faster operational recovery.
A realistic scenario shows the pattern:
1. A warehouse encounters a labor shortage for the evening shift.
2. Real time signals capture the shortfall.
3. A decision system prepares multiple labor allocation options and tests them through simulation.
4. Supervisors receive throughput implications and make a confident choice.
What this means for supply chain leaders
Shorter cycle times require decision systems, not dashboards. Simulation gives operators a way to validate options quickly without increasing operational risk.
Executives have increased expectations for transparency and risk control in analytics and AI systems. Gartner identified AI security, digital provenance, and confidential computing as essential for enterprise adoption. Deloitte pressed the point that governance determines whether agent based systems succeed in production in their analysis.
SimWell sees governance as the invisible accelerator in Decision Intelligence programs.
CFOs and CIOs want confidence that planning decisions can be traced. Planning leaders want model lineage and scenario control so colleagues across regions rely on the same logic. Operators want to know that recommended actions respect the constraints they face every day. Simulation sits at the center because it enforces explicit assumptions, constraints, and data. This makes audits practical and reduces friction in executive approvals.
Governance matters once decisions become traceable. A well structured model shows which assumptions were active, which constraints shaped the outcome, and how each scenario performed. Leadership signs off faster because the decision path is transparent and defensible.
What this means for supply chain leaders
Trust drives adoption. Adoption drives performance. Teams with structured governance will scale Decision Intelligence faster than teams with sophisticated models that cannot be audited.
Three actions will help organizations gain traction in the year ahead.
Identify the decisions, not the processes, that shape you're P&L
Identify the planning, routing, inventory, capacity, and risk decisions that shape your P&L. Design your models and analytics workflows around those decisions.
Use simulation as the verification engine
AI proposes ideas. Simulation reveals consequences. A controlled scenario environment gives planners confidence and exposes second order effects before execution.
Build trust early through clear governance
Document lineage, scenario assumptions, and model updates. Strong governance reduces debate and accelerates acceptance when automated recommendations enter the workflow. Trust is a feature, not an outcome. Built it intentionally.
SimWell supports organizations that want to move from isolated analytics to operational decision systems. The strongest programs often begin with one critical decision node. A small pilot establishes the decision framework, the verification model, and the governance pattern that later scales across departments and regions.
When leaders start small and verify decisions with simulation, they create a path to Decision Intelligence that is both practical and defensible.