The Shift from Planning to Supply Chain Situational Awareness
The inspiration for Supply Chain Situational Awareness from John Boyd’s pioneering work on the OODA (Observe–Orient–Decide–Act) loop and its application for situational awareness and decision support across a wide range of mission-critical environments.
It is also informed by our own experience building and deploying ZFlow to orchestrate workflows that span organizations and supply chains—across teams, functions, partners, and enterprise systems. Leading manufacturing organizations use ZFlow to run complex cross-functional processes such as supply chain orchestration, new product introduction (NPI), and master data management. In many of these scenarios, the operating procedure is known, documented, and repeatable. Workflow orchestration provides an effective mechanism for ensuring that the right activities occur in the right sequence and are executed consistently.
However, not every situation can be reduced to a predefined workflow. Supply chain disruptions, supplier failures, demand shocks, quality incidents, geopolitical events, and unexpected interactions between multiple constraints often require responses that cannot be fully anticipated in advance. In these situations, the challenge is no longer one of workflow execution but of situational awareness, rapid sense-making, decision support, and coordinated response.
Traditional workflow orchestration answers the question, “What should happen next according to the process?” Situational Awareness and Response Orchestration answers a different question: “Given what is happening right now, what is the best course of action?”
This shift—from executing predefined plans to continuously observing, orienting, deciding, and acting in a dynamic environment—is the foundation of the approach described in this post.
Everybody has a plan until they get punched in the face
Aside from being one of the greatest heavyweight boxers of all time, Mike Tyson is also credited with the famous line: “Everybody has a plan until they get punched in the face.”
The 2020s have been one punch after another — pandemics, wars, unexpected tariffs, shortages, inflation. Teams, organizations, and entire supply chains are constantly thrown into situations where no standard operating procedure exists: supply chain disruptions, first-of-their-kind product introductions, unique suppliers, one-off orders, tariff turbulence, sudden shortages.
These unique and dynamic scenarios are where the effort, the risk, and the cost concentrate — and by definition, they’re the workflows you can’t design in advance. Increasingly, supply chain teams are facing more and more of these unique and dynamic scenarios on a daily basis.
OODA (Observe-Orient-Decide-Act)
OODA comes from John Boyd, a U.S. fighter pilot and strategist who set out to explain a puzzle: why pilots in slower, less capable aircraft kept winning dogfights. His answer was that combat is a cycle — you observe the situation, orient yourself to what it means, decide on a move, and act — and the pilot who runs that cycle faster, and misreads the situation less, ends up acting against a picture his opponent hasn’t finished forming. Boyd called this getting inside the other side’s loop.
John Boyd’s OODA loop
Credit: Diagram by Patrick Edwin Moran, CC BY 3.0, via Wikimedia Commons; model after John Boyd.
Many supply chain scenarios now fall under the unique, dynamic and evolving. A standard operating procedure, a workflow, a playbook is not the right solution for these scenarios. We believe the OODA is the right framework. Boyd intended the loop as a general account of how any entity, from a single operator to an entire organization, adapts under pressure. That is why it applies well to a supply-chain function absorbing daily disruption.
The picture below explains “the pilot who runs that cycle faster, and misreads the situation less”
How Supply Chain Situational Awareness and Response Orchestration maps to OODA
Situational Awareness = Observe + Orient. Response Orchestration = Decide + Act.
Situational awareness is Observe plus Orient. Observe is seeing what’s happening. Most companies already have this in the form of transactional data, feeds, real-time visibility, control towers and dashboards. Orient is the hard part of understanding what a signal actually means for your business. A supplier’s credit downgrade is just noise until you know that supplier is the only source for a part that goes into an order you’ve already promised a customer next quarter. That understanding comes from knowing how everything in your supply chain connects, which supplier provides which part, which part goes into which product, which product is committed to which customer.
Response Orchestration is Decide plus Act. Decide is choosing the right next step, often the best among many options. Act is completing it, which in a supply chain means coordinating a chain of work across people and systems. That coordination is orchestration, and workflow engines (like ZFlow) are very good at it.
How Big is Your Response Space
Many supply chain visibility platforms and control towers do a good job of helping teams understand what is happening. They can identify delayed shipments, inventory shortages, supplier disruptions, and customer order risks.
But once a disruption is understood, a more important question emerges:
What can the enterprise actually do about it?
In many planning and visibility-oriented systems, the practical response space is limited to logistics and inventory actions—expediting shipments, rerouting freight, reallocating inventory, adjusting fulfillment priorities, or updating delivery commitments.
These are valuable actions, but they represent only a small portion of the available response space.
For many high-impact supply chain disruptions, the best response is not a logistics move at all. It may involve qualifying an alternate supplier, accelerating supplier onboarding, approving an alternate component, launching a supplier development initiative, changing a sourcing strategy, increasing manufacturing capacity, adjusting a product launch, or coordinating procurement, engineering, quality, and manufacturing readiness activities.
Organizations with access to a larger response space are often able to respond more effectively because they are not constrained to moving inventory and freight within the existing system—they can change the structure or the system itself.
The difference between narrow planning and logistics-focused response orchestration and full response orchestration is the size of the response space available to decision makers.
Frontier LLM Models in the OODA loop
In OODA loop, observe is the ability to see the dots. In a supply chain setting that would be equivalent to having a picture of the state of the supply chain. Orient is the ability to connect the dots. The best supply chain practitioners and teams do this well. But it gets difficult to impossible for people to get a proper orientation when the problem space is complex with many moving parts.
This is where frontier models help. They connect dots across a far larger space than a person can hold — every part, supplier, plant, and order at once — and find the combination that matters. A change in delivery schedule, supplier disruption, a failing lot, and a demand spike might each sit unnoticed in a different system; the model reads all three together and sees that a single-sourced part is now exposed against a committed order.
Decide is the model’s other strength. It can generate realistic options, evaluate them for cost and effectiveness, and put them in front of the team, which makes the decision. Once the team decides, a workflow engine like ZFlow takes over and orchestrates the execution.
Two things have to hold for this to be useful. Orient must be grounded in master data and the current state of the supply chain, or the model orients against a picture that isn’t real. The options must be grounded the same way — what is realistically possible, master data, live status, existing workflows, available tools — so a chosen decision can be orchestrated.
Generic architecture for OODA based Supply Chain Situational Awareness and Orchestration
The architecture below is the OODA loop turned into layers you can build. The top four are the loop itself: Observe and Orient make up situational awareness — seeing the state of the supply chain and connecting the dots — while Decide and Act make up orchestration — weighing the options and carrying out appropriate activities A frontier model does the reasoning in Orient and Decide, people make the consequential decisions, and a workflow engine is used for orchestration of activities (Act).
Beneath them sit the layers the reasoning is grounded in: connectivity to the source systems, the master data those systems hold, and the semantic model that turns that master data into a connected picture of dependencies.
Putting OODA into practice for Supply Chain Situational Awareness and Orchestration
Until recently, most supply chain processes, from planning to execution, could be handled with standardized workflows. Organizations could rely on commercial off-the-shelf software to support stable, repeatable processes.
Since 2020, supply chains have been buffeted by volatility that shows no sign of relenting, and may be the norm for the foreseeable future. Even routine purchase orders and fulfillment now carry enough unpredictability that running by exception has become the default, and standard planning and workflows carry less of the load than they used to.
If exceptions are increasingly the norm, OODA-based situational awareness and orchestration should be part of the toolbox, and built into how companies operate on a day-to-day basis. Let’s see how this is put into practice using a scenario.
Observe
The goal for observe layer is to assemble the status of the supply chain that you can trust and current enough to act on.
We’ve seen all kinds of solutions for this problem—from simple dashboards to full control towers and supply chain visibility platforms. Many of them can do the job. And if nothing on the market fits what you need, building something from scratch isn’t especially difficult.
If you do end up building it yourself, the best place to start is by answering a simple question: What does “the current state of our supply chain” actually mean?
This is where it helps to borrow ideas from fields that have been thinking about state and situational awareness for decades.
Air traffic control is probably the clearest example. Controllers don’t try to hold every detail about every aircraft in their heads. Each plane is represented as a track: where it is, where it’s headed, how fast it’s moving, and the flight plan it’s supposed to be following. Control engineers use the same concept and call it state—the small set of variables you need to understand where a system is now and where it’s likely to go next.
We found it useful to track four categories of information, each tagged with when it was last updated and how much confidence we have in it.
1. What you have
This includes inventory by location, work in process, available capacity, and customer commitments that have already been made. Think of these as the system’s memory—the facts that describe where things stand right now.
2. What’s moving
Every open purchase order, shipment in transit, and production order in progress belongs here. Much like aircraft in the sky, these are quantities moving from one place to another, each with an expected arrival date and a level of confidence in that date.
3. What’s planned
This is the forward-looking view: demand forecasts, production schedules, and the S&OP plan.Your planning horizon should extend at least as far as your response lead time—the time required to do something meaningful about a problem. And potential responses to a situation can be many, including negotiating with the supplier to prioritize, qualifying a new supplier, purchasing from another contract manufacturing partner.
4. How everything connects
Much of this belongs to the next analytical layer, but you can’t reason about the future without understanding the network that connects everything together. Supply chain master data (products, suppliers, bills of material, alternates, cost, transportation lanes, lead time..) plays a big role here.
However you choose to design the “State of your supply chain”, whether that’s a star schema in a lakehouse, a wide table, or directly in ZFlow, the implementation matters less than the purpose.
Orient
Individual supply chain events – a delayed shipment, a quality issue, or a supplier schedule change – by themselves are consequential. Supply chains generate thousands of such events every day. The challenge is determining which combinations of events actually matter.
This is the role of the Orient layer.
The Observe layer provides a trusted view of the current state of the supply chain: inventory positions, open purchase orders, shipments in transit, production orders, forecasts, customer commitments, capacity constraints, and other operational facts. These facts describe what is happening right now.
Master data provides the structural definition of the supply chain: suppliers, products, bills of material, manufacturing sites, approved vendors, alternate parts, transportation lanes, lead times, costs, and customer relationships. This information describes how the supply chain is organized and how materials, products, and commitments flow through the network.
To understand the implications of an event, the two must be combined into a connected representation of the supply chain.
This is the role of the semantic model.In many ways, the semantic model is the supply-chain equivalent of Palantir’s ontology—a connected operational representation of how the enterprise actually works. It captures not only the entities that exist within the network but also the relationships between them: which suppliers provide which parts, which parts are used in which assemblies, which assemblies are used in which products, where products are manufactured, which customer commitments depend on them, what alternate sources are available, and what operational constraints govern the network.
The LLM determines why those connections matter.
Consider a supplier that pushes a delivery date out by three weeks. Viewed in isolation, this appears to be a routine delay. Separately, a recent quality rejection has reduced available inventory. At the same time, demand has increased for a strategic customer program.
Traditional systems can identify each of these events independently. The challenge is understanding their combined significance.
The LLM reasons over the semantic model and the current state simultaneously. It evaluates how the delayed shipment affects inventory, how inventory affects production, how production affects customer commitments, and whether alternate sources or mitigation paths exist. Rather than evaluating individual events, it evaluates the situation as a whole.
What initially appears to be several unrelated operational issues becomes a coherent business finding: a committed customer order is now at risk because a sole-sourced component is constrained, available inventory has been reduced by quality issues, and no qualified alternate source currently exists.
That conclusion does not exist in any individual source system. It emerges from reasoning across the connected supply chain model.
The outcome of Orient is not simply an alert. It is situational awareness: an explanation of what is happening, why it matters, who is affected, what constraints define the problem space, and what risks are likely to materialize if no action is taken.
Decide
Once the system understands the situation, the next question is: what can the enterprise realistically do about it?
This is the role of the Decide layer.
The output of Orient is a clear understanding of the problem: a committed customer order is at risk because a sole-sourced component is constrained, available inventory has been reduced by quality issues, and no qualified alternate source currently exists.
But understanding the problem is not the same as choosing a response.
The Decide layer takes the oriented situation and evaluates the available response space. That response space is grounded in the same semantic model, master data, current supply chain state, business rules, and available execution capabilities used in Orient.
In this scenario, the model may evaluate several possible responses:
- Recover the original delivery date from the supplier.
- Place a bridge buy from a distributor at a higher cost.
- Reallocate inventory from lower-priority orders.
- Qualify an alternate supplier or alternate component.
- Adjust the production schedule.
- Renegotiate the customer commitment.
Each option has different implications for cost, time, risk, feasibility, customer impact, and long-term resilience.
The LLM helps evaluate these options by reasoning across constraints that are usually spread across functions and systems: supplier lead times, available inventory, approved vendor lists, quality requirements, engineering qualification timelines, procurement policies, production schedules, customer priority, and contractual commitments.
The goal is not for the model to make the decision automatically. The goal is to produce a decision package that helps the supply chain team make a better decision faster.
A good decision package should include:
- The recommended options.
- The assumptions behind each option.
- The expected cost, lead time, and risk.
- The constraints or approvals required.
- The likely impact on the customer commitment.
- The operational steps required if the option is selected.
For example, the system may determine that qualifying an alternate supplier is the best long-term answer but cannot protect the current customer order in time. It may also determine that a bridge buy from a distributor is expensive but feasible within the required window. The decision package would present both options clearly: one as the immediate mitigation, the other as the structural fix.
In complex supply chain situations, the supply chain team retains the final say. They may choose one of the options presented, decide not to act, combine multiple options, or identify a new course of action that the model did not propose. The purpose of the Decide layer is to improve the quality and speed of human decision-making, not to remove human judgment from the loop.
The outcome of Decide is not an automated decision. It is a structured decision package that gives the supply chain team a clear view of the feasible choices, trade-offs, assumptions, and consequences so they can decide what action, if any, to take.
That decision then becomes the input to Act.
Act
Once a decision has been made, the challenge shifts from determining what to do to ensuring that it gets done.
This is the role of the Act layer.
In this scenario, the supply chain team decides to place a bridge order with a distributor because the additional cost is justified by protecting a strategic customer commitment.
Executing that decision requires far more than creating a purchase order. Procurement approvals may need to be obtained. Supplier commitments may need to be confirmed. Production plans may need to be updated. Inventory allocations may need to be adjusted. Customer delivery commitments may need to be reviewed. Stakeholders across procurement, planning, manufacturing, quality, engineering, finance, and customer operations may need to be informed and coordinated.
The workflow orchestration layer translates the selected response into a coordinated set of activities spanning people, teams, partners, and enterprise systems. The workflow itself may span ERP, PLM, MES, CRM, supplier portals, email, collaboration tools, and human approvals.
As execution progresses, every action is captured: the original signals, the situational analysis, the options considered, the decision made by the team, and the activities performed to implement that decision.
Execution also changes the state of the supply chain. Inventory positions change. Purchase orders are created. Supplier commitments are updated. Production schedules shift. Customer orders are protected or replanned. These changes become new observations that flow back into the Observe layer.
The OODA loop runs continuously across every supplier, part, product, and customer commitment. Observe assembles the state of the supply chain. Orient turns that state into situational awareness. Decide evaluates the response space and supports human decision-making. Act orchestrates execution across people and systems. Together, they create a standing capability for sensing, understanding, deciding, and responding to a dynamic supply chain environment where exceptions are no longer rare events but part of everyday operations.
Closing thoughts
For decades, supply chain software has focused on planning, optimization, visibility, and workflow execution. Those capabilities remain essential, but they were designed for a world in which most situations could be anticipated, modeled, and reduced to repeatable processes.
Increasingly, that is not the world supply chain teams operate in.
The disruptions of the last several years have exposed a different reality: many of the most consequential supply chain decisions occur in situations that are unique, dynamic, and difficult to predict.
This is why the OODA loop is such a useful framework. It shifts the focus from planning to adaptation, from planned workflow execution to situational awareness and response.
The technologies required to build these capabilities have become dramatically more accessible. Data platforms are cheaper. Integration is easier. Workflow orchestration is mature. Frontier models can reason across large, interconnected problem spaces. And the raw material for the semantic model already exists inside most enterprises in the form of supply chain master data.
As a result, capabilities that once required enormous investments and specialized teams are increasingly within reach of most organizations.
The organizations that adapt best will be those that can continuously observe what is happening, orient around what it means, decide on a response, and coordinate execution before disruptions propagate through the network.
What’s Next: Operationalizing OODA for Supply Chain
This framework describes the ideal. In practice, organizations run into real constraints: incomplete master data, fragmented systems, approval bottlenecks, the challenge of grounding LLM reasoning in what’s actually possible.
In our next post (coming in July), we’ll walk through how to operationalize this framework and what we’ve learned deploying OODA-based orchestration with customers across manufacturing, high-tech, chemicals, and life sciences.
We’ll cover:
- Supply chain semantic model design in practice
- Master data prerequisites
- Designing the response space
- Decisions by AI agents and people
- Orchestration using workflows
- The usage patterns that work
In the meantime, if you’re thinking through how to operationalize situational awareness in your supply chain, we’d welcome the conversation.