LinkedIn use-case series
Sixteen operational decisions agents should not own alone.
I wrote this series because operational AI becomes vague very quickly. These sixteen cases start with a concrete decision, the people who live with its consequences, and the boundary an agent must respect. The sequence moves from the factory, through planning and network choices, to supplier and customer commitments.
Explore the interactive relationship map
Factory physics gets the first and last word.
Models help only when maintenance, quality, planning, and production teams can challenge the signal and act on it.
Predictive maintenance is a decision problem first
An agent can detect a pattern. Maintenance and production still decide whether to stop, inspect, defer, or accept the risk.
Computer vision in quality is not AI magic
False positives and false negatives land in a quality system, not a demo. Quality owners define validation and escalation.
Scheduling agents cannot remove factory physics
Setup times, skills, material, release status, and the surprise at 2 p.m. remain real. Production planners own the compromise.
Digital twins need boring process truth
A useful twin is honest enough to disappoint you. People closest to execution know where the model stops matching the process.
Batch-size agents cannot ignore changeover pain
Clean mathematics collides with cleaning, changeovers, labour, shelf life, and service promises. Team leads shape the rules.
Better visibility creates sharper trade-offs, not automatic answers.
Planning agents can compare scenarios and keep signals moving. They cannot decide which shortage, buffer, customer promise, or network commitment the organisation should accept.
Control towers do not remove trade-off owners
The dashboard is the easy part. Operations leaders decide what happens when service, inventory, capacity, and cost point in different directions.
Forecast agents need decision discipline
The forecast is not the decision. Planners translate error into capacity, inventory, customer commitments, and escalation.
Inventory agents make trade-offs visible
Too much stock hurts. Too little stock hurts louder. Decision owners balance service, cash, shelf life, capacity, and trust.
Local inventory agents can create global mess
A warehouse can look efficient while the network gets worse. Network-level owners protect the system from one-site optimisation.
Segmentation shows agents where not to generalise
Products, customers, suppliers, and routes are not interchangeable. Commercial and operations leaders define meaningful exceptions.
Risk agents need escalation rules
A warning without an agreed response is another alert. Risk owners define thresholds, priorities, and who acts next.
Network-design agents simulate options, not commitments
Models can compare footprints, suppliers, buffers, and lead times. Leaders must live with the commitments after the model closes.
Trust becomes part of the operating system.
Supplier data, compliance evidence, dispatch exceptions, and customer promises cross organisational boundaries. Automation needs explicit rights and limits.
Supplier agents need trust boundaries
Data sharing and commitments do not become neutral because software moves them. Supplier-facing owners define what the agent may see and do.
Compliance agents should chase documents, not hide exceptions
Agents can follow up missing evidence. Functional owners decide what happens when documents, approvals, and reality do not match.
Routing agents need human exception logic
A neat route can ignore a driver, loading dock, safety issue, or customer promise. Dispatch leads define the exceptions that matter.
Fulfillment agents must protect customer trust
Shortages and delays become customer-facing decisions. Fulfillment leaders decide which promises can flex and which ones protect trust.
Move from the argument to the artifact.
The operations map connects the sixteen cases through process, people, systems, data, and AI roles. The broader public library contains 25+ interactive HTML presentations across source, make, plan, and deliver.
These summaries are adapted from Frank Kienle's LinkedIn article series and public supply-chain analytics lectures. They use educational examples and contain no employer-, customer-, or supplier-confidential information.