AI in Operations
I connect AI with the decisions people make in production, supply chain, quality, and regulated operations. The work starts with process truth and decision ownership, then moves to models and agents. In that order.
- Digital operations leadership grounded in enterprise execution
- Former university lecturer with reusable learning material
- 19k+ LinkedIn community around practical digital operations
- YouTube lectures and GitHub repositories as inspectable proof
AI in operations starts with decision ownership, not tool enthusiasm.
The useful question is not whether agents can automate something. The useful question is whether the workflow is bounded, the data is reliable, failures are visible, and a human review gate is clear.
AI in Operations
Use agents where workflows are bounded, reviewable, and connected to real operational decisions.
Supply Chain Analytics
Map analytics and AI use cases to planning, sourcing, production, risk, and delivery decisions.
Analytics Translator 2.0
Bridge business, analytics, IT, AI, process, governance, and adoption in the agentic era.
Twenty-seven decisions agents should not own alone.
The original LinkedIn series has grown into a decision library structured on the ASCM SCOR Digital Standard. Every case names the trade-off, the decision owner, and the point where agent authority must stop.
Orchestrate & Plan
S&OP, segmentation, risk, network design, control towers, forecasting, inventory, and VMI make trade-offs visible before commitment.
Explore 9 decisionsSource & assure
Supplier trust, performance, capacity, incoming quality, and compliance need agreed definitions, evidence, and escalation rights.
Explore 5 decisionsTransform
Maintenance, vision, scheduling, batch size, OEE, manufacturing cost, and digital twins remain constrained by factory physics.
Explore 7 decisionsOrder, Fulfill & Return
Customer signals, fulfillment, ETA, routing, freight cost, and returns turn predictions into promises, spend, and disposition decisions.
Explore 6 decisionsExplore the connections Check all 27 decision boundaries Read 25 analytical cases
Useful work leaves an artifact a human can inspect.
Not every experiment belongs inside the business story. Some are here because they show what agents can do well: inspect a source, build something bounded, test it, and leave a clean review path.
Inspect the sources, lectures, and working artifacts.
Everything here is currently open: source collections, lecture routes, and GitHub artifacts. Optional email updates and downloadable packages can be added later.
For selected talks, guest lectures, or professional education inquiries around analytics translation, digital operations, agentic AI, physical AI, and regulated operations, reach out directly.
Personal educational content by Frank Kienle. Views are personal. Examples are based on public, educational, historical, or synthetic material unless stated otherwise. No employer-confidential, customer-confidential, or supplier-confidential information is shared.