Practical AI for real operations

AI in Operations

Practical frameworks, public lectures, and GitHub-backed examples for applying AI in production, supply chain, quality, and regulated operations. Start with the decision, prove the workflow, then automate.

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Built on public proof

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

Start here

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.

Read the AI in Operations section Resource sheet

Use-case readinessBound the workflow, data, failure modes, and human gate

Decision ownershipRemove coordination loops without hiding accountability

OEE before AI hypeProduction AI starts with losses, not models

Evidence library50 public sources for agentic AI in operations

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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.

Operational use cases

Start with the operating problem, not the model.

These public lectures connect AI methods to production constraints, process ownership, data quality, risk, and human decisions.

OEE and root causes

Separate schedule, availability, performance, and quality losses before applying machine learning.

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Predictive maintenance

Conceptually simple, operationally difficult: sensor resolution, feature engineering, risk, and maintenance decisions all matter.

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Vision systems and quality

Start with proven classical vision. Add machine learning when the use case needs it and sufficient training images exist.

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Production scheduling

Make objectives, constraints, data quality, and intraday rescheduling explicit before optimizing the plan.

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Digital twins

Begin with one decision and a clear abstraction level. Modeling everything is a fast route to parameter hell.

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Document compliance

Use OCR and NLP to reduce paperwork while keeping human verification where compliance risk demands it.

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Resource and proof layer

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.

Agentic operations AI resource sheet50 source links for credible agentic AI in operations

Public lecture archiveStructured learning paths across operations, analytics, and AI

supplychainanalyticsSupply-chain analytics use cases

operations_use_case_selectionAI agent readiness workflows

View curated GitHub projects

Talks & professional education

For selected talks, guest lectures, or professional education inquiries around analytics translation, digital operations, agentic AI, physical AI, and regulated operations, reach out directly.

Talks / Contact

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.