Smart Maintenance

A strategic guide for business leaders — how intelligent maintenance planning delivers immediate value, and how predictive models unlock even more. Written by Mikko Alutoin, Head of AI at Cloudamite, ex-KONE Principal Data Scientist.

What’s inside

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– WHY THIS GUIDE

Intelligent maintenance planning before models

Most organisations assume that optimising maintenance starts with predictive models. This guide argues it starts with intelligent planning — and the data that planning generates is exactly what makes prediction possible.

Immediate value — no ML required
Coordinating work across a fleet — bundling jobs, optimising travel, allocating resources — delivers value today, even on scheduled intervals alone.
Build the right data foundation
Structured planning creates structured data: standardised work orders, recorded outcomes, consistent labelling. This is what makes CBM and PdM feasible later.
Measure whether it actually works
A two-stage feedback loop solves the prediction paradox: how do you prove a model works when the prevented failure never occurs?
AI-orchestrated maintenance is here
LLMs for orchestration, ML models for number-crunching. Together they deliver a compounding advantage — but only if you build deliberately.

– WHAT’S INSIDE

Everything you need to get started and scale

From strategy to data architecture to AI — a complete framework for maintenance leaders at any maturity level.

Two-tier framework

Tier 1 (need identification) and Tier 2 (integrated planning) — two concerns that are often tangled together, clearly separated.

CBM vs. PdM — the right question

Condition-based and predictive maintenance aren’t competing — they’re complementary. Learn which to apply where, and why.

Model selection framework

Cox PH, Random Survival Forests, DeepHit, WTTE-RNN — a decision flowchart based on your data volume and requirements.

Measuring business impact

The prediction paradox solved: a two-stage feedback loop that quantifies value without deliberately letting things break.

AI agents in maintenance

How LLMs and ML models combine for a compounding advantage — and the order in which to build for maximum leverage.

Fleet-level optimisation

Single-asset timing is straightforward. Optimising dozens of assets simultaneously — bundling, resources, production windows — is combinatorial.

– FREE DOWNLOAD

Get the Smart Maintenance guide

44 pages of strategic guidance for industrial leaders. Written by Mikko Alutoin — nearly a decade building predictive maintenance systems at scale.