FM Predict.

The problem

Retail facilities management is reactive by design: work orders are only created after something breaks. For an FM Coordinator managing 80 stores, refrigeration failures are always a surprise, always expensive, and always a store-manager escalation.

Each missed failure means €500–2,000 in food spoilage plus an emergency vendor premium of 2–3x the planned rate — and the same unit often gets repaired three or four times before anyone notices the pattern. Refrigeration alone accounts for ~40% of total FM spend, yet there is no failure foresight, no repeat-offender flagging, and no predictive tooling.

The solution

FM Predict is an AI layer on top of the existing ServiceChannel CAFM system that flags likely refrigeration failures before they become emergencies. It analyzes historical work-order history per asset, detects repeat repair patterns and shortening intervals, and presents coordinators with prioritized alerts, evidence, and estimated savings.

Each alert carries a risk score, the asset's repair history, a cost comparison (emergency vs. preventive), and a pre-filled preventive work-order draft the coordinator can approve with one click. It sits inside ServiceChannel with zero new tools, capped at five alerts per coordinator per week in a deliberate precision mode, shifting operations from reactive tracking to foresight-driven maintenance.

How it works

A nightly job analyzes ServiceChannel WO history across 500+ stores. When an asset matches a repeat-failure threshold — for example, ≥3 same-trade work orders in 18 months with decreasing intervals — the system calculates a failure-probability score and cost delta, then uses Claude (Haiku 4.5 in the deployed prototype) to generate a plain-English alert and pre-filled WO draft.

Strict rules govern behavior: only same-trade repeats count, scheduled PM work orders are excluded, WO IDs are never invented, and thresholds are enforced (HIGH ≥75%, MEDIUM ≥50%, LOW ≥40%). Output is valid JSON, no RAG needed since Claude's long context handles 24 months of history in the API call. Live testing reproduced WO IDs and interval patterns exactly at 87% confidence, with a 97.6% objective pass rate (41/42 criteria) across six test cases; a human always approves before any WO is created.

Who it's for

The primary end users are FM Coordinators — each managing 50–150 stores and creating 20–50 work orders a week, working reactively today — for whom one missed failure is a costly, stressful escalation. Regional FM Managers own the budget and get a portfolio dashboard of active alerts, prevented incidents, and estimated savings.

Within the retailer (Aldi, a 10,000+ store chain), the internal buyers are Regional FM Managers and the Head of FM, whose decision criteria are measurable cost reduction, risk mitigation, and minimal disruption to existing ServiceChannel workflows.

Why it matters

The global predictive maintenance market is projected to grow from ~$6B (2024) to ~$28B (2029), a ~36% CAGR, with refrigeration-specific predictive maintenance among the fastest-growing sub-segments due to food-safety and energy mandates. Retail FM is uniquely underserved: no sensors, no predictive tradition — yet years of structured ServiceChannel WO history form an unmatched data moat no external vendor can replicate.

By converting emergency repairs into planned ones, FM Predict targets cost avoidance at scale. The launch is a phased 90-day pilot (120 stores, refrigeration only) with a KPI of ≥3 prevented failures and ≥€5,000 documented savings, expanding regionally then nationally, with human-in-the-loop approval preserving accountability throughout.

At a glance

Project

FM Predict

Built by

Dennis Beireis

One-liner

FM Predict is an AI layer on top of an existing CAFM system that flags likely refrigeration failures before they become emergencies.