| |
By Bill Murphy · Powered by Colony Spark |
THE OPERATIONS
BRIEF |
|
ISSUE #20 · JULY 31, 2026 |
|
| |
|
| |
|
Hi there,
New orders are still the record of committed demand. They just arrive too late to be the only signal the team should trust. Long-lived equipment produces evidence before a replacement order, an overhaul, or a parts request appears: rising service frequency, repeated warranty codes, condition alerts, and operator questions moving from “how do I run this?” to “what replaces this?”
This issue is about turning that evidence into an operating forecast the parts, service, finance, and account teams can act on together.
Bill
|
| |
The Installed Base Is Becoming the Forecast.
New orders describe demand that committed. Installed-base signals describe demand about to.
One service call is noise. Three calls against the same serial number, a recurring component failure, an expiring warranty, and a compatibility question form a demand signal. The forecast improves when those events are joined at the asset level, not left scattered across the ERP, field-service system, warranty database, telemetry platform, and technician notes.
The infrastructure to do it is finally in place, and the market is putting money behind it.
|
Three signals from 2026
· 1.6 million connected and reporting Caterpillar assets, tied to $24 billion in 2025 services revenue. The connected fleet creates an earlier view of asset need; the service network turns that view into planned parts, repairs, and lifecycle revenue (Caterpillar 2025 Annual Report Highlights).
· Predictive maintenance systems now combine IoT sensors, CMMS data, work orders, maintenance records, and environmental monitoring, and can auto-trigger work orders, inspections, and parts activity (IBM, June 2026).
· $13.89B in 2026, projected $23.79B by 2031 for the predictive maintenance market. Connected equipment and AI drive the growth. Data integration and accuracy remain the constraints (MarketsandMarkets, 2026).
|
More signals do not create a better forecast on their own. The advantage belongs to operators who can connect the signal to the correct asset, customer, component, and decision window. The winners are changing three operating habits: they keep a governed serial-number record across sales and service, they classify field evidence into a small set of actions (inspect, stage a part, revise a warranty assumption, offer an upgrade, or begin replacement planning), and they compare predicted events with what actually happened so the system learns which combinations lead to demand.
|
The installed base was once the record of what had already been sold. It is becoming the earliest reliable view of what will be needed next.
|
|
|
| |
The fleet ages either way.
Brent Norwood, CFO of Deere & Company, on what installed-base age signals under muted orders
Brent Norwood is CFO at Deere & Company. On Deere’s Q2 2026 earnings call in May, he described what the installed base is already telling the finance and operations teams while new orders remain soft.
|
“We’ve managed field inventories tightly of new equipment and made significant progress on used. All the while, machine hours continue to accrue, aging out the fleet and driving a base-level need for replacement.”
|
Norwood is describing Deere’s own operating reality in a down cycle. New orders remain muted. Machine hours and fleet age are already revealing the replacement demand building underneath them. The installed base can signal the next order well before the order book does.
|
The takeaway
The order book is a trailing measure of demand that already committed. The installed base is a leading measure of demand about to. Both belong in the same forecast, weighted differently.
|
|
|
| |
The Installed Base Demand Radar.
Service activity translated into a 30, 60, and 90-day operating view.
Not a replacement for the order forecast. The layer that says what will surface before an order exists.
Build a serial-number-level demand radar. It reads service activity, warranty patterns, parts consumption, maintenance intervals, condition alerts, and operator questions, and returns a weekly exception view of demand about to commit.
Inputs
|
· Asset register: installed equipment, age, and configuration.
· Service activity: calls, technician notes, work orders, parts consumed.
· Warranty: claims and coverage expiration dates.
· Condition and interval data: preventive-maintenance schedules and telemetry alerts.
· Commercial signals: open quotes and inbound operator questions.
|
How it works
|
Three layers
1. Join. Every event is tied to an asset, customer, product family, and component. No orphan events.
2. Rules. Known intervals and thresholds fire the obvious signals: warranty expiring, maintenance due, part reaching end of life.
3. Classification. AI reads unstructured notes and questions for failure symptoms, upgrade interest, obsolescence concerns, and replacement language. Each signal carries a demand type, time window, confidence level, evidence trail, and named owner.
|
What the operator gets, weekly
|
· Parts staging list by component, location, demand window, and confidence.
· Asset watchlist for repeated service, accelerating parts use, approaching maintenance, warranty exposure.
· Customer-intent queue separating likely repair, upgrade, and replacement conversations.
· Warranty hotspot report by model, component, age band, and operating environment.
· Forecast-vs-actual log showing which signals produced a work order, quote, parts order, or replacement.
|
Two paths to build it
|
Path 1: Internal starting point
Start with one equipment family and two signals the team already trusts, such as service frequency and parts consumption. Back-test six to twelve months of history. Ask whether the radar would have identified the demand early enough to change a parts buy, a technician schedule, a warranty reserve, or a customer call. Add telemetry and free-text classification only after serial numbers, component codes, and outcomes reconcile.
|
|
Path 2: ERP or field-service partner
An existing ERP, field-service, IoT, or supply-chain partner can connect the source systems and automate the weekly exception run. The operator keeps ownership of signal definitions, confidence thresholds, planning windows, and the decision attached to each alert. No alert enters the forecast without its underlying evidence, and no AI-classified safety or warranty action bypasses human review.
|
|
The goal is not to predict every failure. It is to give parts, service, finance, and account teams the same early view of what the installed base is already saying.
|
|
|
| |
|
THE OPERATIONS BRIEF
By Bill Murphy · Powered by Colony Spark
|
|
Already treating the installed base as a forecast, or watching service data pile up in five systems that don’t talk? Hit reply, I read every one. Bill
|
|
| |
|