Equipment & OEE holds a structured registry of every machine, line, cell and work centre in the plant, each with its technical specification, ideal cycle time, criticality and location in the asset hierarchy. Against that registry it captures production and stoppage data in real time, whether streamed automatically from controllers and sensors or entered by operators at the line. From this stream it computes overall equipment effectiveness continuously, decomposed into its three constituent factors, and rolls the result up from individual asset to line, area, site and enterprise.

The module records every downtime event against a reason code, distinguishes planned from unplanned stoppages, and tracks speed loss and quality loss with the same rigour. It maintains a predictive and preventive maintenance regime driven by condition data, run hours and cycle counts, raising maintenance demand before failure rather than after it. Live dashboards, Pareto loss analysis, trend charts and shift reports present the picture to operators, supervisors and plant managers, each at the level of detail their role requires.

At the centre of the module is the notion of a piece of equipment: a durable, identifiable asset with a rated capability and a place in a hierarchy that runs from the smallest component up through the machine and the line to the whole site. Equipment does not exist in isolation; it exists to convert time into good output, and everything the module records describes how well that conversion happens.

The unit that captures conversion is the production run: a period during which a given asset makes a given product against an expected rate. A run has an ideal output, defined by its cycle time, and an actual output, and the difference between the two is never anonymous. It is always explained as loss, of one of three kinds: time the asset was not running when it was scheduled to, time it ran slower than its rated speed, and output that failed to meet quality. Each loss event carries a reason drawn from a governed taxonomy, so that losses aggregate cleanly into causes rather than remaining a formless total.

Alongside runs and losses sits the maintenance regime: the schedules, condition thresholds and interventions that keep an asset healthy. The model ties maintenance to the same equipment and the same time base as production, so that the cost of keeping an asset available and the loss incurred when it is not are two views of one history rather than separate records.

Operators work against the line in real time. As a shift proceeds, the module records production counts automatically or through simple confirmation, and prompts for a reason whenever the asset stops or slows. Capture is deliberately fast: an operator classifies a stoppage in a moment, and the module handles the arithmetic. At shift end, a structured handover summarises output, losses and open issues for the incoming team.

Supervisors and engineers work against the loss picture. They review OEE by asset, line and shift, drill from a headline number into the Pareto of contributing reasons, and open structured root-cause investigations on the losses that matter most. Recurring or high-cost losses become improvement actions with owners and target dates, and the module tracks the effect of each action on subsequent OEE so that improvement is evidenced rather than asserted.

Maintenance planners work against asset health. Condition readings, run hours and cycle counts trigger maintenance demand, which the module schedules around production so that intervention lands in a window that costs the least output. Every intervention closes the loop by updating the asset's history and its expected time to next service.

The module computes OEE to the standard definition: availability, the ratio of running time to planned production time, multiplied by performance, the ratio of actual output to what the ideal cycle time would have produced in that running time, multiplied by quality, the ratio of good units to total units produced. The product is a single percentage that no single factor can flatter, since a strong score demands that an asset run when scheduled, run at rate, and run right first time. The module also derives total effective equipment performance by extending the base to calendar time, exposing the loss embedded in how the plant is scheduled as well as how it runs.

Loss is classified against the six major loss categories: breakdowns and setup or adjustment losses under availability; minor stoppages and reduced speed under performance; and start-up rejects and running defects under quality. Reason codes hang beneath these categories in a governed hierarchy, so that a single stoppage is at once a specific cause and part of a comparable class. Reliability is quantified through mean time between failures and mean time to repair, computed per asset from the same event history, and condition monitoring compares live readings against alarm and trip thresholds to flag degradation early. Ideal cycle times, quality tolerances and reason taxonomies are governed centrally and versioned, so that a rate change or a reclassification is auditable and comparisons across periods remain sound.

Equipment & OEE draws its production context from Production Scheduling, which tells it what each asset is meant to be making and when, so that planned time, product and ideal rate are never manually maintained. Downtime and condition signals flow into Maintenance & Work Orders, which turns maintenance demand into scheduled, resourced jobs and returns completion history to the asset record. Quality loss reconciles with the Quality Management module, so that a reject counted against OEE and a non-conformance raised in quality are the same event, not two.

The asset registry aligns with Fixed Assets and Finance, giving depreciation, replacement and lifecycle costing a shared definition of the equipment base, and loss data feeds cost-of-poor-performance analysis. Consumption of materials and spares reconciles with Inventory & Materials, shift patterns and operator activity align with Workforce & Shifts, and energy drawn per unit of output connects to Energy & Utilities. Across all of these, the Analytics and reporting layer of Nashua 360 consumes OEE as a first-class metric, so equipment effectiveness appears in enterprise dashboards beside output, cost and service.

AI Workers are first-class users of Equipment & OEE, operating on the same data and through the same controls as their human colleagues. A supervisor asks in plain language why line three lost eight points of OEE overnight, and a Worker answers with the Pareto of reasons, the shifts involved and the comparison against the trailing average. Workers execute actions within their authority: reclassifying a mislabelled stoppage, opening a root-cause investigation, or raising maintenance demand when condition readings cross a threshold.

They watch continuously for anomalies, an asset drifting below its performance band, minor stoppages clustering on a particular product, or a reliability metric decaying, and alert the right people before a shift report would surface it. Workers extract structured data from maintenance notes, inspection sheets and supplier documentation, attaching it to the asset record without manual re-keying. In decision support they weigh the output cost of deferring an intervention against its failure risk and recommend a window. And they participate directly in workflows as an approval or review node: signing off a reason-code reclassification, reviewing an ideal-rate change before it takes effect, or endorsing an improvement action, always within governed limits and with every step recorded for audit.