Before the
Dashboard.
A five-level readiness model for manufacturers deciding where digital investment can produce operational value.
What must be true before a digital manufacturing investment can work?
A dashboard cannot repair an undefined process, inconsistent data, or unclear ownership. Digital readiness is a system condition: process discipline, measurement quality, information flow, decision rights, and improvement capacity must advance together.
NIST defines smart-manufacturing readiness as the maturity required for a factory to improve through data-intensive technology. The implication is important: readiness is not a list of software purchases. It is an organization’s ability to connect data, process knowledge, controls, and improvement activity.
01 · A five-level readiness model
The process is understood through direct observation, but performance definitions and records remain inconsistent.
Critical work, quality requirements, ownership, and escalation paths are documented and repeatable.
Inputs, outputs, defects, downtime, and cycle time use stable definitions with traceable collection methods.
Information moves across equipment, people, and systems with controlled identifiers and defined interfaces.
Teams use the information to test causes, compare interventions, and sustain measurable gains.
02 · Sequence investment around decisions
Digital manufacturing can link software, data, controls, modeling, and analysis across a product lifecycle. The value appears when those capabilities improve a specific decision. A useful sequence is therefore: identify the decision, establish the baseline, stabilize the data, test the intervention, and only then automate or scale.
Write the operational decision and the consequence of getting it wrong.
Collect only the variables required to explain or improve the decision.
Assign an owner who can act on the signal and verify the result.
03 · Questions for a readiness review
- Are the process boundary, customer requirement, and critical-to-quality characteristics defined?
- Do operators, engineering, quality, and management use the same metric definitions?
- Can a measurement be traced to its source, time, unit, and responsible system?
- Is there a documented response when a signal exceeds an operating limit?
- Can the team distinguish common-cause variation from an assignable cause?
- Will the proposed technology reduce decision time, improve quality, or lower total effort?
04 · Limitations
This model is a SKAR synthesis for preliminary decision support. It is not a certification standard and does not replace cybersecurity, safety, regulatory, equipment-control, or quality-system requirements. A factory assessment must be adapted to the production system, product risk, and applicable standards.