Yes, but the practical goal is usually not to turn manufacturing engineers into data scientists.
The better goal is to make manufacturing engineers strong domain counterparts who can frame the right process questions, interpret plant context, spot bad data, and help move analytics into controlled operational use. In regulated manufacturing, that boundary matters. A technically impressive model can still fail if it ignores routing logic, equipment state definitions, genealogy gaps, calibration status, revision control, or change control requirements.
Focus on a short list of capabilities that improve collaboration quickly:
Problem framing: translate production pain points into specific, testable questions such as yield loss by operation, queue-time effects, setup variation, scrap drivers, or rework recurrence.
Data literacy: understand common plant data sources, timestamps, identifiers, missing data patterns, sampling limits, and why ERP, MES, historian, QMS, and spreadsheet extracts often disagree.
Process context for analytics: explain routings, standard work, machine states, part genealogy, revision changes, inspection steps, and exception handling so models are not trained on misleading data.
Basic statistical reasoning: distinguish signal from noise, correlation from causation, and process drift from one-off events.
Validation mindset: know that any operational use of analytics may require documented testing, versioning, approvals, retraining controls, and evidence trails depending on how outputs influence decisions.
Communication with technical teams: write clearer requirements, review assumptions, define acceptable error, and identify where false positives or false negatives would create operational risk.
Do not start with a broad curriculum on advanced machine learning tools and expect adoption. That often produces slide-level understanding without improving plant decisions.
Also avoid treating manufacturing engineers as data labelers for a centralized team. If they are only asked to clean data after the fact, collaboration usually breaks down because the real issue is upstream process definition, identifier consistency, or system integration debt.
A workable model is usually part training, part applied delivery:
Select 2 or 3 real use cases with measurable operational value, such as scrap reduction, bottleneck identification, or cycle-time variance.
Pair engineers with data scientists in short sprints. The engineer owns process context and operational constraints. The data scientist owns analytical method and model evaluation.
Train on the plant’s actual data landscape, not generic examples. Include MES events, historian tags, QMS records, maintenance logs, and manual workarounds where relevant.
Create a common working vocabulary for identifiers, event definitions, state models, and quality status so teams are not arguing over inconsistent meanings.
Require documented assumptions for data filters, exclusions, feature definitions, and decision thresholds.
Review outputs with operations and quality before wider use. Some findings will be technically valid but operationally unusable.
Success usually looks like manufacturing engineers being able to do the following:
Bring better-defined use cases to analytics teams
Challenge misleading outputs using process knowledge
Identify data collection gaps early
Help operationalize useful models into existing workflows
Support traceable updates when process changes affect the model or KPI logic
It does not necessarily mean they build production-grade models on their own.
In most plants, upskilling efforts succeed or fail based less on classroom content and more on system conditions. If MES transactions are incomplete, historian tags are poorly mapped, part and lot identifiers do not reconcile across systems, or quality events live in disconnected workflows, engineers and data scientists will spend most of their time debating data trust.
That is why coexistence with current systems matters. In regulated, long-lifecycle environments, full replacement of MES, ERP, PLM, or QMS just to support analytics is often unrealistic. The qualification burden, validation cost, downtime risk, integration complexity, and traceability impact are usually too high. A more practical path is to improve data contracts, mappings, and governance around the existing stack while targeting a few high-value workflows first.
Breadth versus depth: broad training raises awareness, but role-based training tied to actual use cases usually changes behavior faster.
Speed versus control: rapid experimentation is useful, but if outputs influence production or quality decisions, governance needs to catch up before scale-out.
Centralization versus plant ownership: centralized data science can improve consistency, but local engineering ownership is usually necessary for adoption and sustained accuracy.
Automation versus explainability: more complex models may perform better on paper but can be harder to validate, trust, and maintain in regulated operations.
If you want durable results, train manufacturing engineers to be disciplined translators between process reality and analytics, not generic citizen data scientists.
Whether you're managing 1 site or 100, Connect 981 adapts to your environment and scales with your needs—without the complexity of traditional systems.
Whether you're managing 1 site or 100, C-981 adapts to your environment and scales with your needs—without the complexity of traditional systems.
