AI & Machine Learning Proof of Concept

Validate One Factory AI Decision With Real Historical Data

A generic "AI pilot" with no decision behind it is not accepted here. This program takes one named decision, one owner, one prediction horizon and real history, runs a data-readiness gate before promising a model, and compares the result against a simple baseline rather than against nothing.

Validate my AI use case Talk to a manufacturing engineer

Typical duration6–12 weeks

Pilot scopeOne use case, one decision owner, real historical data

Primary buyerDigital Transformation Manager

Decision at the endScale or no-go recommendation with the reasoning behind it

Is this the problem you need to solve?

There is pressure to "do something with AI" but no decision anyone would change because of it.
A previous model looked excellent in a notebook and nobody ever acted on its output.
The data exists but nobody has checked whether it can answer the question being asked.
Nobody has costed what a false alarm actually does to the operation.

Primary buyer: Digital Transformation Manager · Operations Director · Data / AI Lead · Plant Manager · Reliability Manager

What this PoC will prove

Is the data good enough, complete enough and long enough to answer this question at all?

Does the model beat a simple baseline — a rule, a moving average, current practice — by a margin worth having?

Is the prediction available early enough for anyone to act on it?

What does a false alarm cost, and how many will the operation tolerate per week?

Who acts on each output, and what exactly do they do?

Recommended pilot scope

Exactly one use case with a named decision and a named decision owner.
An explicit prediction horizon — the answer is useless if it arrives after the decision.
Representative historical data, with labels or outcomes where the use case needs them.
A defined baseline method to compare against, agreed before any model is trained.
The operational action each output leads to.

What will be live during the PoC

A reproducible evaluation on held-back data, not a one-off notebook result.

Baseline comparison on the same data and the same measure.

Error analysis showing where and when the model fails.

A defined operational workflow: output, recipient, action.

How this PoC runs

Week 1–2

Discovery and decision definitionDefine the decision, the owner, the horizon, the baseline method and the business cost of a false positive and a false negative.Exit gate: A named decision with a named owner and an agreed baseline. No decision, no project.

Week 2–4

Site, process and data readinessData-readiness gate: coverage, completeness, timestamp integrity, label quality, known process changes and whether the history is long enough for the horizon.Exit gate: Readiness verdict issued before any model performance is promised.

Week 4–8

Model training and offline validationBuild and evaluate the model against the baseline on held-back data, with the metric appropriate to the use case rather than the flattering one.Exit gate: Evaluation reproducible end to end from raw data.

Week 8–11

Validation and acceptanceError analysis, business interpretation, false-alert costing and design of the operational workflow and drift monitoring.Exit gate: The decision owner confirms the output is actionable in practice.

Week 11–12

Rollout decision and business casePresent the readiness report, the evaluation, the business interpretation, the monitoring design and a scale or no-go recommendation.Exit gate: Go, adjust or stop.

Durations are typical, not guaranteed. What extends a schedule: missing or incomplete data, security and network approvals, hardware lead times, sample collection, installation access, the production schedule, ERP test access, and the time your team needs to review results.

No unplanned shutdown is expected. Any installation window or controlled interruption is agreed with you in advance and scheduled around production.

How success will be measured

How success will be measured
Metric	How it is defined	Where the number comes from	Type
Data coverage and completeness	Share of the required period and variables actually present, with gaps and known process changes listed.	Your ERP or existing system	Technical
Baseline comparison	Model performance against a simple baseline on the same held-back data and the same measure.	Held-back validation set	Technical
Use-case-appropriate performance metric	Precision and recall for classification, or an error measure for regression — chosen in discovery, not after the results are in.	Held-back validation set	Technical
Decision lead time	How far ahead of the decision the output is available, against the horizon the owner needs.	Held-back validation set	Operational
False-alert cost	Expected false alerts per week multiplied by what each one costs the operation to investigate.	Observation and user interview	Financial
Actionability	Share of outputs the decision owner confirms they would genuinely act on.	Observation and user interview	Adoption
Drift monitoring plan	What would be monitored after deployment, at what threshold, and who is alerted when performance degrades.	MSF platform data	Technical

Before implementation, MSF and your team agree how each metric is calculated, where the baseline comes from, what data is excluded, and what result supports a rollout decision. This page lists what gets measured; the actual targets belong in the written PoC scope, not in a marketing claim.

What you need to provide

A data dictionary and the historical data itself, with reliable timestamps.
Labels or outcomes where the use case requires them, and an honest account of their quality.
Process context and known changes — a line rebuild mid-history invalidates a model that ignores it.
The business cost of a wrong answer in each direction, and the domain experts who can judge outputs.

Who does what

Meta Smart Factory provides

Discovery workshop and scope facilitation
Solution configuration for the agreed scope
Integration and connection work inside that scope
MSF hardware listed in the proposal
Training for the pilot users
The KPI definitions and validation method
Issue tracking and support during the pilot
The final result report and rollout design
A data-readiness verdict issued before any performance is promised.
A reproducible evaluation against an agreed simple baseline, with the error analysis attached.

You provide

A named business owner and a named technical owner
Timely access to users, the line, machines and approved systems
An accurate explanation of the process and the master data
Network, power, mounting and safety access
ERP, PLC and vendor documentation, plus the experts who know them
Representative samples or historical data
Validation that the baseline is fair
Feedback and the acceptance decision
A named decision owner who will act on the output, not only review it.
Domain experts to judge whether the errors the model makes are the survivable kind.

Defined in the written proposal

Panel PCs, tablets, servers and GPU servers
Cameras, lenses, lighting and enclosures
Scanners, printers, RFID readers, meters and sensors
Travel, installation, freight, import duty and local electrical work
Whether hardware is rented or purchased
Whether any PoC fee is credited against a rollout

Commercial terms, hardware ownership, travel, integration scope and any rollout credit are defined in the written PoC proposal. They are not the same for every product, and this page does not promise them.

What you receive at the end

Data-readiness report with an explicit verdict.
Reproducible model evaluation including the baseline comparison.
Business interpretation: what the numbers mean for the decision.
Error analysis with the failure cases named.
Operational workflow design — output, recipient, action.
Monitoring design and a scale or no-go recommendation.

Dependencies, exclusions and limits

This PoC depends on

History long enough and clean enough for the chosen horizon.
A decision owner who is available throughout, not only at the final presentation.

Not included in this PoC

Open-ended data exploration with no decision attached.
Production deployment, model operations and retraining infrastructure.

What this PoC does not claim

No predictive capability is claimed where labels and history are insufficient — the readiness gate exists to say that out loud before money is spent. Model performance and business impact are also reported separately: a model can be statistically excellent and still change nothing.

Go, adjust or stop — the decision gate

GoGo: the model beats the baseline by a margin that matters and the workflow is actionable — proceed to a production pilot.

AdjustAdjust: the use case is right but data collection has to improve first; the readiness report is that work package.

StopStop: the data cannot answer this question, or the improvement over the baseline is not worth operating a model for.

Frequently asked questions

Why do you insist on a named decision?

Because it is the difference between a model and a result. Without a decision there is no way to choose a metric, no way to cost an error, and nobody whose behaviour changes when the output arrives. Most failed factory AI projects failed at exactly this point.

What is the data-readiness gate?

A structured check of coverage, completeness, timestamps, label quality and process changes, run before any performance is promised. It regularly concludes that the honest first step is collecting better data — which is cheaper to learn in week three than in month six.

Why compare against a simple baseline?

Because a model has to be worth its own operating cost. If a moving average or a threshold rule performs nearly as well, the rule wins: it is cheaper, explainable and does not drift. Comparing only against zero makes any model look impressive.

Can you do predictive maintenance under this program?

Yes, when there is labelled failure history to learn from. When there is not, the honest program is the Maintenance PoC with condition monitoring and data-baseline creation, and this page will point you there rather than train a model on failures that were never recorded.

Request this Proof of Concept

Tell us the scope you have in mind and we will come back with a written PoC plan: what gets connected, what you provide, how success is measured and what the decision at the end looks like.