Where will quality or throughput deteriorate?
Use production history and process signals to identify where an outcome is likely to move outside the desired range.
← All Decision Packs
GN Manufacturing Decision Pack · Manufacturing Decision Intelligence
Turn process data into the next operational intervention.
For operations and production teams deciding what to run, when, and which process changes to test. Use production, quality and process data to decide what is driving scrap, quality or throughput and which controllable change should happen first.
Decisions this pack supports
Move from signal to intervention.
Which controllable variables actually matter?
Use causal methods where appropriate to distinguish intervention levers from correlations.
Which process change should happen first?
Rank feasible interventions by expected effect, operational practicality and constraints.
What should we manufacture, where, when and in what sequence?
Optimise production schedules using orders, expected demand, machine and labour capacity, materials, setup times and due dates.
From today's process to the next decision
Choose the production plan or process change to make next.
How teams make the decision
Teams combine ERP orders, production dashboards, spreadsheet schedules and engineering judgement to plan runs and investigate quality problems.
What this leaves unresolved
A schedule can miss competing capacity and changeover constraints. A dashboard can show where scrap increased while leaving the team to determine which process change will improve it.
What the GN Pack adds
For scheduling, compare feasible sequences under factory constraints. For quality, use causal methods where appropriate to assess controllable changes and prioritise interventions.
What your team receives
A feasible production plan or ranked process intervention, with affected resources, expected consequences and the operating constraints considered.
See the pack in action
From your business data to a decision you can act on.
Explore what a director or manager could receive. Choose a model, see its output, then compare practical actions with costs and constraints.
Illustrative interactive demoAll figures and businesses are fictional. These are examples of potential deliverables, not actual customer results, guarantees or live predictions.
MANUFACTURING / EXAMPLE DECISION WORKSPACE
Which production change should happen first?
Identify rising operating risk, investigate controllable levers and rank feasible interventions.
01 / Explore a model output
EXAMPLE OUTPUT 01 / 06
8.2%
Forecast scrap risk on Line 3
What we found
Scrap is forecast to rise from 3.1% to 8.2% on the affected shift.
Recommended next move →
Plan quality checks before the next high-volume run.
Decision-relevant outputApprox. €18,000 material value at risk
02 / Compare possible decisions
Which approved intervention should Line 3 use?
Switch between the options and compare expected contribution after intervention costs. Numbers use fixed fictional assumptions.
Gross incremental contribution+€9,600
Intervention cost€1,800
Net incremental contribution+€7,800
Selected action
Recommended combined change
Combine approved settings and a quality checkpoint.
Estimated gross weekly scrap savings €9,600 less €1,800 intervention costs; requires engineering approval.
Scope: 5.4%Feasibility: Within stated scenario rules
Operating constraints considered
See what this pack could recommend for your business →- Only approved operating ranges
- No material safety or quality-specification violation
- Throughput and shift-capacity must remain viable
Read every example output and decision alternative
Quality / Scrap Risk Model: 8.2% — Forecast scrap risk on Line 3
Scrap is forecast to rise from 3.1% to 8.2% on the affected shift. Plan quality checks before the next high-volume run. Approx. €18,000 material value at risk.
Process Driver Model: 3 factors — Priority engineering investigation
Material batch, process temperature and speed correlate with the elevated risk. Ask engineering to confirm plausible mechanisms before adjustments. 3 factors for investigation.
Causal Intervention Model: −1.4 pp — Estimated scrap-rate effect
A specified temperature change within approved operating limits may reduce scrap, if the causal assumptions hold. Validate the intervention under controlled operating conditions. 1.4 percentage-point modelled reduction.
Anomaly / Failure Model: M-204 — Machine inspection priority
An unusual vibration pattern raises concern before the planned high-volume cycle. Inspect the machine before the next production window. 1 priority inspection.
Operational Optimisation: €9,600 — Expected weekly scrap saving
The combined feasible intervention scores better than one setting change alone. Apply after safety, engineering and quality approval. €9,600 modelled weekly gross saving.
Production Scheduling + Capacity Optimisation: 2 orders — Proposed schedule change
Order 418 can shift to Line 2 while Order 425 moves forward without missing material or due-date rules. Review the feasible schedule with the production planner. 2 orders rescheduled.
Decision options
Continue with the present production settings. No estimated improvement to the forecast high scrap rate. Scope: 8.2%.
Use a single validated setting adjustment. Estimated gross scrap savings €5,500 less €1,200 implementation and monitoring costs. Scope: 6.8%.
Combine approved settings and a quality checkpoint. Estimated gross weekly scrap savings €9,600 less €1,800 intervention costs; requires engineering approval. Scope: 5.4%.
Models inside this pack
One product bundle around a recurring business decision.
GN Manufacturing combines the models, business logic and constraints needed to move from your operating data to a decision-ready recommendation.
DECISION PACKGN Manufacturing
Built aroundProduction · quality · machines · materials · process
Quality / Scrap Risk ModelPredicts where quality, scrap or throughput is likely to move outside the desired range.
Process Driver ModelRanks variables associated with the KPI and separates robust signal from noise.
Causal Intervention ModelEstimates whether changing a controllable process variable is likely to change the outcome.
Anomaly / Failure ModelDetects unusual operating patterns and elevated equipment or process risk.
Operational OptimisationRanks feasible interventions against expected impact, tolerances, capacity and production constraints.
Production Scheduling + Capacity OptimisationCombines orders, forecasts, routings, machine and labour capacity, setup times, materials and due dates.
OUTPUTDecision support your team can use
Models are combined only where the decision needs them. The pack returns a business-ready action with supporting evidence and expected impact.
- Risk
- driver
- process intervention
- Expected impact and supporting evidence
- Configured around your real operating constraints
Quality / Scrap Risk Model
Predicts where quality, scrap or throughput is likely to move outside the desired range.
Which line, batch, machine or process needs attention first.
Process Driver Model
Ranks variables associated with the KPI and separates robust signal from noise.
Which process factors deserve engineering investigation.
Causal Intervention Model
Estimates whether changing a controllable process variable is likely to change the outcome.
Which machine, material or operating setting is a credible lever.
Anomaly / Failure Model
Detects unusual operating patterns and elevated equipment or process risk.
Where to inspect, maintain or intervene before the problem becomes expensive.
Operational Optimisation
Ranks feasible interventions against expected impact, tolerances, capacity and production constraints.
Which operational change should happen first.
Production Scheduling + Capacity Optimisation
Combines orders, forecasts, routings, machine and labour capacity, setup times, materials and due dates.
What should run, where, when and in what sequence.
Pack anatomy
One repeatable path from process data to action.
Reusable modelling and intervention logic, configured around the line, process and constraints you operate.
Your production data
Quality, scrap, throughput, machine, material, tooling, environmental and other process variables.
Predictive + causal models
Identify risk, estimate driver effects and test whether a controllable process variable is a credible intervention lever.
Operating constraints
Encode feasibility, maintenance windows, tolerances, production limits and the actions operators can actually take.
A prioritised process intervention
Return the recommended process change, affected unit, expected outcome and supporting evidence.
Production scheduling & capacity optimisation
Turn orders, forecasts and factory constraints into a recommended production plan.
When the decision is what to manufacture, where, when and in what sequence, Graphite Note can build the optimisation around the operating rules of the production environment rather than forcing the factory into a fixed scheduling template.
A demand forecast can feed the schedule, but the scheduling decision still needs an objective and constraints. See forecasting vs optimisation.
Orders + demand + capacity
Orders, forecasts, routings, cycle and setup times, labour, material availability, maintenance windows and deadlines.
Encode how the factory actually works
Machine compatibility, shifts, material limits, minimum runs, changeovers, due dates and service-level rules.
A feasible production schedule
Recommend what runs on which resource, when it starts, the sequence and the expected consequence for throughput, lateness, overtime or another objective.
Evidence
Dashboards said where scrap rose. Causal analysis identified what to change.
In one published case, the team needed to determine which of several correlated process variables were credible intervention levers. The result was a ranked action plan tied to measurable operational outcomes.
Read the full case studyMeasured outcome
4.1% → 2.2% scrap rate
The published case study reports a 1.9 percentage-point reduction in six months and more than €1.4M annualised cost recovery.
Direct answers
Questions about the GN Manufacturing Decision Pack.
What is the GN Manufacturing Decision Pack?
It is a packaged Decision Intelligence solution for recurring manufacturing decisions around quality, scrap, throughput, process drivers and operational interventions.
What data does it use?
Typical inputs include production history, quality outcomes, machine settings, materials, tooling, environmental conditions and other process variables relevant to the KPI.
How is this different from a dashboard?
A dashboard shows where a KPI moved. The pack is designed for the next question: which controllable process change is expected to improve the outcome?
Can Graphite Note work with our specific production constraints?
Yes. Optimisation models can be built around the actual decision variables, objectives and constraints of the operation, including machines, labour, materials, routings, setup times, maintenance windows and due dates.
What comes out?
The output can be a risk forecast, ranked driver set, estimated intervention effect, production schedule or prioritised process change with evidence for operators, planners and engineering teams.
Decision IntelligenceFrom signal and prediction to a governed business action.AutoMLAutomate repetitive model-building work without losing business context.Causal Machine LearningSeparate drivers from correlations and estimate intervention effects.Predictive AnalyticsForecast demand, score customers and anticipate business outcomes.Decision OptimisationCompare feasible actions and rank them by expected business impact.Forecasting vs OptimisationSee where prediction ends and the business decision begins.Decision APIDeliver recommendations, expected impact and evidence into workflows.Embedded Decision IntelligenceAdd a decision layer to ERP, CRM, BI, workflow and vertical software.Build vs BuyA practical framework for deciding whether to build, buy or embed.
Bring the KPI and the process variables you can actually change.
We will structure the pack around a credible, operationally feasible intervention.