Industrial AI solution

Demand Forecasting AI SKU-Level Accuracy at Scale

NeoBram helps industrial teams evaluate and build demand forecasting ai for a defined workflow. The engagement starts with the operating decision, representative data, integration boundary, human owner and acceptance test. Deployment can be designed for offline, on-premises, edge or private-cloud operation when the selected components and licences support it.

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Acceptance before scale

Baseline, representative test set, failure conditions and a named human owner are defined before production approval.

Direct answer

Private deployment available

Why evaluate it

Forecast Accuracy Drives Every Other Cost.

The decision is whether this capability improves a defined workflow safely not whether an AI demo looks impressive.

We deploy forecasts inside SAP IBP, Oracle, Kinaxis or o9 - planners review and override in their existing UI. The AI augments your S&OP, it doesn't replace it.

Uncited universal benchmarks from the legacy page were withheld. Define the baseline and acceptance threshold from customer evidence or a reviewable primary source.

Candidate capabilities

What a production solution may need to do.

Each capability is validated against representative data and the customer's workflow. Product or model names describe possible components, not partnerships, certifications or guaranteed compatibility.

SKU-level multi-horizon forecasting

Daily and weekly forecasts at SKU-store / SKU-DC granularity, with hierarchical reconciliation up to brand, region and total.

Daily and weekly horizons up to 18 months
Hierarchical reconciliation (MinT, top-down, bottom-up)
Calibrated prediction intervals (P10/P50/P90)
Driver decomposition per forecast

Promotion & event lift

Models price elasticity, promotion lift, halo and cannibalisation - so the forecast reflects what the planner actually plans.

Per-SKU price elasticity
Promotion uplift with halo and cannibalisation
Holiday and event calendars per market
Trade-spend ROI estimates

New product introduction (NPI)

Forecasts new SKUs using analog-based and attribute-based models when no history exists.

Analog SKU selection with similarity scoring
Attribute-based regression for true-new products
Ramp curves with confidence bands
Iterative refinement as actuals arrive

S&OP-ready governance

Planner override workflow, accuracy tracking by planner / category, and continuous learning from override outcomes.

Side-by-side ML vs statistical vs consensus
Override reason capture for learning
Forecast Value Add (FVA) tracking
Monthly accuracy and bias reports

Architecture context

Select components after the boundary and test are clear.

The list is a design vocabulary. Final selection depends on licences, data location, latency, security, existing systems and customer approval.

Source data

SAP / Oracle ERP
Shipment history
POS / sell-through feeds
Distributor & retail
TPM systems
Promotion calendar
Web analytics
Demand signals

ML & forecasting

Temporal Fusion Transformer
Multi-horizon ML
DeepAR / N-BEATS
Hierarchical models
LightGBM / XGBoost
Tabular features
Prophet / NeuralProphet
Baseline + uncertainty

Planning integration

SAP IBP / APO
Native plan write-back
Oracle Demantra
Forecast publish
Kinaxis RapidResponse
Concurrent planning
o9 Solutions
Knowledge graph sync

MLOps

MLflow / Weights & Biases
Model registry
Airflow / Prefect
Retraining schedule
Monitoring (Evidently, Arize)
Drift detection

Planning ranges

A standard path from decision to operation.

Discovery and qualification

1-2 weeks

Named workflow, owner, baseline, risks and go/no-go questions.

Readiness assessment

2-4 weeks

Data, integration, security, value and operating-readiness findings.

Technical proof of value

4-6 weeks

A bounded test on representative data with documented limitations.

Production pilot

8-12 weeks

One controlled workflow, integrated and evaluated with real users.

Enterprise or multi-site rollout

3-6+ months

Phased scale-out, monitoring, support and change management.

AI capability or CoE programme

3-6+ months

Governance, delivery methods, reusable assets and team enablement.

These are planning ranges, not guarantees. Readiness, validation, hardware, integration, access and change management affect the schedule.

Production safeguards

Private deployment is one control, not the whole control system.

Data boundary

Select offline, edge, on-premises or private-cloud deployment from the real operating constraints.
Document data flows, storage, deletion, backups and support access before production.
The customer approves every interface and any permitted external connection.

Model and application controls

Evaluate representative cases, uncertainty and harmful failure modes before use.
Use suitable access control, input handling and output guardrails for the selected risk.
Treat grounding and citations as testable behaviours, not as a promise of perfect answers.

Traceability and operation

Define identity, roles, logs, monitoring, updates, backup and incident handling.
Keep the evidence needed to investigate outputs and reproduce important decisions.
Assign a named business and technical owner for production operation.

Human authority

Domain, quality, safety and regulatory owners retain decision authority.
Compliance depends on the implemented system and the customer's validated controls.
Escalation and safe fallback are part of the acceptance criteria.

Limitations to plan for

Performance can change with data quality, equipment, process, product mix, documents, users or operating conditions. Third-party model and software licences still apply. AI output does not replace the responsible engineer, operator, quality owner, safety professional, legal adviser or regulator.

Buyer questions

Direct answers before you plan a pilot.

How much accuracy improvement is realistic?+

The right threshold depends on the decision, the cost of each error and representative operating conditions. Define performance by class or scenario, use a held-out evaluation set, include human-review capacity and monitor after data or process changes. NeoBram does not claim an uncited universal benchmark.

How does it work with SAP IBP / o9 / Kinaxis?+

We push the ML forecast into your planning system as a candidate forecast alongside statistical and consensus. Planners review and adjust in their existing UI. There's no parallel tool to maintain. Native connectors exist for SAP IBP, Oracle Demantra, Kinaxis and o9.

How much history do you need?+

This legacy draft included a quantitative benchmark that has not been published with a reviewable source or customer evidence. NeoBram now treats it as an open validation question and defines the baseline, test method, acceptance threshold and limitations during discovery.

Will planners trust an ML forecast?+

Adoption is a design problem, not just a model problem. We ship driver explanations per SKU, side-by-side comparisons, and override tracking that quantifies Forecast Value Add. Planners typically see within 2-3 cycles that the ML beats their consensus on most SKUs, and start overriding only the truly exceptional ones.

What's the deployment timeline?+

A focused production pilot is commonly planned over 8 12 weeks after scope, representative data, owners and acceptance tests are ready. Discovery or a technical proof of value may be shorter; integration, validation, hardware, site access and change management can extend the plan. This is a planning range, not a delivery guarantee.

Where does our demand data live?+

In your environment. We deploy in your private cloud (Azure, AWS, GCP). Shipment, POS and trade data never leave your tenancy.

Bring one workflow

Define the evidence, boundary and acceptance test together.

Your team supplies process authority. NeoBram supplies AI architecture, engineering, evaluation and operating handover.

Plan the first project