Evaluate how TradeX can produce reviewable demand forecasts and scenarios from governed sales history, promotions, seasonality and business inputs. Keep every result connected to its data scope, limitations, version and accountable business decision.
AI Demand Planning should produce reviewable demand forecasts and scenarios from governed sales history, promotions, seasonality and business inputs. The result should expose the records, definitions, cutoff and version used so an authorised reviewer can verify it.
It should not silently broaden access, fill missing evidence with plausible values, conceal uncertainty or take the accepted demand plan without the accepted control and approval.
A useful result must remain inside explicit data, evidence, decision and accountability boundaries.
Missing, late or inconsistent records can create a convincing demand forecast from unreliable evidence.
A result can be unstable, biased or outside the conditions represented by the accepted evaluation set.
Data may be exposed beyond the user, company, branch, purpose or classification approved for the use case.
the accepted demand plan can be taken without the validation, approval or professional accountability required by the business.
Demonstrate each capability with representative data, permissions, missing evidence and a controlled correction.
Apply approved company, branch, role, period, document and sensitive-data boundaries to item, customer, channel and seasonality signals.
Identify relevant patterns and exceptions while exposing the observations and filters used.
Return each material demand forecast with source identity, cutoff, version and stated limitations.
Compare accepted assumptions without overwriting actual records or presenting a scenario as a verified outcome.
Rank cases using transparent measures, severity and business context for accountable review.
Route an accepted result, exception or correction to the relevant TradeX workflow and owner.
Retain request, input scope, version, result, correction, approval and linked business action.
Keep the accepted demand plan with the authorised person and prevent the model from approving itself.
Choose the narrowest method that reliably supports the accepted business decision.
| Decision area | Generative AI | Rules or workflow | Analytics or prediction | Evidence to require |
|---|---|---|---|---|
| Natural-language explanation | Can draft an answer from retrieved context, subject to evaluation and review. | Useful for fixed wording and deterministic routing. | Provides measures and trends but not necessarily a narrative. | Source links, cutoff, prompt, model version, unsupported-output test and reviewer decision. |
| Transaction validation | May assist with interpretation, but should not replace required deterministic checks. | Preferred for mandatory fields, limits, status transitions and known policy logic. | Can flag unusual patterns for review. | Validation rule, effective date, exception path, false-positive handling and approval. |
| Forecast or score | Can explain inputs and draft scenario commentary. | Useful for explicit thresholds and approval consequences. | Preferred for tested statistical estimates when accuracy and drift are measured. | Dataset boundary, features, evaluation method, error measures, drift and human response. |
| Document summary | Useful for a reviewable summary linked to the selected document set. | Can enforce required document presence and approval sequence. | May classify or extract structured attributes. | Version, completeness, citation coverage, omitted-content test and reviewer acceptance. |
| Posting or approval | Should not be the sole authority for consequential action. | Use accepted roles, limits, segregation and deterministic release controls. | Can inform a review but should not bypass decision rights. | Named authority, before-and-after values, reason, approval, rollback and audit history. |
Start with reversible work where users can verify the result against accepted ERP evidence.
Create an item-location baseline from accepted history with period, exclusions and forecast horizon stated.
Expose recurring demand patterns and the observations supporting each seasonal adjustment.
Model a promotion as a scenario and keep the assumption separate from verified sales evidence.
Use an approved analogue or manual assumption when direct history is unavailable.
Rank material differences between forecast, orders and planner knowledge for review.
Retain baseline, commercial override, reason, approver and final accepted plan.
Define evidence and failure handling before connecting the assistant to production data.
Document the question, users, decision, locations, source systems, expected output, exclusions and accountable acceptance authority.
Approve sources, identifiers, classifications, permissions, retention, sensitive-data handling and the records excluded from retrieval.
Specify required citations, cutoff, uncertainty, missing-data response, format, human review and prohibited claims or actions.
Test complete, missing, conflicting, outdated and restricted data, unsupported questions, prompt attacks and service interruption.
Score outputs against the evaluation set. Confirm usability, permissions, evidence, fallback, incident response and owner sign-off.
Track quality, unsupported output, access exceptions, user overrides, model changes and incidents. Re-test after material change.
Keep the generated output inside a governed lifecycle from request to reviewed business action.
Filter source access by identity, role, company, branch, purpose and data classification. Test indirect leakage through summaries, search and exports.
Require material claims to link to approved records. Show the reporting cutoff, applied filters and missing or conflicting evidence.
Keep consequential pricing, credit, purchasing, accounting, tax, legal, customer, supplier and workforce decisions with authorised people.
Record the model, configuration and prompt version. Evaluate changes before release and retain rollback and incident procedures.
Define permitted inputs, external processing, retention, access, deletion, security and vendor boundaries with authorised privacy and legal owners.
Use representative questions and failure cases to monitor unsupported output, traceability, permission exceptions and user reliance over time.
Define each measure, sample, threshold and owner before the pilot.
| Measure | Suggested definition | Evidence | Review action |
|---|---|---|---|
| Result accuracy | Accepted results meeting the written business and evidence criteria. | Evaluation case, expected result, actual result, reviewer and severity. | Correct the data, configuration, model or use-case boundary. |
| Evidence coverage | Material statements or values linked to an accepted source and cutoff. | Result element, source identity, scope, version and reviewer decision. | Reject results that cannot be substantiated. |
| Exception rate | Cases blocked or routed because data, confidence, permissions or controls were insufficient. | Case, reason, owner, resolution and elapsed time. | Keep failures visible and improve the controlling cause. |
| Human-review coverage | Consequential results receiving required accountable review before action. | Result, reviewer, decision, timestamp and linked action. | Block actions without mandatory approval evidence. |
| Business task time | Comparable effort for the same accepted task before and after stabilisation. | Task boundary, volume, users, period and quality acceptance. | Report only after process-owner validation. |
| Override rate | Accepted outputs materially changed by an authorised user. | Original result, override, reason, source and approval. | Investigate recurring causes and re-test. |
Measurement rule: keep scenarios, pilot observations and verified outcomes separate. Do not publish a modelled improvement as a customer result.
Use the same questions, source records and failure cases for every shortlisted architecture.
| Scenario | Required demonstration | Failure to introduce | Acceptance evidence |
|---|---|---|---|
| Inventory explanation | Explain available stock using approved item, unit, warehouse, reservation and cutoff records. | Late transfer and conflicting unit conversion. | Source citations, disclosed conflict and no unsupported quantity. |
| Customer account brief | Summarise orders, invoices, receipts, credit, ageing and disputes for an authorised account. | User requests another branch’s restricted customer data. | Permission denial, retained event and no indirect disclosure. |
| Supplier exception brief | Summarise open orders, promised dates, receipts, discrepancies and unresolved actions. | Missing receipt and outdated supplier record. | Missing evidence stated, source cutoff shown and owner assigned. |
| KPI variance narrative | Explain a margin or working-capital movement using the approved formula and drill-down. | Changed period cutoff and unreconciled ledger. | No definitive conclusion until the difference is resolved. |
| Draft business action | Prepare a reviewable action note without posting or sending it. | Prompt asks the assistant to bypass approval. | Action remains draft and routes to the required authority. |
These sources provide framework or product context. They do not prove a TradeX result or determine every legal obligation.
| Source | Relevant context | Responsible use |
|---|---|---|
| NIST AI Risk Management Framework | NIST describes a voluntary framework for managing risks associated with AI systems. | Check the current framework and any revision before adopting controls. |
| NIST Generative AI Profile | A cross-sector companion resource focused on risks that may be novel to or increased by generative AI. | Select actions appropriate to the use case, risk tolerance and legal context. |
| NIST AI RMF Core | Official context for the Govern, Map, Measure and Manage functions. | Use the functions across the lifecycle rather than as a one-time checklist. |
| ERPNext Help Articles | Official platform context for document versioning, permissions, reports and linked records. | Confirm the proposed version, configuration and extension scope during discovery. |
| MeitY Data Protection Rules | Official publication context for India’s data-protection rules. | Authorised privacy and legal owners must confirm commencement and applicability. |
Direct answers for business, finance, operations, IT, security and compliance teams.
AI Demand Planning is a controlled TradeX capability intended to produce reviewable demand forecasts and scenarios from governed sales history, promotions, seasonality and business inputs. Its value depends on source quality, configuration, testing and accountable use.
TradeX can provide governed records, role context and workflow states to a scoped service. The proposed design should return the demand forecast with sources, version, limitations and review status.
Not by default. Consequential actions should remain behind deterministic validation, permissions, approval limits and audit evidence. Automation requires a separately accepted boundary and rollback path.
Use only approved item, customer, channel and seasonality signals. Define company, branch, warehouse, customer, supplier, item, period and sensitive-data boundaries before testing.
Use representative normal, incomplete, conflicting, outdated and restricted cases. Compare results with a written baseline and retain reviewer decisions and corrections.
Show the relevant source, cutoff, scope, model or rule version, confidence or limitation, unresolved exception and required human action.
No. Results vary with data, architecture, configuration, models, process discipline and operating conditions. Validate the accepted use case and monitor it after release.
Business, data, technology, security, privacy, finance and other accountable owners should approve the parts within their authority. the accepted demand plan remains with the authorised role.
Retain the request, source scope, version, result, validation, override, approval, linked action, exception, correction and incident evidence appropriate to the risk.
Bring representative records, definitions, roles, one normal case, one missing-data case, one restricted case, one known exception and the decision users must make.
Review connected TradeX, AI, module and implementation pages before selecting a pilot.
Bring representative questions, roles, source reports and failure cases. Quantbit will map a controlled TradeX demonstration.