INDUSTRY 5 (i5) Feature List

Demand Planning & Forecasting

Accurately sense, shape, and align demand signals across internal and external sources, time horizons, and granularity—while continuously adjusting forecasts based on real-world volatility and feedback.

Generate forecasts using historical sales, seasonality, and market trends.

i5 generates forecasts via agent logic using synthetic, historical, or integrated data inputs. Forecast agents apply curve shaping and time-relative reasoning across products and regions.

Historical Sales Curve Engine

• Forecast Agent applies shape modeling using prior sales data per SKU, region, or channel. Captures repeatable behavior without static models.

Synthetic Seasonality Modeling

• i5 uses time-relative logic to model seasonal peaks and lulls, even without multi-year historical data. Seasonality can be simulated forward.

Demand Signal Normalization

• Input data (sales, orders, POS, etc.) is normalized across sources and aligned to orchestration time layers before forecast shaping.

SKU-Channel Hierarchy Support

• Forecasts are calculated and stored by product, site, customer segment, or other custom hierarchies, maintaining traceability and roll-up.

Sales Agent Input Reconciliation

• Sales team inputs (volume guesses, new launches) are reconciled against system-generated forecasts using confidence scoring logic.

Forecast Curve Preview & Override

• Users can view the proposed forecast curve, compare alternatives, and override forecast points within simulation before confirming.

Forecast Traceability Layer

• Each forecast value is linked to the underlying source data and agent input (e.g., “This spike reflects Sales input + 2023 Q4 shape”) for explainability.

Support forecast granularity by region, product, channel, and customer segment.

Forecasts are structured by any dimension defined in the orchestration input schema (e.g., site, customer, SKU). All outputs are linked to these entities within the i5 Graph.

Multi-Dimensional Forecast Structure

• Forecasts are modeled across any dimensional stack defined in orchestration schema: product, customer, site, channel, segment, etc.

Hierarchical Roll-Up & Drill-Down

• Users and agents can view forecast values rolled up by category (e.g., global demand) or drilled down to granular nodes (e.g., SKU/customer/site).

Agent-Aligned Forecast Ownership

• Each forecast dimension can be mapped to a responsible agent role—e.g., Sales Agent owns customer segment X, Demand Agent owns SKU Y.

Granularity-Aware Simulation Engine

• Scenario simulations maintain full forecast hierarchy—impacts flow through all levels without flattening or losing attribution.

Contextual Conflict Resolution

• When different agent roles propose values at different hierarchy levels (e.g., Sales overrides region, Ops adjusts SKU), the system reconciles intelligently.

Selective Locking & Override Rules

• Users can lock certain dimensions or hierarchy levels during cycles (e.g., lock customer totals, let SKUs flex), supporting managed forecast shaping.

Dynamic Hierarchy Adjustments

• Forecasts can be restructured mid-cycle (e.g., split a region, reclassify SKUs) without breaking existing simulation threads or agent logic.

Allow cross-functional forecast input and override by stakeholders.

Forecasts can be refined by multiple agent roles (Sales, Inventory, Production). Human users can preview, override, or simulate changes before committing.

Role-Based Forecast Contribution

• Forecast values can be proposed by different stakeholders or agents (Sales, Inventory, Production), each with scoped authority.

Agent Collaboration Threading

• Each agent’s input is time-stamped, scored, and reconciled through forecast collaboration logic—no data overwrite, only intelligent merging.

Input Visibility & Justification Layer

• Users see who entered which forecast value, when, and why—ensuring override transparency and accountability.

Manual Override with Impact Preview

• Human overrides trigger forward simulations to show downstream effect before they’re committed.

Override Governance Configuration

• Rules can require justification for overrides beyond thresholds or require approval escalation (e.g., if >20% over system-generated).

Confidence-Weighted Input Blending

• If multiple agents propose inputs, final forecast uses a weighted blend based on historical accuracy or scenario fitness.

User Interface for Forecast Alignment

• UI provides a structured environment for reviewing, commenting on, and negotiating forecast values before orchestration.

Provide multiple forecast models (statistical, ML-based, heuristic).

i5 uses curve-shaped synthetic generation and can incorporate ML signals via custom inputs. Native forecast models are not off-the-shelf statistical engines.

Forecast Method Selection Interface

• Users or agents can select from multiple methods (synthetic curve, moving average, regression, external ML) based on use case.

Synthetic Forecast Generation Engine

• i5 natively generates demand curves using embedded logic—not regression models—optimized for agent-driven orchestration.

External Model Integration API

• External statistical or ML models (e.g., Prophet, XGBoost) can be ingested into i5 via structured inputs. Results are treated as agent proposals.

Scenario-Based Model Evaluation

• Models can be tested across scenarios to see which method performs better under volatility, demand spikes, or product launch.

Heuristic Logic Modules

• Prebuilt rule sets (e.g., “repeat last year plus 10%”, “fill rate based forecast”) available for segments with limited history.

Model-Agnostic Forecast Threading

• Forecasts generated by different methods can coexist as parallel proposals—evaluated by confidence, convergence, or override logic.

Model Attribution Tagging

• Each forecast line retains metadata for which model created it, with links to training assumptions, version, and last run logic.

Adjust forecasts based on current execution signals (e.g., order, inventory, transport).

Agents continuously simulate forward impact and adjust forecasts as actuals evolve. Real-time updates are scored and propagated through the orchestration graph.

Live Execution Signal Ingestion

• Forecast Agent receives and processes signals from orders, production status, shipment delays, and inventory changes in real time.

Forecast Volatility Adjustment Logic

• When actuals deviate from plan, the system reweights forecast trend curves to reflect changing behavior patterns.

Auto-Correction with Threshold Triggers

• Forecasts are adjusted only when deviation crosses defined thresholds (e.g., ≥15% off-plan), avoiding overreaction.

Execution-Aware Forecast Confidence Updates

• Forecasts with rising deviation or missed commitments automatically drop in confidence score—driving downstream caution.

Upstream & Downstream Cascade Logic

• Execution signals at one node (e.g., stockout in Europe) can prompt demand forecast changes elsewhere (e.g., US replenishment).

Agent-Based Signal Resolution

• Signals are first interpreted by the appropriate agent (e.g., Inventory, Transport), then passed to Forecast Agent with recommendation.

Simulation Preview of Signal Impact

• Before adjusting the official forecast, users can simulate the expected downstream effects of a proposed correction.

Provide automated confidence scoring for forecast lines.

Forecast confidence scores are generated natively by Forecast Agents using historical variance, agent alignment, and simulation divergence.

Confidence Score Generator

• Forecast Agent assigns a 0–100 score to each forecast node, indicating how reliable the prediction is based on known data and agent alignment.

Historical Forecast Error Tracking

• Confidence is adjusted based on historical deviation between forecasted and actual demand across relevant timeframes.

Agent Disagreement Index

• Score incorporates divergence between agent roles (Sales, Ops, Inventory) if forecasts differ significantly.

Scenario Spread Weighting

• Score is lowered when simulation outputs show high variance (e.g., wide confidence interval across scenarios).

Execution Feedback Integration

• Real-world feedback from recent deliveries or plan adherence feeds back into the scoring logic in near real-time.

Forecast Confidence Color Coding

• Confidence scores are displayed in UI with intuitive visual signals (e.g., red = low trust, green = high trust) to support review.

Threshold-Driven Alerts and Overrides

• When confidence drops below defined thresholds, system prompts agents to either simulate alternatives or escalate for review.

Detect and resolve conflicting forecast inputs (e.g., Sales vs. Ops).

Conflicts are flagged, and resolution is proposed by comparing simulation outputs. Confidence-weighted negotiation logic ensures agent alignment.

Conflict Detection Engine

• System monitors input deltas across agent roles and flags forecast nodes where values diverge beyond defined thresholds.

Confidence-Weighted Resolution Logic

• Resolution favors inputs with higher historical accuracy or agent alignment strength—configurable per business rule.

Simulation-Based Tie Breaker

• When two or more inputs are equally weighted, the system runs downstream simulations to determine which input performs better.

Stakeholder Override Workflow

• Conflicts unresolved by agents are surfaced to a human user or planning team for manual resolution and justification.

Input Source Attribution

• Each input is tagged by agent role and submission method (e.g., API, UI, ML), helping teams understand where conflicts originate.

Override Logging and Versioning

• Resolved forecast lines are versioned, and the rationale for final input selection is preserved for audit and future reference.

Recurring Conflict Pattern Detection

• Repeated disagreement patterns (e.g., Sales always over-forecasts a certain region) are flagged over time for process improvement.

Integrate demand signals from external sources (e.g., weather, market events).

External signals can be added via orchestration inputs or API calls; not mined automatically today. Agents respond to structured signals, not raw data.

External Signal Ingestion API

• Structured API allows posting of exogenous signals such as weather, events, promotions, market trends, or economic indicators.

Signal-to-Forecast Mapping Engine

• Signals are mapped to relevant forecast nodes (e.g., signal: “hurricane in Gulf” ? SKU/site/region = Houston; product class = generators).

Impact Weighting Configuration

• Signals can be weighted by expected impact size and likelihood. Agents use this metadata to scale forecast adjustments accordingly.

Event Horizon Modeling

• Signals can include time windows (e.g., “festival: Q3 Week 12–13”) to apply forecast lift or suppression for that period only.

Simulation-Based Signal Validation

• Before applying, signal impact can be simulated in a sandbox to preview effect on demand plans and orchestration response.

Manual and Automated Signal Posting

• Users can post signals via UI; integrated partners can post automatically via webhook or orchestration flow.

Signal Attribution and Expiration Control

• Each signal is tagged with origin, time-to-live, and removal logic—ensuring forecast adjustments don’t persist after context ends.

Simulate demand volatility scenarios and evaluate plan fitness.

i5 supports structured scenario simulation with curve shaping and volatility modeling. Agents evaluate impacts and recommend mitigation.

Demand Volatility Scenario Generator

• Users or agents can define volatility curves (e.g., +30% surge, -20% drop, random variance) by SKU, region, or customer class.

Scenario Execution Engine

• Forecast changes are propagated through the orchestration graph, simulating effects on supply, capacity, transport, and inventory.

Impact Comparison Dashboard

• Results are displayed across multiple scenarios side-by-side, showing service level, margin, and risk exposure differences.

Fitness Scoring per Scenario

• Each forecast scenario is scored for resilience, cost, and SLA fitness using predefined KPIs or custom business rules.

Scenario-Specific Forecast Output

• Scenarios generate distinct forecast versions—enabling optional adoption of the preferred scenario path.

Nested Scenario Capability

• Users can test compounded events (e.g., demand spike + port delay) and assess compound risk amplification.

Scenario Library Management

• Reusable templates (e.g., Black Friday surge, global shortage) are saved with metadata and versioning for future reuse.

Generate multi-horizon forecasts (short, mid, long term).

Forecasts are modeled across nested timeframes using i5’s Temporal Logic engine. Time-based planning layers are resolved independently then reconciled.

Time-Horizon Layering Engine

• Forecasts are structured into nested timeframes (e.g., 0–13 weeks, 3–6 months, 6–24 months), each optimized independently.

Horizon-Specific Agent Roles

• Forecast logic is tiered by agent type—short-term (Inventory Agent), mid-term (Production Agent), long-term (Strategic Demand Agent).

Forecast Resolution Settings

• Each horizon has distinct granularity (e.g., daily vs. weekly vs. monthly), managed independently and reconciled across boundaries.

Cross-Horizon Consistency Checks

• System identifies and resolves conflicts across horizons—e.g., if short-term demand exceeds long-term plan commitments.

Event Anchoring by Horizon

• Certain events (e.g., contract expiration, seasonality, regulatory shift) are linked to specific horizons and shape the forecast curve accordingly.

Orchestration Trigger per Horizon

• Changes in each horizon can selectively trigger downstream orchestration (e.g., short-term triggers PO, long-term prompts sourcing alert).

Synthetic Horizon Simulation

• Entire horizon curves can be tested under synthetic demand environments—ideal for strategic planning, budgeting, or growth modeling.

Optimize forecasts to align with capacity, inventory, and supply constraints.

Forecast Agents reconcile with inventory, BOM, and supply-side feasibility. Agents negotiate changes to avoid unfulfillable demand commitments.

Constraint-Aware Forecast Adjustment Engine

• Forecast Agent evaluates available capacity, inventory, and supplier constraints before confirming demand plans.

Forward Feasibility Simulation

• Forecasts are tested against current orchestration logic to ensure fulfillment is physically and contractually possible.

Dynamic Alignment with BOM and Lead Time

• Forecast shaping respects product BOM complexity, supplier lead time, and downstream availability.

Agent Negotiation Loop

• When forecast exceeds constraints, Forecast Agent triggers discussions with Production, Procurement, and Inventory Agents to find compromise.

Tradeoff Surface Visualization

• System displays what must be flexed—timing, volume, margin—to reconcile the forecast with operational limits.

Scenario-Driven Capacity Rebalancing

• If constraints can be shifted (e.g., reallocation of plant time or transport), system proposes alternate orchestration paths.

Constraint Violations Log with Override Workflow

• Exceptions are documented and may be escalated for override or resimulation—fully traceable and auditable.

Trigger planning or procurement events based on forecast changes.

Smart Agreements and downstream agent logic are activated based on forecast deltas exceeding defined thresholds.

Forecast Change Detection Engine

• Agents continuously monitor for significant forecast changes (e.g., volume, timing, product mix) across all time horizons.

Trigger Threshold Configuration

• Business rules define what constitutes a “material” change—e.g., volume delta, date shift, confidence drop—per product or region.

Auto-Trigger to Procurement Agent

• When material changes are detected, Forecast Agent initiates Smart Agreement re-evaluation or new PO logic.

Triggering of Reallocation or Re-Planning

• Forecast shifts can trigger transport re-routes, production adjustments, or even cancel/rebook cycles—all handled by agents.

Scenario Impact Prioritization

• Triggered changes are scored based on urgency, risk, and cost impact—allowing the system to decide whether to simulate first or execute.

User Notification with Simulation Preview Option

• Optionally, users can be alerted to major changes and review simulated impact before downstream orchestration is committed.

Trigger Logging and Outcome Traceability

• Every trigger is logged with reason, triggering agent, and downstream effect—supporting full auditability and root cause analysis.

Sales, Order & Fulfillment

Integrate order logic with fulfillment reality—ensuring promises, SLAs, and profitability are respected from quote to delivery, even under changing conditions or disruptions.

Capture and validate sales orders from multiple channels (EDI, portal, manual, API).

Orders can be posted via API, synthetic inputs, or manual upload. Data structures are validated against orchestration schema and Smart Agreements.

Omni-channel demand integration

• Ingests orders and forecasts from retail, eComm, direct, distributor, and internal channels.

Order aggregation by customer logic

• Rolls up orders by client ID or contract tier for SLA alignment and prioritization.

Sales signal normalization

• Normalizes inconsistent input formats from sales platforms or spreadsheets.

Demand override tagging

• Flags when sales overrides differ from forecast and stores input source.

Sales-driven forecast reconciliation

• Sales Agent inputs are integrated into forecast agent logic with confidence weighting.

Sales plan vs actual tracker

• Tracks how sales forecasts or deals convert to actual order fulfillment.

Provide Available-to-Promise (ATP) logic considering inventory, production, and transport.

ATP is computed dynamically by Sales Agents using current supply, site constraints, and delivery timelines. Results reflect what can actually be fulfilled.

Order promise engine

• Confirms delivery dates based on inventory, production, and transport logic.

Real-time ATP/CTP calculations

• Calculates Available-to-Promise and Capable-to-Promise at time of order or simulation.

Promise risk scoring

• Scores how likely each promise is to be met based on current volatility and downstream dependencies.

Backorder allocation logic

• Allocates scarce inventory to backorders based on priority, margin, and agreement logic.

Flexible fulfillment recommendation

• Suggests alternate fulfillment paths to meet order terms (e.g., ship partial, alternate DC).

Customer communication hooks

• Updates customers or account teams when orders or promises change.

Confirm, allocate, and track orders through full lifecycle.

Confirmed orders are recorded in the i5 Graph and tracked through production, transport, and inventory stages. Exception logic flags issues.

Smart Agreement-linked ordering

• Sales orders are validated against active agreement terms (e.g., price, quantity, timing).

Dynamic credit & SLA checks

• Order approval logic includes financial risk or service commitments.

Contract breach prevention

• Flags orders that would trigger a contractual or regulatory violation.

Sales tier prioritization

• Elevates processing of orders from premium or strategic accounts.

Order window management

• Enforces order timing constraints based on agreement or inventory status.

Custom term adaptation

• Supports non-standard contract clauses through agent override modules.

Trigger fulfillment actions automatically upon order confirmation.

Order confirmation activates downstream agents—Production, Procurement, Transport. All flows respect current orchestration state.

Order orchestration engine

• Coordinates fulfillment across inventory, production, and transport agents in real time.

Order flow configurator

• Configures routing and sequencing logic by product type, region, or customer segment.

Multi-node fulfillment logic

• Supports complex fulfillment paths (e.g., split ship, vendor drop-ship).

Real-time adjustment capability

• Updates order orchestration plan dynamically based on execution signals.

Orchestration trace visualizer

• Displays end-to-end fulfillment path with traceable changes and responsible agents.

Orchestration resilience scoring

• Scores order paths based on on-time probability, cost, and volatility tolerance.

Allow quote generation based on available inventory and constraints.

Sales Agent generates quotes based on current availability and feasibility. Quote logic is aligned with forecast and Smart Agreements.

Sales-to-operations alignment tracker

• Tracks alignment or divergence between sales goals and operational feasibility.

Sales override workflow

• Supports approval flows and justifications when Sales inputs override agent plans.

Revenue-risk impact preview

• Forecast adjustments by Sales show margin, SLA, and fulfillment impact before commit.

Priority escalation logic

• Triggers cross-agent negotiation when strategic Sales priority conflicts with system logic.

Sales forecast accuracy feedback

• Scores and reports historical Sales input accuracy by time period or product class.

Sales ops scenario sandbox

• Allows Sales to test proposed volume or SKU shifts before execution.

Simulate impact of major or urgent orders on current plans.

i5 simulates any order—real or proposed—and displays impact across demand, supply, capacity, and ESG metrics.

Customer tiering engine

• Defines customer classes with associated rules, priorities, and SLAs.

Service-level goal enforcement

• Order orchestration logic respects target SLAs per customer class.

Order allocation priority logic

• Allocates inventory or production slots based on customer rank.

Tier-aware forecast logic

• Adjusts forecast shaping for strategic vs opportunistic accounts.

Margin-impact override rules

• Overrides that impact top-tier customers must be justified or escalated.

Customer tier scenario modeling

• Tests what-if demand plans under changes to tier mix or service targets.

Prioritize orders based on margin, urgency, or agreement tier.

Orders are scored by agent logic using configurable business rules and Smart Agreement clauses. Higher-priority orders are protected in orchestration.

Order change impact simulator

• Simulates downstream effect of an order change‚Äîinventory, production, transport.

Rescheduling & reconfirmation engine

• Re-triggers confirmations from all agents when key order attributes change.

Partial fill or split recommendation

• Suggests alternate plans to partially fulfill or split-ship when needed.

Disruption-aware re-planning

• Order updates factor in current disruptions or capacity constraints.

Customer approval logic

• Triggers customer re-confirmation if changes exceed contractual or SLA tolerance.

Change history and audit log

• Logs all order changes, responsible agents, and impact assessment.

Adjust order flow dynamically based on execution signals.

Delays or disruptions to any part of the fulfillment flow trigger reallocation, timing shifts, or customer notifications via agent logic.

Sales & fulfillment dashboard

• Combines sales, order, fulfillment, and delay signals in one view.

Fill rate & margin metrics

• Tracks fulfillment metrics against plan, SLA, and financial outcomes.

Customer satisfaction estimation

• Scores how recent fulfillment aligns with customer experience targets.

Exception flagging engine

• Flags and routes fulfillment failures to appropriate agent for response.

Margin-risk tracker

• Shows orders at risk of unprofitability due to routing, cost, or delay.

Sales performance feedback loop

• Provides ongoing feedback to Sales on plan adherence and execution success.

Link customer orders to Smart Agreements with pricing and terms.

Orders inherit or trigger Smart Agreements, which embed price, delivery windows, ESG terms, and penalties. Planning logic respects these terms.

Customer-specific routing rules

• Supports custom fulfillment paths or constraints per customer or region.

Order-level carbon scoring

• Scores carbon footprint of planned fulfillment per order.

Low-carbon fulfillment engine

• Optimizes order fulfillment with emissions minimization goal.

Agreement carbon constraint enforcement

• Ensures order plans don‚Äôt breach carbon limits in contract.

Carbon tradeoff visualizer

• Shows cost/time vs carbon tradeoff for proposed fulfillment options.

Carbon policy scenario testing

• Tests alternate order flows under future ESG or regulatory constraints.

Monitor and alert on order risk (e.g., lateness, margin erosion, carbon breach).

Risk scores are computed continuously. Orders at risk of breaching SLA, margin, or ESG limits are escalated and re-optimized.

Returns simulation engine

• Simulates return volumes and routes under product, channel, or event scenarios.

Reverse logistics planner

• Plans return flows using best-fit nodes and transport paths.

Return condition logic

• Supports routing based on return condition‚Äîresell, rework, scrap.

Return cost integration

• Incorporates return costs and carbon in overall margin or performance metrics.

Customer return SLA enforcement

• Ensures customer-facing returns meet agreed service timelines.

Feedback loop to inventory & forecast

• Completed returns feed back into stock, production, and demand plans.

Simulate quote-to-fulfill scenarios in synthetic environments.

i5-SDG enables complete quote-to-fulfill scenario testing without live data. Used for new SKUs, partners, or sales training.

Order execution status tracking

• Monitors status from receipt to shipment with live updates.

Milestone delay detection

• Flags late picking, packing, staging, or shipment milestones.

Downstream disruption alerting

• Notifies Sales or customer agents when fulfillment delays will impact downstream events.

Execution-triggered re-forecast

• Execution events feed back into demand planning for faster course-correction.

Fill rate deviation logging

• Logs service failures with cause, impact, and agent response.

Real-time escalation workflow

• Enables cross-agent or customer escalation when execution issues arise.

Provide visibility into order status across fulfillment stages.

Order status is tracked natively in the i5 Graph with visibility across agents, scenarios, and UI layers. Timeline, exceptions, and next steps are included.

Sales & order scenario simulator

• Allows sandboxing of volume spikes, mix changes, or disruptions.

Margin & service simulation engine

• Simulates how order changes affect margin and SLA attainment.

Synthetic campaign modeling

• Models new product, promo, or market launch before rollout.

Order mix elasticity tests

• Tests system sensitivity to changes in order mix by channel or geography.

Customer response forecast loop

• Simulates how customers may react to fulfillment changes or delays.

Sales ops training sandbox

• Used for onboarding or testing strategy under synthetic but realistic conditions.

Inventory Optimization

Dynamically manage inventory levels, placement, and policies across the entire network—maximizing service levels with minimal working capital, while adapting to risk, delay, or demand shocks.

Automate replenishment using safety stock, reorder points, and lead times.

Inventory Agents manage replenishment using forecast demand, lead time, and safety stock logic. Updates trigger procurement or production actions.

Multi-echelon inventory calculation

• Computes optimal inventory across raw, WIP, and finished goods across all nodes in the supply chain.

Inventory balance engine

• Reconciles planned vs. actual inventory and highlights imbalances across sites or products.

Dynamic safety stock adjustment

• Adjusts safety stock levels based on volatility, lead time, and forecast accuracy in real time.

Target service level integration

• Optimizes inventory placement based on configurable customer service level targets.

Inventory projection simulator

• Projects inventory levels by week or month under various demand and supply scenarios.

Inventory buffer visualization

• Displays buffer sizes and utilization across the network for planning decisions.

Support multi-echelon inventory planning across DCs, plants, and transit.

Inventory is modeled across nodes, with stock positioning evaluated against demand location, timing, and value.

Inventory segmentation rules engine

• Segments SKUs by ABC/XYZ, cost, or velocity for differentiated inventory policies.

Segment-specific policies

• Applies different reorder points, lead times, and planning methods per segment.

Service vs. inventory tradeoff tool

• Displays how changing service targets impacts required inventory levels.

Segment reclassification triggers

• Flags SKUs for segment reassignment as sales patterns evolve.

Policy override with justification

• Allows planners to adjust policy per SKU and logs rationale for audit.

Segment performance visualization

• Visualizes how each inventory segment performs against targets.

Separate logic for cycle stock vs. safety stock control.

Agents differentiate buffer vs operational stock via orchestration parameters. Buffer logic is scenario-aware.

Reorder point calculation engine

• Automatically calculates reorder points per SKU/location based on demand, lead time, and variability.

Lead time-aware demand logic

• Accounts for forecast demand during lead time windows in reorder logic.

Inventory position tracker

• Tracks on-hand, in-transit, and committed stock to calculate net available inventory.

Min-max and hybrid rule support

• Supports traditional min/max logic as well as advanced agent-based triggers.

Reorder simulation sandbox

• Allows simulation of reorder logic under different demand/supply conditions.

Dynamic reorder trigger alerts

• Notifies planners or agents when reorder thresholds are breached.

Provide visibility into inventory levels by site, product, and status.

Full inventory visibility is native in the i5 Graph. Agents and users can query stock by location, ownership, and availability window.

Slow- and fast-mover SKU logic

• Differentiates inventory logic based on product velocity and variability.

Obsolescence risk scoring

• Scores SKUs based on risk of overstock or aging, triggering alerts or markdowns.

Lot-sizing strategy selection

• Supports EOQ, fixed lot, and MOQ-based lot-sizing with comparison tools.

Inventory aging analysis

• Provides visibility into aging inventory and potential write-offs.

SKU rationalization simulator

• Tests impact of retiring or consolidating SKUs on service and stock.

Stockout risk visualizer

• Maps forecast vs. inventory trends to flag near-term risk zones.

Simulate stockout and overstock risk under forecast volatility.

Inventory scenarios are simulated forward under changing forecasts, BOM shifts, or transport disruptions. Risk alerts are surfaced.

Multi-site inventory allocation logic

• Determines how inventory should be distributed across multiple stocking locations.

Cost-to-serve modeling

• Calculates inventory impact on margin and service at each site.

Allocation simulation engine

• Simulates different allocation strategies under demand uncertainty.

Site-level prioritization rules

• Allows priority weighting per site, customer, or channel.

Transfer feasibility validator

• Checks that stock transfers are physically and contractually viable before proposing.

Auto-balancing logic

• Triggers rebalancing across sites when thresholds or events are breached.

Proactively rebalance inventory across network to avoid exceptions.

Inventory Agents initiate reallocation across nodes based on timing, value, and disruption risk. Triggered dynamically.

Inventory reservation engine

• Reserves stock by order, agreement, or customer priority during planning.

Smart Agreement-linked allocation

• Links reservations to contract terms, SLAs, or penalties.

Dynamic reallocation logic

• Reallocates inventory when priorities shift, new orders arrive, or disruptions emerge.

Simulation-before-commit workflow

• Simulates reallocation scenarios and shows tradeoffs before changes are committed.

Reservation audit trail

• Tracks all reservation activity by user, agent, and reason code.

Order- and event-triggered reservations

• Allows holds based on incoming orders, transport issues, or execution risk.

Align inventory targets to downstream Smart Agreements and demand plans.

Inventory positions are tied to Smart Agreement terms and expected demand. Agents maintain readiness within delivery windows.

Lot and batch-level tracking

• Tracks inventory by lot/batch for traceability and planning accuracy.

Shelf life and expiry logic

• Flags inventory at risk of expiry and integrates freshness into allocation decisions.

FEFO and FIFO enforcement

• Supports first-expiry/first-out and FIFO as configurable policies.

Recall simulation tool

• Simulates the operational and inventory impact of a SKU recall.

Batch availability scoring

• Scores inventory by usability, age, and alignment to active plans.

Production input visibility

• Connects lot-level inventory to upstream production orders.

Optimize inventory for margin, service level, and carbon impact.

Multi-objective optimization includes margin, fulfillment risk, and ESG metrics. Agent logic prioritizes tradeoffs per business rules.

Inventory visibility dashboard

• Displays real-time inventory by product, site, and status (on hand, in transit, held).

Agent-accessible inventory layer

• Allows all agents (e.g., Sales, Transport) to use live inventory data in their logic.

Carbon footprint per SKU

• Attaches carbon data to inventory records for ESG-aware allocation.

Transport-integrated visibility

• Inventory reflects expected receipts and shipments with delay risk overlay.

Agreement-linked visibility filters

• Filters who can see what inventory, based on Smart Agreement permissions.

Out-of-network visibility extender

• Can ingest partner inventory feeds to create virtual availability.

Adjust stock strategy dynamically based on execution signals.

Agents detect transport delays, production misses, or demand surges and adjust safety stock, reordering, or allocation in real time.

Simulate stockout and overstock scenarios

• Models near-term risk windows using forecast, supplier, and transport uncertainty.

Scenario-driven policy adjustment

• Tests inventory policy shifts under simulated demand and supply events.

Carbon-risk scenario logic

• Simulates tradeoffs between low-stock and carbon-heavy replenishment.

Service-level risk scoring

• Scores scenarios based on potential missed SLAs or premium freight triggers.

Stockout escalation protocol

• Triggers alerts and agent-level response when critical items are projected to run out.

Scenario library reuse

• Saves and reuses tested scenarios for future plan calibration.

Visualize tradeoffs between inventory decisions and downstream impacts.

Inventory decisions are simulated through the orchestration graph—showing effects on transport, margin, and SLAs.

Carbon-aware stock planning

• Inventory planning integrates ESG scoring to avoid carbon-heavy overstocking.

Inventory carbon budget setting

• Allows targets per SKU, category, or site for embedded emissions.

Low-carbon replenishment simulation

• Tests stock replenishment paths with minimized carbon impact.

ESG tradeoff visualizer

• Displays tradeoffs between carbon, cost, time, and service in inventory actions.

Supplier ESG data integration

• Uses supplier emissions and compliance as inputs to inventory planning.

Carbon-triggered escalation

• Flags stock plans that exceed emissions thresholds or violate agreement limits.

Score inventory resilience (e.g., reallocation readiness, shock absorption).

Inventory nodes are scored on agility, buffering capacity, and cross-node flexibility. Used by agents during rebalancing.

Transport-aware inventory logic

• Considers transport lead time, mode risk, and disruption in inventory calculations.

In-transit stock tracking

• Tracks shipments and adjusts availability based on transit delay risk.

Dynamic safety time buffers

• Builds in additional coverage when transport is unreliable or high-cost.

Mode-based risk scoring

• Scores replenishment plans based on transport carbon, cost, and reliability.

Inventory-execution sync agent

• Inventory logic is synced with transport and order execution agents.

Replan triggers from transport shifts

• Inventory updates are triggered when transport ETA or mode changes.

Test inventory strategies in synthetic environments before deployment.

i5-SDG generates full synthetic inventory networks for testing reorder logic, positioning rules, and risk posture before go-live.

Inventory plan change history

• Tracks changes to stock plans, reorder points, and allocation rules.

Agent-level audit trace

• Each inventory decision includes responsible agent or user and timestamp.

Plan vs. actual variance tracker

• Continuously compares forecasted vs. actual inventory and alerts deviations.

Override flagging and review

• Overrides are logged and escalated if thresholds are exceeded.

Scenario outcome logging

• Each scenario simulation logs input assumptions, outputs, and selected action.

Compliance-ready reporting

• Generates exportable, auditable logs for inventory and ESG compliance.

Logistics & Transport

Plan and execute transport across modes, lanes, and carriers with live visibility, carbon awareness, and intelligent fallback strategies—coordinating tightly with upstream and downstream nodes.

Select optimal carrier based on cost, lead time, and service level.

Transport Planner Agents evaluate all carrier options based on configurable cost, service, and ESG scoring logic. Selections are auto-logged with rationale.

Transport planning engine

• Calculates optimal transport plan based on demand, inventory, carrier capacity, and cost/service tradeoffs.

Multi-leg routing logic

• Supports multi-leg, multi-modal routing (e.g., air-sea-truck) with handoff points.

Load consolidation rules

• Combines shipments based on weight, volume, route compatibility, or service tier.

Carrier agreement enforcement

• Plans are constrained by Smart Agreement terms‚Äîrate, carbon caps, and preferred routes.

Delay risk modeling

• Estimates likelihood of delays based on mode, lane history, and live disruption signals.

Planned vs. actual reconciliation

• Tracks differences between planned and executed transport and updates future logic.

Optimize routing across modes and constraints (e.g., capacity, timing).

The i5 Move Graph dynamically models all routing options and constraints. Agents plan full or partial multimodal moves based on urgency and feasibility.

Carrier profile modeling

• Stores carrier metadata (modes, region, carbon score, service level history).

Mode eligibility tagging

• Defines which SKUs or flows are eligible for certain modes (e.g., no air for hazmat).

Carrier performance scoring

• Tracks on-time, carbon compliance, and service levels to inform agent decisions.

Lane-level constraint modeling

• Includes regulatory, geopolitical, or capacity limits per lane.

Carrier capacity simulation

• Tests how shifts in demand affect carrier availability or overbooking risk.

Carbon-intensity attribution

• Attaches per-shipment emissions estimates to carriers and lanes.

Plan loads and consolidate shipments efficiently.

i5 Agents auto-consolidate loads by region, mode, and schedule—minimizing underutilization while preserving commitments.

Shipment lifecycle tracking

• Tracks shipment from dispatch to delivery with milestone updates.

Event-driven ETA recalculation

• Adjusts ETA based on delays, exceptions, or real-time signal inputs.

In-transit risk scoring

• Scores risk based on weather, geopolitical disruption, or prior lane issues.

Live status dashboard

• Provides UI and agent-accessible status across all active shipments.

Smart Agreement alert hooks

• Flags when in-transit conditions may violate contractual terms.

Shipment-level feedback loop

• Feeds delivery outcome back into agent learning and future plans.

Provide real-time tracking and visibility of in-transit shipments.

Shipment status and ETA logic are natively modeled. Real-time signal ingestion from TMS/IoT partners via API is supported but not natively sourced.

Transport cost calculator

• Computes expected and actual cost by shipment, lane, and carrier.

Carbon cost integration

• Treats emissions cost as part of shipment total‚Äîreal or shadow priced.

Mode tradeoff analysis

• Compares cost vs. speed vs. carbon across shipment options.

SLA impact scoring

• Shows SLA success likelihood for each shipment under current plan.

Service-tier prioritization

• Plans can elevate shipment priority based on client tier or margin.

Shipment optimization sandbox

• Simulates alternate shipment plans before commitment.

Trigger re-routing based on disruption, delay, or urgency changes.

Agents detect transport risk and automatically simulate alternatives. Feasible re-routes are executed based on system-wide context.

Carrier rebooking trigger engine

• Triggers carrier switch when thresholds are breached (e.g., late ETA, disruption alert).

Rebooking scenario simulation

• Tests downstream impact of switching shipment modes or lanes.

Alternate lane validation

• Validates alternate options for compliance, availability, and carbon limits.

Rebook-and-confirm agent loop

• Confirms rebooked plan with carrier and adjusts downstream orchestration.

Booking history version control

• Logs all booking and rebooking decisions with timestamps and rationale.

Rebooking policy governance

• Rebooking rules configurable by product, urgency, SLA, or escalation level.

Model and manage transport capacity by carrier, lane, and timeframe.

Published transport capacity is modeled as a first-class object. Agents allocate moves based on availability and reservation windows.

Multi-modal orchestration support

• Supports orchestration across air, sea, rail, truck‚Äîincluding handoffs.

Mode transition rules

• Defines how and when mode transitions can occur (e.g., airport ‚Üí DC within 24 hrs).

Customs and compliance triggers

• Flags customs needs or export compliance checks by lane.

CO2 and cost accumulation tracking

• Tracks cost and carbon by leg and totals at delivery.

Hand-off delay risk model

• Includes buffer logic and risk awareness for intermodal transitions.

Mode-based fallback protocols

• Defines backup modes or alternate plans per disruption type.

Include carbon impact in routing and carrier selection.

Emissions profiles are modeled per carrier, lane, and load type. Agents include carbon in tradeoff logic for all move decisions.

Shipment-level carbon reporting

• Provides carbon footprint per order, shipment, and transport leg.

Carrier carbon filtering

• Excludes high-emission lanes or providers based on policy.

Carbon visibility in tradeoffs

• Shows CO2 vs. cost vs. speed in routing decisions.

Carbon policy enforcement

• Enforces limits defined in Smart Agreements or local laws.

Carbon audit log

• Logs all emissions decisions with metadata for export.

Low-carbon routing engine

• Prioritizes lanes with lowest emissions per unit delivered.

Simulate transport disruptions and recovery scenarios.

Transport scenarios (e.g., port shutdown, lane closure) are modeled with agent reallocation, delay scoring, and risk mitigation planning.

Transport simulation engine

• Simulates disruption scenarios: port delays, strikes, lane closures, etc.

Carrier failover logic

• Defines what to do if a carrier or mode fails in a given region.

Transport resilience scoring

• Scores each plan or carrier for resilience (time to recover, alternate lanes).

Scenario comparison dashboard

• Displays impact of different disruption assumptions.

Scenario-generated plans

• Creates live-ready transport plans from simulation results.

Transport training sandbox

• Used for onboarding new staff or agents with synthetic scenarios.

Automatically update downstream plans based on shipment status.

Shipment delays, holds, or exceptions automatically trigger reallocation, reordering, or escalation across agents. No batch re-planning required.

Transport plan change detection

• Monitors for carrier delays, route changes, or missed pickups.

Exception alerting engine

• Triggers alerts based on criticality, SLA risk, or carbon breach.

Dynamic route switching

• Re-plans route in-flight when disruptions occur.

Exception resolution agent loop

• Coordinates between Transport and Inventory Agents for resolution.

Exception dashboard with drill-down

• Displays issue type, root cause, and options with impact score.

Escalation path logging

• Tracks who responded, when, and what action was taken.

Manage Smart Agreements with logistics partners.

Agreements define pricing, capacity, time windows, and emission thresholds. Agents reference these in planning and compliance logic.

Smart Agreement-aware routing

• Plans respect contract limits, cost floors, and carbon terms.

Compliance auto-checks

• Validates shipments meet contract terms before execution.

Contract violation flagging

• Flags shipments violating SLAs or route terms.

Contract-driven prioritization

• Routes can be favored due to premium tier or risk-sharing agreements.

Terms change propagation

• Updated agreements automatically change future plans.

Contract compliance dashboard

• Shows contract status by shipment, carrier, or region.

Track and score carrier performance across cost, timing, and ESG.

Carrier fitness is scored continuously based on actual execution and synthetic scenario performance. Used for partner selection and escalation.

Transport capacity modeling

• Forecasts future carrier capacity vs. planned demand.

Capacity-based planning triggers

• Flags when planned shipments exceed expected capacity.

Peak period buffer logic

• Adds safety time and reserves during peak or congested periods.

Demand surge scenario simulator

• Simulates network impact of demand spikes or panic buying.

Capacity reallocation logic

• Reassigns volumes dynamically across carriers or modes.

Carrier escalation workflow

• Escalates and logs capacity constraints requiring action.

Include ESG and compliance metrics in all logistics planning.

ESG compliance flags (e.g., CBAM, mode restrictions) are built into transport rules. Agents filter out non-compliant options proactively.

Fulfillment order routing engine

• Routes orders to fulfillment center based on stock, capacity, SLA, and transport.

Customer-level prioritization

• Selects fulfillment plan based on client urgency, value, or geography.

Split-order logic

• Supports splitting orders across sites with downstream coordination.

Last-mile mode planner

• Optimizes final delivery mode (e.g., parcel vs. dedicated freight).

Routing compliance enforcement

• Applies geographic, product, or agreement-based constraints to routing.

Fulfillment-to-transport sync

• Ensures final transport plan aligns with order and fulfillment logic.

Production & Capacity Planning

Orchestrate production decisions with full visibility into constraints, lead times, and capacity buffers—supporting alternate routings, BOM substitutions, and flexible manufacturing responses.

Generate feasible production schedules based on demand, BOM, and capacity.

Production Planner Agents align forecasts, BOMs, and site capacity to build executable plans. Constraints are validated pre-commitment.

Production feasibility engine

• Validates production schedules by aligning demand, BOM requirements, and site-level capacity.

Multi-shift capacity modeling

• Accounts for shift calendars, working hours, and downtime to determine usable capacity.

Sequencing constraint enforcement

• Supports sequence-dependent setup times, machine rules, and batch logic.

Pre-commit simulation preview

• Simulates schedule outcomes prior to commitment, flagging infeasible or inefficient plans.

Auto-adjust for material availability

• Schedules flex when materials are delayed or partially available‚Äîwithout breaking orchestration.

Production lock & override logic

• Users can lock certain production slots, override system plans, and log changes with impact.

Support multi-level BOM explosion and materials alignment.

BOM structures are fully modeled within i5 Graph. Cascade logic supports parent-child dependencies with demand roll-down.

Recursive BOM engine

• Explodes multi-level BOMs to identify all raw, intermediate, and packaging material needs.

Phantom and optional component handling

• Handles BOM structures that include conditional or optional subcomponents.

Stage-based consumption mapping

• Maps when each material is consumed during production‚Äîenabling time-phased planning.

Alternate BOM variant support

• Supports multiple valid BOMs per SKU and selects optimal path based on constraints.

Lead-time and shelf-life integration

• BOM explosion logic respects component lead times and product expiry windows.

BOM version control and traceability

• Tracks all changes to BOM structure, usage history, and material substitutions.

Coordinate production across multiple sites or contract manufacturers.

Agents simulate cross-site allocation, optimize based on capacity, and consider Smart Agreements with external manufacturers.

Cross-site production optimizer

• Allocates production loads to optimal sites based on capacity, proximity, and agreement logic.

Contract manufacturer rules integration

• Supports contract-specific terms like volume limits, pricing tiers, and lead time.

Capacity harmonization logic

• Normalizes and aggregates available capacity across internal and external producers.

Multi-site disruption simulation

• Simulates site-specific disruptions and reallocates production dynamically.

Site eligibility filtering

• Filters eligible sites for a product based on BOM compatibility and compliance status.

Supplier integration hooks

• Provides APIs to post plans or retrieve confirmations from 3rd-party manufacturers.

Rebalance production based on disruptions, delays, or new demand.

Agents monitor execution risk and dynamically reassign production to alternate sites or timelines.

Real-time production reallocation

• Agents automatically shift production based on risk, delay, or shortfall indicators.

Demand signal triggers

• New or urgent demand signals trigger agent rebalancing and simulate tradeoffs.

Site priority hierarchy

• Defines fallback site logic (e.g., Tier 1 preferred, Tier 2 emergency) for fast reassignment.

Carbon-aware rebalancing

• Production is reallocated with ESG impact as a tradeoff factor.

Simulation-first override protection

• Users can preview production changes and approve before live update.

Rescheduling visualization tool

• Graphical display shows how orders shift in timeline or location after rebalance.

Include lead times, calendars, and shift schedules in capacity plans.

Calendars, shift constraints, and time windows are modeled in the orchestration layer. Production logic respects them natively.

Site-specific calendar engine

• Supports shift calendars, holidays, working hours, and maintenance windows.

Lead-time embedded planning

• Automatically calculates and applies lead time buffers per SKU and site.

Shift-level capacity allocation

• Agent plans capacity by shift block, optimizing against overlapping or slack time.

Shift-based resource constraints

• Supports different line capacities or resource limits per shift (e.g., 3rd shift reduced staff).

Time window compliance

• Ensures plans stay within contractual production windows per customer or product.

Calendar change propagation

• Any calendar update triggers auto-adjustment across impacted schedules.

Trigger procurement and inventory actions from production plans.

BOM-aware production plans generate material demands, which trigger Procurement and Inventory Agent workflows.

Production-linked demand trigger

• Creates dependent demand for raw materials and triggers procurement logic.

Inventory reservation logic

• Reserves inventory for planned production runs and adjusts reorder triggers.

PO auto-generation interface

• Automatically proposes or adjusts POs based on confirmed production plans.

Orchestration cascade logic

• Downstream actions are orchestrated based on changes in production timing or location.

Production delay signal handling

• Alerts procurement and inventory agents if delays threaten stock availability.

Multi-agent execution sync

• Agents ensure PO, transport, and stock flows are in alignment with production changes.

Monitor production order lifecycle from plan to completion.

All production orders are tracked in the i5 Graph with real-time status updates. Agent exceptions flag delays or scrap risks.

Order lifecycle status tracking

• Tracks all production orders from plan to execution with real-time status updates.

Production exception alerting

• Alerts users and agents to delays, early completions, or quality exceptions.

Execution variance logging

• Logs discrepancies between planned vs. actual production quantity, timing, or yield.

Auto-update trigger logic

• Agents adjust downstream plans (e.g., transport) in real-time based on order status change.

Order reforecast integration

• Completed orders feed back into demand model and execution accuracy metrics.

Order-level audit trail

• Full traceability of order decisions, execution steps, and overrides.

Simulate production capacity under “what-if” scenarios.

i5 supports full-scenario simulations (e.g., surge, shutdown). Agents model production feasibility, lead time shifts, and cost tradeoffs.

Scenario-driven capacity simulator

• Tests alternative demand, site, or shift scenarios to evaluate capacity feasibility.

Capacity bottleneck detection

• Highlights where current or future constraints limit production capability.

Cost vs. time tradeoff simulator

• Evaluates high-cost vs. delayed production alternatives and shows net impact.

New site onboarding sandbox

• Tests how adding a new production site would shift capacity and network performance.

Multi-scenario comparison dashboard

• Visualizes multiple simulations side-by-side to support planning decisions.

Scenario impact scoring

• Assigns a resilience, cost, and ESG score to each simulated plan.

Prioritize production based on urgency, margin, or agreement terms.

Agents reason using time-based priority logic (TNR) and Smart Agreement terms to prioritize what to make, when, and where.

Priority-based scheduling logic

• Schedules jobs based on urgency, margin contribution, or SLA tier.

Agreement-tier enforcement

• Smart Agreement terms (e.g., premium clients) are respected in production sequence.

Dynamic urgency recalculation

• Re-scores job priority based on shifting downstream constraints or risk.

Multi-objective scoring matrix

• Balances cost, urgency, ESG, and capacity in job sequence decisions.

User-adjustable priority weights

• Allows manual tuning of decision weights per planning cycle.

Priority change logging & override

• A full log of any manual overrides or re-prioritizations is captured.

Integrate energy usage and emissions data into production planning.

Energy and carbon impact per product/site are modeled and included in production optimization.

Carbon-intensity per site & product

• Models emissions output per unit by site, SKU, and shift type.

Energy-aware scheduling logic

• Plans can prioritize lower-carbon production windows (e.g., night shift, renewable grid hours).

Carbon-cost tradeoff logic

• Incorporates carbon impact as a tradeoff in scheduling and site selection logic.

Green production mode toggles

• Allows users to enable ESG-prioritized planning mode at runtime.

Scope 1 & 2 tracking hooks

• Allows external emissions data integration per plant for ESG compliance.

ESG dashboard integration

• Displays carbon per order or unit for all production plans.

Adjust production dynamically based on execution signals.

Agents update production plans in real time based on inventory, transport, and demand shifts. Simulation ensures no blind spots.

Execution signal processor

• Receives alerts from transport, inventory, or demand to update production plans in real time.

Partial execution buffer logic

• Handles in-flight changes with time-aware buffers to avoid unnecessary rescheduling.

Plan locking & flex zones

• Supports locking critical production blocks while allowing others to flex in response.

Signal-priority resolution

• Determines which execution signals should drive planning changes (e.g., stockout > delay).

Downstream plan trigger engine

• Adjustments automatically update transport or procurement agents.

Execution change visualization

• Displays timeline and capacity impact of proposed execution-driven shifts.

Simulate new product or line launches in sandbox before live execution.

New SKUs or sites are introduced in synthetic mode using i5-SDG. Scenarios are validated before API posting.

New SKU simulation onboarding

• Simulates demand, BOM, and production flows for new SKUs before go-live.

Synthetic line modeling

• Builds new lines or shifts in synthetic sandbox including capacity, cost, and calendar logic.

Test integration with suppliers

• Tests Smart Agreements and BOM alignment for new products or lines.

Launch resilience simulation

• Tests potential launch failure scenarios‚Äîe.g., component shortage, ramp delay.

Feedback loop validation

• Simulates how a new product affects downstream inventory, transport, and sales commitments.

Launch scenario performance scoring

• Compares different launch approaches by speed, risk, and margin.

Supply Planning & Procurement

Convert demand into executable supply and sourcing plans that are contract-aware, multi-tier, and adaptive—balancing cost, service, risk, and ESG goals.

Calculate raw material needs based on BOM, forecast, and lead times.

i5 interprets forecast and BOM structures to calculate precise material needs by product, site, and period. Constraints and timing are respected across the orchestration window.

Forecast-driven MRP engine

• Automatically computes material requirements by projecting forecast volume and BOM consumption across time periods.

BOM explosion cascade

• Explodes multi-level BOM structures to all component levels to derive precise material demand.

Lead-time buffering logic

• Applies supplier lead-time, transit time, and safety buffer logic when calculating material order dates.

Multi-site aggregation & split

• Consolidates material needs across sites, allowing centralized or zonal ordering strategies.

Production schedule integration

• Links material requirements to production plans, validating feasibility before PO generation.

Exception & alert trigger logic

• Flags material shortages or excesses proactively as deviations emerge.

Maintain master data for suppliers, locations, lead times, and profiles.

Supplier entities and their attributes are managed within the i5 Graph and updated via input templates or APIs. Agents use this data when planning or re-allocating.

Supplier profile modeling

• Stores supplier attributes including location, lead times, performance scores, compliance status.

Lead-time variability tracking

• Tracks lead-time deviations over time and updates supplier profiles for dynamic planning.

ESG and carbon tagging

• Includes carbon intensity and ESG scores as supplier metadata for decision logic.

Contact & SLA storage

• Keeps structured supplier contact, MSA terms, and SLA attributes for contract alignment.

Automated validation and sync

• Enables syncing and validation of master data via API or spreadsheet imports.

Revision history & audit trail

• Logs historical supplier data changes and retains source attribution.

Create and manage purchase orders across the full lifecycle.

Procurement Agents generate and update POs in response to scenario logic. PO lifecycle is tracked with change history and agent-based adjustments.

PO generation engine

• Generates POs based on material need triggers and Smart Agreement parameters.

PO change & version control

• Enables automated PO updates, change tracking, and version history in the orchestration graph.

Confirmation reconciliation logic

• Matches supplier confirmations, back-orders, and change notices to POs and updates status.

PO exception conflict detection

• Detects mismatches between forecast demand and confirmed PO quantities or delivery dates.

Auto-cancel & reschedule logic

• POs are automatically canceled or reissued when demand changes exceed threshold rules.

PO lifecycle dashboard

• Provides visibility into PO status from draft to delivery with exception flags.

Enforce sourcing rules and supplier constraints (e.g., capacity, region, compliance).

Smart Agreements encode constraints like region, MOQ, capacity, or carbon rules. Procurement Agents enforce and optimize sourcing accordingly.

Constraint-aware sourcing engine

• Enforces sourcing rules such as MOQ, tiered priorities, region limits, or carbon caps.

Preferred supplier tiers

• Supports definition and evaluation of preferred vendors by product, region, or compliance status.

Capacity limit logic

• Prevents over-allocation by supplier or site based on real or forecasted capacity constraints.

Dynamic rule update propagation

• Changes to sourcing rules propagate immediately to agent logic and planning flows.

Rule violation alerts

• Flags when sourcing decisions conflict with pre-defined constraints, enabling corrective review.

Sourcing policy versioning

• Tracks changes to sourcing policy rules and retains metadata for audit and revision history.

Dynamically adjust sourcing based on supplier performance or disruption.

System reassigns sourcing in response to delays, disruptions, or new data. Agents trigger reallocation with risk scoring.

Performance-based sourcing engine

• Sourcing logic auto-adjusts based on supplier reliability, ESG, or lead-time deviations.

Disruption detection triggers

• Detects disruptions and automatically recalculates alternate sourcing paths.

Sourcing re-allocation agent

• Automatically reassigns material demand to alternate suppliers proactively.

Risk-weighted sourcing choices

• Supplier selection is weighted by time, cost, carbon, and reliability scores.

Simulation preview before commit

• Supplier changes are simulated before execution to assess downstream impact.

Sourcing change audit log

• Documents sourcing decisions, triggers, and agent negotiation history in the orchestration graph.

Optimize sourcing based on cost, lead time, and supplier ranking.

i5 evaluates sourcing options across multi-criteria scores (cost, time, carbon, reliability). The best-fit is selected and logged with rationale.

Multi-criteria sourcing evaluation

• Scores suppliers across cost, delivery, carbon, and risk to select best-fit source.

Relative ranking engine

• Allows weighting and prioritization of sourcing criteria per business requirement.

Scenario-based sourcing comparison

• Simulates alternative sourcing plans and compares performance across key metrics.

Optimization justification dashboard

• Rationalizes sourcing decisions with visual tradeoffs and scoring breakdowns.

Repeated vendor selection logic

• System favors historically higher-performing suppliers in iterative sourcing cycles.

Override log and escalation

• Sourcing overrides can be made via UI and are logged with rationale and override metadata.

Integrate forecast changes into procurement signals.

Forecast Agents communicate deltas directly to Procurement Agents. Updated PO proposals and sourcing plans are generated in response.

Forecast-procurement delta detection

• Detects significant forecast deviations and flags corresponding procurement triggers.

Automated PO adjustment proposals

• Suggests new POs or PO changes when forecast deltas exceed defined rules.

Agent-based reconciliation loop

• Procurement Agent negotiates with Forecast Agent for optimal ordering response.

Trigger confirmation process

• Supplier confirmation is requested automatically when forecast changes materially impact POs.

Trigger exception visualizations

• Displays forecast impact on POs in dashboard before execution.

PO lock & rollback flexibility

• Allows locking or reversal of PO changes based on user decision or override policy.

Generate and manage Smart Agreements with suppliers.

Agreements are modeled as structured, agent-readable contracts including terms, dates, quantities, and carbon limits. Used in procurement logic.

Smart Agreement schema engine

• Defines structured Smart Agreements with terms, carbon limits, lead times, and SLAs.

Agreement version control

• Tracks versions and amendments made to supplier contracts over time.

Agreement enforcement logic

• Contracts are enforced in procurement, planning, and execution logic.

Trigger-based agreement alerts

• System flags deviations from agreement terms before fulfillment failures.

Agreement-linked scorecard notifications

• Supplier performance and compliance are scored and surfaced in agreement reviews.

Agreement lifecycle dashboard

• Provides visibility across active, expired, and renegotiation-cycle agreements.

Model and simulate supplier disruption scenarios.

i5 supports scenario runs such as port shutdown, capacity drop, or ESG non-compliance. Agents simulate and propose mitigation actions.

Supplier disruption scenario library

• Includes pre-built templates (e.g., factory closure, embargo) for rapid simulation.

Scenario-driven sourcing recompute

• Recalculates procurement plans under disruption and recommends alternatives.

Resilience score comparison

• Compares sourcing options by resilience metrics under risk scenarios.

Escalation-based simulation logic

• High-risk scenario outputs trigger alert or escalation workflows.

Multi-supplier coordination protocols

• System proposes split sourcing or backup options when primary is affected.

Synthetic simulation mode

• Allows live testing of disruption logic in synthetic sandbox before deployment.

Evaluate supplier performance and fitness over time.

Supplier fitness is tracked and scored using historical and simulation-based data. Used by agents for sourcing decisions and agreement ranking.

Supplier performance scorecard

• Tracks supplier reliability, quality, ESG compliance, and lead-time variance over time.

Trend & anomaly detection

• Automatically spots unusual patterns (e.g., lead-time degradation, ESG slips).

Context-aware fitness logic

• Scores adjust based on SKU, region, or delivery mode performance history.

Agent-driven fitness updates

• Supplier fitness is updated continuously and used in sourcing decision logic.

Supplier benchmarking engine

• Allows comparison across suppliers for same product or service category.

Performance exception visualization

• Renders score trends and flags underperformance for review or action.

Trigger transport and production updates from procurement changes.

Procurement Agent actions feed directly into Transport and Production planning agents. All changes are reflected in orchestration flows.

Inter-agent event dispatcher

• Signals from procurement agent automatically trigger production and transport planning agents.

Dependency-aware domino logic

• Ensures downstream flows are updated in time-sensitive sequence to maintain coherence.

Real-time orchestration sync

• Changes propagate immediately with minimal latency across planning modules.

Priority-based job ordering

• Updates consider order priority and urgency when reallocating tasks.

Execution feedback loops

• Downstream execution signals report back to procurement agent for confirmation or adjustment.

Rollback & recovery logic

• Allows safe rollback of triggered flows if simulations flag unmanageable downstream impacts.

Incorporate ESG metrics (e.g., carbon, ethical sourcing) into sourcing decisions.

ESG attributes are embedded in supplier profiles and evaluated in real-time. Sourcing outcomes are scored for ESG compliance.

ESG attribute tagging engine

• Tags suppliers, SKUs, and flows with ESG-relevant attributes including carbon, ethical sourcing, and compliance.

Multi-dimensional ESG scoring

• Evaluates sourcing options with configurable weighting on carbon, social, and governance scores.

ESG-driven sourcing filters

• Filters out non-compliant or low-rated suppliers automatically during sourcing evaluation.

Carbon tradeoff visualization

• Visual UI shows ESG impact alongside cost/time tradeoffs for sourcing decisions.

Scenario-based ESG simulation

• Tests ESG vulnerabilities during scenario runs (e.g., carbon cap breaches, supplier change).

Dynamic ESG policy enforcement

• Sourcing logic respects changing ESG policies or regulatory thresholds in real time.

Sustainability & Compliance

Embed carbon, ethics, and compliance into every plan, policy, and decision—making ESG performance measurable, enforceable, and aligned with enterprise commitments.

Track carbon emissions across shipments, products, and flows.

Emissions are calculated per move, product, and batch based on transport mode, routing, and production inputs. Data is visible and used in orchestration.

Carbon footprint modeling per SKU

• Calculates embedded carbon at SKU level across materials, production, and transport.

Scope 1–3 emissions capture

• Ingests emissions data from internal operations and suppliers (upstream/downstream).

Carbon intensity scoring

• Scores flows and plans by emissions per unit moved or produced.

Carbon data integration hooks

• Allows ingestion of carbon data from LCA tools, carriers, and suppliers.

Dynamic carbon impact tracking

• Tracks real-time impact of changes in transport, sourcing, and production logic.

Carbon attribution to plans and orders

• Attaches carbon cost to every order, PO, shipment, or plan node.

Include carbon impact in planning decisions.

Carbon is treated as a cost or constraint across sourcing, production, and logistics. Agents evaluate carbon tradeoffs along with cost and urgency.

Carbon policy enforcement logic

• Prevents plans that exceed defined carbon limits or targets.

Smart Agreement carbon terms

• Enables contractual carbon thresholds, penalties, or priorities per partner.

Carbon-aware optimization engine

• Agents weigh carbon vs. cost and service in orchestration decisions.

Carbon policy override workflow

• Requires justification and trace for exceeding carbon targets.

Emissions budgeting per node or flow

• Supports carbon budgeting at SKU, site, flow, or network level.

Carbon constraint simulation

• Tests plan feasibility under stricter emissions limits.

Support ESG compliance with global and regional regulations.

Smart Agreements and orchestration logic can enforce CBAM, REACH, CSRD, and other compliance rules by geography or partner.

Regulatory compliance signal tracking

• Flags product, region, or shipment risks based on active regulations.

Global compliance rule engine

• Encodes rules per region‚Äîe.g., REACH, FDA, customs, export controls.

Trade & transport compliance validation

• Ensures routes and shipments comply with local import/export laws.

Restricted material flagging

• Identifies BOMs or flows that include banned or sensitive components.

Sanctions-aware partner logic

• Blocks partners flagged under current trade or legal restrictions.

Compliance update alerting

• Pushes rule updates into planning logic when laws or standards change.

Maintain digital records for ESG audits (e.g., carbon footprint, supplier declarations).

All ESG data and decisions are recorded in the i5 Graph with full auditability and reporting export options.

Ethical sourcing rule enforcement

• Flags sourcing plans that violate labor, conflict mineral, or ESG rules.

Supplier ESG scoring integration

• Integrates partner ESG ratings into sourcing and planning logic.

Sourcing transparency dashboards

• Visualizes partner ESG metrics and links to actual planning flows.

Supplier ethics alert triggers

• Alerts users or agents to ESG violations or reputation flags.

Ethical tier prioritization

• Elevates suppliers with certified ESG performance in orchestration logic.

Ethical sourcing scenario simulator

• Tests impact of shifting to compliant-only supply network.

Integrate supplier compliance data (e.g., certificates, policies).

Supplier ESG attributes can be ingested and referenced in Smart Agreements and sourcing logic. Non-compliant partners are deprioritized or excluded.

Material traceability framework

• Tracks raw materials through multi-tier supply chain nodes.

Batch and lot lineage mapping

• Maintains chain of custody for each lot‚Äîfrom origin to consumption.

Digital product passport hooks

• Supports export or linkage of traceability data to DPP or regulatory tools.

Recall trace-back simulation

• Simulates recall path upstream and downstream to assess response speed.

Chain-of-custody audit logging

• Captures all handoffs and transformations across the product lifecycle.

Traceability compliance triggers

• Flags SKUs or flows lacking required traceability coverage.

Score decisions and flows by ESG impact.

Every option (route, supplier, product) is scored in simulation for carbon, ethics, or waste—then ranked against cost and time.

Circular flow modeling

• Models reverse, reuse, and refurb flows as part of standard orchestration.

Product end-of-life scenarios

• Simulates return, rework, scrap, or recycle decisions at scale.

Reverse logistics orchestration

• Plans movement of returned items back into inventory, repair, or disposal.

Circularity performance scoring

• Scores SKUs by reuse %, waste %, or EoL path quality.

Material recovery trigger logic

• Alerts when recovery plans fall behind target.

Circular flow dashboard

• Displays material reuse, emissions savings, and margin recovery in one view.

Include carbon cost in margin calculation.

True margin includes embedded carbon cost or shadow price, enabling smarter prioritization for low-emissions fulfillment.

Audit-ready reporting packs

• Generates regulatory reports for carbon, sourcing, waste, or trade compliance.

Compliance export logic

• Supports output formats for ESG frameworks (e.g., CSRD, SEC climate rule).

Audit trail integrity engine

• Tracks who changed what plan, policy, or partner logic and when.

Policy adherence reporting

• Reports on which flows and plans are compliant vs. flagged.

Compliance scorecard per partner

• Rolls up compliance outcomes by supplier, carrier, or region.

Audit simulation sandbox

• Tests readiness of plan under mock audit or regulatory review.

Auto-flag or block non-compliant options during planning.

Non-compliant plans (e.g., carbon breach, restricted mode) are filtered before presentation to agents or users.

Sustainability target integration

• Allows ESG targets to be embedded directly into orchestration logic.

Carbon + margin optimization

• Optimizes for both emissions and profitability via multi-objective scoring.

ESG scenario simulation

• Tests alternate plans under new ESG rules, targets, or partner exclusions.

Emissions-reduction pathway modeling

• Models decarbonization strategies across multi-year horizons.

Scope 3 forecast modeling

• Projects future partner emissions based on demand and flow plans.

ESG score impact simulator

• Shows how plan changes impact enterprise ESG metrics.

Simulate ESG impacts of strategic or operational decisions.

Users or agents can simulate the ESG outcome of new SKUs, suppliers, production shifts, or routing changes before committing.

Product sustainability scoring

• Scores SKUs based on inputs, lifecycle, circularity, and emissions.

Sustainability risk triggers

• Alerts when products or flows deviate from sustainability targets.

Product-level ESG metadata tagging

• Attaches ESG tags to SKUs for planning and partner logic.

Sustainable SKU prioritization

• Elevates lower-impact products when other metrics are equal.

Sustainability-flagged decisions

• Logs and visualizes where plans favor ESG outcomes over cost or time.

Customer-facing sustainability data

• Prepares sustainability scores for use in marketing, eComm, or B2B outputs.

Provide ESG metrics per order, shipment, and agreement.

ESG data is available contextually at all orchestration levels—order, flow, partner, agreement—and used for compliance and reporting.

Sustainability + compliance dashboard

• Centralized view of all ESG metrics, risks, and outcomes.

Real-time ESG signal feed

• Displays alerts for emissions, sourcing, or policy breaches in live ops.

Compliance alert center

• Flags emerging risk zones or events needing compliance action.

Drill-down by flow or partner

• View ESG compliance by SKU, route, supplier, or agreement.

Sustainability target tracker

• Tracks progress toward emissions, waste, sourcing, and EoL goals.

Board-ready ESG export

• Summarizes ESG health for executive, board, or investor review.

Generate audit-ready ESG disclosures and data exports.

All ESG events and decisions are logged with metadata and can be exported for compliance reporting or third-party audit.

Supplier emissions forecasting

• Projects Scope 3 emissions based on partner mix and future demand.

Low-carbon partner simulation

• Tests how switching to greener suppliers affects cost and performance.

Supplier engagement toolkit

• Shares ESG scorecards and targets with suppliers via platform.

Incentive-aligned planning logic

• Enables supplier rewards or penalties based on ESG behavior.

Partner path-to-compliance tracking

• Shows how far each supplier is from achieving ESG threshold.

Partner de-risking simulation

• Simulates cost and risk of replacing high-risk ESG partners.

Support dynamic ESG contract terms (e.g., carbon limits, green incentives).

Smart Agreements can include ESG clauses that impact planning behavior—e.g., route caps, supplier filters, emissions penalties.

Enterprise ESG goal alignment logic

• Links i5 orchestration goals to enterprise sustainability strategy.

Emissions cap forecasting

• Models how future orchestration choices impact emissions caps.

Sustainability investment ROI logic

• Tests how changes (e.g., new packaging) pay off in ESG or margin.

Executive ESG performance alerting

• Notifies execs when plans or operations threaten ESG credibility.

Investor-ready reporting triggers

• Links ESG performance to investor frameworks (e.g., CDP, SASB).

Corporate policy propagation

• Pushes new enterprise ESG mandates directly into i5 agent logic.

Resilience, Scenario Planning & Governance

Model and prepare for disruptions, edge cases, and systemic risks—ensuring the supply chain is ready to absorb shocks, recover quickly, and justify decisions with auditable logic.

Model and simulate disruptions across supply, transport, and production.

Agents simulate disruptions (e.g., port closures, factory outages) and recommend mitigation across the orchestration graph.

Node-level disruption simulation

• Models outages at any node (plant, DC, port) with cascading effect tracking.

Path failure propagation

• Simulates downstream failures caused by single-point disruption.

Recovery strategy comparison

• Tests alternate recovery approaches‚Äîreroute, resupply, or delay‚Äîand scores fitness.

Failure probability modeling

• Applies stochastic models to simulate frequency and impact of common disruptions.

Agent-based recovery coordination

• Agents coordinate to reroute, reallocate, or cancel flows as needed.

Resilience cost calculator

• Quantifies the financial and service impact of the proposed recovery path.

Provide pre-configured “what-if” scenarios for risk testing.

Scenario templates (e.g., demand spike, supplier drop) are included and fully customizable per use case.

Time-based scenario modeling

• Simulates short, mid, and long-term scenarios independently or as a chain.

Future-state supply graphing

• Creates synthetic supply:move:demand graphs for future configurations.

Strategic scenario library

• Saves and tags scenarios for reuse during board or ops planning cycles.

KPI simulation output

• Projects impact on service, margin, carbon, and inventory metrics per scenario.

Synthetic demand overlay

• Applies macro events (e.g., new market entry) to existing plans.

Scenario decision approval loop

• Scenario outputs can be promoted into orchestration only after review or trigger.

Score plans for resilience (e.g., time to recover, plan slack, routing flexibility).

Plans are scored by resilience KPIs and used by agents to favor higher-fitness options when tradeoffs exist.

Stress testing engine

• Applies extreme event assumptions (e.g., 90% demand drop, multi-node blackout).

Resilience scoring logic

• Scores plan across resilience dimensions: redundancy, flexibility, response time.

Multi-plan comparison tool

• Compares baseline plan with alternates on cost, service, and risk absorption.

Plan volatility heatmap

• Highlights which parts of the network or graph are most sensitive to disruption.

Recovery time objective modeling

• Estimates time-to-stability for each scenario path.

Stress test template library

• Includes reusable industry and compliance stress templates.

Detect and surface risks from live execution signals.

Agents monitor for emerging risks (e.g., late shipments, demand volatility) and flag potential cascading impacts before failure occurs.

Override governance engine

• Tracks all manual overrides including who, when, and rationale.

Override threshold policies

• Only certain users or roles may override based on risk or value thresholds.

Override justification capture

• Override actions require structured reason entry or escalation approval.

Plan audit timeline

• Shows how a plan evolved over time‚Äîdecisions, triggers, and outcomes.

Audit export engine

• Outputs all plan and override data for audit, compliance, or board review.

Anomaly detection from overrides

• Flags override patterns that deviate from expected or healthy behavior.

Automatically trigger alternative plans when thresholds are breached.

Agents switch plans or reallocate flows once defined risk thresholds are exceeded. New paths are simulated before execution.

Scenario-driven plan creation

• Plans can originate from scenario logic, not just forecast push.

Trigger-based scenario generation

• Events (e.g., disruption, demand shock) auto-generate new scenario thread.

Agent-initiated scenario logic

• Agents can propose and simulate new plans when encountering constraints.

Scenario approval workflow

• Scenario outcomes are approved or escalated before orchestration.

Scenario traceability links

• Scenarios maintain connection to original plan and triggering conditions.

Cascading scenario effects

• Simulates how one scenario layer impacts others‚Äîe.g., transport + production.

Support human override and escalation for risk events.

Agent recommendations can be overridden. Human users can preview, modify, or approve alternate flows via UI or API.

Risk register integration

• Connects supply chain plans to enterprise risk registry or frameworks.

Plan risk flagging logic

• Flags flows, SKUs, or nodes with known or historical risk signals.

Plan-level risk scorecard

• Summarizes each plan‚Äôs risk exposure by location, product, or supplier.

Risk-based alerting engine

• Elevates plans or flows that pass configured risk thresholds.

Risk mitigation trigger hooks

• Links plan changes to mitigation plans, insurance, or alternate contracts.

Risk response simulation sandbox

• Tests potential effectiveness of different mitigation strategies.

Include ESG and regulatory risks in scenario planning.

Scenarios include compliance risk, carbon breaches, and sustainability failures. Mitigations are evaluated in real time.

Disruption event ingestion

• Supports importing disruption alerts (e.g., geopolitical, weather) from APIs.

Event-to-plan mapping engine

• Maps incoming events to relevant nodes, suppliers, or flows.

Event replay & drillback

• Replays past events to test plan response or benchmark improvement.

Auto-triggered scenario creation

• Events trigger fresh simulation or recovery planning.

Event classification logic

• Categorizes disruptions by type, severity, and recurrence risk.

Disruption history analytics

• Tracks which types of events have historically caused biggest performance hits.

Simulate time-based risks (e.g., quarter-end backlog, phased disruptions).

i5 uses Temporal Logic to model nested risks across short, mid, and long-term windows. Outcomes are ranked by horizon.

Governance tier modeling

• Assigns different planning rights to teams (global vs regional vs local).

Cross-tier override resolution

• Handles when lower-tier inputs conflict with higher-tier constraints.

Governance transparency dashboard

• Displays which tier made which decision and with what rationale.

Multi-level plan comparison

• Compares global plan to regional/local adjustments in visual diff.

Tiered escalation logic

• Structured workflow for raising issues up or down the governance stack.

Shared governance policy sync

• Pushes changes in governance policy or thresholds to all affected agents.

Compare multiple scenarios with visual tradeoff impact.

Agents present ranked outcomes by cost, time, margin, carbon, and resilience. Human decision-makers can review all options.

Scenario-driven sourcing plans

• Tests alternative sourcing rules and supplier combinations under future conditions.

Lead time sensitivity modeling

• Models how changes in lead time affect resilience or SLA risk.

Regionalization test scenarios

• Tests shifting supply or production closer to end-market.

Supplier portfolio reshaping

• Simulates consolidation, diversification, or reshoring of supplier base.

Sourcing-carbon policy tradeoff

• Evaluates tradeoffs of local (low carbon) vs global (low cost) sourcing.

Post-scenario sourcing propagation

• Scenario outcomes can update sourcing rules in active agent logic.

Maintain an audit trail of all scenario decisions and overrides.

All scenarios, assumptions, outcomes, and overrides are logged with time stamps and decision traces.

Inventory resilience score

• Scores each SKU or node‚Äôs inventory coverage against disruption windows.

Inventory decoupling simulation

• Tests how safety stock at upstream/downstream nodes buffers volatility.

Inventory cascade risk model

• Shows where inventory shortages would propagate across the graph.

Resilient inventory positioning engine

• Optimizes stock placement to maximize resilience for minimal cost.

Time-to-recovery buffer logic

• Calculates how long stock will sustain operations under upstream failure.

Carbon vs resilience tradeoff model

• Quantifies impact of higher buffer stock on emissions footprint.

Train agents or teams using synthetic risk scenarios.

i5-SDG generates synthetic scenarios for training, onboarding, and stress testing. Full orchestration logic is included in sandbox.

Production reconfigurability modeling

• Tests how flexibly production lines can respond to SKU shifts or disruptions.

Capacity backup scenario generator

• Simulates plans with alternate production nodes or shifts added.

Lead time compressibility score

• Scores which BOMs or processes can accelerate under disruption.

Substitution scenario logic

• Tests BOM swaps, alternate suppliers, or formula changes under pressure.

ESG-compliant rework logic

• Evaluates rework or recovery plans for regulatory or carbon compliance.

Production resilience dashboard

• Displays capacity, reallocation, and substitution readiness across SKUs.

Score customers, suppliers, and nodes by resilience risk.

Entity fitness includes resilience scoring—used to flag weak points in the network and inform sourcing and routing.

Network-wide resilience snapshot

• Summarizes readiness of entire supply:move:demand graph to absorb shocks.

Time to stability metric

• Quantifies how long it would take to restore operations after disruption.

Plan resilience benchmarking

• Compares current plan to past versions or industry peers.

Resilience playbook auto-linking

• Links each risk or plan to relevant playbook section.

Strategic board view interface

• Summarizes scenario readiness and key risk-exposed nodes.

Resilience reporting pack exporter

• Generates exportable pack for leadership, audit, or regulatory use.

Collaboration & Multi-Party Coordination

Enable secure, traceable, and intelligent orchestration across external partners—sharing logic, plans, and events while maintaining governance, compliance, and resilience.

Provide secure access for suppliers, carriers, or partners to view and update forecasts, orders, and shipments.

Multi-party access is supported through role-specific agent portals or API connections. Visibility is controlled by orchestration context and agreement permissions.

Partner orchestration layer

• Enables shared planning and execution logic across external parties in a secure context.

Multi-party agent federation

• Allows agents from partner orgs (e.g., 3PLs, suppliers) to collaborate using shared orchestration threads.

Permission-based data views

• Controls what each party sees and can act on, based on agreement logic.

External action trigger interface

• Triggers events (PO, shipment, plan change) at partner systems via structured APIs.

Inter-org negotiation protocols

• Supports structured exchanges between internal and external agents for conflict resolution.

Partner status integration

• Surfaces real-time partner signals (inventory, transport, production) in i5 planning flows.

Support structured data exchange via EDI, API, and manual upload.

i5 ingests structured data via configurable interfaces. Inputs can be mapped to orchestration schema for full agent logic execution.

Smart Agreement share-out

• Selectively shares relevant contract terms with partners (e.g., SLA, capacity caps).

Agreement-based data partitioning

• Limits shared data by agreement scope (e.g., just for SKUs covered).

Terms compliance monitor

• Tracks whether each partner is operating within the terms of their agreement.

Partner escalation logic

• Triggers alerts or escalations when a partner is at risk of breaching terms.

Multi-party audit logging

• Logs all partner interactions, decisions, and system changes.

Policy sync propagation

• Distributes policy updates (carbon, safety, lead time) across all impacted partners.

Notify stakeholders of exceptions, approvals, or plan changes in real time.

Agents trigger alerts for specific events—delays, demand shifts, re-routes—based on priority and role. Notifications are routed per agreement.

Role-based interaction protocols

• Defines how different roles (planner, supplier, logistics) engage in shared plans.

External comment & justification interface

• Allows external partners to add comments or explain planning choices.

Collaborative forecast editing

• Enables shared editing of forecast curves across internal and partner teams.

Conflict visibility dashboard

• Shows where internal vs. external parties disagree and enables negotiation threads.

Time-stamped input tracking

• Tracks when each party submitted input and when it was accepted or overridden.

Comment audit and version history

• Retains full history of collaboration threads and outcomes.

Track all interactions and changes with audit history.

All orchestration steps are versioned and auditable, with full traceability across agents, inputs, and outputs.

External event sharing engine

• Shares disruption events (e.g., weather, labor) across partner network in real time.

Event-driven plan adjustments

• Partners receive pre-approved changes from i5 (e.g., reallocation, cancelation).

Partner status broadcast

• Allows i5 to publish inventory, ETA, or production status to connected partners.

Exception handoff rules

• Defines how exceptions escalate between parties‚Äîe.g., supplier to planner.

Disruption sandbox for partners

• Enables partners to simulate their own recovery paths in i5 framework.

Multi-party signal reconciliation

• Resolves conflicting signals from multiple partners before altering plan.

Allow multiple parties to contribute to or adjust orchestration logic collaboratively.

i5 supports shared scenario editing, agreement negotiation, and joint simulation across users or agents from different orgs.

Joint KPI definition

• Defines shared KPIs for service, cost, carbon, or responsiveness across parties.

Scorecard publishing framework

• Distributes performance scorecards to partners automatically.

Partner-specific dashboard views

• Each partner sees tailored view of metrics relevant to their role and agreement.

Live SLA risk view

• Shows which partners are at risk of missing targets and why.

Performance-based negotiation hook

• Allows scorecard data to influence Smart Agreement renegotiation or tiering.

Partner performance history

• Stores all performance and exception history by partner and agreement.

Simulate multi-party disruptions or recovery scenarios.

Scenarios can include cross-entity variables (e.g., dual supplier failure, shared transport node issue). Recovery paths are modeled with agent alignment.

Partner onboarding workflow

• Guides setup of new partners, including permissions, data sync, and agent roles.

Template-based partner configuration

• Uses role-specific templates for rapid partner enablement.

Sandbox test harness

• Partners can test integration and behavior before going live.

API/EDI/Manual input support

• Supports structured inputs from any system, spreadsheet, or portal.

Onboarding exception management

• Flags missing data or misaligned logic during setup.

Multi-region compliance flags

• Identifies regulatory conflicts during setup (e.g., GDPR, export rules).

Evaluate and score partner performance on cost, reliability, and ESG.

Partner fitness scores are continuously updated based on real and simulated behaviors. These scores influence agent decisions on routing and sourcing.

Secure document exchange

• Partners can share POs, contracts, docs via i5 interface or API.

Version control & audit trail

• Tracks document versions, access history, and user actions.

Contract annotation tools

• Partners can annotate or flag contract clauses inline.

Multi-language metadata tagging

• Supports metadata and tagging in multiple languages for cross-region ops.

Time-sensitive access logic

• Sets expiry or access limits for shared docs or threads.

Smart Agreement-linked doc logic

• Documents can trigger or constrain orchestration logic (e.g., lead time trigger).

Model Smart Agreements between parties and enforce terms operationally.

Smart Agreements include terms like volume, lead time, carbon, and penalties. Agents monitor compliance and adjust planning accordingly.

Collaboration escalation paths

• Configurable escalation logic between partners, tiers, or internal teams.

Real-time collaboration triggers

• When logic fails or threshold breached, collaboration is auto-initiated.

Shared simulation mode

• Partners can simulate changes collaboratively and co-approve outcomes.

Asynchronous planning protocol

• Allows parties to propose changes and wait for approval asynchronously.

Collaborative override rules

• Overrides can be made jointly with reason code and full traceability.

Partner confidence scoring

• Scores partner inputs based on historical reliability.

Enable real-time negotiations between internal and external agents.

Agents negotiate supply, transport, and timing tradeoffs across roles and orgs—based on alignment logic and scenario feasibility.

Smart Agreement renewal hooks

• Triggers review and renewal workflow before contract expiry.

Terms negotiation workspace

• Structured interface to propose, counter, and agree on updated terms.

Scenario comparison for negotiation

• Shows what-if plan outcomes for alternate terms before signing.

Version comparison tools

• Compares past vs. proposed contract side by side.

Escalation to legal or exec layers

• Escalation rules allow structured handoff to higher decision authority.

Post-renewal change propagation

• New terms automatically update relevant agent logic and plans.

Provide partner-specific views of the orchestration plan.

Views are filtered by role, agreement, and authorization—showing only relevant orchestration paths, not system-wide data.

Multi-tenant compliance logic

• Each partner operates within its own data governance and compliance perimeter.

Audit-ready change tracking

• Every cross-party change is logged, attributed, and timestamped.

Role-based access control

• Granular permissions per user, partner, or role.

Security model transparency

• Partners can review what data they share and receive.

Data masking & tokenization

• PII or sensitive fields can be masked or tokenized in shared views.

Compliance flag trigger engine

• Alerts if data-sharing actions conflict with known rules or agreements.

Share compliance or ESG performance data across entities.

ESG metrics (e.g., carbon by shipment or SKU) can be exposed to partners as part of agreements or visibility layers.

Partner resiliency scoring

• Evaluates partner’s historical responsiveness, reliability, and disruption impact.

Collaborative contingency planning

• Partners contribute to playbooks for blackout, disruption, or recall.

Multi-party scenario testing

• Allows multiple partners to co-simulate under shared what-if assumptions.

Resiliency-informed sourcing

• Score informs upstream sourcing and allocation logic.

Shared escalation decision trees

• Partners co-design decision trees for exception response.

Partner risk alert system

• Triggers warnings when risk profile for partner materially changes.

Allow shared sandbox environments for training or partner onboarding.

i5-SDG supports synthetic onboarding with partner-specific scenarios. New agents or flows can be validated before live integration.

Network-wide collaboration view

• Visual map of partner interactions and collaboration frequency.

Partner engagement heatmap

• Shows who‚Äôs actively engaging vs. lagging in joint planning.

Behavior-based scoring

• Measures responsiveness, override frequency, and plan adherence.

Cross-tier influence logic

• High-impact partners can trigger escalations or policy shifts.

Collaboration tiering model

• Partners can be ranked by engagement maturity or SLA performance.

Partner development toolkit

• Provides tools and recommendations to improve collaboration maturity.

Systemic Intelligence & Control

Treat the enterprise as a living system—using feedback loops, graph logic, and simulation to make intelligent, adaptive decisions at every node, flow, and time horizon.

Full-system scenario simulation

Enables simulation of entire supply chain graph under varied disruption or strategy conditions.

Full-system simulation engine

• Simulates entire supply:move:demand graph behavior under alternate futures.

Scenario layering logic

• Supports nested scenarios‚Äîe.g., demand shift plus transport delay plus carbon constraint.

Synthetic data injection

• Injects stressors (e.g., blackout, recall, spike) to test control response.

Time-bounded simulation cycles

• Runs forward simulation with rollback to anchor or confirm changes.

Simulation result scoring

• Scores each scenario outcome across cost, risk, carbon, service.

Feedback loop simulation

• Models how signals (demand, inventory, etc.) propagate and stabilize across system.

Graph-centric orchestration logic

Treats supply, demand, and move nodes as a connected graph with dynamic dependencies and flows.

Graph-centric orchestration logic

• Plans and actions are derived from demand:supply:move node relationships.

Dynamically linked nodes

• Node behavior changes based on linked node conditions (e.g., plant delay ‚Üí transport shift).

Supply chain topology modeling

• Captures true network structure, including shared suppliers, dependencies, cycles.

Node-level stress propagation

• Models how local events ripple across graph over time.

Graph change detection engine

• Detects when graph structure has materially changed (e.g., new flows, closed site).

Graph-variant simulation mode

• Simulates alternate supply chain structures before implementation.

Feedback loop and control modeling

Embeds closed-loop control logic to enable continuous adjustment based on execution signals.

Control loop framework

• Supports classical control logic: signal, target, error, correction.

Closed-loop plan execution

• Plans auto-adjust based on feedback (e.g., delays, stockouts, capacity changes).

Loop health monitoring

• Flags unstable or oscillating control loops for tuning.

Time-delay handling logic

• Handles signal lags or time-to-effect when making corrections.

Control override buffer

• Enables manual delay or dampening of control response in sensitive flows.

Control parameter scenario testing

• Tests different gain or threshold settings before tuning live control logic.

Systemic deviation and root cause detection

Detects and traces system-wide patterns, not just local failures or noise.

Systemic signal interpreter

• Distinguishes between random noise vs systemic patterns in execution feedback.

Causal link detection engine

• Identifies upstream causes for downstream issues using traceback logic.

Emergent behavior detection

• Flags when multiple small issues create a large systemic deviation.

Signal dampening logic

• Suppresses non-actionable or redundant feedback before triggering response.

Root cause traceability toolkit

• Links downstream risk (e.g., missed SLA) to system-wide contributing factors.

Systemic deviation alerting

• Triggers alerts for system-level drift, not just localized failure.

Model calibration and learning

Continuously tunes model parameters (lead time, volatility, yield) based on observed data.

Model calibration engine

• Auto-adjusts system model parameters (lead time, yield, volatility) based on actuals.

Forecast accuracy feedback

• Continuously evaluates model vs. actual and corrects at node level.

Plan vs. outcome backtest engine

• Replays prior plans against actuals to score and tune decision logic.

Parameter optimization sandbox

• Tests alternate parameter values before applying system-wide.

Model stability scoring

• Scores model based on volatility, delay, error correction speed.

Data sufficiency flagging

• Flags nodes or flows where model lacks sufficient data for reliable control.

Multi-agent negotiation and conflict resolution

Supports negotiation between intelligent agents with different goals (e.g., cost vs. service).

Multi-agent negotiation protocol

• Agents negotiate to optimize outcomes across conflicting objectives.

Agent priority logic

• Supports agent ranking, role-based influence, and goal alignment.

Conflict resolution loop

• Triggers structured debate between agents when plans disagree.

Negotiation audit history

• Logs negotiation decisions, counteroffers, and winning arguments.

Agent-based resilience planning

• Agents propose failover, delay, or resupply strategies when risks arise.

Shared outcome scoring

• All agents agree on shared scorecard to drive resolution logic.

Latency and delay detection

Measures time lags in signal ? plan ? act loop and identifies where speed matters most.

System latency tracking

• Measures time between signal, plan, and execution response.

Loop delay visualization

• Displays delays in feedback, correction, or agent response by node.

Time-to-stabilize metric

• Estimates time needed for network to recover from disruption.

System lag classification

• Flags lag as avoidable (execution) or systemic (contract, geography).

Latency-reduction recommendation

• Suggests fixes (contract buffer, order timing, route) to reduce response time.

Systemic lag heatmap

• Maps where delays most impact network responsiveness.

System entropy and complexity scoring

Quantifies systemic unpredictability, over-planning, and noise.

Order entropy calculator

• Quantifies unpredictability in order timing, quantity, mix.

Signal-to-noise ratio analysis

• Evaluates quality of planning input signals across domains.

Decision entropy tracker

• Logs how many alternate paths were viable vs. selected at time of planning.

Complexity driver detection

• Identifies which nodes or flows add avoidable complexity to system.

Systemic fragility score

• Scores how tightly coupled and fragile the network is to shocks.

Entropy-triggered simplification

• Suggests structural or policy changes to reduce decision volatility.

Plan stability monitoring

Scores and suppresses unnecessary replanning to improve plan consistency.

Plan stability monitor

• Monitors how often plans change for same time horizon and flow.

Replan frequency tracker

• Tracks how often and why each node or SKU is re-planned.

Stability vs agility tradeoff engine

• Shows cost/risk of keeping plan stable vs. reacting faster.

Temporal control tuning

• Supports different stability tuning for different planning horizons.

Plan jitter visualization

• Displays how a plan shifts over time even without external disruption.

Plan stability threshold logic

• Suppresses low-impact replans to maintain consistency.

Systemic carbon and ESG logic

Integrates carbon, compliance, and sustainability directly into orchestration decision loops.

Systemic carbon modeling

• Models carbon emissions not just per shipment, but system-wide per plan.

Carbon delay feedback

• Tracks carbon impact of last-minute or reactive changes vs. planned.

Carbon-budget-aware simulation

• Simulates multiple plans under defined carbon limits.

Carbon tradeoff scoring

• Shows carbon alongside service and cost in orchestration decisions.

Long-horizon emissions forecasting

• Projects carbon footprint of supply chain over quarters or years.

Carbon sensitivity analysis

• Shows which plan changes most affect emissions outcome.

Margin-aware orchestration logic

Tracks and optimizes margin across all flows, adjusting for delay or disruption.

Systemic margin tracking

• Calculates total margin impact of every plan, action, or replan.

Unit cost propagation logic

• Propagates raw, transport, labor cost into systemic cost-to-serve by node.

Margin loss traceback

• Identifies upstream decisions or delays that cause margin loss downstream.

Scenario-based margin testing

• Tests alternate margin paths under different plan strategies.

Execution-margin reconciliation

• Reconciles planned vs. actual margins and flags deltas.

Margin-informed agent logic

• Agents use margin forecasts to prioritize high-impact flows.

Systemic intelligence dashboarding

Provides live visibility into orchestration health, resilience, control loop function, and deviations.

Systemic intelligence dashboard

• Summarizes live orchestration health, stability, resilience, and control.

Control KPI board

• Displays loop-level KPIs: delay, jitter, margin drift, plan volatility.

Graph-wide alert console

• Central view of all critical deviations, from agents and system.

Systemic intelligence pulse

• Scores orchestration logic health and model confidence weekly.

Systemic health trend view

• Shows how resilience, responsiveness, and quality have changed over time.

Executive synthesis view

• Summarizes all relevant control and system-wide performance in one board-ready output.

Platform, Integration & Security

Ensure orchestration logic is extensible, secure, and integrable across legacy systems, APIs, and governance structures—without slowing down innovation.

Modular platform architecture

Supports deployment of independent, composable orchestration services.

Composable module design

• Each i5 capability runs as an independent service with its own API surface.

Elastic orchestration runtime

• Platform dynamically allocates compute to agents and flows as needed.

Service orchestration graph

• Platform services are orchestrated like the supply chain itself—with dependencies and triggers.

Containerized deployment model

• Supports deployment in cloud-native, hybrid, or on-prem Kubernetes environments.

Upgrade decoupling

• Platform upgrades can occur module-by-module with minimal downtime.

Open API framework

Enables full integration with external data, planning tools, and execution systems.

Open API access to all entities

• SKUs, plans, agents, and signals are available via documented APIs.

Secure webhook ingestion

• External systems can push signals to trigger orchestration logic.

Bi-directional integration model

• Supports inbound (e.g. forecasts) and outbound (e.g. plan commits).

Prebuilt API libraries

• Includes Python, GraphQL, RESTful libraries for rapid integration.

Custom API scaffolding

• Enables enterprise-specific API endpoints without altering core code.

Event-driven integration hooks

Allows orchestration logic to respond in real time to signals from external systems.

Event trigger library

• Supports event patterns like stockouts, delays, exceptions as orchestration triggers.

Real-time event relay engine

• Ingests events from WMS, TMS, ERP and maps them to flows/nodes.

Conditional plan hooks

• Only re-plans or alerts when event meets defined condition set.

Agent-initiated response to events

• Agents respond autonomously to mapped events (e.g., delay recovery).

Event trigger traceability

• Every event-response chain is logged for audit and learning.

Data model extensibility

Users can extend or customize key entity types (SKU, node, flow) without vendor code changes.

Entity extension model

• SKUs, nodes, carriers, etc. can all be extended with custom fields.

Flow metadata extensions

• Users can tag flows with metrics, priorities, or compliance tags.

Custom object linkage

• Allows customers to define relationships (e.g., node clusters, priority lanes).

Dynamic schema updates

• Schema changes can be applied without downtime or redeployment.

Domain-specific entity libraries

• Supports industry templates (e.g., Pharma, Automotive).

Master data sync framework

Keeps core planning objects aligned with ERP, MDM, TMS, and external sources.

Master data ingestion scheduler

• Supports timed pulls from MDM, ERP, PLM, etc.

Delta sync optimization

• Only syncs changed records, reducing latency and bandwidth.

Conflict resolution logic

• Resolves discrepancies between source systems and i5 entity state.

Multi-source sync support

• Combines multiple upstream MDM sources into unified view.

Data sync monitoring console

• Visual UI to track sync health, error rates, and timing.

Time-series and stateful data support

Handles both snapshot and streaming data for use in planning, simulation, and agents.

Time-series ingestion engine

• Handles granular inputs like sensor feeds, demand curves, event logs.

Snapshot/state model sync

• Supports “as of” and point-in-time data reconciliation.

Streaming signal processing

• Real-time processing of signals like location pings or order status.

State-aware plan engine

• Considers past and current state of flows when simulating or orchestrating.

Time-series forecasting connector

• Links time-series signals directly to forecasting modules.

Orchestration sandbox environments

Supports safe, isolated testing of new agents, plans, or simulations.

Safe sandbox environments

• Isolated orchestration sandboxes for testing without production impact.

Scenario promotion workflows

• Only approved scenarios move from sandbox to orchestration.

Versioned plan experimentation

• Supports multiple concurrent versions of same plan under test.

Parameter tuning toolkit

• Tune orchestration logic in safe environment with rollback.

Synthetic data simulation mode

• Run orchestration using fabricated demand, capacity, or disruption inputs.

Enterprise-grade security and auditability

Offers robust identity, access, audit, and encryption features across platform.

SSO and identity federation

• Supports SAML, OAuth, SCIM for enterprise login and identity sync.

Audit logging engine

• All user and agent actions are logged with timestamp and context.

Encryption at rest and in transit

• All data secured via AES-256 and TLS 1.3.

Access policy enforcement engine

• Rules determine what data each user, agent, or system can access.

Security event alerting

• Notifies security teams of abnormal login, API, or data behaviors.

Role-based access control (RBAC)

Assigns precise user and agent permissions based on function and sensitivity.

Granular permission roles

• Roles can be set for each capability, entity type, or flow.

Data scoping by region

• limits data access by geography or legal boundary.

Agent-level permission tags

• Agents can be restricted by scope, risk threshold, or plan type.

Partner access control layer

• Limits partner visibility via Smart Agreement constraints.

Custom RBAC template builder

• Supports reusable role templates across teams or orgs.

API-first orchestration deployment

All planning, simulation, and decisioning functions exposed via secure APIs.

All orchestration exposed via API

• Plans, agents, scenarios, and flows are API addressable.

Plan-as-a-Service model

• Orchestration outcomes can be called on demand from external systems.

Embedded orchestration logic

• Core decisioning can be deployed as callable functions.

Auto-generated API docs

• All endpoints are documented and updated via OpenAPI specs.

Integration health reporting

• Tracks latency, errors, and uptime of all API calls.

Cross-system data lineage and traceability

Tracks where each plan input came from, and how it was transformed.

Input source tagging

• Tracks which system provided each data input to a plan.

Plan transformation trace

• Logs every change made to a plan and its rationale.

Agent influence trace

• Shows which agents contributed to a given decision or plan.

Execution vs plan deviation log

• Compares what was planned vs what occurred for traceability.

End-to-end lineage dashboard

• Visual map from raw input to final orchestration action.

Platform performance and scalability controls

Allows tuning and scaling of orchestration workloads across enterprise nodes or clouds.

Auto-scaling orchestration engine

• Adjusts compute capacity based on orchestration load.

Elastic agent scheduling

• Adds/removes agents based on active flow volume.

Parallel simulation threading

• Runs multiple plan tests concurrently for faster performance.

Resource throttling policies

• Prevents any plan or agent from consuming excess resources.

Platform health monitoring dashboard

• Displays real-time platform status, latency, and compute use.

Experience & Exception Handling

Give users and agents shared access to alerts, dashboards, and guided interventions—enabling fast, intelligent response when plans go off course.

Unified orchestration alerting

Aggregates all system, execution, agent, and plan-level alerts in a user-tailored console.

Unified alert console

• Displays all alerts from agents, execution, signals, and external triggers in one place.

Agent-originated alert flagging

• Alerts surfaced directly from agent negotiations or logic triggers.

User-specific alert views

• Customizes what each user sees based on planning role and data domain.

Alert bundling logic

• Groups related alerts (e.g., demand spike, order surge, node overload) for clarity.

Severity & urgency tagging

• Uses system-level scoring to rank alerts and guide response time.

Exception classification logic

Tags and categorizes exceptions by source, impact, and recommended ownership (human vs. agent).

Exception classification engine

• Tags each exception based on cause, impact, propagation potential.

Owner routing logic

• Assigns exception to agent, user, or external party based on rules.

Repeat exception detection

• Flags recurring issues to support long-term mitigation.

Exception pattern clustering

• Groups similar exceptions to allow batch resolution.

Classification override controls

• Allows human users to reclassify or tag exceptions manually.

Guided intervention workflows

Offers human users recommended actions, escalation paths, or override options per exception.

Guided resolution paths

• Offers pre-built workflows (e.g., escalate, wait, override) per exception type.

Action suggestion engine

• Suggests best next action based on similar historical resolutions.

Multi-step escalation triggers

• Allows exceptions to escalate through roles or regions based on risk.

Override justification capture

• Prompts user to explain override decisions with traceability.

Conditional intervention logic

• Only allows override if certain policy or signal thresholds are met.

Plan deviation explanation engine

Explains why a plan changed, what triggered it, and how it differs from the prior baseline.

Plan diff engine

• Displays how the current plan differs from prior version.

Trigger traceback logic

• Traces back to root signal or logic change that caused plan adjustment.

Agent intent display

• Shows rationale behind agent-driven plan changes.

Forecast change impact viewer

• Visualizes how forecast shifts altered planning behavior.

Graph-variant comparison

• Compares how plan changes impact downstream nodes and partners.

Agent-human collaboration loop

Allows agents to request human input, justification, or override before finalizing orchestration.

Agent “ask for input” logic

• Agents can pause orchestration to await human guidance or sign-off.

Human-override flag trigger

• If user contradicts agent plan, system flags and records override.

Collaborative explanation thread

• Users and agents can leave comments and intent notes.

Agent confidence signal display

• Agent shows confidence score to help guide human decisions.

Approval workflow branching

• Different approval logic per flow, value, or region.

Context-aware notification system

Prioritizes alerts by user role, urgency, and historical behavior patterns.

Alert prioritization engine

• Sorts alerts by role relevance, SLA risk, and decision urgency.

Role-based channel logic

• Sends alerts via dashboard, mobile, email based on preference.

Smart suppression mode

• Hides alerts if already acknowledged or addressed by agent.

Alert delivery window config

• Controls when users receive alerts to avoid overload.

Historical alert response analysis

• Improves notification logic based on user behavior.

Dashboard-to-action continuity

Users can act directly from dashboards (e.g. cancel, reroute, approve) without switching systems.

Dashboard-to-action link

• Allows users to act (reroute, approve) from dashboards directly.

Embedded control UI

• Plan and flow controls embedded within visualization panels.

One-click escalation tools

• Sends issue to partner or exec layer with pre-built payload.

Alert-to-plan traceability

• Links dashboard alerts back to full orchestration context.

Scenario injection from UI

• Users can trigger what-if scenarios based on dashboard insights.

Event replay & trace mode

Users and agents can “replay” the decision history for any plan or event.

Replay mode for plans

• Replays decision timeline step-by-step to understand why a plan was chosen.

Event trace console

• Shows how execution or signal events triggered orchestration changes.

Comment & decision thread viewer

• Retains full context of plan discussions and overrides.

Simulation history overlay

• Compares what was simulated vs what occurred in execution.

Exception trace replayer

• Replays cascading exceptions to support audit or root cause review.

Role-based visibility & filtering

Limits what each role sees (alerts, decisions, risks) based on policy or Smart Agreement.

Role-based alert filtering

• Filters what users or partners can see by permissions.

Agreement-scoped visibility logic

• Limits view to alerts covered under Smart Agreements.

Flow-specific alert rules

• Allows user to configure what alerts appear for which flows.

Alert escalation visibility guardrails

• Only higher roles can see certain alert types.

User override of filter defaults

• Allows experienced users to widen visibility when needed.

Exception risk scoring

Scores each exception by margin risk, SLA impact, and systemic propagation potential.

Exception SLA impact score

• Scores each exception by how it threatens delivery or cost targets.

Systemic propagation signal

• Indicates whether the issue will affect other nodes or SKUs.

Exception margin erosion estimator

• Estimates financial impact if exception is not addressed.

Risk-based prioritization tag

• Adds risk flags to help prioritize human or agent action.

Escalation threshold logic

• Only escalates high-risk exceptions to planning team.

Confidence-based override guardrails

Prevents override actions when signal quality is too low or contradiction is detected.

Signal confidence gating

• Blocks override when demand or signal is too volatile.

Policy-based override lockout

• Prevents changes that break enterprise rules or targets.

Override simulation mode

• Tests outcome of override before applying.

Agent override rebuttal mode

• Agent flags concerns if user action reduces plan quality.

Justification library link

• Offers best-practice examples for common override types.

Multimodal interface support

Supports dashboard, mobile, email, and API-based alerting and approvals.

Multichannel alert delivery

• Supports dashboard, email, mobile, and voice alerting.

Approval action from any channel

• User can approve or route via mobile or inbox.

Alert summary compression

• Summarizes alerts into digestible messages for mobile or exec views.

Slack/MS Teams integration

• Sends alerts to preferred enterprise channels.

API-based alert access

• External tools can retrieve alerts or inject responses.