Aniruddha Biswas AI Supply Chain Intelligence
Flagship ProjectEnterprise Planning Intelligence

AI Planning Command Center

Enterprise Planning Decision Intelligence

SAP-aligned planning architecture with Joule-inspired interaction patterns

Enterprise planning teams rarely lack planning signals. MRP exceptions, demand changes, inventory positions, supply shortages and S&OP gaps are all visible somewhere — but usually in separate transactions, dashboards, spreadsheets and meetings, each with its own list and its own idea of what matters most.

The hard part is not finding one more exception. It is converting fragmented planning signals into a planning decision someone can explain afterwards. This prototype demonstrates that connected path end to end: signals are related to their underlying planning condition, translated into business exposure, presented as explainable prioritized responses, and traced back to the synthetic records behind them — with the decision itself left to the planner.

SAP-Aligned PlanningMRP IntelligenceDemand RiskInventory & ShortageS&OPJoule-Inspired AssistantHuman ReviewSynthetic Data
Synthetic data onlyRead-only prototypeHuman-reviewed decisionsNo live SAP connectionNot real SAP Joule

Business Context

Why enterprise planning decisions are hard to make well

A planning organisation can answer “what changed?” in several systems at once. It is much harder to answer why it happened, what is exposed, what the consequence is, which response deserves attention, what evidence supports it, and what should stay under human judgment.

The enterprise problem

Fragmented planning signals, unrepeatable decisions

MRP exceptions, material shortages, demand changes, inventory exposure, supply constraints and S&OP context are reviewed in separate transactions, dashboards, spreadsheets and meetings. The difficulty is structural rather than occasional.

  • Planning signals live in separate placesMRP exceptions, demand changes, inventory positions, shortage conditions and S&OP gaps are each reviewed in their own transaction, report or spreadsheet.
  • Every list has its own idea of priorityWithout a shared measure, each module ranks its own queue, and planner attention follows whichever list was opened first.
  • The cause sits one module away from the symptomA shortage in one view and a demand change in another are frequently the same problem, recognised as one only after someone connects them by hand.
  • Consequence is described, not quantifiedSeverity is discussed operationally while service, material and exposure consequences are argued separately, so trade-offs are debated rather than compared.
  • Executive translation is manual and inconsistentTurning a week of operational findings into an S&OP-ready planning context is repeated effort, rebuilt differently each cycle.
  • Responses are hard to evidence afterwardsWhen a planning response cannot be traced back to the records and logic behind it, it is difficult to review, defend or repeat.

The connected planning response

One governed path from planning signal to reviewed decision

This is not a dashboard that restates the same exception counts in a new layout, a chatbot bolted onto a planning report, or a forecasting model. It demonstrates a planning decision path, with the evidence kept attached at every step.

  • One connected planning surfaceMRP, demand, inventory and S&OP signals are normalized into one risk view rather than reconciled after the fact.
  • Consistent cross-module prioritizationSeverity, risk score and financial exposure are applied the same way across all four modules, so lists become comparable.
  • Root cause kept next to the signalEach record carries its risk type, affected object, location and period, so the planning condition behind a symptom stays reachable.
  • Conversational planning interpretationA planner can ask a cross-module question and receive a structured explanation rather than another filtered table.
  • Explainable prioritized responsesEach record carries a recommended planner action and its related SAP-style process, presented for review rather than execution.
  • Executive translation built inThe same governed planning context is expressed as S&OP-level narrative and as a structured decision pack.
  • Evidence that stays reachableEvery view resolves back to the curated synthetic planning records, so a recommendation can be followed to its source rows.

Decision Framework

Planning decision journey

Each screen in the prototype sits at a defined point in this sequence. The journey is what turns a planning signal into a decision someone can explain afterwards.

  1. Stage 01Planning signalIdentify the relevant MRP exceptions, demand signals, inventory shortages or S&OP conditions currently in scope.
  2. Stage 02Root causeConnect the signal to the underlying planning, demand, supply, material or inventory condition that produced it.
  3. Stage 03Business impactTranslate the planning condition into service, material, priority, inventory or executive exposure.
  4. Stage 04OptionsReview the possible planning responses and the considerations attached to each of them.
  5. Stage 05RecommendationPresent an explainable prioritized response prepared for planner review.
  6. Stage 06Human decisionKeep planning judgment and authority with the accountable business users.
  7. Stage 07Decision evidenceTrace the response back to synthetic planning records, risk signals, supporting logic and decision artifacts.

Functional Coverage

Planning scope

The planning domains represented in the synthetic dataset and surfaced in the experience.

MRP exception management

Materials planning exceptions with severity, risk score, affected object, location and period.

Planning module

Demand risk

Demand signals and forecast variance expressed as prioritized planning risk.

Planning module

Inventory and shortages

Inventory exposure and supply shortage conditions ranked for planner review.

Planning module

S&OP interpretation

Demand-supply gaps and service-level conditions read at planning-review level.

Planning module

Cross-module planning risk

One normalized risk view across all four modules, with severity and exposure applied consistently.

Decision layer

Conversational planning assistance

Structured, deterministic answers to cross-module planning questions.

Decision layer

Decision-pack preparation

Headline, top risks, decision items, next steps and talking points assembled for review.

Decision layer

Evidence exploration

Read-only filtering of the underlying synthetic planning records behind every view.

Reference

Primary Motion Walkthrough

Executive Planning Command Center

Cross-Module RiskSeverity & ExposureExecutive Planning Context

Business question

Where is planning exposure concentrated across MRP, demand, inventory and S&OP, and which issues deserve attention first?

Motion walkthrough of the cross-module planning command center. Values shown are from one captured synthetic scenario.

What the screen brings together

  • Total cross-module exception volume across MRP, demand, inventory and S&OP
  • Severity banding, with the critical and high subsets carried as headline measures
  • Per-module exception counts, so module exposure can be compared directly
  • Total synthetic financial exposure alongside impacted locations and planning objects
  • The current top risk module, stated explicitly rather than inferred
  • Exception distribution by module and by severity, charted side by side
  • The highest-exposure records ranked across all modules
  • A cross-module table of top risks with module, exception, risk type, object, location and severity

Decision supported

Prioritize cross-module planning exposure and determine where planner or leadership attention should begin. Each headline measure resolves into the module view behind it and, from there, into the individual synthetic planning records.

Captured scenario note

In the captured synthetic scenario the command center is tracking 630 cross-module exceptions across the four modules, spanning 8 locations and 217 planning objects, with S&OP carrying the strongest module signal.

Human-review boundary

A read-only view of synthetic planning exposure. Counts and exposure figures are outputs of one captured scenario, illustrate the decision surface, and are not a forecast or a business case.

Signature Interaction · Primary Showcase

Planning Decision Assistant

Joule-inspired conversational planning pattern

The assistant is the part of this flagship that changes how the rest of it is used. Instead of opening four module lists and reconciling them by hand, a planner asks one cross-module question and receives a structured planning explanation with the same governed context behind it.

Conversational PlanningStructured ResponseDeterministicHuman Review

Business question

How can planners interrogate cross-module planning risk and receive a structured explanation of what is happening, why it matters and what deserves review?

Motion walkthrough — a sample planning question is selected and submitted, and the structured cross-module response is read section by section.

How the interaction works

  • A set of sample planning questions is offered alongside a free-text planning question box
  • The walkthrough submits an existing sample question — “What are the top supply chain risks today?”
  • The response opens with the highest-ranked synthetic risks named individually, with module, exception and exposure
  • A confidence qualifier is displayed with the response rather than hidden
  • Each response is deterministic — the same question against the same data returns the same answer

The response is structured, not free-form

  • Executive Summary — the cross-module position in one paragraph
  • Root Cause — the dominant planning pattern behind the current signals
  • Business Impact — the locations and planning objects carried in the current risk view
  • Recommended Action — where to start, and the first record to inspect
  • Decision Items — the specific points a planner is asked to decide or confirm
  • Related SAP Process — the SAP-style planning processes the answer relates to

Decision supported

Lets a planner interrogate the whole planning position in one question and receive an explanation that can be reviewed section by section — summary, cause, impact, recommended action and the decision items that remain for a person to settle.

Joule-inspired, not SAP Joule

This independent synthetic-data prototype uses a Joule-inspired conversational planning pattern; it does not use SAP Joule or connect to a live SAP system.

Responses are produced by deterministic logic in the prototype's own service layer. The assistant does not take autonomous action, execute transactions, or write anything back to any system, and its output is planning decision support that an accountable planner reviews.

Materials Planning

MRP Exception Intelligence

MRP ExceptionsSeverity RankingPlanner Action

Business question

Which MRP exceptions require attention first, and what business context makes them important?

Motion walkthrough — MRP exception KPIs, severity distribution, and a selected record with its recommended planner action.

Key signals on the screen

  • The materials planning exception population banded into critical, high, medium and low severity
  • Exception type per record — including late purchase orders, component shortages and missing supplier confirmations
  • The affected material and its description, the plant location and the planning period
  • A risk score and financial exposure attached to each exception
  • The module's top risk type and most affected location, surfaced as headline context
  • A selected-record panel carrying the recommended planner action and related SAP-style process

Decision supported

Prioritize planning exceptions for review and focus planner attention on the issues with the strongest operational consequence, with the reason for the ranking visible next to each record.

Human-review boundary

A read-only view of synthetic MRP exceptions. It does not correct MRP, reschedule planned orders, expedite purchase orders, contact suppliers or perform any SAP transaction.

Demand Planning

Demand Risk Intelligence

Demand SignalsForecast VariancePlan Challenge

Business question

Which demand signals create meaningful planning risk, and where should planners challenge the current plan?

Motion walkthrough — demand risk KPIs, severity composition, and a selected demand signal with its recommended action.

Key signals on the screen

  • The demand risk population banded by severity, with the critical and high subsets isolated
  • Demand risk types such as demand spikes and new product ramps, stated per record
  • The affected planning object, location and period behind each demand signal
  • A risk score per record and an average score for the module
  • The module's top demand risk type and most affected location
  • A selected-record panel with the recommended planner action for that demand signal

Decision supported

Prioritize demand risks for planner review with visibility into severity, business context and planning consequence, so a plan challenge can be raised on the signals that justify it.

Human-review boundary

A read-only view of synthetic demand risk. It does not adjust a forecast, change demand plans, release consensus demand or write back to any planning system.

Inventory & Supply

Inventory & Shortage Intelligence

Inventory ExposureSupply ShortageCoverage Risk

Business question

Where do inventory positions or supply shortages threaten the current plan, and which exposures should be reviewed first?

Motion walkthrough — inventory and shortage KPIs, severity distribution, and a selected exposure with its recommended review action.

Key signals on the screen

  • The inventory and shortage population banded by severity across the module
  • Coverage and shortage risk types, including days-of-cover conditions, stated per record
  • The affected material, location and planning period behind each exposure
  • Risk score and financial exposure carried at record level and rolled up for the module
  • The module's top risk type and most affected location
  • A selected-record panel with the recommended review action and related SAP-style process

Decision supported

Prioritize inventory and shortage exposure for planner review while connecting each issue to the planning consequence that makes it worth acting on.

Human-review boundary

A read-only view of synthetic inventory and shortage exposure. It does not trigger replenishment, create procurement proposals, move stock or update any live system.

Sales & Operations Planning

S&OP Executive Decision Intelligence

Demand-Supply GapsService Level RiskExecutive Context

Business question

How should planning risks and cross-module findings be translated into a concise executive planning context?

Motion walkthrough — S&OP KPIs, gap and service-level severity composition, and a selected condition prepared for executive review.

Key signals on the screen

  • S&OP conditions banded by severity, including demand-above-supply and supply-above-demand gaps
  • Service-level risk records alongside the planning families and locations they affect
  • The planning period each S&OP condition belongs to
  • Risk score and financial exposure at S&OP level, which in the captured scenario is the largest single module contribution
  • The module's top risk type and most affected location
  • A selected-record panel carrying the recommended action for executive discussion

Decision supported

Translate operational planning signals into structured S&OP context for leadership discussion and human decision-making, rather than presenting leadership with four separate module lists.

Human-review boundary

A read-only interpretation layer. The prototype prepares S&OP context for discussion; it does not approve an S&OP plan, set consensus volumes or commit any planning decision.

Decision Handoff

Decision Pack & Evidence Handoff

The point where a planning review stops being a screen and becomes something that can be circulated, discussed and followed up.

Structured BriefingDecision ItemsExcel & PowerPointRead-Only Evidence

Business question

How can planning findings be converted into a structured artifact for executive review, communication and follow-up?

Motion walkthrough — headline, ranked cross-module top risks, decision items, recommended next steps, leadership talking points and the export controls.

What the pack assembles

  • A headline that states the current cross-module planning position
  • The ranked top risks with module, exception, risk type, location, severity and exposure
  • Decision items — the specific points put to a planner or leadership group
  • Recommended next steps, kept separate from the decision items themselves
  • Leadership talking points for the planning discussion
  • Excel and PowerPoint export of the same content as review material

Decision supported

Converts a planning review into a structured handoff — planning evidence and a decision briefing that a planner or leadership group can take into a meeting, rather than a screen that has to be re-explained each time.

Data Explorer — the supporting evidence layer

Behind the pack, a read-only explorer opens the same cross-module record set with filters for module, severity, location and record limit. It is the evidence layer that lets any figure in the pack be followed back to the synthetic rows that produced it.

Human-review boundary

Exports are review material, planning evidence and a decision briefing prepared for a structured handoff. They are not approved operational instructions, and nothing in the pack is dispatched, scheduled or written back to any system.

Functional Scope

Key capabilities

Each capability is stated with the planning decision it supports and the evidence it draws on.

Cross-module planning risk synthesis

Decision supported

Where is planning exposure concentrated, and which module deserves attention first?

Evidence used

Exception counts, severity banding, per-module distribution, financial exposure, impacted locations and impacted planning objects, drawn from one normalized cross-module risk view.

Conversational planning interpretation

Decision supported

What is happening across the plan, why does it matter, and what deserves review?

Evidence used

A deterministic assistant response structured as executive summary, root cause, business impact, recommended action, decision items and the related SAP-style planning process.

MRP exception prioritization

Decision supported

Which materials planning exceptions should a planner work first?

Evidence used

Exception ID, risk type, material and description, plant location, planning period, severity, risk score, exposure and a recommended planner action per record.

Demand risk review

Decision supported

Which demand signals justify challenging the current plan?

Evidence used

Demand risk records banded by severity with risk type, affected planning object, location, period and score, alongside the module's top risk type and location.

Inventory and shortage exposure

Decision supported

Which inventory positions or supply shortages threaten the plan first?

Evidence used

Inventory and shortage records with severity distribution, coverage-related risk types, affected materials, locations and per-record exposure.

S&OP translation and decision handoff

Decision supported

How should operational planning findings be presented for leadership discussion?

Evidence used

S&OP demand-supply and service-level records, plus a decision pack of headline, ranked top risks, decision items, recommended next steps and leadership talking points with Excel and PowerPoint export.

System Design

Architecture and data basis

Eight layers, from generated planning records through to the point where a person takes over.

  1. Layer 01Synthetic SAP-style planning dataSeeded, repeatable planning datasets that imitate the shape of SAP-style planning records. No real SAP, client, employer or customer data is present anywhere in the stack.
  2. Layer 02MRP, Demand, Inventory and S&OP source modulesFour accepted planning modules, each contributing its own exception population with module-specific risk types, planning objects, locations and periods.
  3. Layer 03Deterministic business and risk processingExplicit, repeatable rules score each record for severity, risk and financial exposure and attach a recommended planner action. The same inputs always produce the same outputs.
  4. Layer 04FastAPI normalized service layerA Python service exposes platform summary, per-module views, cross-module risk, assistant responses, decision-pack content and exports over read-only endpoints with one common record shape.
  5. Layer 05React and TypeScript executive experienceA typed single-page application presents the command center, the four module views, the planning assistant, the decision pack and the evidence explorer in a responsive planning layout.
  6. Layer 06Planning assistant and decision-pack interactionA question-driven assistant and a briefing assembler read the same governed planning context and return structured, section-based output rather than free-form prose.
  7. Layer 07Read-only evidence and export layerA filterable explorer opens the curated planning records behind any view, and Excel and PowerPoint exports package the same content as review material.
  8. Layer 08Human review and governance boundaryRecommendations stop at prepared proposals. Nothing is dispatched, written back or executed, and planning authority stays with accountable business users.

The layers are deliberately separated so each can evolve independently. No layer implies a live SAP connection, and no stage of the architecture performs an automated planning transaction.

Alignment

SAP-aligned planning patterns

What “SAP-aligned” means here, stated precisely — and what it does not mean.

SAP-style planning data and business objects

Records are shaped around planning concepts a SAP practitioner recognises — materials, plants and locations, planning periods, exception identifiers and planning objects.

Module patterns informed by enterprise practice

MRP, demand, inventory and S&OP behaviours follow patterns drawn from enterprise SAP planning practice, including exception types such as late purchase orders, component shortages, coverage breaches and demand-supply gaps.

Related planning process kept visible

Each record and assistant response carries the SAP-style planning process it relates to, so a finding can be placed in a familiar process context.

Joule-inspired conversational pattern

The assistant follows a Joule-inspired interaction concept — a structured, planning-aware response rather than a general chat surface. The pattern is conceptual only.

Adapter-shaped for future integration

Business logic is separated from the data source, so a future SAP-oriented source would be an adapter replacement rather than a rebuild.

The current prototype does not use SAP Joule and is not connected to a live SAP environment. It is not affiliated with, endorsed by or certified by SAP.

Future roadmap — not implemented

A staged, read-only-first integration path

Every stage below is a future direction for the adapter layer. Only the first stage exists today; none of the later stages is implemented, connected or in progress.

  1. Synthetic CSV · today
  2. Excel
  3. Mock OData
  4. SAP read-only OData
  5. SAP BTP destination
  6. Governed write-back simulation

Lab Standards

Explainability, governance and guardrails

The boundary between planning decision support and planning decision authority is part of the design, not a footnote.

Synthetic data only

Every planning record is generated for demonstration. No employer, client, customer or confidential operational data is present.

Independent professional prototype

Built independently as applied-AI portfolio work, outside any engagement, client deliverable or employer project.

No live SAP connection

SAP-aligned in data structure and terminology only. There is no connection to any live or sandbox SAP environment.

Not real SAP Joule

The assistant is an independent deterministic implementation of a Joule-inspired interaction pattern. It does not use SAP Joule, any Joule API, or SAP-endorsed functionality.

No autonomous execution

The prototype explains and proposes. It does not post, reschedule, reorder, release or write anything back to any system.

Human review required

Recommendations are planning decision support. An accountable planner or executive reviews and decides before any action.

Explainable by construction

Risk scores, rules and assistant responses are deterministic and inspectable rather than hidden inside an opaque model call.

Not production-validated

Not validated for production, regulatory or client use, and not a substitute for an operational planning system.

Honest Status

Current prototype maturity

Complete today

  • React and TypeScript executive planning portal
  • Deterministic FastAPI backend over normalized read-only endpoints
  • MRP exception module
  • Demand risk module
  • Inventory and supply shortage module
  • S&OP executive narrative module
  • Unified Planning Assistant with structured section-based responses
  • Decision Pack with headline, top risks, decision items, next steps and talking points
  • Read-only Data Explorer across the cross-module record set
  • Excel and PowerPoint export of the decision-pack content
  • Synthetic SAP-style planning dataset across all four modules

Intentionally not claimed

  • Live SAP connectivity or ERP integration
  • Real SAP Joule integration or Joule API usage
  • SAP endorsement, affiliation or certification
  • Autonomous planning, approval or transaction execution
  • Customer, client or employer deployment
  • Realized business savings or validated benefit cases
  • Production readiness or operational hardening

These are deliberate boundaries of a synthetic-data prototype, not pending features.

What This Demonstrates

Professional capability demonstrated

Building this flagship exercised the following, end to end.

Frame an enterprise planning problem

State the planning decision problem in terms a planning organisation recognises, rather than as a feature list.

Connect SAP planning knowledge with applied AI patterns

Express SAP-style planning objects and exception behaviour inside an applied decision-intelligence pattern.

Integrate MRP, demand, inventory and S&OP flows

Normalize four separate planning domains into one comparable risk view without losing module-specific meaning.

Build React and FastAPI decision-support architecture

Deliver the typed front end and the Python service layer that carry the experience end to end.

Design explainable conversational planning interactions

Structure an assistant response into summary, cause, impact, action and decision items so it can be reviewed rather than trusted blindly.

Maintain human-in-the-loop governance

Design the review boundary into the product rather than appending it as a disclaimer.

Translate technical capability into executive planning context

Document the prototype so a planning business audience and a technical reviewer can both follow it.

Continue through the lab

This flagship sits alongside the other synthetic-data prototypes in the Applied AI Supply Chain Lab.

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