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Performance-to-Plan Dashboard

tags: [fsd] title: "Performance-to-Plan Dashboard" sidebar_label: Performance-to-Plan Dashboard author: Pramod Prasanth​

Performance-to-Plan Dashboard

Version: 1.0
Date: September 7, 2025
Status: Draft

1. Feature Name​

Performance-to-Plan Dashboard

2. Objective & Purpose​

To provide a clear, automated, and insightful review of the previous S&OP cycle's performance. This dashboard serves as the mandatory starting point for each new cycle, fostering accountability and a culture of continuous improvement by forcing a structured discussion on key variances before new planning begins.

3. Target Users​

* S&OP Lead ("Sarah"): Uses the dashboard to facilitate the root cause analysis discussion. * The entire S&OP Team (Demand, Supply, Finance): Uses the dashboard to understand what actually happened versus the plan and to learn from past performance.

4. Functional Requirements​

4.1. Data Sources & Logic​

* The dashboard is automatically generated at the start of a new S&OP cycle. * It compares the locked "One-Number Plan" from the previous cycle (stored in PostgreSQL) against the latest "Actuals" data ingested by the Intelligent Data Ingestion & Validation Engine. * All variances are calculated automatically.

4.2. UI Components & Widgets​

* 4.2.1. Header Widget: * Cycle Title: Clearly states the period under review (e.g., "September 2025 Performance vs. Plan"). * Plan Adherence Score: A single, high-level, color-coded KPI (e.g., 88% - 🟡) that gives an immediate sense of overall performance against the plan.

* 4.2.2. Financial Waterfall Chart Widget: * Purpose: To provide an intuitive, executive-friendly view of the financial story. * Implementation: An "Intelligent Chart" that visually decomposes the variance between "Planned Gross Margin" and "Actual Gross Margin". * Components: * Starts with the Planned Gross Margin bar. * Shows intermediate green (positive) bars for favorable variances (e.g., Favorable Price/Mix). * Shows intermediate red (negative) bars for unfavorable variances (e.g., Unfavorable Volume, Higher COGS). * Ends with the Actual Gross Margin bar.

* 4.2.3. Operational Scorecard Widget: * Purpose: To show performance against the most critical operational KPIs. * Implementation: A clean, concise table comparing Plan vs. Actual for 4-5 key metrics. * Table Columns: Metric, Previous Plan, Actual Result, Variance. * Example Rows: * Forecast Accuracy (MAPE): 85% Target, 78% Actual, 🔻 -7 pts * Service Level (AAA Customers): 99.0% Target, 99.2% Actual, ✅ +0.2 pts * Production Attainment: 98% Target, 94% Actual, 🔻 -4 pts

* 4.2.4. AI-Driven Root Cause Prompts Widget: * Purpose: To "nudge" the team toward a productive, forward-looking discussion instead of just reviewing numbers. * Implementation: The AI Insight Engine analyzes the largest variances from the scorecard and generates 1-2 natural language prompts. * Example Prompt: "The largest negative impact on margin came from Forecast Accuracy, which missed its target by 7 points. The original plan was based on the assumption of a 5% promotional lift, but actuals show the lift was closer to 12%. Was this a one-off event, or do we need to systemically adjust our promotional lift factors for future planning?"

5. User Journey​

1. The S&OP team joins the Demand Review meeting. The Performance-to-Plan Dashboard is the first screen they see. 2. The facilitator ("Sarah") uses the high-level Plan Adherence Score and Financial Waterfall Chart to frame the discussion. 3. She then directs the team to the AI-Driven Root Cause Prompts to begin a structured root cause analysis of the most significant variance. 4. Once the discussion is complete, the facilitator clicks an "Acknowledge & Proceed" button. The system logs that the review has been completed, and the team can then move to the Demand Consensus Workbench to begin the new plan.

6. Dependencies​

* A locked "One-Number Plan" from the previous cycle must exist in the PostgreSQL database. * The Intelligent Data Ingestion & Validation Engine must have processed the "Actuals" data for the completed period. * The AI Insight Engine is required to generate the root cause prompts.