Taming the ERP Hydra: A UX Framework for Consolidating Supply Chain Planning Views
Here's what happened at a global automotive parts supplier:
9:14 AM: A customer (major automaker) calls asking: "Can you deliver 50,000 units of Part #AX-4472 by March 25th instead of March 31st?"
Simple question. Should take 2 minutes to answer.
Here's what the supply chain planner has to do:
Step 1: Check current inventory (SAP MM - Material Management module)
- Log into SAP
- Navigate to transaction code MM03
- Search for Part #AX-4472
- Note: 32,000 units on hand
Step 2: Check scheduled receipts (SAP PP - Production Planning module)
- Exit MM03
- Navigate to transaction code MD04
- Review MRP list
- Note: 15,000 units scheduled to complete March 20th
Step 3: Check raw material availability (Separate procurement system)
- Log out of SAP
- Log into Oracle Procurement Cloud
- Search for 8 raw materials needed for Part #AX-4472
- Note: 2 materials on backorder (delivery March 22nd)
Step 4: Check production capacity (Manufacturing Execution System - MES)
- Exit Oracle
- Open MES system
- Navigate to capacity planning dashboard
- Note: Line 3 available starting March 21st
Step 5: Check competing orders (Back to SAP SD - Sales & Distribution)
- Reopen SAP
- Navigate to transaction code VA05
- Filter by Part #AX-4472
- Note: 3 other customers have orders scheduled for delivery March 28-30th
Step 6: Calculate total available-to-promise
- Open Excel
- Manually enter data from 5 different systems
- Build formula: (Inventory + Scheduled Receipts) - (Committed Orders)
- Result: Can deliver 48,000 by March 25th (2,000 short)
Total time to answer: 43 minutes
Number of systems accessed: 5
Number of screens/transactions: 12
Excel spreadsheets opened: 2
Risk of error: High (manual data transcription across 5 systems)
The customer's response: "That's too long. We'll ask your competitor."
Cost of the delay: $2.8M in lost revenue (annual contract value)
The Fragmentation Nightmare
This is the reality of enterprise supply chain planning in 2025.
The problem isn't the data. Modern ERP systems (SAP, Oracle, Microsoft Dynamics, Infor) collect everything:
- Inventory levels (real-time)
- Production schedules (hourly updates)
- Supplier lead times (historical + predictive)
- Demand forecasts (ML-powered)
- Capacity constraints (equipment, labor, materials)
The problem is the interface.
To answer the simplest planning question—"Can we fulfill this order?"—planners must:
- Access 5-10 different systems
- Navigate 15-20 different screens
- Manually reconcile conflicting data
- Build Excel models to synthesize the information
- Hope nothing changed while they were collecting the data
This is the ERP Hydra:
Can We Fulfill This Order?
↓
┌───────────────┴───────────────┐
│ │
┌────┴────┐ ┌────┴────┐
│ Inventory│ │Production│
│ (SAP MM) │ │ (SAP PP) │
└────┬────┘ └────┬────┘
│ │
┌───────┴───────┐ ┌───────┴───────┐
│ On-Hand (WMS) │ │Capacity (MES) │
└───────────────┘ └───────────────┘
│
┌───────┴───────┐
│ │
┌────┴────┐ ┌────┴────┐
│Raw Mat. │ │Committed│
│(Oracle) │ │Orders │
└─────────┘ │(SAP SD) │
└─────────┘
Every question requires data from 4-8 "heads" of the Hydra.
And unlike the mythical Hydra, cutting off one head (replacing one system) doesn't help—two more grow back (integrations break, data migrations fail, user adoption tanks).
The Cost of Fragmentation
Let's quantify the business impact:
Cost #1: Decision Lag
Time to answer planning questions:
- Simple questions ("Can we fulfill this order?"): 30-60 minutes
- Complex questions ("Which orders should we prioritize if raw material is delayed?"): 4-8 hours
- Strategic questions ("How do we optimize production across 3 plants?"): 2-5 days
Impact:
- Lost sales (customers can't wait 45 minutes for a quote)
- Missed optimization opportunities (by the time the analysis is done, conditions have changed)
- Planner burnout (spending 60% of time data gathering instead of decision-making)
Cost #2: Manual Data Reconciliation
Average supply chain planner spends:
- 40% of time gathering data from multiple systems
- 30% of time reconciling conflicting data (SAP says 32,000 units, WMS says 31,400)
- 20% of time building Excel models
- 10% of time actually making decisions
Annual cost per planner:
- Salary + benefits: $95K/year
- Time spent on manual work: 70% × $95K = $66,500/year of waste
- For a team of 12 planners: $798,000/year
Cost #3: Errors and Expediting
When planners work with fragmented, manually transcribed data:
- 8-12% error rate (wrong numbers copied, stale data, missed updates)
- Each error triggers expediting (rush orders, air freight, overtime production)
- Average expediting cost: $15,000/incident
Annual expediting cost (typical mid-size manufacturer): $1.8M - $3.5M
Why You Can't Just "Replace the ERP"
The obvious solution seems to be: "Replace the legacy ERP with a modern, integrated system."
This almost never works.
Why ERP replacements fail:
- Cost: $5M - $50M for mid-to-large enterprises
- Duration: 18-36 months
- Risk: 60-70% of ERP implementations fail or severely underperform
- Complexity: 15-20 years of business logic embedded in the current system
- Customization debt: Every company has 500+ customizations that must be recreated
- Organizational disruption: Retraining 500+ users, changing processes, managing change resistance
Example: Famous ERP Replacement Failures
| Company | ERP Project | Cost | Outcome |
|---|
| Lidl (German retailer) | SAP replacement | $500M | Abandoned after 7 years |
| Nike | SAP upgrade | $400M | $100M quarterly revenue loss |
| HP | ERP consolidation | $160M | Blamed for 5% revenue decline |
| Hershey | SAP implementation | $112M | Unable to ship candy for Halloween |
The reality: ERP systems are the foundation. Replacing the foundation while the building is occupied is nearly impossible.
Better approach: Build a UX layer on top of the existing systems.
Don't replace the Hydra. Build a cockpit that tames it.
The Unified Planning Cockpit Philosophy
Here's the shift:
Stop trying to replace the ERP.
Start building a unified planning interface that consolidates the 3-4 critical metrics planners need for 80% of decisions.
The Unified Planning Cockpit is:
- A read-only visualization layer (doesn't replace the ERP)
- Built on API integrations (pulls data from SAP, Oracle, MES, WMS, etc.)
- Designed for decision velocity (answer questions in 30 seconds, not 30 minutes)
- Drill-down capable (one click to access detailed historical data in the source system)
Visual Concept:
┌─────────────────────────────────────────────────────────┐
│ Unified Planning Cockpit │
│ Part #AX-4472: Automotive Suspension Component │
├─────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ INVENTORY │ │ PRODUCTION │ │ DEMAND │ │
│ │ │ │ │ │ │ │
│ │ 32,000 │ │ 15,000 │ │ Weekly: │ │
│ │ units │ │ units │ │ 12,500 │ │
│ │ │ │ (Due 3/20) │ │ │ │
│ │ 🟢 Healthy │ │ 🟡 Delayed │ │ 🟢 Stable │ │
│ │ │ │ (2 days) │ │ │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │
│ ┌─────────────────────────────────────────────────┐ │
│ │ Available to Promise (ATP) │ │
│ │ │ │
│ │ Next 30 Days: 48,000 units │ │
│ │ │ │
│ │ ██████████████████░░░░░░ 78% of demand │ │
│ │ │ │
│ │ ⚠️ Constraint: Raw Material B (backorder 3/22)│ │
│ │ │ │
│ └─────────────────────────────────────────────────┘ │
│ │
│ Customer Request: 50,000 units by March 25th │
│ │
│ ⚠️ CAN FULFILL 48,000 (96%) - 2,000 units short │
│ │
│ [VIEW DETAILED BREAKDOWN] [SCENARIO ANALYSIS] │
│ │
└─────────────────────────────────────────────────────────┘
Key Design Principles:
- Single Screen: All critical data visible at once (no tab-switching)
- Visual Hierarchy: Most important metrics (ATP) dominate the screen
- Traffic Light System: 🟢 Healthy, 🟡 Warning, 🔴 Critical
- Constraint Visibility: The limiting factor is called out immediately
- Action-Oriented: Answer is clear: "Can fulfill 48,000 (96%)"
- Drill-Down Available: One click to see detailed breakdown
Time to answer: 15 seconds (vs. 43 minutes with fragmented systems)
Phase 1: Critical Data Mapping
The first step in building a Unified Planning Cockpit is identifying the 4 Core Planning Metrics that answer 80% of planning questions.
The 80/20 Rule for Supply Chain Planning
Most planning questions fall into 4 categories:
- Can we fulfill this order? (Availability)
- When can we deliver? (Lead time)
- Should we prioritize this order over another? (Demand/capacity)
- What's blocking fulfillment? (Constraints)
To answer these questions, you need 4 core metrics:
| Metric | What It Tells You | Source System |
|---|
| Inventory On-Hand | How much do we have right now? | SAP MM, Oracle WMS |
| Scheduled Receipts | How much will we have soon? | SAP PP, MES |
| Committed Orders | How much is already promised? | SAP SD, CRM |
| Lead Time Variance | Are we running late or early? | SAP PP, MES, SRM |
These 4 metrics enable calculation of:
Available to Promise (ATP) =
(Inventory On-Hand + Scheduled Receipts)
- (Committed Orders)
± (Lead Time Variance Impact)
Mapping the Hydra: Where Does Each Metric Come From?
Challenge: Each metric often exists in 2-4 different systems, with slight variations.
Example: Inventory On-Hand for Part #AX-4472
| System | Value | Why Different? |
|---|
| SAP MM | 32,000 units | "Book" inventory (what should be there) |
| Warehouse Management System | 31,400 units | Physical inventory (what's actually there) |
| MES | 31,850 units | Includes WIP on the production line |
| Quality System | 30,900 units | Excludes 500 units on hold for QC |
Which number is "correct"?
It depends on the question:
- For customer promising: Use Quality System value (30,900) — only ship approved inventory
- For production planning: Use MES value (31,850) — includes WIP that will be available soon
- For financial reporting: Use SAP MM value (32,000) — matches books
The Unified Planning Cockpit must:
- Document the source of truth for each use case
- Flag discrepancies when systems disagree by >5%
- Show the reconciliation logic ("30,900 sellable, 950 in QC hold")
Design Pattern: Metric Source Transparency
Visual Design:
┌─────────────────────────────────────────────────┐
│ Inventory On-Hand: 30,900 units │
├─────────────────────────────────────────────────┤
│ │
│ Source: Quality Management System (QMS) │
│ Last Updated: 2025-03-12 09:05:14 EST │
│ │
│ ⚠️ Variance Detected: │
│ │
│ • SAP MM: 32,000 units (+3.6%) │
│ • WMS: 31,400 units (+1.6%) │
│ • QMS: 30,900 units (authoritative) │
│ │
│ Reconciliation: │
│ 32,000 (book) - 600 (shrinkage) - 500 (QC) │
│ = 30,900 (available) │
│ │
│ [VIEW FULL RECONCILIATION REPORT] │
│ │
└─────────────────────────────────────────────────┘
Benefits:
- Trust: Planners know which system is authoritative
- Error Detection: 3.6% variance flags potential data quality issues
- Auditability: Clear reconciliation logic for finance/compliance
- Freshness: Timestamp shows data is current (not stale)
Phase 2: Designing for Decision Velocity
The second phase is designing the interface for maximum decision velocity.
Design Goal: Planner should be able to answer a planning question in <30 seconds (vs. 30-60 minutes today).
Problem: Planners waste time interpreting numbers. Is 32,000 units good or bad? Depends on demand, lead time, variability.
Solution: Use color-coded conditional formatting to show status at a glance.
Visual Design:
┌─────────────────────────────────────────────────┐
│ Part #AX-4472: Inventory Health │
├─────────────────────────────────────────────────┤
│ │
│ 🟢 HEALTHY │
│ │
│ On-Hand: 30,900 units │
│ Safety Stock: 18,000 units │
│ Coverage: 2.5 weeks │
│ │
│ Status: Inventory exceeds safety stock by 72% │
│ │
└─────────────────────────────────────────────────┘
vs.
┌─────────────────────────────────────────────────┐
│ Part #BX-8832: Inventory Health │
├─────────────────────────────────────────────────┤
│ │
│ 🔴 CRITICAL │
│ │
│ On-Hand: 2,400 units │
│ Safety Stock: 12,000 units │
│ Coverage: 0.4 weeks (3 days) │
│ │
│ Status: Below safety stock by 80% │
│ Risk of stockout in 3 days │
│ │
│ [EXPEDITE ORDER] [ALLOCATE FROM PLANT 2] │
│ │
└─────────────────────────────────────────────────┘
Conditional Formatting Rules:
| Status | Condition | Color | Action Required |
|---|
| 🟢 Healthy | Inventory > 150% of safety stock | Green | None |
| 🟡 Warning | Inventory 80-150% of safety stock | Yellow | Monitor closely |
| 🔴 Critical | Inventory < 80% of safety stock | Red | Expedite replenishment |
Benefits:
- Instant comprehension: Planner sees 🔴 and knows immediate action needed
- Prioritization: Focus on red items first
- Reduced cognitive load: No mental math to determine if 2,400 units is good or bad
Design Pattern 2: Sparklines for Trend Visibility
Problem: Numbers without context are meaningless. Is demand increasing or decreasing? Is lead time getting worse?
Solution: Use sparklines (tiny inline charts) to show trend without requiring a separate graph view.
Visual Design:
┌─────────────────────────────────────────────────┐
│ Part #AX-4472: Production Lead Time │
├─────────────────────────────────────────────────┤
│ │
│ Current: 12 days 🟡 (+2 days vs. target) │
│ │
│ Trend: 8 ▁▂▂▃▅▆█ 12 (Last 7 weeks) │
│ │
│ Analysis: Lead time increasing 8% per week │
│ Root cause: Supplier delays (Vendor 4472)│
│ │
│ [VIEW DETAILED TREND] [CONTACT SUPPLIER] │
│ │
└─────────────────────────────────────────────────┘
Sparkline Benefits:
- Trend direction visible at a glance: ▁▂▂▃▅▆█ clearly shows increasing trend
- No screen real estate cost: Fits in one line
- Context for the current number: 12 days is bad because it's trending worse (was 8 days)
When to use sparklines:
- Lead time variance: Increasing or decreasing?
- Demand patterns: Seasonal spikes?
- Inventory levels: Stable or volatile?
- Supplier performance: Improving or deteriorating?
Design Pattern 3: Constraint Visibility
Problem: Even if all the data is visible, planners still have to figure out what's blocking fulfillment.
Solution: Automatically identify and surface the limiting constraint.
Example:
┌─────────────────────────────────────────────────┐
│ Can We Fulfill 50,000 Units by March 25th? │
├─────────────────────────────────────────────────┤
│ │
│ ⚠️ CAN FULFILL 48,000 UNITS (96%) │
│ │
│ Limiting Constraint: │
│ 🔴 Raw Material B (Steel Alloy) │
│ │
│ Issue: 8,000 lbs on backorder │
│ Expected delivery: March 22nd │
│ Required for: 2,000 units production │
│ │
│ Impact: Cannot start production for additional│
│ 2,000 units until 3/22, which won't │
│ complete until 3/27 (2 days late) │
│ │
│ Options: │
│ 1. Accept partial fulfillment (48,000) │
│ 2. Expedite raw material ($4,500 air freight) │
│ 3. Substitute from alternate supplier (+$8K) │
│ │
│ [SCENARIO COMPARISON] │
│ │
└─────────────────────────────────────────────────┘
Constraint Identification Logic:
function identifyConstraint(order) {
const constraints = [];
if (inventory < order.quantity) {
constraints.push({
type: 'inventory',
shortfall: order.quantity - inventory,
severity: 'high',
});
}
const requiredCapacity = (order.quantity - inventory) * unitsPerHour;
if (availableCapacity < requiredCapacity) {
constraints.push({
type: 'capacity',
shortfall: requiredCapacity - availableCapacity,
severity: 'medium',
});
}
for (const material of requiredMaterials) {
if (material.onHand < material.required) {
constraints.push({
type: 'raw_material',
material: material.name,
shortfall: material.required - material.onHand,
expectedDelivery: material.nextDelivery,
severity: 'critical',
});
}
}
return constraints.sort((a, b) =>
severityScore(b.severity) - severityScore(a.severity)
)[0];
}
Benefits:
- Immediate root cause: Planner doesn't have to detective-work through multiple systems
- Quantified impact: "2,000 units short" is actionable
- Options presented: Not just the problem, but potential solutions
- Decision support: Cost/benefit of each option shown
Design Pattern 4: The Drill-Down Contract
The Cockpit Principle:
The consolidated view provides the insight. A single click provides the detailed historical context from the underlying legacy system.
Why this matters:
The Unified Planning Cockpit is not a replacement for the ERP. It's a navigation layer.
For 80% of questions, the cockpit provides the answer. For the other 20%, planners need to drill into the source system for detailed history, audit trails, or to make transactional changes.
Visual Design:
┌─────────────────────────────────────────────────┐
│ Production Schedule: Part #AX-4472 │
├─────────────────────────────────────────────────┤
│ │
│ Scheduled Receipts (Next 30 Days): 47,000 │
│ │
│ ┌─────────────────────────────────────────┐ │
│ │ Week of 3/18: 15,000 units 🟡 (2 days late)│
│ │ Week of 3/25: 18,000 units 🟢 (on time) │
│ │ Week of 4/1: 14,000 units 🟢 (on time) │
│ └─────────────────────────────────────────┘ │
│ │
│ 🟡 Week of 3/18 is delayed due to: │
│ - Raw material delay (2 days) │
│ - Supplier: Acme Steel (Vendor #4472) │
│ │
│ [📊 VIEW IN SAP PP (MD04)] │
│ [📧 EMAIL SUPPLIER] │
│ [📅 RESCHEDULE PRODUCTION] │
│ │
└─────────────────────────────────────────────────┘
[WHEN USER CLICKS "VIEW IN SAP PP"]
→ System opens SAP transaction MD04
→ Pre-filtered to Part #AX-4472
→ Highlighted: Production order scheduled for 3/18
→ User can now:
- View full MRP list
- See all production orders (past and future)
- Modify production schedule
- Access audit trail
Key Design Decisions:
- Deep linking: Don't just open SAP—open the exact screen and filter to the relevant item
- Context preservation: When user returns to cockpit, they return to the same view (no lost state)
- Bi-directional sync: If user makes changes in SAP, cockpit refreshes automatically
- Audit trail: Log when users drill down (for compliance)
Benefits:
- Fast exploration: 15 seconds to check status, 1 click to get full details
- No data duplication: Cockpit doesn't try to replicate all ERP functionality
- Minimal training: Planners still use familiar ERP systems for detailed work
- Lower risk: Not replacing the ERP, just adding a navigation layer
Real-World Case Study: Automotive Tier 1 Supplier
Company: Global automotive parts manufacturer (5 plants, 18,000 SKUs, $1.2B revenue)
Problem:
- Supply chain planners (team of 18) spent 65% of time gathering data from 7 systems
- Average time to quote customer on expedited order: 4.2 hours
- Missed revenue opportunities: $12M/year (customers went to competitors due to slow response)
- Expediting costs: $3.8M/year (due to poor visibility into constraints)
Solution: Unified Planning Cockpit
Implementation:
- API integrations with SAP (MM, PP, SD), Oracle WMS, MES, SRM
- 4 core metrics: Inventory, Scheduled Receipts, Committed Orders, Lead Time Variance
- Traffic light conditional formatting
- Sparklines for trend visibility
- Automated constraint identification
- Drill-down links to source systems
Architecture:
┌─────────────────────────────────────────────────┐
│ Unified Planning Cockpit (React + D3.js) │
│ (Read-only visualization layer) │
└─────────────────┬───────────────────────────────┘
│
↓
┌─────────────────────────────────────────────────┐
│ Integration Layer (Apache Kafka + REST APIs) │
└──┬─────┬─────┬─────┬─────┬─────┬──────────────┘
│ │ │ │ │ │
↓ ↓ ↓ ↓ ↓ ↓
┌────┐ ┌────┐┌───┐┌───┐┌───┐┌────┐
│SAP │ │SAP ││SAP││WMS││MES││SRM │
│MM │ │PP ││SD ││ ││ ││ │
└────┘ └────┘└───┘└───┘└───┘└────┘
Results (After 12 Months):
| Metric | Before | After | Change |
|---|
| Time to Quote Expedited Order | 4.2 hours | 8 minutes | -98% |
| Planner Time on Data Gathering | 65% | 18% | -72% |
| Missed Revenue (Slow Response) | $12M/year | $1.4M/year | -88% |
| Expediting Costs | $3.8M/year | $1.1M/year | -71% |
| Customer Satisfaction (NPS) | +12 | +47 | +292% |
| Planner Satisfaction | 4.2/10 | 8.9/10 | +112% |
ROI Calculation:
Investment:
- Cockpit development (React, D3.js): $280K
- API integration layer (Kafka, REST): $420K
- Deployment + training: $85K
- Total: $785K
Annual Benefit:
- Recovered revenue: $10.6M/year
- Reduced expediting: $2.7M/year
- Total: $13.3M/year
Payback Period: 21.7 days
3-Year ROI: 4,979%
CEO Quote:
"For 20 years, our planners have been drowning in SAP screens. We tried to replace SAP twice—failed both times, spent $8M. This cockpit cost us less than $1M and solved the problem in 6 months. Our planners are finally doing strategic work instead of data archaeology."
Design Checklist: Building a Unified Planning Cockpit
Phase 1: Discovery (Weeks 1-4)
✓ Identify Core Planning Questions
✓ Map Data Sources
✓ Quantify Current State
Phase 2: UX Design (Weeks 5-8)
✓ Core Metrics Dashboard
✓ Drill-Down Navigation
✓ Responsive Design
Phase 3: Integration Architecture (Weeks 9-14)
✓ API Layer
✓ Data Reconciliation
✓ Security
Phase 4: Pilot (Weeks 15-18)
✓ Pilot Team
✓ Refinement
Phase 5: Rollout (Weeks 19-24)
✓ Training
✓ Phased Rollout
✓ Monitor Adoption
Advanced Patterns: Beyond the Basics
Pattern 1: Scenario Analysis
Use Case: "What if raw material is delayed 5 days? Which orders are at risk?"
Design:
┌─────────────────────────────────────────────────┐
│ Scenario Analysis: Raw Material Delay │
├─────────────────────────────────────────────────┤
│ │
│ Scenario: Raw Material B delayed 5 days │
│ (New ETA: March 27th instead of March 22nd) │
│ │
│ Impact Analysis: │
│ │
│ 🔴 4 Orders at Risk (Total: 22,000 units) │
│ │
│ Order #8847: 50,000 units (due 3/25) │
│ Impact: Can fulfill 48,000 (96%) │
│ Shortfall: 2,000 units (3 days late) │
│ │
│ Order #8848: 15,000 units (due 3/28) │
│ Impact: Can fulfill 0 (0%) │
│ Shortfall: 15,000 units (5 days late) │
│ │
│ [... 2 more orders ...] │
│ │
│ Total Revenue at Risk: $2.8M │
│ │
│ Mitigation Options: │
│ 1. Expedite material ($18K air freight) │
│ 2. Allocate from Plant 2 inventory │
│ 3. Negotiate delivery extensions with customers│
│ │
│ [COMPARE SCENARIOS] │
│ │
└─────────────────────────────────────────────────┘
Benefits:
- Proactive risk management: Identify problems before they happen
- Quantified impact: "$2.8M revenue at risk" gets executive attention
- Decision support: Compare mitigation options (cost vs. benefit)
Pattern 2: Multi-Plant Optimization
Use Case: "Should we fulfill Order #8847 from Plant 1 or Plant 2?"
Design:
┌─────────────────────────────────────────────────┐
│ Multi-Plant Fulfillment Optimization │
│ Order #8847: 50,000 units (Part #AX-4472) │
├─────────────────────────────────────────────────┤
│ │
│ Option 1: Plant 1 (Ohio) 🟡 Recommended │
│ ───────────────────────────────────────────── │
│ Inventory: 30,900 units │
│ Production: 15,000 units (due 3/20) │
│ Capacity: Available │
│ Shipping: 2 days (customer is 150 mi away) │
│ Total Lead Time: 15 days │
│ Cost: $42,500 │
│ │
│ Option 2: Plant 2 (Mexico) 🟢 Lower Cost │
│ ───────────────────────────────────────────── │
│ Inventory: 42,000 units │
│ Production: Not required │
│ Capacity: N/A │
│ Shipping: 5 days (customer is 800 mi away) │
│ Total Lead Time: 5 days │
│ Cost: $38,200 │
│ │
│ 🔴 CONSTRAINT: Plant 2 inventory committed │
│ to Order #8850 (larger customer) │
│ │
│ Recommendation: Fulfill from Plant 1 │
│ Reason: Plant 2 inventory already allocated │
│ │
│ [SCENARIO COMPARISON] [ALLOCATE NOW] │
│ │
└─────────────────────────────────────────────────┘
Benefits:
- Cost optimization: Planner sees all options, ranked by cost
- Constraint-aware: System flags when "optimal" solution isn't feasible
- Strategic allocation: Considers existing commitments (don't rob Peter to pay Paul)
Pattern 3: Predictive Alerts
Use Case: "Alert me 3 days before we're likely to stock out."
Design:
┌─────────────────────────────────────────────────┐
│ 🔔 Predictive Alert │
├─────────────────────────────────────────────────┤
│ │
│ Part #BX-8832: Predicted Stockout │
│ │
│ Current Inventory: 8,400 units │
│ Burn Rate: 2,800 units/day │
│ Predicted Stockout: March 15th (3 days) │
│ │
│ Confidence: 87% (based on 12 weeks history) │
│ │
│ Next Scheduled Receipt: 15,000 units (3/18) │
│ ⚠️ Too late (3 days after stockout) │
│ │
│ Recommended Action: │
│ Expedite production (move up from 3/18 to 3/14)│
│ Cost: $3,500 (overtime + expedited materials) │
│ │
│ [EXPEDITE PRODUCTION] [SNOOZE ALERT] │
│ │
└─────────────────────────────────────────────────┘
Benefits:
- Proactive intervention: Fix problems before they become crises
- ML-powered forecasting: More accurate than static safety stock calculations
- Cost transparency: Planner knows cost of expediting vs. cost of stockout
Metrics: Measuring Cockpit Effectiveness
Metric 1: Time to Answer
Definition: Average time from question to answer
Before (Fragmented Systems): 30-60 minutes
After (Unified Cockpit): <30 seconds (for 80% of questions)
Target: 95%+ of questions answered in <60 seconds
Metric 2: System Context Switches
Definition: Number of different systems accessed to answer a question
Before: 5-8 systems
After: 1 system (cockpit) + optional drill-down
Target: 80%+ of questions answered without leaving cockpit
Metric 3: Decision Quality
Definition: % of decisions that achieve optimal outcome (cost, speed, quality)
Measurement: Retroactive analysis (did we choose the best plant? Did we expedite correctly?)
Before: 62% (many suboptimal decisions due to incomplete data)
After: 91% (better visibility into constraints and options)
Target: 85%+
Metric 4: Planner Productivity
Definition: % of planner time spent on strategic work (vs. data gathering)
Before: 30% strategic, 70% data gathering
After: 75% strategic, 25% data gathering
Target: 70%+ strategic
Conclusion: Governance Over Chaos
Here's the fundamental truth about enterprise supply chain planning:
The ERP Hydra will never be slain. It's too big, too complex, too embedded in the business.
But it can be tamed.
The Unified Planning Cockpit strategy:
- Don't replace the ERP. Build a UX governance layer on top.
- Focus on the 4 core metrics that answer 80% of planning questions.
- Design for decision velocity: Traffic lights, sparklines, constraint identification.
- Maintain the drill-down contract: Cockpit provides insight, source systems provide details.
The ROI:
- 98% faster time to answer (4.2 hours → 8 minutes)
- 72% reduction in data gathering time (65% → 18% of planner time)
- 88% reduction in missed revenue ($12M → $1.4M/year)
- 71% reduction in expediting costs ($3.8M → $1.1M/year)
The result:
Supply chain planners who spend 75% of their time making strategic decisions (instead of 70% of their time hunting for data in SAP).
Because in supply chain planning, velocity is value.
Want to learn more about designing interfaces for complex enterprise systems?
Have you designed interfaces for enterprise planning systems? What strategies have you used to consolidate fragmented data sources and reduce decision lag?
