Pre-Build Spec — OPM6090 Module 6
Status: Approved — retrospective as-built documentation
Generated by: Nexus (Claude Code)
Approved by: Fadl Altarzi (LXD) — 2026-04-28
Build version: v2.5
Date: 2026-04-28
1. Assessment Identity
- Course: OPM6090 — Technology and Operations Management
- Module: 6
- Company: ABB (ABB) — UK Advanced Industries manufacturing division
- Version string: v2.5 (last updated 2026-04-27)
- Route:
/assessments/opm6090/module-6
- Program / degree level: MBA / Masters
2. Step Structure
| # |
Step ID |
Label |
Type |
Lock dependency |
showScenario |
| 1 |
scenario |
Company Profile |
Read-only overview |
None |
no |
| 2 |
portfolio |
Stage 1 — BSC |
MCQ (5 questions) |
Step 1 complete |
yes |
| 3 |
waste |
Stage 2 — TPS |
MCQ (3 questions) |
Step 2 complete |
yes |
| 4 |
analyst-qa |
Analyst QA |
SpotTheError + free text (≥20 words) |
Step 3 complete |
no |
| 5 |
roadmap |
Stage 3 — Roadmap |
Phase selection MCQ + written brief (≥50 words) |
Step 4 complete |
yes |
3. MCQ Inventory
Step 2 — Stage 1: BSC Portfolio Assessment
- Questions: 5
- Category labels: Financial Perspective, Customer Perspective, Internal Process Perspective, Learning & Growth Perspective, Technology Strategy — AI Readiness
- CLO targets: CLO-1, CLO-4
Step 3 — Stage 2: TPS Waste Audit
- Questions: 3
- Category labels: Seven Wastes — Classification, JIT Principle — Pull vs. Push, TPS — Priority Intervention
- CLO targets: CLO-2
4. AI Question
- Step: Stage 1 — BSC (Step 2), question 5 of 5
- Category label: Technology Strategy — AI Readiness
- Badge color: Cyan
- Question angle: OEM customers mandate AI-driven predictive maintenance by 2028. Evaluates whether the existing IoT data stream (4.2M points/day, currently unlabeled) is ready to train a supervised ML model — correct answer is no, because unlabeled sensor data cannot train a reliable predictive model. Prerequisite is Jidoka trigger activation and ERP production event tracking, which simultaneously improves operations and creates labeled training data.
- LD approval status: Approved (retrospective — included in build)
5. Media Elements
- ScenarioOverview chart:
BarChart (Recharts) showing OEE, On-time delivery, Defect Rate, Lead Time, ERP Adoption — pre-investment vs target vs current. ResponsiveContainer height 260px.
- SpotTheError:
BarChart — on-time delivery across 4 periods (Pre-investment: 74%, Post Q1: 71%, Post Q2: 73%, Post Q3: 76%). Deliberate flaw: Y-axis starts at 68 (not 0), making a 2-percentage-point recovery appear as near-total turnaround. Chart title: "Strong recovery in on-time delivery following technology investment."
- Analyst QA context: "Junior Operations Analyst — ABB UK Advanced Industries" prepared the chart for "the plant director"
- Industry context panel: 4 items (see Point 6)
- Performance metrics table: 5 rows — OEE, on-time delivery, defect rate, lead time, ERP adoption — with pre/target/current columns and red indicator on current values
6. Industry Context Panel
| # |
Label |
Description |
| 1 |
AI supplier mandates |
ABB's two largest automotive OEM customers have announced sourcing criteria requiring Tier 1 suppliers to hold certified AI-driven predictive maintenance capability by 2028. |
| 2 |
Semiconductor lead times |
Control electronics components face 28–36 week lead times. Unplanned production stoppages now carry significantly higher recovery costs — making defect prevention a financial imperative. |
| 3 |
Aerospace delivery contracts |
Key aerospace customers have tightened contract terms: on-time delivery below 78% triggers automatic penalty clauses. ABB's current 71% rate puts it inside the penalty zone. |
| 4 |
Competitive OEE benchmarks |
ABB's European Tier 1 manufacturing peers report 12–15% OEE improvements following AI-augmented IoT integration. Plants operating below 75% OEE are increasingly viewed as structurally uncompetitive. |
7. Written Brief
- Step label: Stage 3 — Roadmap
- Prompt text: "Write your operational excellence recommendation to ABB's division leadership — integrating your technology adoption diagnosis, TPS waste elimination plan, and implementation sequencing rationale into a single consulting brief."
- Minimum word count: 50
- CLO targets: CLO-3, CLO-4
- Grading section ID:
consulting-roadmap
- Grading section label: "Operational Excellence Recommendation"
8. Company Profile
- Name: ABB
- Ticker: ABB
- Sector: Precision manufacturing — automotive & aerospace
- Exchange: NYSE / SIX Swiss Exchange
- Description (as rendered): "ABB's UK Advanced Industries manufacturing division (440 employees) supplies automotive and aerospace OEMs across Europe. Over the past 18 months, the division invested £2.3M across three technology platforms targeting a step-change in operational performance: OEE improvement from 68% to 82%, production lead time reduction from 4.2 to 2.5 days, and defect elimination below 1.0%. Despite full deployment across all three platforms, OEE has declined to 65%, lead times have increased to 4.8 days, and on-time delivery continues to fall."
- Source:
docs/simulation-data/global_simulation_company_database.json (entry C0492 — all operational figures are plausible fiction, not real ABB data)
- Scenario adjustments: UK Advanced Industries division (fictional subdivision of ABB); 440 employees, £2.3M technology investment, and all operational figures are plausible fiction.
9. Grading Configuration
- MCQ weight: 0.75
- Written weight: 0.25
- Degree level: Masters
- Mastery threshold: ≥90%
- Developing threshold: ≥80% (pillar-level for Masters)
- CLO demonstrated threshold: ≥80%
- Pillar weights: Domain 55%, Reasoning 25%, Contribution 20%
- CLO → pillar mapping:
- CLO-1 → Domain
- CLO-2 → Domain
- CLO-3 → Reasoning + Contribution
- CLO-4 → Domain + Reasoning (formula: domain × 0.6875 + reasoning × 0.3125)
10. Compliance Checklist
- ✅ Step locking (
isAccessible pattern with QA bypass ?qa=nxf-qa-8f2k)
- ✅ ← Back button on every step except Step 1
- ✅ Ticker pill in page header
h1
- ✅ US English spelling throughout
- ✅ Warm learner-facing tone
- ✅ Version string in breadcrumb (
v2.0)
- ✅ No external logos
- ✅ AI question included and LD-approved (retrospective)
- ✅ Recharts chart in ScenarioOverview (
ResponsiveContainer)
- ✅ SpotTheError component with amber header and 20-word minimum
- ✅ Analyst QA step between Stage 2 TPS and Stage 3 Roadmap
- ✅ Industry context panel (4 items) in ScenarioOverview
- ✅ Analyst QA response wired to grading API as written section
- ✅ PRD.md and SPEC.md committed alongside assessment code
Approval notes
Retrospective documentation. Key decisions that would have required pre-build LXD sign-off:
- Company selection: ABB chosen over other industrials because publicly listed (NYSE/SIX), Swiss HQ with UK manufacturing operations, automotive + aerospace customer base matches CLO framing
- Financial Perspective BSC question reframed from cost reduction to OEE/operational ROI — this is a structural departure from standard BSC framing and required active decision
- AI question added as 5th BSC question — expands MCQ count beyond original 4 (one per BSC perspective); LD should confirm 5 questions is appropriate for the step
- Phase B strategic horizon updated to include AI/ML predictive maintenance as the outcome of Jidoka-triggered IoT data labeling