OPM6090

Product Requirements Document

OPM6090 · Module 6 · Deeply Embedding Technology and Practices for Continuous Innovation

PRD — OPM6090 Module 6

Status: Retrospective (documented post-build) Program: MBA Degree level: Masters Documented by: Claude Code Date: 2026-04-24


Zone 1 — LXD Inputs

Course identity

Module learning intent

Module 6 is the synthesis capstone for Technology and Operations Management. Learners must integrate the Balanced Scorecard framework, Toyota Production System principles, and technology portfolio evaluation into a single consulting engagement — diagnosing why a multi-platform technology investment is failing to improve operational performance, classifying the resulting waste patterns, and sequencing a phased improvement roadmap that addresses root causes rather than symptoms.

Course Learning Outcomes (CLOs)

CLO Text Pillar mapping
CLO-1 Evaluate technology investments using the Balanced Scorecard framework across all four perspectives Domain
CLO-2 Apply TPS principles (JIT, Jidoka, Seven Wastes) to classify operational failure patterns and identify interventions Domain
CLO-3 Construct a sequenced, dependency-aware implementation roadmap integrating BSC diagnosis and TPS waste elimination Reasoning + Contribution
CLO-4 Analyze the impact of technology adoption decisions on operational performance outcomes Domain + Reasoning (CLO-4 formula)

Grading weights (LD-confirmed)

Constraints / special requirements


Rubric Descriptors

Status: Retrospective draft — generated by Nexus based on module learning intent and CLO mappings. Requires LXD review and sign-off before these descriptors are wired into the grading prompt (#28).

Domain Pillar (55%)

Rating Descriptor
Meets Expectations (90–100%) Correctly applies BSC across all four perspectives (Financial, Customer, Internal Process, Learning & Growth) with OEE and operational KPIs leading the Financial Perspective (not cost accounting figures); classifies at least four of seven TPS waste types present in the scenario with specific evidence; technology portfolio evaluation references at least three of the five platforms with named performance data (CLO-1, CLO-2).
Mostly Meets (80–89%) BSC analysis covers three of four perspectives adequately with appropriate KPIs; three TPS waste types classified with evidence; technology portfolio covers two platforms with scenario-grounded evidence.
Somewhat Meets (70–79%) BSC present but Financial Perspective framed around cost reduction rather than operational ROI (OEE, throughput, lead time); fewer than three TPS wastes classified; technology portfolio assessment is generic without specific platform evidence.
Does Not Meet (<70%) BSC framework absent or applied to fewer than two perspectives; TPS waste analysis absent or classification is incorrect; technology portfolio not evaluated with scenario data.

Reasoning Pillar (25%)

Rating Descriptor
Meets Expectations (90–100%) Implementation roadmap is dependency-aware — each phase logically follows from the prior and the dependency is explained; roadmap phases are explicitly connected to the BSC diagnosis (e.g., Phase 1 addresses the Internal Process gap identified by a specific BSC metric); AI readiness judgment correctly identifies labeled historical data as the key prerequisite constraint for predictive maintenance viability (CLO-3, CLO-4).
Mostly Meets (80–89%) Roadmap is sequenced and mostly dependency-aware; BSC-to-roadmap connection is present but one phase lacks an explicit justification for its position in the sequence; AI readiness judgment is correct but the labeled data reasoning is incomplete.
Somewhat Meets (70–79%) Roadmap phases are listed in an order that is plausible but sequencing logic is not explained; connection to BSC diagnosis is asserted without demonstrating which finding drives which phase; AI question response is partially correct (identifies AI as relevant but does not identify the specific prerequisite).
Does Not Meet (<70%) No dependency-aware sequencing; roadmap is a generic list of interventions not connected to the BSC diagnosis; AI readiness judgment is absent or incorrect.

Contribution Pillar (20%)

Rating Descriptor
Meets Expectations (90–100%) Identifies a non-obvious root cause or second-order effect — e.g., explains why eliminating a specific TPS waste type will unlock performance gains across multiple BSC perspectives, or why sequencing Phase 1 before Phase 2 reduces risk rather than just enabling capability; frames the roadmap as a prioritized consulting recommendation rather than an implementation checklist (CLO-3).
Mostly Meets (80–89%) Roadmap shows prioritization logic beyond the obvious; recommendation is consulting-appropriate but relies on scenario prompts for most of its framing.
Somewhat Meets (70–79%) Roadmap reads as a summary of the MCQ steps rather than an independent analytical recommendation; no explanation of why the chosen sequence is preferable to alternatives.
Does Not Meet (<70%) No independent recommendation; response is a list of steps without analytical synthesis or prioritization rationale.

LXD Sign-off — Rubric Descriptors

These descriptors were drafted retrospectively by Nexus (Claude Code) on 2026-04-27 based on existing CLOs and scenario content. They require LXD review and approval before being wired into the grading prompt (punch list #28).

Role Name Date Status
LXD (review & approve) Fadl Approved
Head of LD (final approval) Pending

Once approved, update Status above to "Approved" and add the approver names and dates here. Changes after approval require a new sign-off round and a version bump on the PRD.