8316-607 Assignment Example

Developing excellence in operations is about building organisations that deliver consistently high-quality outputs efficiently, adaptably, and sustainably. Unit 8316-607 moves beyond operational management (making things work day-to-day) into operational strategy (designing systems that produce excellence as a default rather than an aspiration).

This assignment example follows a production director in a 400-person UK pharmaceutical contract manufacturer, applying operational excellence principles to reduce batch failure rates that were costing the company £2.1 million annually in rejected product.

Learning Outcome 1 — Understand How to Evaluate Operational Performance

AC 1.1 — Critically Evaluate Current Operational Performance Against Strategic Objectives

The company’s strategic plan (2024-2027) targets three operational objectives: reduce batch failure rate from 4.8% to 1.5%, achieve 98% on-time-in-full (OTIF) delivery, and reduce cost per unit by 12% through operational efficiency. Current performance: batch failure 4.8% (£2.1M annual cost in rejected product, rework, and client compensation), OTIF at 91% (below the 98% target and the contractual SLA of 95%), and cost per unit rising 3% annually against a target of 12% reduction. The gap between strategic ambition and operational reality is significant. Slack and Brandon-Jones (2024) identify this as a ‘performance frontier’ challenge — the organisation’s current operating model has reached its maximum capability without structural redesign.

AC 1.2 — Analyse Root Causes of Operational Underperformance

A Six Sigma DMAIC investigation (Pyzdek and Keller, 2023) identified three root causes of batch failure. Environmental control variability: temperature and humidity fluctuations in two of four manufacturing suites exceeded the validated range on 12% of production days, causing product instability. The HVAC systems were installed in 2016 and have not been upgraded despite increased production volumes generating higher heat loads. Raw material inconsistency: three of seven active pharmaceutical ingredient (API) suppliers delivered material outside specification on 8% of deliveries. The incoming quality testing regime detected these deviations, but the production schedule pressure meant borderline batches were sometimes processed rather than rejected — a decision that transferred quality risk from incoming goods to finished product. Skills variation: batch failure rates varied from 2.1% (best operator) to 7.4% (worst operator) — a variation that indicates technique-dependent quality rather than process-robust quality. The manufacturing process relied on operator judgement at three critical steps where automated controls should have eliminated human variability.