8316-603 Assignment Example

Optimising organisational capacity is about making the most of what the organisation has — its people, money, technology, and processes — to deliver more without simply spending more. Unit 8316-603 requires strategic analysis of how your organisation deploys its resources, identification of capacity constraints, and development of a plan to optimise performance within realistic resource boundaries.

This assignment example follows a director of customer operations in a 600-person UK insurance company, analysing and optimising the capacity of a 120-person claims processing department that was struggling to meet service-level agreements during seasonal demand peaks.

Learning Outcome 1 — Understand How to Analyse Organisational Capacity

AC 1.1 — Analyse Current Capacity Against Strategic Requirements

The claims department processes an average of 2,400 claims per month, with seasonal peaks of 3,800 during winter months (October-February) driven by weather-related property and motor claims. The 120-person team operates at an average utilisation rate of 78% during normal months but 112% during peaks — meaning demand exceeds capacity by 12%, which is absorbed through overtime (£186,000 annually), temporary staff (£94,000), and SLA failures (average settlement time increases from 14 days to 23 days during peaks, generating 34% of annual customer complaints in just five months).

The strategic plan (2025-2028) targets three objectives: reduce average claims settlement time to 10 days (from 14), achieve a customer satisfaction score of 90% (from 82%), and reduce operational cost per claim by 15%. Current capacity cannot deliver any of these objectives — the department needs to process more claims, faster, at lower cost, without additional headcount. This is the classic capacity optimisation challenge: doing more with the same resources rather than doing the same with more resources (Slack and Brandon-Jones, 2024).

AC 1.2 — Identify and Evaluate Capacity Constraints

Goldratt’s Theory of Constraints (as discussed by Slack and Brandon-Jones, 2024) identifies three bottlenecks. Constraint 1: Manual data entry. Claims handlers spend an average of 22 minutes per claim on data entry — inputting information from claim forms, policy documents, and third-party reports into the claims management system. This represents 38% of total processing time and is the single largest capacity constraint. The data entry is largely transcription — moving information from one format to another without adding analytical value. Constraint 2: Approval bottleneck. Claims above £5,000 require senior claims manager approval. Three senior managers handle all escalated claims, creating a queue that averages 2.3 days during normal periods and 5.1 days during peaks. The constraint is not the approval decision (which takes 15 minutes) but the queue to access the decision-maker. Constraint 3: Skills concentration. Complex claims (subsidence, fraud investigation, large commercial) can only be processed by eight specialist handlers. When these handlers are absent or at capacity, complex claims stall regardless of overall department capacity — the skills are concentrated rather than distributed.