8607-523 Assignment Example

Lean production is not just for factories — it is a systematic approach to eliminating waste and improving flow in any process where work passes through multiple stages and people before reaching the customer. Unit 8607-523 asks you to take lean methodology out of the textbook and into your workplace: identify a real service delivery problem, plan an improvement using lean tools, implement the change, monitor its impact, and report on the outcomes with honest evaluation. This is a competence unit — ‘be able to’ means you must demonstrate that you have done it, not just that you understand it.

This assignment example follows a service operations manager in an NHS outpatient department applying lean methodology to a persistent operational problem: excessive patient waiting times in the fracture clinic, where the average patient wait between arrival and consultation had reached 47 minutes — against a trust target of 20 minutes and a national benchmark of 15 minutes. The project used the DMAIC (Define, Measure, Analyse, Improve, Control) framework to structure a systematic improvement that reduced average waiting time by 53% within twelve weeks.

Learning Outcome 1 — Be Able to Undertake a Service Delivery Improvement Project Using Lean Production and Improvement Methodologies

AC 1.1 — Develop a Plan to Improve a Workplace Project Using Lean Production and Improvement Methodologies

The improvement plan followed the DMAIC framework — a structured lean methodology that provides both the analytical discipline and the implementation sequence that service improvement requires (Slack and Brandon-Jones, 2024).

Define. The problem statement was formulated using the lean principle of starting from the customer’s perspective: ‘Patients attending the fracture clinic wait an average of 47 minutes between arrival and consultation, against a 20-minute target. This creates patient dissatisfaction (complaint rate: 14 per month relating to waiting times), overcrowding in the waiting area (a 42-seat space regularly holding 60+ patients), staff frustration (the 2025 staff survey scored the fracture clinic lowest in the department for ‘I am able to do my job effectively’), and a clinical risk from delayed assessment of potentially deteriorating fractures.’ The project scope covered the entire patient journey from arrival at reception to the start of the clinical consultation — a process involving five handover points and three separate staff groups (reception, nursing, medical).

Measure. Before improving the process, the current state had to be measured precisely. A two-week data collection exercise (March 2025) captured timestamp data for every patient at each stage: arrival time, registration completion, triage assessment start, triage completion, clinician consultation start. The data (n=340 patients) revealed the actual process timeline: registration averaged 4 minutes, triage averaged 8 minutes, and clinical consultation averaged 12 minutes — a total of 24 minutes of value-adding activity. But the average total time from arrival to consultation was 47 minutes — meaning 23 minutes (49% of the patient’s time) was non-value-adding waiting. Ohno’s (2022) original lean principle applies directly: if less than half the elapsed time adds value, the process contains substantial waste.

Analyse. A value stream map (VSM) of the current state identified where the 23 minutes of waste accumulated. Three waste hotspots emerged. Waste 1 — Waiting (Muda type 1): patients waited an average of 11 minutes between completing registration and starting triage. Root cause: the triage nurse operated on a batch system — collecting four patients’ notes before calling any of them, rather than processing each patient as they arrived. This created artificial batching that transformed a 4-minute registration into an 11-minute wait. Waste 2 — Over-processing (Muda type 6): the triage assessment included re-collecting information already captured at registration (patient demographics, GP details, medication history). This duplication added 3 minutes to every triage assessment unnecessarily. Root cause: the triage proforma was designed independently of the registration system, and nobody had reviewed whether the two processes duplicated each other. Waste 3 — Motion and transport (Muda types 2 and 4): physical patient notes were printed at reception, carried to the triage room by a healthcare assistant, and then carried again to the consulting room. Each physical handover took 2-4 minutes and created a queue when the healthcare assistant was unavailable. Root cause: the clinic operated a hybrid paper-digital system where notes were digital but printed for clinical use — a legacy practice from the pre-digital era that nobody had challenged.

Improve. Three improvement interventions were designed, each targeting a specific waste source. Intervention 1: replace batch triage with single-piece flow — the triage nurse processes each patient immediately upon registration completion, eliminating the artificial batch queue. Intervention 2: redesign the triage proforma to pre-populate with registration data, eliminating duplicate data collection. Intervention 3: transition the clinic to fully digital notes — clinicians access patient records on screen rather than waiting for printed notes to be physically transported. Each intervention was designed with the lean principle of ‘smallest change, biggest impact’ — none required capital investment, new technology, or additional staffing (Bicheno and Holweg, 2022).

Control. Planned as part of AC 1.3 — the mechanisms to sustain the improvement after the initial implementation.

AC 1.2 — Implement an Appropriate Improvement to Service Delivery, Using the Plan

Implementation followed a phased approach over four weeks (April 2025), with each intervention introduced sequentially to isolate its individual impact and manage the change without overwhelming the clinical team.

Week 1-2: Single-piece flow triage. The triage nurse transitioned from batch processing to single-piece flow — calling each patient immediately after their registration was completed. Implementation required a brief process change discussion with the triage nurse (who initially resisted because the batch approach ‘allowed me to organise my workspace’), a repositioning of the triage workstation closer to the reception desk to reduce physical distance, and a visual signal system (a green light triggered by the receptionist when a patient completes registration, visible from the triage station). The change was immediately measurable: the average wait between registration and triage dropped from 11 minutes to 2.5 minutes within the first week. The triage nurse acknowledged after three days that the single-piece flow was ‘actually less stressful — I’m never looking at a queue of four patients waiting and feeling behind.’

Week 2-3: Triage proforma redesign. The triage proforma was redesigned in collaboration with the senior triage nurse and the clinical lead. Six data fields that duplicated registration data were removed and replaced with a pre-populated header drawn from the electronic patient record. The redesigned proforma reduced triage time from 8 minutes to 5.5 minutes per patient — a 31% reduction. The clinical lead confirmed that no clinically relevant information was lost in the redesign.

Week 3-4: Digital notes transition. Clinicians were supported to access patient records digitally rather than from printed notes. Two of four clinicians already preferred screen-based access; two required familiarisation sessions with the electronic patient record system’s clinic view. By week four, printed notes were eliminated for routine fracture clinic appointments. The healthcare assistant previously occupied with note transport was redeployed to patient communication — updating patients in the waiting area on estimated wait times, which addressed a secondary source of patient dissatisfaction identified in the complaint data.