Synthetic demonstration case — fixture previewCalculated from fixture dataRoot causes remain hypotheses

Late validation and repair loops may be the leading opportunities to improve payment SLA performance — requires validation.

The synthetic fixture achieves 76.67% SLA attainment. Missing beneficiary bank codes and invalid account formats contribute 76.19% of observed SLA breaches.

Recommended first action: Prevent the highest-volume field defects before queue entry, then fast-track corrected cases through revalidation.

Illustrative scenario: 2,572 capacity hours and £82,320 annual capacity value, subject to the stated assumptions.

SLA attainment

76.67%

42 breaches / 180 records

Median turnaround

191 min

Mean 175.73 min

P90 turnaround

269.2 min

P95 284.05 min

First-time-right

38.33%

Rework rate 61.67%

Define

Understand the problem

Fixture-backed

Problem statement

In the frozen synthetic sample of 180 released payment instructions, the mean turnaround was 175.73 minutes and SLA attainment (≤ 240 minutes) was 76.67%, corresponding to 42 breaches at CALCULATION_VERSION 1.0.0.

The accepted illustrative capacity scenario indicates that avoiding 4,410 defect-driven cases (2,572.5 hours) would release £82,320 of annualised capacity value — an illustrative capacity scenario, not verified savings.

Primary Y— the main outcome being improved

Payment instruction turnaround time

Source factCalculated result

CTQ— the performance requirement that matters to the customer or business

Complete payment instructions within four hours. Target: released within 0.9 minutes.

Methodology — primary Y, CTQs and compact SIPOC

Seeded methodology — fixture-backed demonstrator (payments)

CTQs (max 3)

Complete payment instructions within four hours
Controlling Y
Need: Corporate clients require predictable same-day settlement of routine payment instructions.
Metric: SLA attainment rate (share of instructions with turnaround_minutes ≤ 240)
Specification: ≤ 240 minutes per released instruction
Population: All released instructions in the synthetic sample
Defect rule: Any released instruction with turnaround_minutes > 240 is counted as an SLA-breach defect.
Target provenance:Illustrative assumption
AssumptionCalculated result
First-time-right release without rework
Secondary Y
Need: Repair loops consume operational capacity and delay customers.
Metric: First-time-right rate (no recorded defect and no rework)
Specification: ≥ 70% of released instructions first-time-right (illustrative demonstrator target)
Population: All released instructions in the synthetic sample
Defect rule: Any released instruction with defect_category ≠ none or rework_count > 0 is counted against first-time-right.
Target provenance:Illustrative assumption
AssumptionCalculated result

Compact SIPOC

Suppliers

Corporate client
Portal channel
Email channel

Inputs

Payment instruction payload
Beneficiary bank details
Purpose code and reference data

Process

Receive instruction
Validate mandatory fields
Repair on defect (loop)
Screen and secondary-approve
Release for execution

Outputs

Released payment instruction
Rejected or held instruction

Customers

Corporate client
Downstream payment execution
Operations reporting

Scope, assumptions and missing evidence

In scope

Receipt, validation, repair, screening, approval and release of the synthetic corporate payment instructions represented by the frozen 17-column CSV contract.
The single frozen batch of released instructions (n = 180) used as the demonstrator sample.

Out of scope

Payment execution steps performed after release for execution.
Event-level per-repair-cycle timestamps and any timestamp-derived analytics (future pilot only).
Statistical process control, correlation, regression and hypothesis-test libraries (V1 exclusions).

Assumptions

The 240-minute SLA threshold used to define an SLA-breach defect is an illustrative seeded assumption for the demonstrator, not a sponsor-approved or source-defined SLA.
The ≥ 70% first-time-right target is an illustrative seeded demonstrator target, not sponsor-approved.
The dataset is a synthetic frozen sample of 180 instructions; no event-level timestamps are collected.
Benefit inputs (annual volume, adoption rate, loaded hourly cost) used to derive the illustrative £82,320 capacity value are editable assumptions rather than measured facts.

Missing information

Originator-level volumes and repeat defect behaviour
Timestamp for each repair-loop event
Actual loaded cost and annual volume
Confirmed ownership of mandatory-field validation

Measure

Establish current performance

Fixture-backed

Measure details

Record count: 180
SLA breach count: 42
Median turnaround: 191 min
P90 turnaround: 269.2 min
Defect rate: 60.00%
Rework rate: 61.67%
Mean queue wait: 116.42 min
Mean processing: 59.33 min

Pareto — SLA breaches by defect category— a ranking of where observed failures are concentrated

Top two categories: 76.19% of observed breaches

The first two categories account for 76.19% of observed SLA breaches.

Data quality

Population / denominator: All 180 released instructions in the frozen synthetic 17-column sample; single batch; no event-level timestamps.

Required columns present: Yes
Rows: 180
Duplicate case IDs: 0
Null required values: 0
Invalid types: 0

Warnings

The dataset does not contain a timestamp for each repair cycle.
The benefit model uses editable assumptions because annual volume and labour cost are not in the dataset.

Why this? Every Measure number is a deterministic calculation from the frozen 17-column operational CSV (n=180) at CALCULATION_VERSION 1.0.0, using PERCENTILE.INC / Type-7 percentiles. No AI-generated number appears in this section.

Methodology — operational definitions and Data Collection Plan

Seeded methodology — fixture-backed demonstrator (payments)

Operational definitions

Turnaround minutes
Elapsed minutes from receipt of an instruction to release for execution.
Unit: minutes
Population: All released payment instructions in the synthetic sample
Time window: Single frozen batch (n=180)
Calculation identity: baseline.meanTurnaroundMinutes / medianTurnaroundMinutes / p90TurnaroundMinutes / p95TurnaroundMinutes · CALCULATION_VERSION 1.0.0
SLA attainment rate
Share of released instructions with turnaround_minutes ≤ 240.
Numerator: Count of released instructions with turnaround_minutes ≤ 240
Denominator: Count of released instructions
Unit: ratio (0..1)
Population: All released payment instructions in the synthetic sample
Time window: Single frozen batch (n=180)
Calculation identity: baseline.slaAttainmentRate · CALCULATION_VERSION 1.0.0
First-time-right rate
Share of released instructions with defect_category = none and rework_count = 0.
Numerator: Count with defect_category = none AND rework_count = 0
Denominator: Count of released instructions
Unit: ratio (0..1)
Population: All released payment instructions in the synthetic sample
Time window: Single frozen batch (n=180)
Calculation identity: baseline.firstTimeRightRate · CALCULATION_VERSION 1.0.0
Queue and processing minutes (operational drivers)
Descriptive means of the supplied aggregate `queue_minutes` and `processing_minutes` fields. Presented as operational Measure/process-driver context for the primary Y — NOT a CTQ and NOT an acceptance threshold.
Unit: minutes
Population: All released payment instructions in the synthetic sample
Time window: Single frozen batch (n=180)
Calculation identity: baseline.meanQueueWaitMinutes / meanProcessingMinutes · CALCULATION_VERSION 1.0.0
Defect Pareto by category
SLA-breaching instructions grouped by defect_category and ranked by breach count.
Unit: count and cumulative share
Population: SLA-breaching released instructions only
Time window: Single frozen batch (n=180)
Calculation identity: pareto.breachesByCategory · CALCULATION_VERSION 1.0.0

Data Collection Plan (max 4 rows)

Measure	Purpose	Source	Method	Owner	Frequency	Population rule	Status
Turnaround minutes	Baseline primary-Y performance and SLA attainment	Frozen 17-column operational CSV	Batch upload of the synthetic dataset (no event-level timestamps)	Presenter (demonstrator role)	Single synthetic batch	All released instructions in the sample	Collected (synthetic)
Defect category and rework count	Rank SLA-breach drivers and quantify repair-loop exposure	Frozen 17-column operational CSV	Batch upload of the synthetic dataset	Presenter (demonstrator role)	Single synthetic batch	All released instructions in the sample	Collected (synthetic)
Queue and processing minutes	Distinguish waiting time from active processing time as operational drivers of the primary Y (not a CTQ).	Frozen 17-column operational CSV	Batch upload of the synthetic dataset	Presenter (demonstrator role)	Single synthetic batch	All released instructions in the sample	Collected (synthetic)
Per-repair-cycle timestamps	Quantify each repair-loop delay to validate the repair-loop hypothesis	Event-level operational log (future pilot)	Not activated in demonstrator — event-level-v2 fixture retained for reference only	Future pilot data owner — to be assigned	Not yet collected	All repair-loop events per instruction	Deferred — not collected; future-pilot only

Analyse

Identify what to validate

Fixture-backed

Candidate Xs— a factor that may influence the outcome and requires validation

Prioritised by: observed contribution · evidence strength · likely influence on Y · controllability · feasibility of validation

1. Missing or invalid mandatory payment fields
Calculated result
Missing beneficiary bank code and invalid account format account for 32 of 42 SLA breaches.
Validation needed: Confirm field-level failure data and first-submission completeness by originator.
2. Repeated repair and revalidation loops
Inference
Rework count has a strong positive association with turnaround time in the synthetic fixture.
Validation needed: Add event-level timestamps to quantify each repair-loop delay.
3. Queue waiting before specialist review
Calculated resultInference
Average queue wait materially exceeds average processing time in the fixture.
Validation needed: Confirm queue ownership and staffing pattern by hour/day.
4. Email-originated instructions require more manual intervention
Unconfirmed hypothesis
The SOP describes manual interpretation for non-portal instructions; the dataset contains channel information.
Validation needed: Compare defect and SLA rates by channel with a larger sample.
5. Secondary approval creates additional delay for high-value instructions
Unconfirmed hypothesis
High-value instructions require a second approval under the SOP.
Validation needed: Separate approval waiting time from other queue time.

Priority hypotheses

1. Mandatory-field validation may be occurring too late, which is associated with incomplete instructions entering the repair process — requires validation.
Unconfirmed — further evidence required
The largest breach categories are missing beneficiary bank code and invalid account format, both detectable before full processing.
What would validate this? Field-level first-submission completeness by originator, plus a timed pilot of pre-submission validation.
2. Repair cases may be re-entering a shared queue rather than receiving controlled fast-track revalidation — requires validation.
Unconfirmed — further evidence required
The SOP requires repair and revalidation, while queue wait is the largest component of turnaround time.
What would validate this? Queue-entry/exit timestamps for each repair cycle, and an A/B trial of a repair fast-track lane.
3. Unstructured email instructions are associated with avoidable interpretation and completeness defects — requires validation with a larger sample.
Unconfirmed — further evidence required
The SOP allows email intake and the data identifies originator channel, but a larger sample is required to confirm the effect.
What would validate this? A larger, channel-stratified sample comparing defect and SLA rates by channel.

Improve

Decide what to test

Fixture-backed

1. Introduce pre-submission validation for beneficiary bank code, account format and purpose code before an instruction enters the processing queue.

Initial assessment

Impact HighEffort Medium

Risk: Validation rules must be aligned with approved payment formats and exception handling.

Addresses hypothesis 1 · Addresses hypothesis 3

2. Create a controlled repair fast-track with a standard repair reason and direct return to validation rather than re-entry to the general queue.

Initial assessment

Impact HighEffort Medium

Risk: Queue routing and ownership must be agreed.

Addresses hypothesis 2

Benefit scenario

Planning scenario — replace with validated Operations and Finance inputs

Basis:AssumptionCalculated result

Annual volumeInstructions released in a full year. Operations input required.Affected case share (%)Proportion of yearly cases the action could touch. Planning assumption — demonstration value, not derived from breach, defect or rework rates.Target reduction in affected cases (%)Reduction in affected cases the action aims to achieve. Pilot hypothesis.Adoption rate (%)Share of the affected population working to the new way. Implementation assumption.Minutes saved per avoided caseHandling minutes released per avoided case. Time-study input required.Loaded hourly cost (GBP)Fully loaded cost of one operational hour. Finance input required.

Showing the outputs of the saved analysis record.

Annual avoided cases

4,410

Annual capacity hours

2,572.5

Annual capacity value

£82,320.00

Control

Define sustainment

Fixture-backed

First-time-right rate for submitted payment instructions

Proposed control design — requires implementation and validation

Percentage of released instructions with no recorded defect and no rework.

Basis:Controlled guidanceAssumption

Baseline

38.33%

Target

70.00%

Frequency

Weekly

Trigger

Below 65% for two consecutive weeks

Tail-performance outcome

P90 turnaround time (minutes)

90th-percentile elapsed minutes from instruction receipt to release, nearest-rank convention, across all released instructions in the period.

Baseline

269.2

Proposed target

240

Target provenance

Illustrative assumption

Leading indicator

First-time-right rate at first validation

Share of instructions passing validation at the first attempt with no recorded defect and no rework, over all instructions submitted in the period.

Baseline

38.33%

Proposed target

70.00%

Target provenance

Illustrative assumption

Data source: Weekly extract of the operational payment-instruction record, filtered to released instructions in the reporting period.

Escalation: If the response does not restore performance within two further reporting periods, escalate to the process owner and the operations governance forum.

Closure criteria: The control action closes when the leading indicator holds at or above its proposed target for four consecutive reporting periods with no reopened defect category.

Linked action: Introduce pre-submission validation for beneficiary bank code, account format and purpose code before an instruction enters the processing queue.

Target provenance: Illustrative assumption

Protects: First-time-right release without rework · Linked actions: recommendation 1, 2

Response: Review top defect categories, affected originators and repair-loop ageing; agree corrective action.

Trust statement

Root causes remain hypotheses until field-level and event-timestamp evidence is collected.