---
name: 3pl-accountability-sla-compliance-tracker
description: "Converts raw 3PL operational data into a structured SLA compliance scorecard with breach severity ranking, root-cause categorization, dollarized impact estimates, and a prioritized accountability action list with owners and deadlines. Use when reviewing daily or weekly 3PL performance, preparing partner business reviews, escalating chronic SLA breaches, or building an accountability trail for underperforming fulfillment partners."
argument-hint: "[reporting-period] [sla-data]"
allowed-tools: ["Read"]
---

# 3PL Accountability & SLA Compliance Tracker

## Purpose
Turn raw fulfillment metrics from a 3PL partner into a clear compliance scorecard against contracted SLAs, then generate ranked accountability actions with named owners and deadlines — so a fulfillment operations manager can walk into any partner review or escalation with evidence instead of anecdotes.

## When to Use
Invoke this skill when the user asks to:
- Summarize daily or weekly 3PL SLA performance against contracted targets
- Prepare a partner business review or QBR from operational data
- Identify which SLA breaches happened, how severe they were, and who owns the fix
- Build an accountability trail for a chronically underperforming fulfillment partner
- Escalate a recurring problem (late shipments, high error rate, damage claims) with documented severity
- Track whether prior corrective actions actually moved the metrics period-over-period

## Inputs Required
Before starting, collect:
- **Reporting period**: The date range being evaluated (e.g., "Week of Mar 3–9" or "Mar 12 daily")
- **SLA targets**: The contracted thresholds for each metric (e.g., on-time ship ≥98%, order accuracy ≥99.5%, dock-to-stock ≤24h)
- **Actual performance data**: The raw numbers for the period — can be pasted metrics, a table, or a data dump
- **Order/unit volume**: Total orders or units for the period (needed to weight breach severity and dollarize impact)
- **Cost/penalty context**: Per-unit cost of a miss, chargeback rate, or SLA penalty clause if defined
- **Prior period data**: Last period's numbers, if available, to show trend
- **Known context**: Any peak events, weather, systems outages, or excused exceptions

If SLA targets are missing, apply standard fulfillment benchmarks (on-time ship 98%, order accuracy 99.5%, inventory accuracy 99%, dock-to-stock 24h, RTS/damage <1%) and flag them as assumed. If cost context is missing, report volume-based severity without dollar figures and note the gap.

## Instructions

### Step 1: Build the Compliance Scorecard
For every metric provided, produce a row with:
- **Metric name**
- **SLA target**
- **Actual result**
- **Variance** (percentage points or absolute, signed +/-)
- **Status**: ✅ Met / ⚠️ At Risk (within 1 point of target) / ❌ Breached

Cover the core 3PL SLA categories where data exists:
- **Speed**: On-time ship rate, dock-to-stock time, order cycle time, same-day cutoff adherence
- **Accuracy**: Order accuracy (right item/qty), pick accuracy, inventory record accuracy, shipping label accuracy
- **Quality**: Damage rate, return-to-sender rate, mis-ship/reship rate, claims rate
- **Availability**: Backorder/OOS incidents attributable to 3PL, receiving delays
- **Responsiveness**: Ticket resolution time, exception notification timeliness

### Step 2: Rank Breaches by Severity
Score each ❌ Breached and ⚠️ At Risk item on a composite of three factors:
- **Magnitude**: How far below target (variance size)
- **Volume exposure**: How many orders/units the miss touched
- **Trend**: Improving, flat, or worsening vs. prior period

Assign a severity tier:
- **Critical**: Large variance × high volume, or a metric worsening for 2+ periods
- **High**: Meaningful variance on high-volume flow, or first breach on a customer-facing metric
- **Moderate**: Small variance or low-volume exposure
- **Watch**: At-risk items trending toward breach

### Step 3: Categorize Root Cause
For each breach, assign the most likely root-cause bucket based on the data pattern (state it as a diagnosis, not a certainty):
- **Labor/capacity**: Misses cluster on high-volume days or specific shifts
- **Process/SOP**: Accuracy or damage issues consistent across volume levels
- **Systems/integration**: Sync errors, inventory mismatches, EDI/order-flow failures
- **Inbound/receiving**: Dock-to-stock and OOS issues trace to receiving delays
- **External/excused**: Peak surge, carrier failure, weather — flag as candidate for exception review

### Step 4: Dollarize the Impact
Where cost context exists, estimate the financial impact per breach:
- Missed shipments × per-miss cost (expedite, chargeback, or penalty)
- Accuracy misses × reship/return cost
- Damage/claims × unit cost + claim handling
- SLA penalty clauses triggered, if applicable

Present a period total and per-metric breakdown. Where cost data is absent, quantify in affected-order counts instead.

### Step 5: Generate the Accountability Action List
Produce a ranked list (Critical first) where each action has:
- **The issue** (one sentence, tied to the specific breach)
- **The action required** (specific and verifiable — "Provide RCA on the 47 mis-shipped orders from Mar 6," not "improve accuracy")
- **Owner**: 3PL partner / internal ops / joint
- **Deadline**: Concrete date or SLA-standard response window (e.g., RCA within 48h)
- **Verification**: How next period's data will confirm the fix worked

### Step 6: Trend & Escalation Flags
- Note any metric breached in 2+ consecutive periods → mark as **chronic** and recommend formal escalation per contract terms
- Note any metric that recovered vs. prior period → credit it, so the record is balanced
- Recommend one escalation path if a Critical or chronic issue exists (documented notice, penalty invocation, QBR agenda item, or partner-change evaluation)

## Output Format
- **Structure**: Scorecard table → severity-ranked breach summary → root-cause + dollar impact → accountability action list → trend/escalation flags
- **Length**: 500–900 words plus tables (scales with number of metrics)
- **Format**: Compliance scorecard as a table; breaches and actions as ranked structured lists; a short executive summary paragraph up top
- **Sections**: Executive Summary (2–3 sentences) → SLA Compliance Scorecard → Breach Severity Ranking → Root Cause & Financial Impact → Accountability Action List → Trend & Escalation Flags
- **Tone**: Operations manager — factual, evidence-first, non-emotional. Written to survive being forwarded to the partner. No hedging on numbers, no blame language beyond documented cause.

## Quality Checklist
Before delivering output, verify:
- [ ] Every metric provided appears in the scorecard with target, actual, variance, and status
- [ ] Each breach has a status, severity tier, and root-cause category — none left unclassified
- [ ] Severity ranking accounts for volume exposure, not just variance size
- [ ] Every accountability action has an owner, a deadline, and a verification method
- [ ] Financial impact is dollarized where cost context exists, or stated in order counts where it does not
- [ ] Any assumed SLA target (used because none was provided) is explicitly flagged as an assumption
- [ ] Metrics breached across consecutive periods are flagged as chronic when prior data is available

## Notes
- The single most useful output for partner reviews is the accountability action list — resist the urge to bury it under analysis; keep actions specific enough that "done / not done" is unambiguous next period.
- Always credit recovered metrics. A scorecard that only lists failures loses credibility with the partner and weakens escalation leverage when a real Critical issue arises.
- For daily runs, keep the scope tight (scorecard + top 3 actions). Reserve full root-cause and dollarization depth for weekly and QBR-level reviews.
- When data is ambiguous about cause, state the most probable bucket and request the specific data that would confirm it — this itself becomes an accountability action on the partner.

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## How to use this skill

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