# Kanban Cycle Time & Throughput Coach Import created→started→resolved timestamps from CSV (Jira-style or simple) and get flow metrics the way agile coaches read them: lead/cycle time percentiles, WIP age, throughput per day/week/month, a cumulative flow diagram and a Little's Law consistency check. > Canonical page: https://elysiatools.com/en/tools/kanban-cycle-time-throughput-coach - **Category:** Data Analysis - **Keywords:** kanban metrics, cycle time, lead time, throughput, cfd, wip, jira csv ## Overview The Kanban Cycle Time & Throughput Coach parses issue tracking timestamps from CSV exports to generate agile flow metrics, including lead and cycle time percentiles, aging work in progress (WIP), throughput histograms, a Cumulative Flow Diagram (CFD), and Little's Law consistency validation. ## Inputs - **Work items CSV (id, title, created, started?, resolved?)** (textarea): Issue key,Summary,Created,Started,Resolved PROJ-101,Login fix,2026-07-01,2026-07-02,2026-07-05 - **Throughput bucket** (select) - **Percentiles** (text): 50,85,95 - **Analysis clock (defaults to now)** (text): 2026-08-01 — pin this to get reproducible WIP-age numbers - **Treat Created as work start when no Started column** (checkbox) ## When to use - Analyzing team delivery predictability and cycle time percentiles (p50, p85, p95) from Jira or issue tracker CSV exports. - Investigating bottlenecks, aging in-progress items, and flow stability during sprint retrospectives or agile coaching sessions. - Checking process stability and throughput trends using cumulative flow diagrams and Little's Law validation. ## How it works - Paste or import your work item CSV containing identifier, title, created, started (optional), and resolved (optional) timestamps. - Configure your throughput aggregation bucket (daily, weekly, or monthly), target percentile cutoffs (such as 50, 85, 95), and an optional analysis snapshot date. - Set fallback options like using the creation date as work start if explicit started timestamps are missing. - Review generated metric cards, WIP aging tables, throughput breakdown charts, the visual Cumulative Flow Diagram, and Little's Law check results. ## Use cases - Agile team retrospectives evaluating service level expectations (SLE) against actual p85 cycle times. - Engineering managers monitoring weekly throughput volume and identifying stalled in-progress tickets. - Delivery coaches validating flow continuity and work accumulation trends using Cumulative Flow Diagrams. ## Frequently asked questions ### What CSV columns are recognized by the tool? The parser accepts standard headers for item identifier, summary or title, created date, started date, and resolved date, including Jira-style CSV exports. ### What happens if my CSV does not contain a Started date column? Enabling the 'Treat Created as work start' option will calculate cycle time and WIP metrics using the creation timestamp as an approximation. ### Why would I set a specific Analysis Clock date? Pinning the 'asOf' date ensures reproducible WIP age calculations for historical reporting instead of calculating age against the current date. ### How are cycle time and lead time percentiles calculated? The tool computes duration across completed work items and calculates distribution percentiles based on your configured percentile thresholds (e.g., 50th, 85th, and 95th percentiles). ### What does the Little's Law check evaluate? It tests the mathematical consistency between average WIP, average throughput, and average cycle time across your dataset to highlight delivery flow equilibrium. ## Related tools - [Time Series Forecast & Seasonality Analyzer](https://elysiatools.com/en/tools/time-series-forecast-seasonality-analyzer): Forecast future periods from CSV or JSON time-series data and inspect trend, seasonal, and residual decomposition in one report - [Header Remover](https://elysiatools.com/en/tools/header-remover): Remove headers from CSV data to create clean header-less files. Perfect for database imports, data processing pipelines, API integrations, and systems that require header-less CSV format. 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Perfect for business intelligence, statistical analysis, data exploration, and reporting. Features: - Multiple aggregation functions (sum, count, average, min, max, median) - Flexible row and column grouping - Percentage and ratio calculations - Row/column totals and grand totals - Multi-dimensional analysis - Conditional formatting support - Statistical significance testing - Custom sorting and filtering - Export-ready formatting Common Use Cases: - Sales analysis by region and product - Customer demographics analysis - Financial statement analysis - Survey response analysis - Inventory turnover analysis - Performance metrics tracking - [CSV to Database Migration Planner](https://elysiatools.com/en/tools/csv-to-database-migration-planner): Infer a relational schema from CSV data and generate create-table plus ALTER migration plans for PostgreSQL, MySQL, SQLite, or SQL Server - [Short URL + UTM + QR Bundle Builder](https://elysiatools.com/en/tools/short-url-utm-builder-qr-bundle): One pass replaces bit.ly + Campaign URL Builder + QR generator: GA4-validated UTM parameters, a deterministic self-hosted short link (FNV-1a base62 slug), QR pair, redirect snippets and a tracking CSV row. ## Samples - [CSV Samples](https://elysiatools.com/en/samples/csv-samples): Sample CSV files with various data types, sizes, and complexity levels - [Python Samples](https://elysiatools.com/en/samples/python): Essential Python code examples and Hello World demonstrations - [Windows String Processing - C# Samples](https://elysiatools.com/en/samples/windows-string-processing-csharp): Comprehensive C# string processing examples for Windows platform including string manipulation, splitting, joining, regex operations, and text analysis - [Time Zone Workflow Scheduler ICS Samples](https://elysiatools.com/en/samples/time-zone-workflow-scheduler-ics-samples): ICS files generated in the same structure returned by the Time Zone Workflow Scheduler, with multiple VEVENT meeting candidates exported from overlap windows