https://engineering-leadership.hinshelwood.com/tags/lead-time/ Recent content in Lead Time on Engineering Leadership in AI & Software Hugo en Tue, 16 Jun 2026 17:40:35 +0000 https://engineering-leadership.hinshelwood.com/articles/rethinking-capacity-planning/ Mon, 21 Jul 2025 09:00:00 +0000 https://engineering-leadership.hinshelwood.com/articles/rethinking-capacity-planning/ Capacity planning should focus on optimising system flow and predictability, not tracking individual hours or task assignments. Shifting from micromanagement to managing work as a system at portfolio, category, and team levels helps prevent overload, improves value delivery, and enables reliable forecasting. Development managers should prioritise system-level metrics, enforce work-in-progress limits, and empower teams to pull well-prepared work, creating sustainable and predictable delivery. https://engineering-leadership.hinshelwood.com/signals/why-measuring-individual-cycle-time-fails-to-help-teams/ Sat, 15 Mar 2025 16:30:02 +0000 https://engineering-leadership.hinshelwood.com/signals/why-measuring-individual-cycle-time-fails-to-help-teams/ Measuring individual cycle time does not help teams improve because it focuses on people instead of the overall system. Real bottlenecks come from process issues like queues and overloaded work, not individual speed. To improve team performance, focus on system-level metrics such as lead time, throughput, and process efficiency, and address process bottlenecks rather than monitoring individuals. https://engineering-leadership.hinshelwood.com/videos/stop-guessing-how-to-make-work-visible-and-drive-real-improvement-with-azure-devops-flow-metrics/ Mon, 25 Aug 2025 06:00:00 +0000 https://engineering-leadership.hinshelwood.com/videos/stop-guessing-how-to-make-work-visible-and-drive-real-improvement-with-azure-devops-flow-metrics/ Relying on gut feeling leads to mediocre results, so making work visible with real data is essential for improvement. Azure DevOps provides a strong data foundation but its built-in metrics and visualisations are basic; using tools like Flow Viz or Actionable Agile Metrics gives deeper insights and actionable flow data. To drive better outcomes, move from guesswork to evidence-based decisions by leveraging these tools to make your team’s work and bottlenecks visible. https://engineering-leadership.hinshelwood.com/signals/why-tracking-individual-cycle-time-distorts-team-behaviour/ Wed, 12 Mar 2025 16:30:03 +0000 https://engineering-leadership.hinshelwood.com/signals/why-tracking-individual-cycle-time-distorts-team-behaviour/ Tracking individual cycle time leads people to focus on looking good rather than improving team performance, causing them to pick easy tasks, rush work, and avoid collaboration. This does not improve actual delivery time and results in local optimisations that do not help deliver value. Focus on measuring and improving team flow metrics like lead time, work in progress, and throughput instead. https://engineering-leadership.hinshelwood.com/tags/lead-time/ Fri, 11 Apr 2025 06:00:00 +0100 https://engineering-leadership.hinshelwood.com/tags/lead-time/ Lead Time is an essential observability metric that quantifies the duration from the initiation of a work item to its delivery to the customer. It is a practical implementation of Cycle Time often used in flow-based systems such as Kanban. This metric provides end-to-end visibility into workflow performance, helping teams identify inefficiencies and optimise delivery for improved predictability and responsiveness. By exposing how long it takes to deliver value, Lead Time enables real-time insight into system health and team effectiveness, supporting continuous improvement across Agile, Lean, and DevOps environments.