July 1st, 2024

Streamlining Workflow with kanban in a forming team: a case study

In March 2024, a newly formed team in the energy sector was grappling with establishing a consistent workflow. The initial work primarily focused on architectural runway tasks, making it challenging to set a fair performance baseline. However, tracking initial metrics was crucial to gauge future improvements.

Engagement Overview

Starting in March 2024, I collaborated with the team to implement Kanban, focusing on enhancing their workflow efficiency and predictability. A custom Kanban workshop was designed to help the team better approach their stand-ups and manage work in progress (WIP).

Initial Challenges

 1. Inconsistent Metrics: The team’s initial work was not indicative of performance, making it difficult to establish a baseline.

 2. High WIP: The team struggled with managing multiple work items simultaneously.

 3. Lack of Focused Stand-Ups: Stand-ups were not effectively centered around the work.

Approach and Solutions

  1. Kanban Workshop Implementation:

    • Visualizing Work: Educated the team on the importance of visualizing their workflow and making work visible on the board.

    • Managing Flow: Introduced practices to ensure the movement of items to the right during daily stand-ups.

    • Limiting WIP: Encouraged the team to ask how they could help once they finished work, thus keeping WIP low without explicitly defining limits initially.

    • Making Policies Explicit: Defined clear criteria for each column on the Kanban board.

  2.  Focus on Metrics and Continuous Improvement:

    • Cycle Time and Throughput: Used metrics to track and improve cycle time and throughput.

    • Feedback Loops: Established regular feedback sessions to identify and address bottlenecks and blockages.

    • Team Autonomy and Coaching: Shifted the focus of stand-ups to be more about the work rather than individual contributions.

    • Collaboration and Communication: Fostered a culture of collaboration by encouraging team members to help each other complete tasks.

    • Monte Carlo Simulations: Improved delivery predictability using Monte Carlo simulations, reducing the original timeline from 2026 to September 2024.

    • Kanban Board Adjustments: Worked with the team developer lead to modify the Kanban board for better visual representation of ongoing work.

Setting Up for Success:

To ensure the team continued to thrive after the engagement ended, I set up a framework for retrospectives led by the team lead. These retrospectives are expected to focus on analyzing outliers in cycle time data and discussing system improvements to prevent recurrence of such outliers. This approach promotes continuous improvement and team autonomy.

Flow Data Analysis

Initial metrics (march 2024):

 • Average System Lead Time (Cycle Time): 40.72 days

 • 50th Percentile Lead Time: 32 days

 • 85th Percentile Lead Time: 62 days

 • Throughput: 13.33 work items/month

 • The Cycle Time variability as shown on this scatter plot is very volatile

The initial metrics for March 2024 provide insight into the early stages of this newly formed team’s workflow. The average system lead time of 40.72 days indicates that tasks are currently taking a considerable amount of time to complete, which is expected for a team still in its formative phase. The throughput of 13.33 work items per month reflects the team’s early output levels as they establish their processes.

The variability in lead times, with the 50th percentile at 32 days, the 85th percentile at 62 days, and the 98th percentile at 105 days, suggests a broad range of completion times for different tasks. This scatter in cycle times, as depicted in the distribution plot, is typical for teams still refining their workflow and adjusting to new methodologies. These initial metrics serve as a baseline to understand current performance and identify areas for improvement as the team matures and optimizes their processes.

Final Metrics (June 2024):

After implementing the Kanban workshop and continuous coaching from March to June 2024, the team’s metrics showed substantial improvement:

 • Average System Lead Time (Cycle Time): 18.76 days - a 54% improvement

 • 50th Percentile Lead Time: 15 days - 53% improvement

 • 85th Percentile Lead Time: 53 days - 42% improvement

 • Throughput: 21.25 work items/month - a 59% improvement

 • The Cycle Time variability as shown on this scatter plot shows considerably more consistency

These metrics indicate a significant reduction in cycle times and an increase in throughput, reflecting enhanced efficiency and productivity within the team. The variability in cycle times also decreased, leading to more predictable and reliable delivery timelines.

Summary of results

Improved Efficiency and Predictability:

  • Significant Reduction in Lead Times: Achieved substantial improvements in average system lead time and lead time percentiles, enhancing overall efficiency.

  • Increased Throughput: The team increased its throughput by 59.4%, reflecting improved productivity and workflow management.

  • Enhanced Predictability: The reduced variability in cycle times led to more predictable delivery timelines.

Business Outcomes:

  • Accelerated Delivery Timelines: Monte Carlo simulations showed a significant reduction in projected delivery timelines, moving from an initial estimate of 2026 to September 2024.

  • Better Visualization and Communication: The modified Kanban board provided a clearer representation of ongoing work, facilitating better communication and collaboration within the team.

Conclusion

This case study highlights the transformative impact of implementing Kanban principles in a newly formed team within the energy sector. By focusing on visualizing work, managing flow, limiting WIP, and fostering team collaboration, we achieved significant improvements in efficiency, throughput, and predictability. The continuous improvement approach and regular feedback loops were critical in sustaining these gains

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