Advanced Workshop: Predictability, Variability, and Risk – De-risking Delivery

Advanced Workshop: Predictability, Variability, and Risk – De-risking Delivery (9am-1pm Local Time)

with Daniel Vacanti & Peter the Barvarian

Overview

You’ve got a handle on flow metrics, and you can do basic probabilistic forecasting, so now what? This advanced workshop is designed for practitioners who want to go further: turning data into decisions, variability into advantage, and risk into something that can be seen—and managed—early.

Starting from the customer’s perspective, we’ll go deep into the dynamics of flow, the impact of variability, and practical techniques for improving predictability while managing delivery risk.

Who Should Attend

This workshop is ideal for:

• Delivery leads, Product Managers, and Engineering Managers responsible for meeting customer deadlines

• Agile Coaches and Scrum Masters who support teams beyond the basics of velocity and burndown

• Executives and Portfolio Managers who need to manage risk and make investment decisions

• Teams or orgs that have adopted flow metrics but struggle to use them effectively

What You’ll Learn

By the end of this workshop, you will be able to:

• Identify how customer-facing problems (missed dates, inconsistent delivery, unmet expectations) stem from unmanaged variability and hidden system risks

• Use advanced flow metrics to visualize and manage variability

• How to incorporate multiple teams and dependencies into a probabilistic forecast

• Recognize and act on system-level risks before they impact delivery

• Explain why predictability matters to your customers—and how to improve it

• Use tools like Process Behaviour Charts, and Monte Carlo simulations to drive better conversations and decisions

Pre-requisites

• A passing score on ProKanban.org’s Professional Applied Metrics assessment is required
• Pre-read of  "ActionableAgile Metrics for Predictability" and "When Will It Be Done" by Daniel Vacanti
• Optional but recommended to have taken ProKanban.org’s Applying Metrics for Predictability class