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Agile Value Delivery Optimization through Flow KPIs

To succeed in an Enterprise Agile Transformation, and more importantly, to continue to maintain the achieved new state (Agile Value Delivery Optimization) and to grow towards higher levels of success in the market through becoming a continuously improving and optimizing organization, we need to have a clear vision on where we are, how each one of our Value Streams are contributing to that state, and where we need to invest our focused energy to improve the new status quo and push towards our next desired future state.

We cannot improve what we are not measuring. We cannot know how much we have truly improved, without a proper baseline. Without proper measurement, we cannot tell if all of our Values Streams are performing and improving towards the strategic goals, or some are faltering and working against that goal.

Establishing metrics for all of our value delivery pipelines and aggregating and presenting them as a live view on the most recent numbers and the past incremental movements in the observed values, is vital to our successful transformation and sustaining the momentum forward.

Flow KPIs has been around for a while, and have been called by a variety of names under different disciplines. They specialize on observing, measuring, and monitoring the key factors impacting the flow of value in all of our Value Streams.

As a growing number of organizations have been failing in their Agile transformation or facing serious obstacles in finding and selecting their starting points to ignite the process (mostly due to lack of proper baselining and establishing the needed transparency on current state and incremental evolutionary change in silos and cross-silos), we have seen a raised understanding of the importance of measuring the “Flow” and monitoring its attributes.

Flow KPIs are the essential indicators of Business Agility, showing how well an organization is progressing forward with its transformation plan, and becoming a more competitive and stronger market player. They set performance standards for their Value Streams’ effectiveness and agility in responding to shifting market conditions, and their optimized approach in doing so.

What to Measure?

Over the past 1.5 decades, an extensive amount of energy, time and budget has been invested in answering that question, leading to identification, and fine-tuning of valuable, high impact KPIs in organizations.

In its most basic definition, the ultimate goal of a business is to provide the highest amount of Value to the customers in the fastest possible way. Value is essentially what the customer wants and is willing to pay for, hence generating revenue for the business. Value generates the Business Outcome that the organization is looking for and wants it to sustain its existence and to continue to grow indefinitely.

An organizations’ success in generating more of the Business Outcome it seeks,  can be identified as the aggregate effect of the efficiency of the Flow of the Value through its Value Streams, the organizations quality of function and ability in generating and delivering that Value, and ultimately the return from all of the activities, aka the Business Outcome created due to matching or exceeding customer expectations.

In this article we focus on the Flow of Value and the required KPIs in observing, tracking, and measuring it across the Value Streams.

Flow Balance

Also called as Flow Distribution, refers to the workload distribution across teams and shows what kind of activity is taking what percentage of teams’ capacity in any timeframe. For example, if teams serving a Value Stream are spending 25% of their capacity fixing issues with the product, that they are responsible for, then they are not able to invest that amount in creating more valuable products (or features) to be provided to customer, hence generating less revenue.

In another example, if teams serving multiple Value Streams are spending 15% of their capacity in synchronizing their efforts toward joint product releases due to the existing internal complexities in their inter-team / inter-group co-ordinations, or their relationship with the lines of business, then that is 15% of capacity not in service of creating and delivering value to customers.

Also, another example would be if too much of the teams’ capacity is allocated to new work items, and in pursuit of new business opportunities, then the organization may be avoiding necessary incremental technical-debt retirement activities in a “glory hunting” leadership anti-pattern trying to push forward into the market at the cost of growing the existing technical issues that need to be addressed.  

This metric is measured at team level, group / program level, large solution group level and at enterprise level and raises red and yellow flags in areas where a considerable amount of workload is composed of non-value-generating activities (roughly equivalent of wasteful-activities) needed to just keep the lights on and push forward in an under-optimized way or growing patterns are being observed in that context.

Flow Delivery Speed

Also called Flow Velocity,  shows the amount of work (measured in number work items / features / deliverables) that the teams are completing in a specific timeframe. This metric measures the speed at which teams, groups, Value Streams can deliver Value to the customers.

In general, the higher the Flow Delivery Speed, the higher the organizational output (which can be an outcome of the process optimization efforts in the enterprise through streamlining the workflow, decentralizing decision making checkpoints and empowering teams).

An excessively rapid growth in Flow Delivery Speed may be an indicator of teams pursuing faster work delivery by skipping the needed quality checks and reviews in product and service components, exposing the enterprise to risk of introducing missed defects into the products and services, lowering the quality of customer experience, damaging the brand, and dropping revenue levels.

This metric shows the capacity of teams (at multiple levels) in delivering work items through the pipelines. The internal review of this metric’s observed and tracked value at team and group / program level, allows for assessment of improvements (or degradations) of capacity in teams, programs and enterprise level and can be used to identify heated areas where impediments are throttling Value Streams’ ability to deliver.

Flow Lead Time

Also called Flow Time or Lead Time, is an indicator of the time that is elapsed from when work items enter the pipelines to when they are release to production and become available to customers.

This metric serves as an indicator of how fast an idea created by the business side (or fine-tuned by the interaction of customers with our prototypes or earlier versions of our products and services), can become the next delivered Value to the customer and push toward creation of more of the desired Business Outcome.

This metric is also measured at team level, group / program level, large solution group level and at strategic portfolio level, showing the elapsed time it takes the strategic decisions to cascade down to the teams and turn into value delivery to customers.

Flow Density

Also called Flow Load or Work-In-Progress (WIP), is an indicator of number of parallel existing work items in one or multiple pipelines. Studies show that the higher the number of WIP, the more damage to the efficiency of the teams (and their Value Streams) due to the time wasted by context-switching and allowing for unfinished work items to create a line-up and extending the wait time for the next work items coming into the pipeline.

High WIP limits encourage teams to suspend a work item the moment it hits an obstacle and start a new one, instead of summoning the team’s resources (or even escalate to get management support) to resolve the impediment and break through it.

Cumulative Flow Diagrams are good visualization tools showing the WIP in several stages of the workflow. The wider the width of a stage during a certain timeframe, the higher the WIP and the stronger level of risk to that stage.

This metric is also measured at team level, group / program level, large solution group level, serving as an ongoing optimization practice to adjust the WIP Cap in search for finding the most optimized number for every  stage of each pipeline.

Flow Quality

Also called Flow Efficiency, or Workflow Efficiency, is a comparative indicator of the portion of the time the work items are in “Active” status (i.e., work being done on them), versus “Waiting/Idle” status (i.e., in a line up waiting to be pulled into the next stage of the workflow) while they are travelling through pipelines.

This is a key indicator of bottle necks in Value Streams and can raise red and yellow flags upon their observation and be tracked over time to visualize the trends. In traditional organizations where no prior optimization has been done, the Flow Quality is very low (within single digit range) and the traditional “Approval Chains” and “Gated Checkpoints” serve as the most serious anti-patterns that the organization needs to resolve.

This metric is also measured at team level, group / program level, large solution group level, and Strategic Portfolio level, serving as an ongoing optimization practice to push for higher rates in each of the stages of every pipeline.

Flow Reliability

Also called Flow Predictability or Commitment Predictability, is an indicator of the accuracy of teams, groups/ programs planned delivery versus actual work that is completed and provided to customer.

Predictability is a sensitive measure that needs to evaluated and adjusted with considerations in maintaining organizational Agility, versus a sustainable and reliable level of predictability across the pipelines.

The executive management needs to resist the temptation of falling back into the traditional command and control ways of leadership, where they used to plan entire fiscals and impose rigid quarterly expectation on teams.

This is to allow the teams to maintain the essence of customer-centric innovation, rapid prototyping and fast market response based on the new feedbacks (serving the innovative aspect of the business), while providing enough predictability to safely set business objectives for program increments (short intervals of synchronized work across the teams) towards certain incremental delivery of products and services.

In other terms, the more predictable we are, the less innovative we will be.

Flow Analysis

The established Flow KPIs gather an invaluable amount of information that can be presented in dashboards setup for different levels of the organization.

This live and raw information is extremely useful and provides immediate insights on where we are in our continuously improved Agile transformation journey, but there is more in-depth wealth of knowledge to be extracted through performing analytical work on the collected metric values.

Flow analysis extends the depth of that visualization into the underlying parameters and create insights for all engaged teams and their management in health of the pipelines, the moving trends of improvement or degradation, and the impact of incremental changes in one section to other lateral or downstream sections.

Flow KPIs enable the lines of business to monitor the delivery rate and status of products and services against the roadmaps established, track the performance of Value Streams, and participate in brain storming and action items in reducing waste and raising productivity.

They can benefit tremendously from the combined power of business intelligence with Flow Analytics and their power in data-driven decision making.

Product managers can use Flow KPIs in their product discovery and management, visualizing the progress in delivery of every feature and their aggregate progress as part of a solution and even summing up into a portfolio value delivery pipeline.

The technology teams (Development Value Streams at program and solution level), benefit from the established transparency in all key metrics and the aggregate “single source of truth”.

They can use the visualization in identifying performance inhibitors or degraders ahead of time and trigger remedial and mitigatory actions to prevent them to reduce their impacts. They also benefit from AI enriched DevOps tools using the analytical data from Flow analysis to fine-tune their processes.


The Flow KPIs are the de facto elements in establishing the required lines of sight into current state and the on-going situation with all key components of our value delivery pipelines.

It is also imperative to reiterate that the entire purpose of Flow KPIs and Flow Analysis is to create visibility into the quality of the enterprise value delivery efforts and to create insights on the aggregate and program and team quality of work and improvements, to allow for better strategic navigation and more effective approaches in optimizing and improving all moving parts as a holistic system thinking framework.

They are not to be used to compare one group against another (generate the destructive internal competition) or penalize teams or individuals based on the measured metrics. Any abuse or mis-use of these KPIs will lead to their gaming by the teams and programs, depriving the enterprise leadership from true and realistic measures, leading to decision making based on manipulated (and unrealistic) numbers and will ultimately lead to direct negative impact to the organization’s competency and capacity in delivering real value to the customers and generating the desired Business Outcome.

Exclusive 💬

Arman Kamran

About author

Enterprise Agile Transformation Coach, CIO and Chief Data Scientist

Arman Kamran is an internationally recognized executive leader and enterprise transition coach in Scaled Agile Delivery of Customer-Centric Digital Products with over 20 years of experience in leading teams in private (Fortune 500) and public sectors in delivery of over $1 billion worth of solutions, through cultivating, coaching and training their in-house expertise on Lean/Agile/DevOps practices, leading them through their enterprise transformation, and raising the quality and predictability of their Product Delivery Pipelines.

Arman also serves as the Chief Technology Officer of Prima Recon Machine Intelligence, a global AI solutions software powerhouse with operations in US (Palo Alto, Silicon Valley), Canada (Toronto) and UK (Glasgow).

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