After embracing the foundational ideas of “Eliminating Waste,” “Amplifying Learning,” and “Deciding as Late as Possible” in my previous articles, we now arrive at the fourth principle, which acts as the engine driving the entire system: “Deliver as Fast as Possible”. In their book, Lean Software Development: An Agile Toolkit, Mary and Tom Poppendieck present rapid delivery not as a reckless rush to the finish line, but as a strategic capability that unlocks immense business value for you and your team, and enables the other principles to function effectively.

Two decades after the publication of the book, the imperative for speed has only intensified. In an era where market windows open and close in months, not years, and where the instant gratification of consumer technology shapes user expectations, the ability to deliver value quickly can be your organization’s baseline for survival. Revisiting this chapter reveals that the Poppendiecks’ insights into speed were prescient. The tools they described, pull systems, queuing theory, and economic modelling, are not just relevant; they are the theoretical underpinnings of the modern DevOps and Continuous Delivery movements that define the high-performing IT organizations of today.

Why Speed Is More Than Just a Schedule

Traditional project management often views speed as one corner of a rigid triangle, with speed standing in direct opposition to cost and quality. The old sayings “haste makes waste” and “faster is dearer” suggest that accelerating delivery inevitably leads to higher expenses and sloppier work on the software development side. Thankfully, lean thinking dismantles these misconceptions. The Poppendiecks argued that speed, when achieved by optimizing the entire value stream, is a catalyst for better outcomes.

Rapid delivery offers several profound benefits:

  • It enables customer responsiveness: The primary advantage is delivering value to your customers quickly. The sooner a feature is delivered, the sooner your customer benefits, and the sooner your organization can start earning a return on its investment. Of course, another vital advantage is the enablement of timely customer feedback and the optimization that can follow from it..
  • It reduces risk: Long development cycles are inherently risky. Partially completed work can become obsolete. Market needs can shift. And defects can remain hidden for months, thus becoming exponentially more expensive to fix. Short cycles reduce the inventory of unfinished work and its associated risks.
  • It powers learning and late decisions: The principles of “Amplify Learning” and “Decide as Late as Possible“, which we looked at in earlier articles, are entirely dependent on the ability to execute quickly. Your team can only afford to delay a decision if it can implement it rapidly once it’s made. Fast delivery shortens the feedback loop, which is the engine of learning.

In essence, delivering fast is not about cutting corners; it’s about building a highly efficient system that transforms an idea into delivered value with minimal delay and waste.

Key Mechanisms for Delivering Fast: Poppendieck’s Toolkit

Chapter four of Lean Software Development introduces three powerful thinking tools to help organizations structure their work for speed. Let us explore these concepts and their modern manifestations.

1. Pull Systems

Traditional development often operates on a push system. A master schedule or a detailed plan dictates what work a development team should do and when. This work is then pushed onto the team, regardless of their actual capacity. This leads to overburdened teams, context switching, and large batches of work-in-progress, all of which create waste and slow down the system. The lean alternative is a “pull” system, inspired by Toyota’s kanban method. In a pull system, work is only started when there is a clear signal of demand from a downstream station and the capacity to handle it. This prevents the system from being overloaded and ensures a smooth, continuous flow.

Relevance in 2025: Pull systems are now the operational backbone of modern Agile and DevOps practices. A Kanban board is the most direct implementation, where work items, such as features or user stories, are “pulled” from a To Do column into a Work in Progress column by the development team only when they have the capacity. This explicitly limits work-in-progress, which, as queuing theory shows, is critical for reducing cycle times. Continuous integration and continuous delivery pipelines also function as sophisticated pull systems. A developer committing code signals a demand, which pulls the code through an automated sequence of building, testing, and deployment stages. The work is self-directing; developers don’t wait to be told what to do but are signaled by the state of the system. Visual controls, such as dashboards showing build status or Kanban boards, are essential for making the signals in a pull system visible to everyone in your team, enabling their self-organization and rapid response.

2. Queuing Theory

The Poppendiecks brilliantly apply the mathematical principles of queuing theory to software development. Any process where work arrives, waits to be processed, and is then serviced can be analyzed as a queue. The key metric is cycle time, which is the total time from when a request enters the queue to when it is completed. Queuing theory provides several critical, and often counterintuitive, insights:

  • High utilization is the enemy of speed. As the utilization of a resource (like a testing team or a server) approaches 100%, the length of the queue in front of it, and thus the cycle time, increases exponentially. A system with no slack or buffer capacity will inevitably grind to a halt.
  • Variability kills flow. Variability in the arrival of work (lumpy batches) or in the time it takes to process work will dramatically increase queue lengths and cycle times.
  • Large batches are a primary source of variability. Large batches of work (e.g., saving all testing for the end of a project) create long queues and massive delays.

Relevance in 2025: Queuing theory is the mathematical justification for nearly every core Agile and DevOps practice.

  • Limiting work-in-progress: The core tenet of Kanban is a direct application of queuing theory. By limiting the number of items in the “in-progress” queue, teams reduce cycle time and increase throughput.
  • Small Batch Sizes: The emphasis on minor, frequent releases in Agile and Continuous Delivery is the most effective way to reduce variability and shorten queues. Instead of a single large batch of features to be implemented, your team’s work flows in a continuous stream of small batches, dramatically reducing the “traffic jams” in their development process.
  • Addressing Bottlenecks: The theory of constraints, which complements queuing theory, teaches that the throughput of any system is determined by its primary bottleneck. In 2025, this could be a manual testing process, a slow security review, or a cumbersome deployment approval board. Applying queuing theory means focusing improvement efforts on these bottlenecks by automating them, adding capacity, or reducing the variability of work arriving at them. Running your testing team at 95% utilization may look efficient on a spreadsheet, but queuing theory proves it creates crippling delays for your entire organization.

3. The Cost of Delay

To make effective, fast decisions, teams need to understand the economic impact of their choices. The Poppendiecks advocate for creating simple economic models to quantify the cost of delay. Such a quantification could involve building a basic profit and loss model for a product and then calculating the financial impact of delaying its launch by a month or more. This often reveals that the cost of a delay (in terms of lost revenue, reduced market share, or missing a market window) is vastly higher than the cost of development itself.

Relevance in 2025: This concept is more critical than ever. In digital markets, first-mover advantage can be significant, and user loyalty can be very fickle. Understanding the cost of delay provides a robust framework for making trade-off decisions.

  • Prioritization: Frameworks like Weighted Shortest Job First, popular in the Scaled Agile Framework (SAFe), are direct implementations of this thinking. They force teams to quantify the cost of delay for features and divide it by the job duration to determine the highest-value work to tackle next.
  • Justifying Investment: An economic model can justify investments that accelerate delivery. Should we spend $100,000 on better testing automation? If the model shows that the cost of delay is $50,000 per week and the automation will speed up delivery by three weeks, the decision becomes obvious.
  • Empowering Teams: Providing your team with an economic model empowers them to make smart, decentralized decisions. Instead of a project manager making trade-offs in isolation, an entire team understands the financial impact of their choices, leading to a better alignment with business goals. No matter whether it is a product’s profit-and-loss model or a simpler model for an internal application, such as modeling the value of reduced call-handling time, the act of quantifying the cost of delay frames every decision in terms of business value.

Challenges and Nuances in 2025

Applying these principles in the modern context requires navigating new complexities:

  • The Microservices Bottleneck: While microservices enable parallel development, they also create a complex web of dependencies. A delay in one critical service can create a cascading queue that blocks multiple teams. The root causes are usually too fine-grained services, sync call chains, and shared data models. If teams cannot own services end-to-end, including the schema and runtime, flow stalls. Managing flow in such a distributed system requires sophisticated CI/CD, contract testing, and observability. However, tooling can mitigate, not remove, the coupling.
  • The Illusion of Infinite Cloud Capacity: The cloud seems to provide near limitless capacity, which can mask queuing problems. It is easy to spin up more servers to handle the load, but this often hides underlying inefficiencies in the code or architecture. True speed comes from optimizing the system, not just throwing more resources at it.
  • The Human Factor: The most significant queues are often not technical but organizational. Manual approval processes, change advisory boards, and handoffs between siloed teams are the biggest impediments to flow. Delivering fast requires a cultural shift towards trust, automation, and empowered teams.

Conclusion: Speed as a Foundational Capability

Revisiting the principle “Deliver as Fast as Possible”  confirms its status as a cornerstone of modern software engineering. The tenets of pull systems, queuing theory, and cost of delay are not abstract academic concepts. They are the proven principles of efficient value delivery. And they explain why Agile, Lean, and DevOps practices work.

In today’s competitive landscape, the ability to rapidly and reliably deliver value is the ultimate measure of your organization’s effectiveness. Teams that master this capability can learn faster, adapt to market changes more quickly, and make better economic decisions. They don’t achieve speed by working harder or taking shortcuts, but by systematically designing a development process that minimizes queues, reduces batch sizes, and eliminates delays. The Poppendiecks provided the blueprint over two decades ago, and its wisdom continues to be a reliable guide for organizations. We will continue looking at their work in my next article on the chapter “Empower the Team”.