Adaptive control systems designed to track dynamic goals are different than those designed to conform to static goals. Because product development has inherently high variability, it is critical to recognize situations where our goals should be dynamic. In such cases, we should strive to constantly reduce the gap between our current state and the economically optimal state, even when this economically optimal state is constantly changing. For example, this is commonly done in consumer marketing, where continuous preference-testing is used to detect market shifts that take place during development.
In a Markov process, the elapsed time between arrivals is exponentially distributed. This simply means that short inter-arrival times are more probable than long ones. This is also known as a memoryless arrival pattern, because each arrival is statistically independent from the next.
we should remember that our control system can add economic value in two ways. It can help us to reduce negative deviations from the plan, and it can attract effort to emergent opportunities, thereby increasing positive deviations from plan.
We want to control measures of performance that have strong influence on economic success. We can identify these parameters using the economic framework discussed in Chapter 3. If a 10 percent increase in project expenses reduces profits by 1 percent, and a 10 percent increase in unit cost reduces profits by 50 percent, we should focus our attention on controlling unit cost, not project expenses.
A granular timeline subdivides time intervals into very small buckets. When we do this, the coefficient of variation for each of these buckets becomes very high. This makes variance very high and conformance unlikely. Even worse, if we incentivize conformance, people will insert contingency reserves to prevent their tasks from missing the schedule. The more granular the schedule, the larger the schedule reserves. And these reserves aggregate into even longer timelines. The more we increase planning detail and the harder we try to incentivize performance, the worse our problem becomes.
when we emphasize flow, we focus on queues rather than timelines. Queues are a far better control variable than cycle time because, as you shall see, queues are leading indicators of future cycle-time problems. By controlling queue size, we automatically achieve control over timelines.
there can be strong diseconomies associated with large batches. Furthermore, the modest reduction in variability due to the pooling of variances will be completely overwhelmed by the geometric increase in uncertainty caused by the longer planning horizons associated with large batches.
how do we prevent all these small review meetings from driving up overhead? We conduct these review meetings on a regular time-based cadence. Every Wednesday afternoon at 1:00 pm, we review all the drawings completed in the last week. There is no need for a meeting announcement and no need to coordinate schedules. Meetings that are synchronized to a regular and predictable cadence have very low set-up costs. They contribute very little excess overhead.
We must recognize that our original plan was based on noisy data, viewed from a long time-horizon. For example, we may have started development believing a feature would take 1 week of effort and it would be valued by 50 percent of our customers. As we progressed through development, we may have discovered that this feature will require 10 weeks of effort and it will only be valued by 5 percent of our customers. This is a factor of 100 change in its cost-to-benefit ratio. This emergent information completely changes the economics of our original choice. In such cases, blindly insisting on conformance to the original plan destroys economic value.
To manage product development effectively, we must recognize that valuable new information is constantly arriving throughout the development cycle. Rather than remaining frozen in time, locked to our original plan, we must learn to make good economic choices using this emerging information.
This leads them to load their processes to dangerously high levels of utilization. How high? Executives coming to my product development classes report operating at 98.5 percent utilization in the precourse surveys. What will this do? Chapter 3 will explain why large queues form when processes with variability are operated at high levels of capacity utilization. In reality, the misguided pursuit of efficiency creates enormous costs in the unmeasured, invisible portion of the product development process, its queues.
Since high capacity utilization simultaneously raises efficiency and increases delay cost, we need to look at the combined impact of these two factors. We can only do so if we express both factors in the same unit of measure, life-cycle profits. If we do this, we will always conclude that operating a product development process near full utilization is an economic disaster.
There are two important reasons why product developers are blind to DIP. First, inventory is financially invisible in product development. We do not carry partially completed designs as assets on our balance sheet; we expense R&D costs as they are incurred. If we ask the chief financial officer how much inventory we have in product development, the answer will be, “Zero.” Second, we are blind to product development inventory because it is usually physically invisible. DIP is information, not physical objects. We do not see piles of DIP when we walk through the engineering department. In product development, our inventory is bits on a disk drive, and we have very big disk drives in product development.
Any subprocess within product development can be viewed in economic terms. The total cost of the subprocess is composed of its cost of capacity and the delay cost associated with its cycle time.
Few developers realize that queues are the single most important cause of poor product development performance. Queues cause our development process to have too much design-in-process inventory (DIP). Developers are unaware of DIP, they do not measure it, and they do not manage it. They do not even realize that DIP is a problem.
let me point out one more subtle implication of this approach towards buying information. It implies that there is an economically optimum sequence for risk-reduction activities. Low-cost activities that remove a lot of risk should occur before high-cost activities that remove very little risk.
