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.
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.
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.
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.
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.
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.
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.
