IN MILAN ON A RAINY SUNDAY MORNING, MARCH 23, 1919, A FEW dozen angry men crowded into a muggy meeting room of the Industrial and Commercial Alliance in Piazza San Sepolcro. After hours of talk, they stood, clasped hands, and pledged their readiness “to kill or die” in defense of Italy against all enemies. To dramatize their unity, they chose for their emblem the fasces, a bundle of elm rods coupled with an ax that in ancient times had represented the power wielded by a Roman consul. The manifesto they signed bore just fifty-four names, and their foray into electoral politics that autumn was barely noticed, but within a couple of years the Fascist movement had more than two thousand chapters, and Benito Mussolini was their leader.
Unlike a monarchy or a military dictatorship imposed on society from above, Fascism draws energy from men and women who are upset because of a lost war, a lost job, a memory of humiliation, or a sense that their country is in steep decline. The more painful the grounds for resentment, the easier it is for a Fascist leader to gain followers by dangling the prospect of renewal or by vowing to take back what has been stolen.
Fascism should perhaps be viewed less as a political ideology than as a means for seizing and holding power. For example, Italy in the 1920s included self-described Fascists of the left (who advocated a dictatorship of the dispossessed), of the right (who argued for an authoritarian corporatist state), and of the center (who sought a return to absolute monarchy). The German National Socialist Party (the Nazis) originally came together around a list of demands that catered to anti-Semites, anti-immigrants, and anti-capitalists but also advocated for higher old-age pensions, more educational opportunities for the poor, an end to child labor, and improved maternal health care. The Nazis were racists and, in their own minds, reformers at the same time.
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.
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.
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.
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.
product development has asymmetric economic payoff-functions. Not all deviations have negative economic consequences. Because product developers encounter unexpected opportunities, conformance to plan may not represent the best economic choice. Our control system must enable us to exploit unexpected opportunities by increasing the deviation from the original plan whenever this creates economic value.
Before we discuss queues, we must introduce a little vocabulary. When we describe queueing systems, we refer to the queue, which is the waiting work, and the server, which is the resource performing the work. The pattern with which work arrives, which is usually unpredictable, is known as the arrival process. The time it takes the server to accomplish the work may also be unpredictable. This is known as the service process. We handle waiting work in a certain sequence, such as first-in-first-out (FIFO). This sequence is known as the queueing discipline.
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.
