Other companies prioritize on the basis of project profitability measures like return on investment (ROI). On the surface, this appears to be an economic approach, but this is just an illusion. By prioritizing, we choose to service one project before another. In general, it is best to delay the project with a low cost of delay. This suggests that we should not prioritize on the basis of project profitability, but rather on how this profitability is affected by delay. Of course, this can only be done when we know the cost of delay, information that 85 percent of developers do not have.
When economics change, we must reevaluate the economic wisdom of our choices. Remember Principle E1, our primary objective is to make good economic choices. To blindly conform to the original plan when it no longer represents the best economic choice is the act of a fool.
we should make each decision at the point where further delay no longer increases the expected economic outcome. By avoiding “front-loading,” we can take advantage of the fact that market and technical uncertainty decrease with time. Instead of waiting for the “last responsible moment,” we can recognize that the cost of certain decisions can rise steeply past a certain point, or that the value created by waiting may have stopped increasing. The timing of economic choices should be based on their economics, not broad philosophical concepts like “front-loading” or “responsible deferral.”
Reducing risk, which is the primary mission of testing, clearly creates economic value for product developers. In fact, reducing risk is so centrally important to product development that it is indispensable for us to quantify its economic impact.
this is a blind spot for many modern managers who are heavily influenced by the concept of the Pareto Principle, which observes that 80 percent of the leverage lies in 20 percent of problems. The dark side of the Pareto Principle is that we tend to focus excessively on the high payoff 20 percent. We overmanage this 20 percent, and undermanage the other 80 percent. This leads to what we might call the Pareto Paradox: There is usually more actual opportunity in the undermanaged 80 percent than the overmanaged 20 percent.
Unhappy with late deliveries, a project manager decides he can reduce variability by inserting a safety margin or buffer in his schedule. He reduces uncertainty in the schedule by committing to an 80 percent confidence schedule. But, what is the cost of this buffer? The project manager is actually trading cycle time for variability. We can only know if this is a good trade-off if we quantify both the value of cycle time and the economic benefit of reduced variability.
We need COD to evaluate the cost of queues, the value of excess capacity, the benefit of smaller batch sizes, and value of variability reduction. Cost of delay is the golden key that unlocks many doors.
For simple single variable decisions, we only need to know the direction of the change. For multivariable decisions, we also need to know the magnitude of the change, and most importantly, we need a method to express all changes, in all variables, in the same unit of measure. This is the only way we can evaluate the overall economic consequences of changing multiple proxy variables simultaneously.
we commonly use five key economic objectives as measures of performance for a project. We vary each measure independently and assess its influence on life-cycle profits. In effect, we are trying to determine the transfer function between each measure of performance and life-cycle profitability. This method is known as sensitivity analysis.
WIP constraints are a powerful way to gain control over cycle time in the presence of variability. This is particularly important for systems where variability accumulates, such as in product development. WIP constraints exploit the direct relationship between cycle time and inventory, which is known as Little’s Formula.
The current orthodoxy does not focus on understanding deeper economic relationships. Instead, it is, at best, based on observing correlations between pairs of proxy variables. For example, it observes that late design changes have higher costs than early design changes and prescribes front-loading problem solving. This ignores the fact that late changes can also create enormous economic value. The economic effect of a late change can only be evaluated by considering its complete economic impact.
manufacturing deals with predictable and repetitive tasks, homogeneous delay costs, and homogeneous task durations. As a result, manufacturing sequences work in a simple first-in-first-out (FIFO) order. FIFO prioritization is almost never economically optimal in product development, because product development deals with high variability, nonrepetitive, nonhomogeneous flows. Different projects almost always have different delay costs, and they present different loads on our resources.
The king was the law, and once the law was dead, chaos reigned. Nobles fastened their cloaks, pulled on their boots, and saddled up their horses. Some grabbed gold, some grabbed documents, and some even grabbed the meat and wine in the royal cellars.1 Meanwhile, as they rushed back and forth, Chilperic remained where he had fallen from his horse2 in the stable yard. The sky turned from violet to black and the shadows of the surrounding forest fell across the crumpled king, now completely alone.
If pressed, Cook will disagree, politely, with the portrait painted by some that he is a spreadsheet guy, a suit running a company built by a man who disdained suits. “Steve saw this—one of the things I loved about him was he didn’t expect innovation out of just one group in the company or creativity out of one group,” Cook says. “He expected it everywhere in the company.” Including in operations, where Cook worked: “When we were running operations, we tried to be innovative in operations and creative in operations, just like we were creative elsewhere. We fundamentally had to be in order to build the products that we were designing.”
Pot odds are the payout offered by the pot expressed in comparison to the price to call a bet. For example, if there was $100 in the pot and the player had to put in $20, the pot was offering 5-to-1 odds. These odds were then compared to the likelihood the player making the decision would win the hand, either by having the best cards or drawing to the best hand.
