good enough curvegood enough curve - - A concave curve with a relatively flat top with goal units (e.g., net profit, throughput, or happiness) on the vertical or Y axis and the decision variable (e.g., number of open projects, orders released to processing or level of multitasking) on the horizontal axis. There are three phases to the curve: too little, good enough, and too much of the decision variable.
Usage: On the left side of the curve the impact of having too little of the decision variable (e.g., too few open projects or orders) is depicted. The effect of this level of the decision variable is a low number of goal units (e.g., throughput is low due to starvation of the constraint). In the middle or flat portion of the curve the goal units are relatively stable over a wide range of the decision variable. The right side of the curve shows the effect on goal units of values of the decision variable that are too high. Three points are significant: 1. No one optimum solution exists for the decision variable to achieve the maximum goal units. This concept is counter to the academic search for an optimum solution for many situations. Instead, there is a wide range of values for the decision variable that provides equally acceptable numbers of goal units. 2. One of the basic concepts of the theory of constraints is that managers should strive to develop a good enough solution to a problem and then buffer the solution to minimize the risk that the planned goal units will not be achieved. 3. In the first phase (i.e., the left portion of the curve) the limiting factor is not enough work, while in the third phase (the right portion of the curve) the limiting factor is too much work, which causes confusion, resource
conflicts, and multitasking, with a consequent drop in productivity.
In a real situation, the investigator would identify the undesirable effects of the current situation and could then, using cause-and-effect logic ( a logic branch or CRT), tell where on the curve the organization is. If the undesirable effects are caused by too little of the decision variable then more of the decision variable is applied. If the undesirable effects relate to too much of the decision variable then the decision variable is usually reduced by 25% (e.g., 25% of open projects are frozen). This action usually brings the organization to the good enough region of the diagram. Buffer management can then be used to continually refine the solution.