revenue lost due to poor consumer (public) perception. No one quite knows how to accurately measure the amount of revenue lost based on "what if the perception would have been different." In other words, it is almost impossible to find data based on estimates of "what could have been" that will be reliable enough to draw accurate conclusions on lost revenue.
Several other factors also add to this complication, including some that are directly related to current accounting practices. For instance, the conventional accounting practice does not provide enough guidance regarding how to account for lost benefit of added market share (where a government has to compete with the private sector to provide services) or the value of added quantity in moving an organization further along the learning curve. In spite of this difficulty, an organization must have some knowledge of the costs it would incur (even how rudimentary that may be) due to lost revenue from poor perception. One way to deal with the problem is to develop some changes in the current accounting practices consistent with changing conditions in the market, or the economy in general.
Quality control deals with methods, tools, and approaches that are useful for an organization, in particular government, to improve the quality of its goods and services. As the demand for quality control increases, so is the need for better control measures to ensure that the goods and services a government provides meet the quality standards. Although it is difficult to be precise as to what quality means for many of these goods and services, it is possible to identify specific areas of service operation where the control measures available to a government could be effectively applied to ensure quality.
This chapter has presented several different measures of quality control, in particular those related to process control. Most of these measures are simple enough that one should not have any difficulty in constructing them. However, two things must be properly addressed when using these measures, especially in government: one, the data must be reliable to produce useful results; two, efforts must be made to identify specific factors that affect quality. In most instances these factors are preventable, meaning that if properly identified they can be removed from the process before they can cause any major damage to it.