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Engineering Systems Integration
Processes have inputs, outputs, and losses to achieve those outputs.
Processes are measurable with objective measures such as cost, number of
people, and the amount of loss to achieve certain results. Processes are comparable to other processes subjectively. Yet it is quite difficult to say that one
process is better than another. What can be said is that there are noticeable
differences between processes, some taking more labor, some requiring
more resources, and some costing more money. However, in isolation, these
objective measures have little meaning as their bases are quite different and
therefore not comparable. Were there only a few processes that when combined produce a certain result, the combinatorial advantages and disadvantages of these processes might be discernable compared to another set of
like-kind processes. In this case, the comparison would be to ascertain if
there was a combinatorial advantage determined by the number of people
involved with the process, the total costs of the processes, and the amount of
time it takes to complete the work prescribed by the processes. For example,
two sports team compete in a “game,” each team bringing its different processes to test the consequences of their processes on “game day.” In a similar
fashion, processes of a like kind can be “tested” given that the competing
process sets agree to a set of rules and standards by which to measure the
outcomes of the “test.” Short of a game-play equivalency, there would not seem
to be an objective, rational basis on which to measure a process empirically.
Further, processes can be measured and improved relative to themselves
(Goldberg et al. 1994). If the same process is measured according to a set of
objective measures in a simulated “game-play,” then enacted again using the
same rules and standards in a subsequent “game-play” situation, then the
before and after comparisons of objective measures indicate the degree of
controls that are operative on the activities within the process. If there are
random sources of perturbations, then the variations in the objective measures can be collected and evaluated for a set of “game-play” “tests.”
The basic unit of dimension for a process is an act—a single factor signifying that a process might be evaluable in isolation is termed as an act, a
single step in a string of steps that when combined are recognizable as an
activity. Activities combine into processes. At the level of an act, the actor
(in this example, the human) may take form as “walking” between a desk
and a lab. That “walking” is part of a series of like-kind acts, concatenate to
the activity of “going to the lab.” That the combination of “going to the lab,”
“setting up an experiment,” “running the experiment,” and “taking data”
is considered the process of “running an experiment” signifies the manner
in which processes and their subtasks can be granularized (or partitioned).
There are many ways to granularize acts and activities, and there is no
standard. So the practice of valuing a process is problematic. You might
note an advantage to moving your desk into the lab to ‘save time’ (a function). Changing the activities changes the processes. It is notably difficult
to perform the same routine task in the same way each time that task is
performed. Consequently, there is variation in the acts, in the activities, and
Engineering Systems Integration
Processes have inputs, outputs, and losses to achieve those outputs.
Processes are measurable with objective measures such as cost, number of
people, and the amount of loss to achieve certain results. Processes are comparable to other processes subjectively. Yet it is quite difficult to say that one
process is better than another. What can be said is that there are noticeable
differences between processes, some taking more labor, some requiring
more resources, and some costing more money. However, in isolation, these
objective measures have little meaning as their bases are quite different and
therefore not comparable. Were there only a few processes that when combined produce a certain result, the combinatorial advantages and disadvantages of these processes might be discernable compared to another set of
like-kind processes. In this case, the comparison would be to ascertain if
there was a combinatorial advantage determined by the number of people
involved with the process, the total costs of the processes, and the amount of
time it takes to complete the work prescribed by the processes. For example,
two sports team compete in a “game,” each team bringing its different processes to test the consequences of their processes on “game day.” In a similar
fashion, processes of a like kind can be “tested” given that the competing
process sets agree to a set of rules and standards by which to measure the
outcomes of the “test.” Short of a game-play equivalency, there would not seem
to be an objective, rational basis on which to measure a process empirically.
Further, processes can be measured and improved relative to themselves
(Goldberg et al. 1994). If the same process is measured according to a set of
objective measures in a simulated “game-play,” then enacted again using the
same rules and standards in a subsequent “game-play” situation, then the
before and after comparisons of objective measures indicate the degree of
controls that are operative on the activities within the process. If there are
random sources of perturbations, then the variations in the objective measures can be collected and evaluated for a set of “game-play” “tests.”
The basic unit of dimension for a process is an act—a single factor signifying that a process might be evaluable in isolation is termed as an act, a
single step in a string of steps that when combined are recognizable as an
activity. Activities combine into processes. At the level of an act, the actor
(in this example, the human) may take form as “walking” between a desk
and a lab. That “walking” is part of a series of like-kind acts, concatenate to
the activity of “going to the lab.” That the combination of “going to the lab,”
“setting up an experiment,” “running the experiment,” and “taking data”
is considered the process of “running an experiment” signifies the manner
in which processes and their subtasks can be granularized (or partitioned).
There are many ways to granularize acts and activities, and there is no
standard. So the practice of valuing a process is problematic. You might
note an advantage to moving your desk into the lab to ‘save time’ (a function). Changing the activities changes the processes. It is notably difficult
to perform the same routine task in the same way each time that task is
performed. Consequently, there is variation in the acts, in the activities, and