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Parallelism : Differential management - KPI - System
dynamics - System evolution
Differential management - KPI
At driving a car, we are highly sensitive to
small or instantaneous variations of the environment even when we
are steadily looking far ahead.
The fact that that our views arrow the time in
a perspective allows to propose a survey - i.e. of the KPI (Key
Performances Indicators) and balanced business indexes - in a
similar differential manner.
This utilizes the geometrical property that
enables to project time series into closed and finite clusters.
The two examples here below show that time
series can be transformed in closed clustered zones where time
variations can be visually recorded and graded.
System dynamics
The two figures here above provide with a good
support to illustrate operational system dynamics management and
practices - say to exemplify the difference between what we named
"SD-Drive" and "SD-Management".
In the first figure, the daily operations had
to sometimes cope with dangerous variations but they could get
back "in the green". The mean trends did
not required for long-term targets adjustments.
In the second figure, the daily operations have been
more often "deeply in the red" so that the alert belled more
often. It indicated first that the operational team "as-it-is"
could not actually cope with the current trends (SD-Drive).
In second, as the situation lasted for too
long, it indicated also a sound trend shift - say the mean dropped
nearly 50% - so that the long-term targets had to be reviewed.
In term of holotomial analysis, it turned that
the configuration space had changed so much that it had to be
re-mapped.
It is a current experience that what we called
"SD-Management" requires more study, investigation or engineering
- say more "seating works" - while what we named "SD-Drive" is
more talking to people, answering the phone or traveling at
various places - say more "running works".
The advantage of the perspective view is to
provide the two work styles with a common communication support.
System evolution
System evolution is
classically referred as the system components evolutions - say
motions - described from a given frame - or configuration - of
reference taken as a view point of observation.
The recognition - or the
concept - that a system
evolution may have an influence on the frame - or configuration -
of reference itself has been - slowly - proposed and accepted in several fields - i.e.
like in life sciences and some socio-economic domains, even
physics is "weakly" recalling for it with
background-independent visions - i.e. like Einstein or more recently
Lee Smolin who has been
quoted - to our understanding - to recall for a configuration
space evolution from the stand point of the string theory.
If you are not a scientist but
only someone with a bit of life experience, we may also accept
that a given organization schema - that is an organizational
configuration - mostly stand efficient for a while or for a
given set of conditions - i.e. a group size - but finally "always"
needs to be corrected or amended because of its obsolescence.
We have not soundly tackled
the problem of systems evolution per se but we are highlighting
that particles motions may induce changes in the configuration
space itself and vice versa - i.e. for the only reasons that - in
the holotomial analysis - a configuration of reference is
reflecting particles densities and that particles are also
themselves configurations.
This paragraph is here also to underline that
- in any configuration - particles can "capture" particles,
particles can "liberate" particles and particles can "combine"
with particles so that an initial system configuration may after a
"while" become obsolete and translated within another one.
We do not argue that this
implies system evolution but we assess that an holotomial analysis may allow system evolutions.
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