Specific
KPI can be defined for Marketing, IT, Sales, production, etc.
The
problem, in any event, appears to be a subjective
one, namely that of chosing the
right parameters and, most
importantly, understanding the
business well. In KPI definition
the so-called intangibles are avoided since they cannot be measured.
Science
teaches us that even though everything is relative, there do exist
objective and tangible properties of systems which may be measured.
When dealing with mechanical systems energy is one. Mass and
temperature are other examples.
Complexity
is an attribute of systems which today can be measured. Growing
complexity in all spheres of social life is the biggest threat to
sustainable development and to a resilient economy.
It would
be great to measure it and to use it as a new meta-KPI.
The pop-science definition of complexity, which equates it to a
"process of spontaneous self-organization at the of
edge-of-chaos" (by the way, ho do you measure that?) is not of
much use. We propose a definition of complexity which combines
topology and entropy (now that can be measured), i.e. C=f
(T; E).
Let's see
a example of how this definition of complexity can be used as a new
KPI.
In Supply
Chain Management, for example, KPIs will detail the following
processes (see the Wikipedia):
- sales forecasts
- inventory
- procurement and suppliers
- warehousing
- transportation
- reverse logistics
Without
going into further detail, let us suppose that each of the above
items is already a KPI. The idea we propose is to combine these into
a single holistic KPI.
In other words, all of these KPIs are computed at certain intervals
and the resulting data is analyzed to produce one
combined measure of global performance,
in a Complexity Map.
The big
advantage of this approach is that it is unnecessary to come up with
subjective weights. The information of how much a particular KPI
influences the meta-index is already contained in the data.
The
question is: how much does each single KPI contribute to this
picture? The answer lies in the so-called Complexity Profile, which
provides a breakdown of the total system complexity into components.
The advantage
of this approach is that it overcomes the weaknesses and risks
inherent in (arbitrary) functions containing subjective weights. It's
all in the data.
Nessun commento:
Posta un commento