In the 1950′s Stafford Beer managed the largest Operations Research firm in
England. A large corporation would come and say “Our inventory is out of
control. Can you develop a system for us to get the inventory costs down?”
Stafford always said something like “We certainly can. You have come to the
right place. However, to make sure the new inventory control system will
really and truly fit your company, we had better poke around a bit. We need
to interview some folks and spend some time observing how your plant
actually operates.”
After a bit of poking around, Stafford’s team would discover that the
inventory control problems were merely symptoms of some other problem. The
key problem always turned out to be an inappropriate compensation scheme
for the sales force or an inadequate production control system or some
thing else equally far from the inventory control system. For example, in
one manufacturing company the salesmen earned the same commission for
selling a manufacture-to-order item as for selling their most popular
product. This compensation scheme insured that the inventory would never be
adequate, because the salesmen kept selling unique items. The inventory
problem was fixed, along with the firm’s overall profitability, by making
the sales commission depend upon the profitability of the item sold.
Stafford’s firm had dozens of these inventory control engagements. They
almost always carried them to a successful conclusion — and they never
built a single inventory control system. The real problem always turned out
to be something else and the inventory problems were merely symptoms.
Now imagine what would happen if the consultant engaged to fix the
inventory control problem was the world’s best expert on inventory control
systems but didn’t know much else. In the example cited above where the
salesmen kept selling unique items, the firm might sink massive amounts
into improving the inventory control system. They might become extremely
good at keeping precisely the optimum quantities of all of their standard
products in the warehouse. And all of this effort would mask the real
problems and make it even less likely that anyone would stop and examine
the structure of the sales commissions.
Companies are complex systems, with multiple circular chains of cause and
effect. For example, reliance on sending orders down the chain of command
discourages initiative on the part of the lower ranks – and this loss of
initiative means that you can’t get anything done without issuing lots of
orders. In such a company, the executives and the workers are engaged in an
unrecognized conspiracy to ensure that the executives stay busy issuing
orders and the workers don’t do anything except what they are explicitly
told to do. The workers become cynical and irresponsible, and save their
thinking for when they are off the job. The executives lament the sorry
state of morale, the low quality of their work force, and the excessive
time it takes to actually get anything done. The executives never realize
that they have “trained” their work force to be irresponsible and
unmotivated.
Peter Senge provides many other examples of circular causal chains in
companies. He says that the symptoms that we struggle with are generally
far removed from the real causes. Consider our two examples above:
(1) The symptoms are that inventory is out of control. The real problems
turn out to be the structure of the sales commission or the production
scheduling system.
(2) The symptoms are unmotivated and irresponsible workers. The real
problem is a management team that has unwittingly encouraged these
behaviors on the part of the workers by an over-reliance on issuing orders.
To paraphrase Russell Ackoff, it is far more useful to fail at solving the
right problem, than to succeed in solving the wrong problem. Determining
the right problem requires skills in observing and modeling. All companies
do this, but usually only in an ad hoc way. Observing includes gathering
data, statistical analysis, examination of processes, and often just plain
old fashioned conversation and direct observation. Modeling is making sense
out of our observations. It puts the data in a context for making
predictions. It is often facilitated by tools, such as systems dynamics (a
method for modeling in terms of circular causes and effects) or statistical
analysis. A good model interprets our observations, provides a road map for
action, predicts results, provides measures, and can even be useful for
supporting or helping to define a desired vision (see Corporate Forums 1.1
and 1.2).
For a company to consistently produce good models requires a set of skills.
These skills can be facilitated and learned. They include the use of
powerful tools such as those mentioned above. More importantly they include
the abilities to determine the focus (appropriateness), to exercise rigor
(reproducible observations and valid generalizations), to develop an open
and true sharing of ideas (dialog), and to define expectations (measures
for success). When such skills are explicitly facilitated and learned,
observation and modeling become more consistent, predictable, and less
dependent on personalities. The company willing to make such an investment
will find itself more consistently attacking the right problem. Most
importantly, it will have developed a distinct competitive advantage: it
will have learned to learn.
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Originally published as
Volume 1.8, November 4, 1998 of The Corporate Forum
Department of Engineering Management, Old Dominion University