Peeved by queues,
manufacturing experts line up solutions to the waiting
game
BY BRUCE GOLDMAN
If you've ever worked in a
retail store, you've probably noticed that a long, angry
line can suddenly sprout from nowhere at what was a quiet
cash register only moments before.
It's a fact of life: Lines
happen. And not just in retail, or on your local commuter
highway, or on the World Wide Web. You even find them in
factories. Despite society's image of a factory as the
ultimate embodiment of clock-like precision, the most
painstakingly planned production line can get blindsided
by profit-gobbling backups.
At an April 28 workshop
sponsored by Stanford's Alliance for Innovation in
Manufacturing, or AIM (formerly known as the Stanford
Integrated Manufacturing Association), several experts
attacked the problem of how and why lines happen, and
what can be done to prevent them.
The workshop, titled
"Variability, Throughput and Cycle Time in
Manufacturing and Product Development," was
organized by Stanford Professors J. Michael Harrison and
James M. Patell of the Graduate School of Business and J.
G. "Jim" Dai, a professor of engineering and
mathematics at Georgia Institute of Technology. AIM is a
campus-based joint venture, initiated by Stanford's
Graduate School of Business and School of Engineering and
a number of large corporate partners, whose mission is to
encourage advances in manufacturing and to disseminate
these advances throughout industry and academia.
High utilization plus
variability equals long processing times
The production backups
that factory managers often witness typically "don't
have anything to do with bad attitude, malfeasance, poor
corporate strategy or bad organization structure"
per se, Harrison told an audience of about 40 attendees.
Rather, congestion and delay will occur wherever systems
working near full capacity are subject to high
variability.
A productive resource
be it a worker or a machine may be working as hard as
he or she or it can, but this doesn't automatically
translate into a fast completion rate of the jobs being
processed. That's because the time it takes the resource
to complete its task is only part of the total time it
takes for a job to get done. The remainder is accounted
for by the time the widget spends in a queue, waiting its
turn to get worked on. The more heavily utilized
workstations that a job has to thread its way through,
the more bottlenecks can crop up, Harrison said.
Variability in a
manufacturing environment arises from unreliable
equipment, unpredictable yields, glitches in human
performance, fluctuations in order rates and sizes, and
numerous other sources. When a production system whose
components are working at close to full capacity is
subjected to the stress of such variability, resulting
waiting times can become very long compared with actual
processing times. "This is a scientific
principle," Harrison noted, predicted by a rigorous
mathematical treatment known as queuing theory.
Thus, in any production
system beset by variability in its many guises, a paradox
emerges: Using resources at close to full capacity, far
from ensuring an efficient operation, is almost a sure
guarantee of time-eating delays. Moreover, these delays
aren't meted out equally. While the average ratio of
waiting to processing time in a production system
overwhelmed by variability and high utilization rates may
be 9 to 1, which is bad enough, that ratio may look more
like 20 to 1 for some jobs.
"Keep in mind,"
Harrison reminded the audience, "the delivery time
you quote to your customers shouldn't be your average
performance, but rather a delivery time you can hope to
achieve 95 percent of the time."
Prescriptions to reduce
congestion
To alleviate congestion,
Harrison recommended a five-pronged approach:
- Eliminate all
unnecessary tasks and artificial constraints on
the order in which pieces of a project are
sequenced. Put simply, organize production
systems so that some things can get done while
other things are waiting to happen.
- Reduce the load on
individual resources by combining tasks, adding
capacity or lowering the order backlog by, for
example, raising prices.
- Reduce variability in
the operating environment whenever possible. Talk
your customers into scheduling their orders in a
staggered fashion. Get your error rates down to
avoid having to do the same thing twice.
- Pool resources.
Whenever possible, use standardized parts and
machinery. Cross-train your employees so they can
pinch-hit for each other in a crunch. Of course,
there are limits, Harrison acknowledged
lawyers and engineers are not interchangeable,
but engineers can hand off some tasks to
technicians.
- Stay flexible. Be
ready to reroute tasks and resources as new
information comes in.
Real-world implications
Other speakers at the
workshop discussed the practical implications of these
prescriptions and the difficulty of implementing them in
the real world.
"Manufacturing is
like Rodney Dangerfield," said Michael P. Kuntz, a
senior engineer at Boeing Corp. "It really doesn't
get much respect. But that's changing." Kuntz
stressed the difficulty of reforming ongoing as opposed
to first-time manufacturing operations. "When a new
program comes along, they always take the best and
brightest from older operations and move them into the
new thing. Those people get jazzed up." But the
existing operations suffer a brain-drain.
Alas, said Kuntz,
"the time frames for the development of solutions
you need are wider than your management team can fathom;
those solutions may yield results 10 years down the road,
when the management team will only be in place for two or
three years."
Paul Pickerskill, a
manager in the Lean Manufacturing Team at Visteon (a
wholly owned parts-making subsidiary of Ford Motor Co.),
agreed that slack capacity has value in improving
congestion performance. But increasing capacity
inevitably costs money, he said, and can be a tough sell
to higher management. Practically speaking, it may be
smarter to locate sources of variability and reduce them:
Make sure that a plant is laid out properly, for example,
or cut machine set-up times or schedule preventive
maintenance. One of the hidden advantages of the
just-in-time manufacturing methods developed in Japan and
widely adopted in the United States is that they
significantly reduce the variability introduced by
long-term forecasting, he said.
Applying queuing theory
to product development
Queuing theory has been
applied satisfactorily to the factory floor, but the
theory applies equally well to information flow as to
material flow substitute "in box" for
"queue" and "desk" for
"workstation." It might take 10 minutes to read
and act on a memo, but if it sits in someone's in box for
a month, whoever is downstream may learn a painful lesson
in applied queuing theory.
Vien Nguyen of Morgan
Stanley Dean Witter recounted a detailed study she and
several colleagues carried out while she was a
postdoctoral student at Stanford in the Graduate School
of Business. The study was an attempt to apply these
techniques to the area of product development in a
relatively large company specializing in high-technology
materials. Specifically, the Stanford research team
performed a detailed analysis of all the tasks performed
by the product development group, the order in which
those tasks were carried out and the time it typically
took to complete them.
"When we went in to
find out how many hours each person spent at each of a
number of defined tasks during the course of a year
how much time an engineer spent in, say, prototyping or
administrative work or support we got
resistance," Nguyen said. "They told us, 'This
is creative work! Each project is totally different from
the others.' But in fact, a lot of tasks are similar from
project to project, and so are the sequences in which
those tasks are performed."
Another common reaction
people had, said Nguyen, was: "You're gonna use
these numbers as punitive measures, to get rid of
me."
Antidote to
management's overoptimism
Once persuaded that tasks
can indeed be quantified and that nobody's going to get
laid off, harried workers nonetheless don't particularly
like logging their task time, Nguyen said, and they may
not always do so with perfect accuracy. Thus, resulting
estimates of task time may be off by 30 percent. But
without this kind of analysis, she said, managerial
estimates are more likely to be off by a factor of 3 to
10. ("And always in the same direction!"
interjected Harrison, to the mirth of the audience, who
appeared quite familiar with management's perennial
overoptimism.)
High-tech manufacturers in
particular are working hard to get inventory down, said
Gerald R. Feigin of i2 Technologies, and for a very good
reason. Feigin cited a study by big computer maker
indicating that of the $6.7 billion it was carrying in
inventory in 1997, about 60 percent $4 billion in
non-earning assets was the result of uncertainties,
largely due to variations in demand. He suggested that,
just as telephone companies smooth demand by having
different rates for different calling times,
manufacturers could perhaps charge different prices
depending on the order's urgency.
Feigin said he hoped
participants would take home at least one lesson from
this workshop: "Uncertainty is bad." On the
other hand, it's not so easy to avoid. As Feigin put it,
"Question: How do you get God to laugh? Answer: Tell
Him your plans." SR
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