The Right Person for the Job
by David Pescovitz
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Professor Rhonda Righter, an alumnus of UC Berkeley's IEOR department, returned as a faculty member in July of this year.
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What do a sandwich
shop, a bank and an automobile plant have in common? They're
examples of places that could benefit from research in UC Berkeley's
Department of Industrial Engineering and Operations Research (IEOR).
IEOR professors Rhonda Righter and Hyun-soo Ahn are teasing out
general "rules-of-thumb" from the esoteric mathematics
of IEOR to help managers train employees and efficiently distribute
a company's workload, thereby amping up workplace productivity.
Perhaps the most ubiquitous workflow approach is for a single employee
to follow one task from beginning to end. While this may seem like
a logical approach, Righter and Ahn's research reveals that
it may not be the most efficient. One method they're investigating
is known as "last-buffer, first-served" (LBFS). The idea
is simple: employees should prioritize projects that are closest
to being completed even it had been another employee's responsibility.
"If you have people coming and going in a company or getting sick
during the course of a large project, there are often many unfinished
tasks," Righter explains. "Under certain circumstances,
it's better for someone else to pick up where something was
left off rather than waiting for the first person to come back
and finish it."
LBFS may seem like common sense, but it's often underutilized,
Righter says. Take the process a bank uses to approve a loan. Typically,
one employee follows a loan from start to finish. In reality though,
there may be ways to dramatically speed things along by assigning
various components to a number of employees. Perhaps, Righter says,
certain individuals may be trained to float through the office,
helping those who are bogged down at certain stages in the approval
process.
Professor Hyun-soo Ahn also conducts research on supply chain and service operation management.
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"We're especially interested in situations where there could
be random factors," Righter says. "In the case of bank
loans, the approval process could be held up because more data
is needed. We'd like to figure out how to model these kinds
of uncertainties to develop better policies."
To mathematically prove their rules-of-thumb theories, Righter
and Ahn employ stochastic models — mathematical models that
contain random variables representing random factors. Specifically,
they look at so-called Markov chains, models of a sequence of events
where the probability of one event depends on whether the preceding
event occurred or not.
"By using these models to prove theorems, we can determine that
a particular workflow policy is optimal, the best you can do in
that situation," Righter says.
Righter and Ahn are also studying another workflow policy called
a "bucket brigade." Named for the way old-time firefighters
passed containers of water down the line to quench a blaze, here
products are progressively completed as they move down the line.
Once the last employee puts the finishing touches on a product,
the employee walks “upstream” to take over the task
of the person ahead. The trick to maximizing the efficiency of
a bucket brigade, Righter explains, is to ensure that the workers
are sequenced so that the fastest employee is placed at the end
of the line. This results in a workflow that "tunes" itself
so that the brigade produces the most products possible given the
employees' varying speeds.
From order-pickers
in warehouses to submarine sandwich makers, a carefully orchestrated
bucket brigade can dramatically improve
production, Righter says. But setting one up is not so simple.
In a busy photocopy and printing shop, for example, should every
person be trained in every step of completing a job, from customer
service to collating? If only one person knows the design software,
will that create a bottleneck so severe that it makes financial
sense to train other employees in the software?
Those are the kinds of questions Righter and Ahn will tackle next.
They hope to develop a set of rules to determine how flexible employees
should be. Increased flexibility, Righter says, means more on-the-job
training.
"It's expensive to train your employees to perform every task," Righter
says. "And once they know how to do many different jobs, how
do you determine who is assigned what task and in what order?"
UC Berkeley Industrial Engineering and Operations Research
Rhonda Righter's
former home page at Santa Clara University
Hyun-soo
Ahn's
Home Page
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