Strengthening the Empirical Base of Operations Management

Fisher: Strengthening the Empirical Base of Operations Management
Manufacturing & Service Operations Management 9(4), pp. 368–382, © 2007 INFORMS 375
rework, and scrap. We could continue to add inspectors,
steadily reducing the defect rate while increasing
cost and thus moving along a cost-quality tradeoff
curve. On the otherhand, if we identify one ormore
root causes of defects and modify the process to
remove these root causes, then the process will produce
fewerdefective units so we will both lowerthe
defect rate and reduce cost because of reduced rework
and scrap. Total cost will be reduced as long as the
cost of diagnosing and fixing root causes of defects is
less than the savings in rework and scrap expenses.
Total Quality Management (TQM) wisdom suggests
sorting root causes on the number of product defects
they cause and fixing first those root causes responsible
for the most product defects. If this approach is
followed, we will likely improve quality and reduce
cost as we attack the most serious process defects initially;
eventually we will be fixing process defects that
cause so few product defects that the savings in rework
and scrap will be less than the cost of process
improvement—then improved quality comes at the
expense of increased cost.
Several authors have suggested principles that
align with these observations. Clark (1996), Hayes and
Pisano (1996), and Porter (1996) observe that most
companies operate off the efficient frontier between
quality and cost and are therefore initially able to
improve on both dimensions. But eventually they
reach the frontier and face a tradeoff between cost and
quality. Ferdows and DeMeyer (1990) suggest that
companies should and do give priority to quality over
cost. If they are off the efficient frontier, then they
improve both quality and cost but give higher priority
to quality improvement. If they face a tradeoff
between quality and cost, then the improved quality
is at the expense of increased cost. Once they reach
a position of high quality, they may then focus on
cost reduction and eventually move to a new efficient
frontier in which both quality and cost are improved.
Lapre and Scudder (2004) examine these hypotheses
within the context of the airline industry using
public data collected and reported by the U.S. Department
of Transportation (DOT). Their quality metric is
the numberof customercomplaints made to the DOT
per100,000 passengers, theircost measure is cost per
seat-mile flown, and theirasset utilization measure is
fleet utilization, the average percentage of the time in
a 24-hourday that a plane was available forservice
that the plane was in active use; a period of active
use is defined from when the plane first moves under
its own power from the boarding ramp at the departure
airport until it comes to rest at the ramp for the
destination airport. Using DOT data, these three variables
were tabulated for each of the 11 years from
1988–1998 forthe 10 majorairlines operating during
this time: Alaska, America West, American, Continental,
Delta, Northwest, Southwest, TWA, United,
and U.S. Airways. The study qualitatively examined
the cost-quality path followed by each airline over
the 11 years and concluded that when an airline was
forced to make a tradeoff between quality and cost,
it generally elected first to improve quality at the
expense of cost, and, in some instances, subsequently
also improved cost to arrive at an overall superior
position. Their empirical research thus provides support
for the hypotheses of Clark (1996), Ferdows and
DeMeyer(1990), Hayes and Pisano (1996), and Porter
(1996).
As another example: of integration of theory and
empirics, Cachon and Lariviere (2001) and Terwiesch
et al. (2005) both considera demand planning problem
between a manufacturer and supplier, the first
from a theoretical and the second from an empirical
perspective. A manufacturer is launching a new product
and will be purchasing a specialized key component
from a supplier. In advance of the product
launch, the supplierneeds to build capacity, which
can only be used forthe specialized key component.
To help the supplierdecide what level of capacity to
build, the manufacturer provides the supplier with its
forecast of demand in the form of a probability density
function of demand. Note that the manufacturer
has an incentive to inflate its forecast to encourage
the supplierto build ample capacity and thus minimize
the risk that it might loose business because of
inadequate supply. The supplier, on the other hand,
knows the manufacturer has this bias and is therefore
inclined to discount the forecast.
This is a very real problem. As one personal example,
I worked once with the manufacturing division
of a large company that had tracked the forecasts prepared
by the sales and marketing division and found
they exceeded actual demand by 30%. Consequently,
they began to divide the sales and marketing forecast