Strengthening the Empirical Base of Operations Management

Fisher: Strengthening the Empirical Base of Operations Management
378 Manufacturing & Service Operations Management 9(4), pp. 368–382, © 2007 INFORMS
progress of new product development from company
records ��� � We retained 220 new products for which
complete histories on time to prototype, time to volume
production, and engineering expenditures were
available.” They used these data to identify features
of new product development structures that correlate
with a short time to market.
DeHoratius and Raman (2004) worked with a retailer
that audited its stores to identify discrepancies
between the quantity of a SKU as counted on the
shelf in the store compared with the same quantity as
listed in corporate records. They analyzed the data to
understand the level and drivers of inventory record
accuracy.
Mukherjee et al. (1998) used company data on 62
quality improvement projects conducted by N. V.
Bekaert, S.A., the world’s largest supplier of steel
wire, to determine the impact these projects had on
the way the organization learned.
Existing company data have the advantage of being
easy to assemble, but those data might not have
all the information needed to answer the research
questions being addressed, so researchers will often
augment existing data with additional data collected
for the purpose of their research project. As mentioned
previously, Clark and Fujimoto (1991), Jaikumar
(1986), Jordan and Graves (1995), Krafcik (1988),
Macduffie (1991), MacDuffie et al. (1996), and Terwiesch
et al. (2005) all provide examples of researchers
constructing a database within a company.
Anotherexample is provided by DeHoratius and
Raman (2007), who worked with an audio electronics
retailer that had changed the store manager incentives
of another retailer it had acquired to reduce the incentive
the store managers had to minimize inventory
shrink. They found that shrink did indeed increase,
but the cost of this increase was more than offset by
the profit on increased sales, which resulted because
activities that reduce shrink tend to also reduce sales.
Khanna and Iansiti (1997, p. 413) worked with all the
mainframe computer manufacturers in the world and
“collected observations on all major multichip module
related projects ���through multiple interviews with
the key managers and engineers as well as through
questionnaires” to better understand how these firms
allocated resources during different stages of a development
project. Sterman et al. (1997) sought to understand
why financial performance at Analog Devices
worsened after a dramatically successful Total Quality
Management program that doubled yield, cut cycle
time in half, and reduced defects by an order of magnitude.
To do this, they “used econometric estimation,
interviews, observation, and archival data to specify
and estimate” (p. 503) the parameters of a simulation
model that linked productivity and quality
variables with accounting systems. Ton and Raman
(2005) worked with a book retailer that was concerned
about what it called “phantom stock outs,” instances
in which a book was in a store but could not be found
in response to a customer request. They tabulated data
on instances of phantom stock outs and used the data
to assess the level and causes of phantom stock outs
as a precursorto designing countermeasures.
In all the above examples, the data are about companies,
gleaned from public or internal sources. Ulrich
and Pearson (1998) sought to understand product
design issues by directly examining products. Using
an approach they called “product archaeology,” they
took apart 20 coffee makers, estimated manufacturing
cost using techniques from Design for Manufacturability,
and then correlated cost with attributes of the
product’s design to understand how design attributes
influence cost.
As well as directly observing products, a researcher
can directly observe processes. MacDuffie (1997) spent
one week each at a GM, Ford, and Honda plant, documenting
and comparing their approaches to solving
waterleaks, paint defects, and electrical defects.
Unlike other examples I have cited, his results were
comprised of qualitative descriptions of the processes
used in each of the plants, but they are nonetheless
interesting forthat fact. In a similarfashion, Tucker
(2004, p. 4), “a management researcher with a background
in quality engineering in manufacturing settings,
spent 239 hours shadowing 26 different nurses
at nine hospitals and recording detailed information
about theirwork activities” to betterunderstand how
they dealt with operational failures.
Experimentation is a standard tool of empirical
research, a tool that has also proven useful to operations
management researchers. Experiments can be
conducted in a laboratory type setting or in a company.
One example of laboratory experimentation
already mentioned is the beer game (Sterman 1989),
which asks students to make supply decisions in the