Strengthening the Empirical Base of Operations Management

MANUFACTURING & SERVICE 

OPERATIONS MANAGEMENT 

Vol. 9, No. 4, Fall 2007, pp. 368–382 

issn 1523-4614 � eissn 1526-5498 � 07 � 0904 � 0368 

Strengthening the Empirical Base of 

Operations Management 

Marshall Fisher 

Operations and Information Management Department, The Wharton School, University of Pennsylvania, 

Philadelphia, Pennsylvania 19104, fisher@wharton.upenn.edu 

informs ® 

doi 10.1287/msom.1070.0168 

© 2007 INFORMS 

Isuggest that the prospering fields of physics, medicine, and finance illustrate the value of a strong empirical 

dimension to research that is well integrated with theoretical research. I use empirical research in these fields 

to formulate a framework for classifying empirical research and illustrate that framework with a few selected 

examples in operations management. I offer some advice on data sources and approaches to conducting empirical 

research and suggest ways strengthening empirical research in operations management. This is obviously 

a partial treatment of a large subject and represents my personal point of view. This paper should encourage 

comments by others to further develop the topic and to offer alternative points of view. 

Key words: empirical research; econometrics; experiments; case research; data sources 

History: Received: December24, 2005; accepted: February 21, 2007. 

1. Introduction 

The field of operations management has accomplished 

much of which we can be proud, but like all healthy 

fields, we should constantly strive for improvement. 

One way to improve is to learn from role models. 

Please join me in a bit of introspection. What academic 

fields do you think are prospering and would be useful 

role models for operations management? 

My list would include academic medicine, physics, 

and finance, because all three have blended deep intellectual 

content with profound impact on the world. 

These fields have been guided by big questions that 

have led to big ideas such as the germ theory of disease, 

the theory of relativity, the Manhattan Project 

(however you feel about the result, this project, which 

created the atomic bomb during World War II, was 

a remarkably ambitious endeavor), the idea that in a 

perfect capital market, the price history of a stock is 

of no value in predicting future prices, and the analytic 

approach to investing that has emerged in the 

last three decades. 

By contrast, operations management has had big 

ideas such as the industrial revolution, mass production, 

the assembly line, the Toyota Production System, 

and statistical process control. Yet these ideas have 

368 

not come from academia. 1 We have been late to the 

game, focusing on developing a deepermathematical 

foundation and understanding of these concepts 

once they had been identified. Although this is doubtless 

a contribution, it has tended to position our field 

as proving these were good ideas decades after they 

have been well accepted in the market place as great 

ideas. 

Why has this happened, and what can we learn 

from other fields to change it? I believe we are at risk 

of falling victim to the malaise von Neumann (1956, 

p. 2063) warned of: 

Mathematical ideas originate in empirics �� But once 

they are so conceived, the subject begins to live a 

peculiarlife of its own and is bettercompared to a 

creative one, governed by almost entirely aesthetical 

motivations �� As a mathematical discipline travels 

1 Some might argue that this is overly harsh. The fathers of statistical 

process control were Walter Shewhart and W. Edwards Deming. 

Both had PhDs (although Shewhart’s was in physics), Shewhart 

worked at Bell Labs doing academic type research, and Deming 

was a professor during part of his career in the business schools of 

New York University and Columbia. A recent big development in 

operations management, large-scale software systems for inventory 

management and factory scheduling, draws heavily on voluminous 

theory in inventory control and factory scheduling published in the 

operations research literature.

Fisher: Strengthening the Empirical Base of Operations Management 

Manufacturing & Service Operations Management 9(4), pp. 368–382, © 2007 INFORMS 369 

far from its empirical source, ��there is a grave dangerthat 

the subject will develop along the line of least 

resistance, and that the stream, so far from its source, 

will separate into a multitude of insignificant branches. 

��whenever this stage is reached, the only remedy 

seems to me to be the rejuvenating return to the source: 

the reinjection of more or less directly empirical ideas. 

When I read this quote, I see what I fearourfield 

might become in a decade ortwo if we continue to 

move along our current trajectory. Certainly “the line 

of least resistance” in the journal review process leads 

to a stream of incremental, unobjectionable papers 

giving rigorous answers to narrow questions. If excellent 

research is an important question well answered, 

I believe we have been betterat answering than at 

posing important and interesting questions. 

I am certainly not the first to make this observation. 

Bertrand and Fransoo (2002, 2006) and Keys (1991) 

are among those who have argued for strengthening 

our empirical dimension. The following excerpt from 

Bertrand and Fransoo (2006) strikes an eerie parallel 

with von Neumann’s 1956 warning: 

Initially, ��operational research was oriented ��towards 

solving real-life problems �� Especially in the 

USA, a strong academic research line in OR emerged 

in the 1960s, working on more idealized problems �� 

However, much of this research lost its empirical foundations. 

Research methods have been primarily developed 

for ��theoretical research lines, leaving the more 

empirically oriented research lines for more than 30 

years in the blue �� (p. 241). 

I would agree with von Neuman; the way to avoid 

the risk of separating “into a multitude of insignificant 

branches” is to have a healthy injection of empirics. 

I believe medicine, physics, and finance have 

prospered because they have, each in their own way, 

figured out how to integrate empirics with theory. 

Medicine has evolved a variety of refined protocols 

for conducting and evaluating empirical research, 

including clinical trials fordrugs and othernew treatments 

and epidemiological studies that mine population 

data fordisease correlates. Medicine also takes 

a broad view of empirical research that includes less 

structured contributions; for example, the New England 

Journal of Medicine publishes descriptions of interesting 

cases. In recognition of the importance of their 

three missions—research, teaching, and patient care— 

medical schools now have three distinct types of faculty: 

PhDs doing basic lab research; MDs, who teach, 

do basic lab research, and see patients; and clinician/ 

educators who teach and see patients. The integration 

of theory and practice is greatly facilitated because 

medical schools are both conducting lab research and 

seeing patients in close proximity. They have coined 

the phrase “from bench to bedside” to connote their 

ideal model. What a medical faculty membersees in 

clinical practice motivates his orherlab research, the 

fruits of which are then made available to patients 

via the clinical practice. In fact, medicine has created 

a name foractivities that bridge the lab and clinical 

practice: translational research. 

Theory and empirics have also been synergistic in 

physics and finance. Things observed empirically, be 

they new particles in an accelerator or a tendency 

for stock prices to rise in January, provide interesting 

phenomena to be explained by new theories, and 

that theorizing produces new conjectures to be tested 

in the “lab.” Examples of theory guiding empirics 

would include conjectured new particles to be discovered 

in an accelerator and the perfect market theory, 

which asserts that one can’t earn supernormal profits 

in the stock market from an analysis of a stock’s price 

history. 

A taxonomy of the types of empirical research 

will be helpful in exploring how we might learn 

from medicine, physics, and finance to create something 

similarforoperations management. Empirical 

research can be grouped by a number of attributes; 

two that make sense are how structured and formal 

the interaction with the world is and whether the 

goal of the research is to describe the world or to 

determine a recommended course of action based on 

empirical observations. This taxonomy can be represented 

by the 2 × 2 matrix shown in Figure 1. 

To help in understanding the matrix, Figure 2 has 

various types of empirical research in medicine placed 

into the cells where they seem to best fit. A clinical 

trial to gain FDA approval for a new drug is a 

highly structured and defined process, and I placed it 

in the prescriptive box because the goal of the trial is 

to make a decision on whetherto introduce the drug. 

At the other extreme, medical journals will report 

accounts by physicians of interesting cases they have 

seen. While there probably is an agreed format for 

reporting cases, I placed this in the lower-right box 

because the collection of data seems less structured


370 Manufacturing & Service Operations Management 9(4), pp. 368–382, © 2007 INFORMS 

Figure 1 A Taxonomy of Empirical Research 

Interaction with the world 

Highly 

structured: 

Data and 

algorithms 

Less structured: 

Interviews and 

observations 

Prescriptive 

Goal of the research 

Descriptive 

than a clinical trial and there is no immediate action 

prescribed by the observations of a single patient. 

Lab research on mice and epidemiological studies 

seem more structured to me, but aimed at a general 

understanding of a disease. It is hoped that this 

general understanding might eventually lead to a recommended 

treatment, but that is not the immediate 

goal. I had to think hard to come up with an entrant 

for the lower-left box but concluded that critical care 

paths qualify. These are detailed descriptions of the 

exact procedure to be followed in a surgical procedure, 

including pre- and post-surgical steps. (See 

Wheelright and Weber1995 fora more extensive discussion 

of critical care paths.) As such, critical care 

paths are clearly prescriptive, but in the examples 

I have seen, the care path is determined largely by 

interviewing doctors and other health care practitioners 

regarding their opinion of best practice. Health 

outcomes research attempts to discover best treatment 

options by looking at a large sample of patients who 

Figure 2 Empirical Research in Medicine 


Highly 

structured: 

Data and 

algorithms 



observations 


Prescriptive Descriptive 

Clinical trial for a 

new drug 

Critical care path 

Laboratory research 

on mice 

Epidemiological studies 

that mine population 

data for disease 

correlates 

Observation of 

interesting cases 

Figure 3 Empirical Research in Operations Management 


Highly 

structured: 

Data and 

algorithms 



Engineering 

Software implementation 

of algorithm deployed in 

a company and run daily 

Principles 


Ohno invents Toyota 

Interviews and Production System, 

observations inspired by the principles 

of U.S. supermarkets 

Operations management 

econometrics 

Statistical analysis of 

large data sets to 

discover drivers of 

success in operations 

Case studies 

Interview and observe 

managers 

Research cases 

received different treatments for the same disease and 

identifying the treatment variant that correlates with 

the best results. To the extent that critical care paths in 

the future will be guided by outcomes research, they 

would move toward the upper left box. 

Now we can think about how prior empirical 

research in operations management fits into this 

matrix. Figure 3 shows the matrix labeled with types 

of empirical research in operations management and 

with one ormore examples of each type. As one 

can infer from the array of activities depicted in this 

matrix, my own definition of empirical research is 

broad and includes any effort to gather and report 

information about real operations that is accurate, is 

intellectually deep, raises interesting research questions, 

and contains enough correct analysis to at least 

partially answer those questions. If you object that 

this definition of empirical research is overly broad 

and that only what I have called operations management 

econometrics deserves to be called research, feel 

free to think of this matrix of activities as field work. I 

would certainly acknowledge that some entries in the 

matrix, such as interviews with managers, may not 

be a stand-alone publishable product. However, as 

described in §8, such activities can be extremely valuable 

as part of a broader program of activity involving 

othercells of the matrix. 

Including case studies and the implementation of 

an algorithm in a single company in the matrix raises 

the question of whether research based on a single 

observation is valid. Certainly research based on a 

large number of observations is to be encouraged, but 

there is also much to be learned from deep and exten-



sive analysis of a single orlimited numberof observations. 

Eisenhardt (1989), Voss et al. (2002), Voss 

(2006), and Yin (1994) contain extensive discussions of 

why and how to conduct effective case research. Voss 

(2006) in particular directly considers the question of 

whether research based on a single observation makes 

sense. 

The next foursections will describe some examples 

of each of the four types of research. I want to emphasize 

that what follows is not intended to be a comprehensive 

survey of empirical research in operations 

management, but a few illustrative examples. Clearly, 

a great deal of other excellent work is omitted here 

because of space constraints. The reader will detect, 

and I hope forgive, a Wharton bias in this survey; it is 

not that this research is necessarily more meritorious, 

but that it was more familiar to me. 

2. Engineering 

Engineers in industry and academia alike design and 

build things, then test theircreations, first in the 

lab and eventually in the field. In most engineering 

disciplines what gets built is a piece of hardware, 

but in our field the analog to hardware is usually 

the software implementation of an algorithm. Since 

the earliest days of operations research and management 

science, a core paradigm has been to model a 

real-world problem, devise an algorithm to analyze 

the model, create a software implementation of that 

algorithm, and then observe whether the algorithm 

improves the performance of the function that was 

modeled. 

Using company data to estimate the parameters of 

a model is a form of empiricism. Moreover, while it 

might seem that deploying an algorithm is the end, 

rather than the beginning, of research, I have found 

that so much is learned during the implementation 

process that this itself constitutes a type of empirical 

research. During implementation you are forced 

to verify and refine the details of your model, so you 

evolve a very precise definition of how a particular 

operations function works. You also discover properties 

of real data that influence algorithm design. 

Forexample, the application of the simplex method 

to real linear programming problems revealed that 

real problems have very sparse constraint matrices, a 

property that could be exploited in developing a more 

efficient algorithm. Finally, putting an algorithm into 

the hands of users teaches you how they think about 

problem solving. For example, most users want to see 

more than the final solution for a given data set. They 

want to see the logic path that leads from the input 

data to the final solution, and they want a sensitivity 

analysis that tells them whetherit is worthwhile 

forthem to take an action that might relax one of the 

constraints of the problem. 

Vehicle routing is one context where much has been 

learned from the deployment of algorithms. Vehicle 

routing focuses on the efficient use of a fleet of vehicles 

that must make a numberof stops to pick up 

and/ordeliverpassengers orproducts. The problem 

requires one to specify which deliveries or pickups 

should be accomplished by each vehicle and in what 

order so as to minimize total cost subject to a variety 

of constraints such as vehicle capacity and delivery 

time restrictions. Fisher (1995) surveys research in 

vehicle routing and describes a number of successful 

applications of vehicle-routing algorithms. Bartholdi 

et al. (1983) is one of my favorite vehicle-routing 

applications because of the novel algorithm used and 

the social contribution of its application. The authors 

describe the application of a routing system based 

on space-filling curves that they developed for the 

Atlanta, Georgia, branch of Meals-on-Wheels, which 

delivers hundreds of meals daily to those who cannot 

shop forthemselves. 

A space-filling curve is a mapping between a lower 

and a higherdimensional space. A space-filling curve 

from R 2 to R 1 induces a sequencing of all points in 

a plane and hence provides a heuristic for the planer 

traveling salesman problem. The authors use a particular 

space-filling curve to sequence all required deliveries 

for a given day. They then assign deliveries in 

orderto a vehicle until furtherassignment would 

exceed vehicle capacity. This process is repeated with 

successive vehicles until all deliveries have been 

assigned to a vehicles. The resulting algorithm is so 

simple that it was implemented on two Rolodex card 

files and shown to have significant positive benefit. 

As the result of decades of similar research in creating 

and applying vehicle-routing algorithms to particular 

real problems, we now have a rich understanding



of the many nuances of vehicle routing that arise in 

practice, a taxonomy of the various versions of real 

vehicle routing problems, and a knowledge of vehiclerouting 

data structures. For example, Fisher (1994) 

observes that the delivery points in real vehiclerouting 

problems are highly clustered and discusses 

how to exploit this in an optimization algorithm. If 

the essence of empirical research is gathering and 

understanding information about the world, all of this 

clearly constitutes significant empirical research. 

Bowman (1963) presents an excellent example of 

learning through implementation. The paper was 

already a classic by the late 1960s when I was a graduate 

student, and it profoundly influenced me and 

many otherstudents at that time. Holt et al. (1955) 

describe decision rules to determine optimal monthly 

production, inventory, and workforce levels as linearfunctions 

of priorvalues of these variables and a 

demand forecast. Bowman (1983) applied these rules 

to ice cream, chocolate, and candy plants in three separate 

studies. He first used accounting data to derive 

accurate estimates of the coefficients required in the 

decision rules and obtained results that improved on 

existing practice. But, surprisingly, he got still better 

results by choosing these coefficients so the output of 

the decision rules most closely matched, on average, 

managers’ past decisions. Amazingly, rules fitted to 

managers’ prior decisions did better than the managers 

themselves! 

Bowman’s findings illustrate that one often makes 

interesting discoveries in the process of implementing 

an algorithm. Bowman’s discovery was a principle 

that he summarized as follows. Managers make 

good decisions on average, but they are hurt by variation 

in theirdecision making. Thus, “A decision rule 

with mean coefficients estimated from management’s 

behaviorshould be betterthan actual performance [of 

the managers]. It may also be better than a rule with 

coefficients supplied by traditional analysis” (p. 321). 

3. Operations Management 

Econometrics 

Many fields apply regression and other statistical 

analysis tools to data sets in an attempt to discover 

evidence to support various hypotheses. In fact, this 

is the type of research that most people in our field 

associate with the term empirical research. 

Without question, the International Motor Vehicle 

Program study of 70 automobile assembly plants 

worldwide is the “mother” of all operations management 

econometrics research efforts. This ambitious 

study, which began at MIT. in the late 1980s and 

continues to this day, seeks to discoverand validate 

management practices associated with high levels of 

quality and productivity in an automobile assembly 

plant. The average numberof defects pervehicle as 

measured by the J. D. Powers survey that tests most 

car models is used as the measure of quality. Productivity 

is equated to the numberof hours to assemble 

a vehicle, a metric tracked by most automobile 

plants. To compare two different plants, this metric 

is adjusted to normalize for differences in vehicle 

complexity. 

The study 2 found large differences in quality and 

productivity between plants. Moreover, those plants 

with the highest productivity also tended to have the 

highest quality, contradicting the conventional wisdom 

that productivity and quality trade off against 

each other. High performance was correlated with 

certain practices that have come to be called “lean 

production” and include just-in-time inventory management 

and a high degree of worker involvement. 

Although these practices are typically associated with 

Japanese manufacturers, it was shown that this is 

not merely a Japanese effect; there were a significant 

number of poorly performing plants within Japan 

that did not follow lean production principles and 

an equally significant numberof highly performing 

plants outside of Japan that did follow lean production 

principles. 

The automobile industry has proven to be a fertile 

context forthe application of econometric methods. 

Anotherhighly successful example is Clark and 

Fujimoto (1991), which reports results of an extensive 

study of product development in the auto industry. 

The authors examined a large number of new model 

development projects at 20 automobile manufacturers 

worldwide to understand the management practices 

that influenced design quality, product development 

time, and product development productivity as measured 

by the engineering hours required by a project. 

2 The results of this ongoing study have been published in many 

papers. Early work can be found in Krafcik (1988) and MacDuffie 

(1991), with more recent results in MacDuffie et al. (1996).



Table 1 Impact of Variety on Supply Chain Costs 

Impact on Impact on 

production market Ideal supply 

Type of variety costs mediation costs chain 

Material High Low Large, centralized 

plant 

Frame geometry Low High Local 

and size 

Colors Low High Local 

Components Low High Local 

They found big differences between companies and 

regions, although generally the Japanese companies 

were more effective on all dimensions, producing 

higher-quality designs with fewer engineering hours 

in about half the lead time of the U.S. companies. 

They coined the term “heavy-weight project manager” 

for the management style used in these projects, 

a concept that has become pervasive in product development 

management. 

Writing about bicycles, not cars, Randall and Ulrich 

(2001) sought to understand the relationship between 

product variety and supply chain structure in the 

world bicycle industry. As shown in Table 1, the 

authors identified four types of bicycle variety and 

conjectured as to their impact on production costs and 

market mediation costs, the cost of mismatch between 

supply and demand. Based on this, they hypothesized 

as to the ideal supply chain fora company with a 

given level of each type of variety. They then analyzed 

public and survey data from 48 firms comprising 

70% of the bicycle industry to determine their level of 

variety, supply chain structure, and return on assets. 

They found that 71% of the firms used their hypothesized 

appropriate supply chain, and those firms that 

did had a higherreturn on assets than those that 

did not. 

4. Case Studies 

We should not underestimate the value of less structured 

empiricism. Something as simple as a conversation 

with a manageroverlunch can be extremely 

useful in identifying problems and hypotheses for furtherinvestigation, 

especially if a series of these conversations 

over time all point in the same direction. 

The preparation for writing a case usually includes 

numerous relatively unstructured conversations with 

managers. Cases are written both to support teaching 

and directly for research. Because they document 

a particular operations issue in a single company, 

they are also a wonderful way to begin to formulate 

research problems and hypotheses. 

Jaikumar(1986) provides a good example of how a 

case can reveal research issues. A description of this 

case and its research implications was provided previously 

in Fisher(1991), and the description here is 

based on that paper. 

As described in the case, the maintenance of turbine 

generators is a major challenge for the U.S. electric 

power industry because 40% of its generators are 

more than 20 years old, and even a single day of 

down time can cost as much as $500,000. In this environment, 

it is vital for a utility to be able to detect 

that a generator is about to crash, because this allows 

that utility to bring the generator down “softly” and 

fix the problem more quickly and less expensively 

than if the generator crashed without warning. For 

this reason, utilities have armed their generators with 

hundreds of on-line sensors to measure in real time 

voltage, temperature, pressure, and other key variables. 

But then the utilities face the problem of making 

sense out of all this data. 

It was to satisfy this need that the steam turbine 

division of Westinghouse Electric developed an expert 

system that can continuously monitordata from up to 

110 turbine sensors, looking for patterns that signal 

trouble. The system uses a base of 1,300 rules 

that were developed in consultation with a numberof 

Westinghouse engineers. They identified 350 

failure conditions corresponding to different components 

within a generator that can fail. At any time, 

if the system detects that something is amiss, it can 

recommend that personnel at the generator site perform 

additional confirming tests while the generator 

is running. Depending on the results of these tests, 

the system can then recommend that a generator be 

stopped, inspected, and fixed if necessary. The system 

has been used since 1984 by the Texas Utilities 

Generating Company and is credited with a number 

of “early warnings” on imminent failures that have 

saved millions of dollars in down time. 

One interesting research question motivated by this 

case is whethera modeling/algorithmic approach 

could be developed forproblems of this type that



would be more effective than an expert system. 

Clearly, the work at Westinghouse has structured the 

problem to the point where one can visualize applying 

ideas like those in Ross (1971). Forexample, it is 

tempting to think of the 350 identified failure conditions 

as states in a Markov process and the actions 

available to the expert as the actions in Ross’s model. 

However, the scale of real problems is several orders 

of magnitude bigger than the problems addressed in 

the literature, so computational tractability could be a 

majorchallenge. 

5. Principles 

Prescriptions derived from empirical research can be 

numeric and detailed, such as the output of an algorithm, 

or more qualitative principles that provide general 

guidance but require some interpretation on the 

part of the user. Perhaps the best example of prescriptive 

principles is the Toyota Production System, which 

provides guidance on how to structure a production 

process and manage workers to achieve a high level 

of quality and productivity. 

My favorite example of principles reported in the 

academic literature is the study by Jordan and Graves 

(1995), which describes several principles of manufacturing 

flexibility derived from empirical research 

within the auto industry. They consider a company 

that makes N products with uncertain demand in M 

factories with fixed capacity. Because of the limited 

capacity, it may not be possible to satisfy fully a particulardemand 

realization. The amount of demand 

that is satisfied can be increased by providing the 

flexibility to make a particular product in more than 

one plant. In the extreme, when every product can 

be made in all plants, demand can be fully satisfied 

as long as total demand forthe N products does 

not exceed the total capacity of the M plants. However, 

providing this maximal level of flexibility is usually 

prohibitively expensive, so Jordan and Graves 

(1995) ask how closely more limited flexibility would 

come to achieving the demand satisfaction benefits 

of full flexibility. Starting with this natural question 

and drawing on insights gleaned from extensive interaction 

with General Motors managers, they derive 

several principles of flexibility and provide analytic 

support for these principles. 

Their sharpest result is stated as a property of the 

product-plant assignment graph, which has a node 

foreach product and foreach plant and an arc connecting 

product node i with plant node j if product i 

can be made in plant j. They show that when M = N , 

the ability to make each product in just two plants 

provides nearly the same level of demand satisfaction 

as full flexibility, provided the product-plant assignment 

graph is connected. 

6. Integrating Theory and Empirics 

As discussed at the start of this paper, medicine, 

physics, and finance have benefited from a healthy 

synergy between theoretical and empirical research. 

Theoreticians develop hypotheses that are verified 

by empiricists, and empiricists identify interesting 

phenomena in the world to be explained through 

additional theories. Some examples of research that 

integrates theory and empiricism are beginning to 

emerge in our field. 

One such example focuses on a betterunderstanding 

of the relationship among the four traditional 

performance measures in operations management: 

cost, manufacturing conformance quality, delivery 

speed, and flexibility. Traditionally, these measures 

have been thought to trade off against each other. 

For example, improving quality meant increasing cost 

(we will use cost and quality as ourexamples in 

the rest of this section, although it should be clear 

that the concept is also applicable to the otherperformance 

measures). More recently, Clark (1996) and 

Hayes and Pisano (1996) have suggested that companies 

can and do follow improvement paths that simultaneously 

increase quality and reduce cost. 

A little thought makes it clearthat a company 

can take various actions to improve quality; some of 

those actions will increase cost and some will reduce 

cost. To illustrate, consider a process that produces 

a product with a 10% defect rate. An inspector at 

the end of the process attempts to cull out defective 

units to be reworked or scrapped. But because 

inspection is imperfect, the inspector only catches half 

the defects, resulting in a 5% defect rate reaching 

the market. Adding a second inspector who independently 

detects half the remaining defects would 

reduce the defect rate to 2.5% but would increase cost 

because of the expense of the additional inspector,



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



by 1.3 for planning purposes. Sales and marking got 

wind of this, and you can guess what they did; they 

began to inflate theirforecasts by 60%! 

Cachon and Lariviere (2001) consider this complex 

planning context and ask how a manufacturer that is 

telling the truth about its forecast can convince the 

supplierof this. They design contracts the manufacturercan 

offerthe supplierthat would not be attractive 

to the manufacturer if the true forecast were less 

than they were representing. 

Building in part on this research, Terwiesch et al. 

(2005) take an empirical approach to this problem. 

They worked with a manufacturer and 78 of their 

suppliers to collect data on 3,000 instances over two 

years when the manufacturer shared a forecast with 

a supplier. They note that the problem is often a 

repeated game, so when a supplier receives a forecast 

from a manufacturer, its faith in that forecast 

will depend on the accuracy of all of the forecasts 

received from that manufacturer in the past. Those 

suppliers that have received forecasts that were relatively 

poor(biased high and/orchanged frequently in 

the past) provided significantly worse service, delivering 

less than was ordered and delivering it late. Conversely, 

the manufacturer tended to inflate its forecast 

to the extent that it had been short shipped by 

the supplierin the past, thus creating the conditions 

of what would appearto be a downwardly spiraling 

relationship. 

Experimentation is a common form of empirical 

research in the physical sciences and, as described in 

Croson and Donohue (2002), is emerging as a useful 

technique in operations management. Probably 

the most famous laboratory experiment in operations 

management is the beergame described in Sterman 

(1989). Participants play the role of managers of firms 

in a beersupply chain, comprised of a manufacturer, 

a wholesaler, a distributor, and a retailer. They 

make supply decisions based on recent downstream 

demand or orders, but with no knowledge of future 

demand ororders. The beergame has been used 

almost universally in courses on supply chain management 

and has been the source of an important conjecture 

about supply chains called the bullwhip effect. 

It is usually observed in the beergame that ordervariability 

increases as one moves upstream in the supply 

chain, just as the movement of a bullwhip increases 

from the handle to the tip. For example, the variation 

in manufacturer orders is usually much greater than 

retail demand. 

Formany years it was believed, and anecdotally 

observed, that real supply chains exhibited this same 

phenomenon. Then Lee et al. (1997) developed analytic 

results for various supply chain planning contexts 

that would explain why the bullwhip effect 

could be expected to occur. Guided by this framework, 

Cachon et al. (2007) used data from the U.S. 

Census Bureau and the Bureau of Economic Analysis 

on sales, inventory, and prices to search for instances 

of the bullwhip effect. They found the bullwhip effect 

in some situations but not in others, which led them 

to develop a more refined framework of the factors 

that increase demand variability and those that attenuate 

demand variability as one moves upstream in a 

supply chain. 

The research reviewed in this section that was conducted 

using both theorizing and empirical research, 

with each stimulating the other, has many positive 

features. However, some might argue that our field 

lacks a cohesive and general theory of operations, 

and therefore any discussion of integration of theory 

and empirics must be postponed until we have 

such a theory. I would argue that, as suggested by 

von Neuman (1956), the best theories are the result 

of efforts to understand real phenomenon: thus, theorizing 

based on empirics increases the chances of 

improving the theoretical base of operations management. 

Others have suggested that relations between 

theoreticians and empiricists are often contentious in 

otherfields, including medicine, physics, and finance. 

I would counterthat vigorous debate about issues is 

the mark of a healthy field. Doing research on important 

issues that people care about will always invite 

controversy, but that’s a good thing, not a bad thing. 

7. Data Sources 

Data are the raw materials of empirical research, so a 

crucial question for an empirical researcher is where 

to get data. I examined a numberof papers on empirical 

research in operations management to identify 

data sources. The results are compiled in Table 2. 

Many of the papers cited as examples of various types



Table 2 Data Sources 

Data source Examples 

Public accounting and other public data: Annual reports, Compustat, Trade Cachon and Olivares (2007), Cachon et al. (2007), Fisher et al. (1999), 

and Industry Index, Dow Jones News Service, Department of Transportation, Gaur et al. (2005), Hendricks and Singhal (1997), Hendricks and 

U.S. Census Bureau, Bureau of Economic Analysis, and other similar 

sources 

Singhal (2003), Lapre and Scudder (2004) 

Private accounting data Datar et al. (1997), DeHoratius and Raman (2007), Mukherjee et al. (1998) 

Data created by the researchers from company records or survey of Clark and Fujimoto (1991), DeHoratius and Raman (2007), Jaikumar 

managers (1986), Jordan and Graves (1995), Khanna and Iansiti (1997), Krafcik 

(1988), Macduffie (1991), MacDuffie et al. (1996), Randall and Ulrich 

(2001), Sterman et al. (1997), Terwiesch et al. (2005), Ton and Raman 

(2005) 

Direct observation of products Ulrich and Pearson (1998) 

Direct observation of processes augmented by discussions with managers MacDuffie (1997), Tucker (2004) 

Experiments in a “lab” Schweitzer and Cachon (2000), Sterman (1989) 

Experiments in companies Bartholdi et al. (1983), Bowman (1963), Burchill and 

Fine (1997), Lapre and Van Wassenhove (2001) 

of data have been discussed already in this paper. 

Others are new and are discussed in this section. 

It is readily apparent from Table 2 that there are 

many sources for data. Happily, much useful data are 

available in the public domain. Annual reports and 

otherpublic accounting data published by companies 

are the most obvious example, but there are many 

other sources of public data. As already discussed, 

Lapre and Scudder (2004) found rich data on airlines 

available through the DOT, which they were able to 

use to analyze improvements in quality and productivity. 

Cachon et al. (2007) used data from the U.S. 

Census Bureau and the Bureau of Economic Analysis 

on sales, inventory, and prices in their search 

forthe bullwhip effect. Gauret al. (2005) used Standard 

and Poor’s Compustat database to construct a 

data set of inventory, total assets, current assets, and 

othervariables for311 public retailers forthe period 

1985–2000 and used it to explain variation in inventory 

turnover using gross margin, capital intensity, 

and sales surprise. Hendricks and Singhal (1997) used 

news articles from the Trade and Industry Index and 

the Dow Jones News Service to identify instances in 

which companies were late to market with new products 

and then measured the impact this had on share 

price. Hendricks and Singhal (2003) followed a similar 

approach, using articles from the Dow Jones News 

Service and the Wall Street Journal to identify instances 

of supply disruption to measure the impact that had 

on share prices. 

It is worth noting that some public data exist not as 

a database but as transaction data created for another 

purpose that can be assembled by a researcher into a 

database to support specific research. In their study 

of parts sharing in the auto industry, Fisher et al. 

(1999) needed to know which brake components were 

shared across which cars. To assemble this information, 

they used a data service created by a third-party 

provider that is consulted by auto salvage yards to 

determine if a brake salvaged from car A can be used 

in carB. Anotherintriguing example is provided by 

the Cachon and Olivares (2007) study of competition 

among auto dealers. To facilitate customer shopping, 

General Motors auto dealers provide real-time information 

overthe Internet of theirinventory availability 

by Vehicle Identification Number(VIN). Cachon and 

Olivares (2007) programmed and ran a Web crawler 

against this data service nightly and were able daily 

to construct information on dealer sales, transfers, 

receipts, and inventory. 

In addition to the accounting results they report 

publicly, all companies maintain substantial databases 

for internal managerial purposes and can often be 

persuaded to provide this data for research purposes 

in exchange forassurances formaintaining confidentiality 

of sensitive information. The virtue of these 

“private accounting data” is that they already exist 

and hence can be shared by companies with relatively 

little effort. Datar et al. (1997) worked with three 

companies and “obtained critical information on the



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



context of a mythical beersupply chain. Many other 

studies have placed students in supply chain-related 

decision contexts to understand the human side of 

supply chain decision making. Schweitzerand Cachon 

(2000) provide a good example. Students were asked 

to make various types of newsvendor decisions to 

understand how and why their decisions differed 

from those recommended by the standard newsvendorformula. 

It is harder to convince a company to use itself as 

a laboratory, and it is therefore noteworthy that some 

researchers have succeeded in doing this. The application 

of a model and algorithm within a company is a 

kind of experiment, and I would regard the algorithm 

application examples in Bartholdi et al. (1983) and 

Bowman (1963) as examples of experiments within 

organizations. Burchill and Fine (1997) persuaded a 

numberof companies to test a process the authors had 

developed forconcept engineering and to compare 

results using the new process versus their standard 

process. Lapre and Van Wassenhove (2001) worked 

with N. V. Bekaert, S.A. to conduct experiments using 

one of its production lines to learn the impact on 

productivity of various production parameters. The 

results of these experiments are credited with enabling 

huge production improvements. 

8. Approaches to Conducting 

Empirical Research: Navigating 

the Matrix 

I have found two strategies to be particularly effective 

in conducting empirical research. Both involve 

conducting various types of empirical research corresponding 

to different cells of the matrix (Figure 1). 

I have always found it useful when contemplating 

research on a topic to start in the lower-right cell with 

discussions with one ormore companies. These discussions 

enable a deeperunderstanding of an issue so 

that subsequent research can be guided by better questions. 

It may make sense to write a case on one of the 

companies with which one is interacting, which is a 

useful way to further deepen one’s understanding of 

issues. 

Sometimes, one will find a single company that 

has a well-defined problem that can be modeled and 

analyzed, so we start in the lower-right unstructured 

descriptive cell and move to the upper-left structured 

Figure 4 Navigating Matrix Cells—Approach 1 


Highly 

structured: 

Data and 

algorithms 



observations 



Engineering 

Software implementation 

of algorithm deployed in 


Principles 

Ohno sees U.S. 

supermarket and 

invents Toyota 

Production System 


econometrics 

Statistical analysis of 

large data sets to 



Start here 

Case studies 

Interview and observe 

managers 

Research cases 

prescriptive cell, following the path shown in Figure 

4. Many traditional projects that apply operations 

research/management science to a particular problem 

follow this path. 

Another approach is illustrated in Figure 5. We start 

by interacting with a group of companies to form 

some hypotheses, then assemble a data set from an 

expanded set of companies to test the hypotheses, 

and finally use the validated hypotheses to design an 

agenda forimprovement. 

The research reported in Fisher et al. (1999) and 

Ramdas et al. (2003) followed this path forthe issue 

of the best way to share parts across various finished 

products. We started in the lower-right cell by engaging 

in discussions with executives at several auto companies 

to understand how they thought about this 

issue. We learned that they viewed designing cars to 

maximize shared components as an important tool 

Figure 5 Navigating Matrix Cells—Approach 2 


Highly 

structured: 

Data and 

algorithms 



observations 

Prescriptive 


Descriptive 

Engineering 


econometrics 

Software implementation Statistical analysis of 

of algorithm deployed in large data sets to 




Optimization 

Validation 

Hypotheses Start here 

Principles 

Case studies 

Ohno sees U.S. Interview and observe 

supermarket and 

managers 

invents Toyota 

Production System 

Research cases



for providing maximum variety to customers while 

retaining less variety in plants. We also learned that 

many functional components could be rank ordered 

on a single quality metric. Brakes are an example, 

where the metric is stopping power. One could support 

an entire product line with a single brake type if 

it had the powerto stop the heaviest carin the line 

within a requisite distance, but the per unit production 

cost of this brake would be inordinately high for 

smaller cars. Conversely, you could minimize per unit 

production costs by having a unique brake for each 

car, but this would result in high plant complexity 

costs. 

We then sought a principle that would guide our 

subsequent research and showed, for a stylized version 

of the problem, that the number of brakes that 

minimizes perunit production costs and the cost of 

plant complexity from a greater number of brakes 

could be found via a model that resembled the economic 

order quantity model. From this we hypothesized 

that the numberof brakes an auto company 

would create to support a given product line would 

depend on factors such as the number of cars in the 

line, the variance in weight of those cars, and their 

production volumes. We then moved to the upperright 

cell to verify these hypotheses by analyzing a 

database assembled fora large numberof auto companies 

using public data on auto specifications and 

production volumes and data provided as a service 

to salvage yards that showed commonality of brakes 

across cars. Finally, we moved to the upper-left cell by 

formulating and analyzing a model for determining 

the optimal number and type of brakes to support a 

defined product line. 

The approaches to research outlined here might be 

contrasted with a more common one of reading a 

paperin a journal and identifying a variant of it to 

be analyzed. While much good research falls in this 

category—and it’s fine to have this as part of a portfolio, 

if that’s all we do—we would be at risk, in 

von Neuman’s words, to “separate into a multitude of 

insignificant branches.” 

9. Conclusions and Some Suggested 

Action Steps 

I have suggested that the field of operations management 

can benefit from strengthening its empirical 

dimension. As evidence, I have offered the examples 

of physics, medicine, and finance, all of which have a 

strong empirical tradition and are prospering. Moreover, 

these fields provide role models for how empirical 

research should be conducted, which I have 

attempted to summarize in this paper. 

Some advantages of a strong empirical component 

to our research include the following: 

1. Identifying and verifying important phenomena 

2. Identifying and characterizing important questions 

on which we can do useful research 

3. Validating models and assumptions that we have 

made 

4. Establishing the relevance of our research by 

demonstrating how the research outputs apply to 

practice. 

If you agree with these assertions, then a natural 

question is what action steps should be taken. We can 

separate actions into those to be taken by individuals, 

academic departments, and professional societies. 

As an individual, you could, I hope, considereither 

adding an empirical component to your own research 

portfolio orcontinuing yourempirical research if 

you already do it. Academic departments can considerdevoting 

some of theirhiring slots to faculty 

doing empirical research, introducing courses on 

empirical research into their PhD programs (the PhD 

course Empirical Research in Operations Management 

designed and taught by Christian Terwiesch in the 

Operations and Information Management Department 

at the Wharton School is one example) and giving 

PhD students a clinical experience via working on 

research projects within companies. A more ambitious 

goal would be to create more institutionalized 

opportunities for a clinical experience that might constitute 

a “teaching hospital” foroperations management. 

Some interesting initiatives in this direction 

are the internships provided within the MIT Leaders 

for Manufacturing program and the University 

of Michigan Manufacturing Applications Project program, 

in which faculty work with students on field 

projects with companies. 

There is also a huge leadership opportunity for professional 

societies such as the Institute forOperations 

Research and the Management Sciences (INFORMS) 

and the Production and Operations Management Society 

(POMS). Clearly, publication of empirical research



by journals such as Manufacturing & Service Operations 

Management and Production and Operations Management 

is important. This special issue of M&SOM on 

empirical methods in operations management, which 

is guest edited by Aleda Roth, is clearly a positive step 

in this direction. Gupta et al. (2006) also report encouraging 

evidence that publication of empirical research 

is increasing. I hope the journals will take the same 

broad view of empirical research offered in this article 

and consider publishing interesting cases. Increased 

publication of broader empirical research will both 

require and encourage forging standards of what is 

good empirical research. 

Professional societies could also sponsor industryacademic 

conferences to provide a context for academics 

to interact with practicing managers to learn 

the issues they face and identify meaningful research 

topics. An example is the conference “Improving 

Supply Chain Synchronization and Strategy through 

Industry-Academia Collaboration” organized by the 

POMS Supply Chain College and held at the University 

of Chicago on May 3, 2005. 

Finally, those who have conducted empirical research 

know that access to data is a challenge. Finance 

research into capital markets starting in the 1960s was 

greatly facilitated by the CenterforResearch in Security 

Prices at the University of Chicago; it provided 

a rich data set on security prices and related variables. 

It would be wonderful if a professional society 

would considerwhat data would be useful for 

researchers in operations management and then take 

on the task of maintaining those databases. Alternatively, 

one of the societies might act as a clearing house 

to enable individual researchers to make their data 

publicly available to others. Happily, data sharing is 

starting to happen. Willems (2007) provides a data 

set that describes 38 real-world multiechelon supply 

chains that is publicly available through Manufacturing 

& Service Operations Management at the journal’s 

website (http://msom.pubs.informs.org). 

I sincerely believe that the pursuit of these activities 

will have a tremendously vitalizing impact on our 

profession, and I hope these remarks will stimulate 

comments by others on this issue. 

Acknowledgments 

The author is grateful to Garrett van Ryzin for suggesting 

this paperand forproviding encouragement and advice on 

drafts. The author also appreciates the helpful comments 

of Gérard Cachon, Nicole DeHoratius, Jan Fransoo, Vishal 

Gaur, Steve Graves, Michael Lapre, Hau Lee, John Paul Mac- 

Duffie, Kamalini Ramdas, Zeynep Ton, Karl Ulrich, Chris 

Voss, Luk van Wassenhove, and two anonymous reviewers. 

This paperis based on three talks the authorhas given on 

this subject: a talk in the MSOM Fellows Award session of 

the 2002 INFORMS San Jose Meeting and plenary talks at 

the 2005 POMS Chicago Meeting and the 2006 INFORMS 

Hong Kong Meeting. 

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Strengthening the Empirical Base of Operations Management

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