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OCR for page 115
Operations Research and the
Services Industries
RICHARD C. LARSON
A dispatcher is using a color-graphics computer workstation to design
efficient routes for 14 trucks that tomorrow will carry liquid nitrogen
to customers in the Northeast.
After consulting a computer printout, an operations engineer has just
directed crews at 6 dams in a 12-dam hydroelectric system to increase
outflows by 10 percent for the next 3 days.
The chief executive officer of a major forestry products company is
using a video-game-like system to understand better how mill operators
in the field can increase profitability by improving their log-cutting
. . .
activities.
A social service volunteer in Atlanta, Georgia is using two card files
to assign drivers to vans and vans to routes to deliver "meals on
wheels" to elderly and handicapped individuals.
A vice president of a large railroad is scrutinizing a consultant's report
that recommends an ambitious $1 .5 billion capital investment program
over the next 5 years.
What; do all these activities have in common? In each case the individual
mentioned is a consumer of a product of operations research. Representing
a quantitative knowledge-based service industry, operations research has
established a foothold in corporate America, both in the goods-producing
and non-goods-producing (service) sectors.
Despite the growing importance of the field, as demonstrated by numerous
documented case studies, relatively little is known about it outside its own
"inner circles." As illustrated above, the material product of operations
115
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RICHARD C. LARSON
research can assume numerous forms, making the field ill-defined to out-
siders. Operations research can be highly mathematical and is often embedded
in less-than-transparent computer software or mathematical models. As a
result, reactions of fright, suspicion, distrust, insecurity, and irrelevance are
not uncommon.
In many ways, operations research is a perfect example of an emerging
class of technologies (often provided in the marketplace as a service) that
might be characterized as decision-aiding "software technologies." Decision-
aiding software technologies range from spread sheet programs to complex
optimization algorithms specially tailored to a specific application. The
professional fields that provide such software and services include computer
science (especially with recent advances in "fourth generation" languages,
relational data base systems, and "expert systems"), various branches of
engineering and operations research/management science (or, "OR/MS".
Despite an impressive array of successful implementations, the market
penetration of OR/MS in services industries in the United States is low,
raising several questions related to operations research. How does a firm
choose to invest in ORJMS and what determines success or failure in im-
plementation? Does low market penetration reflect difficulties in evaluating
the likely returns to investment? Does it reflect difficulties in translating the
analytically rigorous academic discipline of OR/MS into application?
This chapter, therefore, has two purposes. First, to provide an overview
of successful applications of OR/MS in services industries. Second, by ex-
ample and with some admitted speculation the paper addresses the ways in
which investment decisions are made about OR/MS applications and the
ways in which those investments are evaluated. To get to that point we must
first backtrack, spending a little time on a description of the field and a brief
review of its history and some of its major accomplishments.
OPERATIONS RESEARCH: BACKGROUND
Decision-aiding Technology
Operations research focuses on developing improved procedures for plan-
ning and operating complex systems. To distinguish it from other fields
having similar objectives, operations research tends to utilize the scientific
method to discover the "laws of physics" of the system under scrutiny. By
a process of trial and error not dissimilar from that of a physicist who is both
an experimentalist and a theoretician, the operations researcher attempts to
develop an accurate mathematical abstraction (i.e., mathematical model) of
the system. By manipulating the model, the operations researcher tries to
discover improved ways for operating the system. Operations research does
not exclude inputs from social scientists and organizational theorists, and a
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OPERATIONS RESEARCH AND THE SERVICES INDUSTRIES
117
large number of the founding members of the Operations Research Society
of America (ORSA) were from these fields (in the year 19521; however, the
mathematical model seems to remain a central feature of operations research
studies and products.
Most operations research models contain decision variables whose values
are to be "optimized" subject to certain constraints. If one views a decision
as an irrevocable allocation of resources, the values of the decision variables
represent a particular allocation of resources. The optimization objective may
be to maximize profits or to minimize costs or to maximize customer sat-
isfaction, or it may be multidimensional, including two or more such objec-
tives. In any event the desired goal of an operations research enterprise is
the identification and implementation of improved decisions (i.e., allocation
of resources).
Operations research is a state-of-the-art technology. It uses the latest sci-
entific knowledge from such diverse fields as mathematical programming
(i.e., computer-based optimization of mathematical functions subject to often
complex constraints), stochastic processes, graph theory, and computer sci-
ence. Since its focus is on improved decisions, we may regard operations
research as a decision-aiding technology and thus admissible to a discussion
of technologies in services industries.
Institutionally, operations research is carried out by consultants, in-house
technical groups, university professors, and-ever more frequently soft-
ware firms. Operations research is itself a services industry. And, as will be
argued below, its services are most often sought by other services industries,
including transportation, finance, government, health care, education, and
consulting.
Brief Histor'?
Operations research was identified as a field of scientific inquiry and named
during World War II. The initial important work, focusing on radar utili-
zation, antisubmarine warfare, and other military operations, was done by
two groups of diverse scientists, one (in the United Kingdom) under the
direction of the physicist P. M. S. Blackett and the other (in the United
States) under Philip M. Morse, also a physicist. The group in the United
Kingdom christened the new field "operational research" Esee Morse (1977)
and McCloskey (1987) for more details]. Among the numerous accomplish-
ments of these groups was the creation of "search theory," a new integrated
mathematical formalism combining ideas of probability, geometry, and math-
ematical optimization and used initially to deploy planes and ships to find
enemy submarines. Search theory has subsequently found wide application
elsewhere, including design of search strategies to find lost items over vast
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RICHARD C. LARSON
areas; for instance, it was instrumental in helping search parties to locate the
wreckage of the Shuttle Challenger crew module.
After the war there was considerable interest in developing and applying
methods of operations research to problems of the private sector and the
nonmilitary public sector. Considerable momentum was given to this effort
by the simultaneous developments in computers and algorithmically oriented
mathematical optimization, arising initially as "linear programming" with
the celebrated "Simplex method" due to George Dantzig. Subsequent ad-
vances in algorithmic optimization, many developed at the Rand Corporation,
included dynamic programming (Bellman, 1957), various special forms of
linear programming (see Dantzig, 1963), and network flows (Ford and Fulk-
erson, 1962~; the methodological developments fit nicely with the concurrent
technological advances in digital computation.
The ORSA was founded in Cleveland in 1952. The first academic programs
in operations research were established at Case Institute of Technology and
(under the direction of Philip M. Morse) at Massachusetts Institute of Tech-
nology. The first Ph.D.s were awarded in the late 1950s.
Although the field coalesced as a result of the war effort and subsequent
developments, important components of operations research reach back prior
to 1940. Thomas A. Edison, serving during World War I as head of the U.S.
Naval Consulting Board, used statistical and gaming ideas to develop im-
portant early results in antisubmarine warfare (Whitmore, 19531. Queuing
theory, which focuses on the development of mathematical models of waiting
lines, had its roots in Denmark during the period 1910 to 1915 when the
Danish telephone engineer Erlang used probabilistic reasoning to develop
the first queuing models to help engineers determine the capacity of telephone
switching systems. Graph theory, which has been used extensively by op-
erations researchers to model transportation networks, is rooted in the efforts
of the Swiss mathematician and physicist Leonhard Euler who in 1736 at-
tempted to route a parade over the seven bridges of Konigsberg (now Ka-
liningrad) in such a way that each bridge was crossed exactly once; in
developing the initial important results of graph theory Euler proved that
such a route did not exist (and he showed how to design a minimal length
parade route that crossed each bridge at least once) (Larson and Odoni, 1981,
p. 3851. With regard to linear programming and the Simplex method, the
now famous vertex-to-vertex descent method was at least suggested by the
mathematician Fourier in 1826 and additional important early work was done
by other mathematicians: Farkas in 1902, von Neumann in 1937, and, es-
pecially, Kantorovich in 1939 [see Dantzig (1963, Chapter 2) for details!.
As this brief history shows, operations research uses techniques and ap-
proaches from many disciplines. A persistent problem for the field has been
a labeling one, in which it has often proved difficult to determine how
operations research is distinguished from various branches of applied math
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OPERATIONS RESEARCH AND THE SERVICES INDUSTRIES
119
emetics, physics, or engineering, or (more recently) computer science and
artificial intelligence.
Prize-winning Works
The Lanchester Prize of the ORSA is given annually to the book or paper
(in the English language) judged to be the most outstanding contribution to
operations research during the year of publication. Since the award's incep-
tion in 1954, 39 publications have been honored as Lanchester Prize winners.
Using a rough categorization scheme, 15 of these publications have been
general in nature and 24 have focused on applications in a particular industry
or service category. Of the 24 applications-driven winners, 14 (or 58%) are
clearly directed at services industries. These include applications in trans-
portation (Leslie Edie, in 1955, was awarded the first Lanchester Prize of
ORSA for his 1954 work on traffic management over the bridges and through
the tunnels of the Port Authority of New York), banking transactions, uni-
versity operations management, urban systems, library management, com-
munications, criminal justice, logistics, postal operations, and health care
provision. Of the remaining ten applications-focused winners, only two were
prompted by problems from the manufacturing floor; the other eight were
directed at less easily categorized problem areas: search theory, inventory
management, military operations, mining exploration, and purchasing poli-
cies (data provided to the author by ORSA).
Each year The Institute of Management Sciences (TIMS) selects up to six
finalists for the Edelman Award for Management Science Achievement. Each
entry is judged on its use of operations research and management science
techniques within an implementation context in which real dollar savings or
service level improvements or both are reported and verified by external
referees. Of the most recent 28 finalists, 19 (or 68%) are clearly in services:
transportation and logistics, 8; financial planning and asset management, 3;
marketing and sales, 3; urban services, 2; work force planning, 1; postal
services, 1; general corporate planning services, 1. Only 3 are motivated
directly by concerns in manufacturing. The remaining 6 focus on water
systems, inventory, and energy systems.
This review of OR/MS prize winners was not meant to imply that operations
research is unimportant in the goods-producing sector. Many services such
as logistics are often "services in support of manufacturing." In specific
manufacturing processes, ORIMS can provide design and analysis tools to
assist in the engineering design and operations control of those processes.
A Roadmap
In the following three sections I have selected cases from three general
areas of OR/MS application to illustrate the variety of contextual settings for
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RICHARD C. LARSON
ORIMS, the driving forces behind the decision to invest in OR/MS, the types
of products that emerge, some broader organizational impacts of OR/MS,
and estimates of the return on investment in OR/MS. The first and third topic
areas are directly focused on services industries: logistics and work force
planning. The second is "production-related services," selected to indicate
how OR;MS provides technical engineering service in the goods-producing
sector. Most of the cases are drawn from the open literature, particularly the
flagship OR/MS applications journal Interfaces. Several were nominated for
the prestigious Edelman Award, thus no claim is made that the sample is
"random" in any sense. On occasion the discussion is augmented with
information provided to me by the authorts).
The space-constrained limited descriptions of these cases do not give a
full picture of the institutional and organizational factors that come into play
in ORIMS work. Thus I have included, as an appendix, a more detailed case
in the area of work force planning. It focuses on the scheduling of emergency
telephone (911) operators in New York City.
Following the cases I offer a suggested set of conclusions and discuss the
problem of estimating the value added by OR/MS, particularly focusing on
how managers make the decision to invest in OR/MS.
ILLUSTRATIVE CASES IN DISTRIBUTION
AND LOGISTICS
One of the most successful areas of application of operations research has
been in improving operations of spatially dispersed systems. Usually the
problems focus around issues of transportation, deployment of vehicles,
location of facilities, design of service territories, and inventory management.
"Distribution/logistics" is the label we assign to these types of problems.
Tactical Planning
A recent well-publicized example involves the efficient routing and sched-
uling of trucks delivering industrial gases (nitrogen, oxygen, and argon) to
spatially dispersed customers. In the production and distribution of industrial
gases the major costs are due to electricity (to separate the gases from the
air) and to distribution. Typically distribution costs amount to 30 to 40 percent
of total direct costs. In day-to-day operations a dispatcher matches customer
orders with available trucks and drivers, tries to identify other customers
who may benefit from a delivery, and attempts to devise cost-efficient routes
for the trucks while not violating any one of myriad constraints (e.g., De-
partment of Transportation-imposed constraints on maximum allowable driver
time per trip). The potential number of combinations is often enormous, and
operating in manual mode the dispatcher must rely on experience and intuition
to devise the trip assignments.
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OPERATIONS RESEARCH AIID THE SERVICES INDUSTRIES
121
In 1983 Marshall Fisher (University of Pennsylvania) and his colleagues
devised an operations research procedure based on mathematical program-
ming techniques that allowed the testing and sorting of thousands (perhaps
millions) of trip combinations; the new procedure produced solutions typically
10 to 12 percent less costly than those produced manually. In implementation
at Air Products, Inc., the reported savings were reduced to approximately 6
percent, still representing substantial dollar volume when projected over the
entire corporation. This work was honored by TIMS, which awarded the
authors the Edelman Award for excellence in the practice of management
science (Bell et al., 19831. The work has been favorably reviewed in the
Wall Street Journal, the New York Times, and elsewhere. fAlso see Bodin
et al. (1983) for additional references in vehicle routing and scheduling.]
The "product" of the Fisher et al. work is a complex computer program
that must be executed at least once daily to devise the next day's trip as-
signments. The success of the implementation in Air Products, Inc., is due
in part to corporate commitment from top management to quantitatively
based, computer-implemented tools for improving operating efficiencies. The
record of success and failure of similar attempts indicates clearly that a
necessary condition for implementation success is the existence of a broadly
based constituency within the organization (including top management) sup-
porting the effort and able to maintain the delivered product after the oper-
ations researchers have left the scene.
Not all operations research products in the logistics area are sophisticated
computer programs. In 1983 John Bartholdi and Loren Platzman (Georgia
Institute of Technology) studied the problem of "meals on wheels." In this
application a charitable organization in the inner city delivers meals daily to
elderly and infix individuals in their homes. The organization is staffed
with a combination of volunteers and near-minimum-wage employees; re-
sources are scant, and a state-of-the-art computer for solving complex math-
ematical optimization problems is out of the question. Yet distribution costs
represent a large fraction of direct costs of operation, and even casual ob-
servation of operations revealed that then current methods of distribution
were far from optimal. Ingeniously, applying some ideas from the mathe-
matical field of "space-filling curves," the authors devised a scheme for
assigning drivers to vehicles and vehicles to routes that (1) only required
two card files (no computer); (2) produced solutions vastly superior to pre-
vious solutions; and (3) naturally included certain operating realities, such
as, the fact that the number of drivers showing up for work on a given day
is a random quantity. As in the Air Products case, the procedure must be
performed daily, but it only requires 15 minutes; before the procedure was
introduced, 3 hours had been consumed daily to construct routes. The devised
operations research procedures are now used widely throughout the United
States for delivering "meals on wheels" and in a variety of commercial
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RICHARD C. LARSON
endeavors as well (package delivery, supplying fresh pastries to restaurants,
servicing banks' automated teller machines) (Bartholdi et al., 1983; Bar-
tholdi, private communication, 19871.
Strategic Planning
Whereas routing of industrial gas trucks and vans for meals on wheels
required vastly differing computational power, each represents an example
of tactical or operational planning in a logistics setting. Many of the successful
operations research ma plementations in logistics have been of this type, nary ely
involving near-ter - .^ decisions in an operational setting. Yet perhaps a more
powerful area for application in logistics is in strategic or long-term planning.
Rails Consider the case of the Boston and Maine Railroad. Between 1977
and 1982 the Boston and Maine Railroad made extensive efforts to improve
its operating perforce ance, especially in the areas of freight service, terminal
control, and freight car utilization. In the arena of long-haul transportation,
railroads achieve their competitive advantage by using a single locomotive
to pull a great many freight cars. The "operating plan," the most fundamental
control at the disposal of the railroad, governs the movements of cars and
trains (blocking policy, train schedules, and dispatching policy). Procedures
for developing and modifying operating plans are extremely important aspects
of the railroad control system. Specific problems arise in establishing con-
sistent standards for yard, train, and system performance, due in part to
availability of only aggregate information and to the limitations resulting
from analyzing each train and yard somewhat independently, despite their
clear interdependence.
As reported in 1986, Carl D. Martland and his colleagues (MIT) developed
the "Service Planning Model" (SPM) that estimates the service and cost
impacts of alternative railroad operating plans. Using available data on traffi
flows over the network, costs, operating constraints, and parameters describ-
ing the proposed plan, SPM estimates yard performance, trip times, aggregate
perforce ance per user-defined traffic categories, and numerous types of costs.
As a consequence of analyses conducted with SPM, major changes were
made in the organizational structure and decision-making processes of the
company, as well as in physical facilities and information systems. Savings
attributable to this effort amounted to more than $3 million annually, or
roughly 3 percent of total operating expenses (Martland et al., 19861. Ac-
cording to Martland, "The Service Planning Model in and of itself did not
cause the benefits, but provided an impetus to create an effective interde-
n~rtm~.ntn1 Planning process" (Martland, private communication November
rim r~-^^^^^^^= rip - ~
19874.
Operations research has produced other major strategic planning impacts
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OPERATIONS RESEARCH AND THE SERVICES INDUSTRIES
123
in the rail industry. As another example we consider the Canadian National
(CN) Railway, Canada's largest railway. In 1984 CN Railway's total traffic
volume was 174.3 billion gross-ton-miles, which generated C$3.S billion in
revenues, resulting in a net income of C$304 million. In the late 1970s traffic
volumes through 1990 were projected to double on CN Railway's already
congested single track main line. Faced with complex constraints that were
physical, financial, and operational, CN Railway embarked on a "Plant
Expansion Program" (PEP) whose implementation would handle the in-
creased traffic of 1990 while maintaining existing levels of service. Proposals
called for capital expenditures during the 1980s of C$2.2 billion, of which
C$1.3 billion would provide double track in some congested links of the
system.
After analyzing state-of-the-art line-capacity methods, CN Railway de-
cided to develop detailed simulation models to estimate the capacity of trans-
port on segments of the line. An important component of the analysis was
the Signal Wake Model that determines for a given configuration of signals
the minimum train headway that can be maintained as a given fleet of trains
follow each other in the same direction over a specified track layout. Another
component was the Route Capacity Model that estimates train delays by
simulating operations of trains over a rail line under specified track main-
tenance activities. The resulting analyses produced a package of cost-effective
improvements for capacity expansion, which included a combination of con-
trol technology (closely spaced signals) and strategically located sections of
double tracks. The major cost savings of the analysis was the identification
of 128 miles of track, originally slated for expansion to double track, that
with extra signaling could remain single track until after 1990. This allowed
CN Railway to defer C$350 million in capital expenditure beyond 1990
(Welch and Gussow, 19861.
Banking Banking is a major (financial) service industry that is not usually
associated with logistics. However, as the case of BancOhio demonstrates,
logistical concerns can play a key role in efficiency of banking operations.
Partially due to the relaxation of branch banking restraints, U.S. banks are
increasing their branch networks. The wider geographical dispersion of bank
branches can complicate the check-processing function resulting in the need
to determine (1) how many operations centers should be used and where
should they be located; (2) which branches should be served by each center;
and (3) what costs and performance measures should be included in evalu-
ating alternatives?
From an operations research point of view the flow of checks through a
bank can be viewed as a "pipeline inventory model." Items are input at
various entry points (banking offices), flow through the processing pipelines
and exit in the form of outgoing cash letters dispatched to clearing banks.
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RICHARD C. LARSON
The problem becomes complex due to external time constraints imposed on
outputs of the system (clearing deadlines). Transportation is a significant
component of the system as checks are moved from receiving branches to
encoding sites to capture sites.
In January 1984 the BancOhio network consisted of 266 branches repre-
senting 42 individual banks. Checks were encoded in 31 of these locations
and eventually transported to one of two capture sites (Columbus or Cleve-
land) for computer processing and clearing. Management felt that centralizing
processing facilities would achieve economies of scale, and initiated an op-
erations research analysis to determine the validity of their views.
The analysis used a check-processing simulation model (CHECKSIM) to
generate efficient transportation routes for the messengers who pick up checks
and deliver them to the processing center. The simulation was run for each
processing center configuration under consideration. The analysis showed
the expected result that consolidation would produce economies of scale, but
perhaps even more importantly, that even greater savings could be accrued
by moving certain ancillary support functions to consolidation centers. In
fact, the savings in transportation and encoding efficiencies ($287,000 per
year) were dwarfed by savings associated with transferring certain retail and
operations functions ($1,381,000 per year). The total identified savings rep-
resented approximately 9 percent of then current operating expenses. In
implementation the most difficult problems were associated with reassign-
ment (and displacement) of personnel (Davis et al., 19861. According to
Davis, the OR/MS implementation effort lasted 270 days and cost BancOhio
$80,000 (Davis, private communication, November 19871.
Urban Services The use of operations research in logistics is not confined
to the private sector. Let me briefly cite two projects that I recently directed
involving agencies of New York City.
The first was with the New York City Department of Sanitation (DOS).
In 1981 DOS was confronted with imminent closings of major in-city land-
fills, resulting in a projected doubling of daily refuse tonnage transported by
barge to the world's largest landfill on Staten Island (Fresh Kills Landfill).
The strategic planning rule then "in good currency" was to "size" the fleet
of barges in direct proportion to daily tonnage carried. If tonnage doubles,
barge fleet size should also double, according to this tradition-based rule-of-
thumb. If the fleet size were to double the city would have had to purchase
an additional 40 barges, estimated then at $1 million per barge, representing
a potential commitment in capital expense of $40 million.
Not willing to trust the "linear rule-of-thumb" for such an important
decision, DOS commissioned an operations research study to determine the
required fleet size to handle the projected new loads. The study resulted in
the creation and implementation of a simulation model Barge Operation
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OPERATIONS RESEARCH AND THE SERVICES INDUSTRIES
125
System Simulator (BOSS). DOS personnel, while performing numerous pro-
duction runs with BOSS, identified a savings through 1990 of at least 10
barges and perhaps 20 barges without impeding service levels. The savings
were not unexpected by the operations researchers who saw the barge and
tug refuse transportation system as a closed "multiserver queuing system;"
such systems almost always display economies of scale, in which workloads
may be increased and servers (i.e., barges) need not be increased in direct
proportion, while still maintaining similar (acceptable) performance char-
acteristics.
At the time of contracting for new barges (1983) the ship-building industry
was severely depressed, resulting in new barge purchase cost of only $600,000
per barge; hence, as a result of using BOSS for fleet sizing, the city has
saved $6 million and may save an additional $6 million (if an additional 10
barges are not ordered in 19901. BOSS cost New York City $100,000,
yielding an immediate return on investment of 60:1. (Larson et al.,19881.
New York City's Department of Environmental Protection (DEP) com-
missioned an operations research analysis in September 1985 to provide a
computer-based tool to help DEP planners design a new logistics system to
transport sewage sludge to a new ocean dumping site. Sewage sludge is the
final product of primary and secondary sewage treatment; it is 97 percent
water, 3 percent solid and has a specific gravity of 1. For decades New York
City had been dumping its sewage sludge a few miles outside New York's
harbor entrance. In 1983 the federal Environmental Protection Agency (EPA)
placed New York City under court order to commence a scheduled process
whereby eventually all of the city's sludge would be transported to a new
EPA-designated site approximately 106 miles south southeast of the harbor
entrance. It is at this "106-mile site" that sludge is to be dumped in the
future.
After requesting bids to transport the sludge from "private haulers," DEP
decided that the new sludge transport system should be primarily under DEP's
(not a private hauler's) control. The commissioned operations research model
was to be able to depict alternative ways of operating the new sludge transport
system, including computation of costs and performance characteristics of
alternative fleet sizes and fleet mixes, use of transshipment points, impact
of dredging and other capital improvements, and increased land-based sludge
storage capacity at one or more sites.
The model that was ultimately developed, Strategic Logistical Unified
Design GEnerator (SLUDGE), accomplished all the desired tasks; it operated
on an IBM PC AT desktop computer and required only approximately 2 to
3 seconds for each production run. DEP planners have executed the model
in production runs well over 1,000 times in determining the appropriate type
and size of vessel to assign to the oceangoing link and to the inner harbor.
SLUDGE has also been used to determine the best locations for a primary
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OPERATIONS RESEARCH AND THE SERVICES INDUSTRIES
· additional revenue generated by improved service levels
· benefits from the use of SMPS in contract negotiations
133
· savings from reduced support staff requirements
· savings from reduced manual scheduling efforts
· reduced training requirements (lIolloran and Byrn, 19861.
According to Holloran, the development cost of SMPS was approximately
$500,000, allocated over a 260-day development period (Holloran, private
communication, November 1987~.
Services associated with "income tax" time are highly seasonal, requiring
careful scheduling of personnel during short peak work load periods. For the
Financial Services Group (FSG) of Canada Systems Group, Inc., the "season
for giving" to one's tax-deductible retirement fund apparently lasts approx-
imately 6 weeks (late January to early March). In 1984 the incremental cost
to FSG for managing this intense period was approximately $500,000. Prior
to the following season, the FSG developed a linear programming work force
planning tool that, based on projected work loads, developed hiring needs
and shift assignments for the 6-week period (well in advance of that period).
The incremental cost of managing the 1985 season, with the new tool, was
reduced to $170,000, a 64 percent reduction, despite somewhat higher wages
and a 25 percent increase in volume. As has been shown to be common with
other operations research installations, other intangible benefits were also
reported, particularly an enhanced reputation for reliability of service that
has resulted in successful acquisition of new clients and all but one major
client renewing contracts (Haehling von Lanzenauer et al., 19871.
Spatial Deployment
Work force planning may also relate to the allocation of personnel over
service territories. In fact the subfields of "optimal location" and "optimal
districting" are two of the most active fields in operations research.
In the 1970s the literature of both operations research and marketing began
to offer detailed consideration to the use of mathematical programming mod-
els to assist in sales territory design decisions. Models were developed to
allocate work load among a fixed number of salespersons, to calculate the
best number of salespersons, and to determine territory boundaries. Later
refinements dealt with constraints on time limitations of the salespersons,
supervision, salesperson experience and competition, as well as adjustments
to boundaries taking into account natural obstacles.
The Houston-based Variable Annuity Life Insurance Company (VALIC)
markets annuity contracts to not-for-profit organizations and governments.
In 1982 VALIC decided that it needed quantitative guidance in the design
of its service territories and in the structure of its field offices. At that time
there were 336 salespersons nationwide, allocated over 16 regions, each with
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RICHARD C. LARSON
a manager and an office. Among the management issues were the number
and design of sales territories and regions, while considering equity of "mar-
ket potential" in each and morale problems associated with redesigns. An
operations research analysis commenced whose purposes were threefold:
(1) to determine the cost associated with the current 16-region configuration;
(2) to determine the lowest cost solution in both number of regions and their
geographic configuration; and (3) to estimate expected cost savings if the
change in configuration were to be adopted. The analysis presented an in-
teresting trade-off between fixed and variable costs, the fixed costs associated
with regional offices and the variable costs associated with intraregion travel
times.
The first use of the resulting program was focused on the then present
regional configuration, showing a model-derived cost of $1S,826,000. Also,
it was determined that by closing one regional office and moving a few
regional boundaries, VALIC could reduce total costs by 4 percent. More
interesting was the cost difference when the number of regions was allowed
to vary. The total cost of the solution resulting from 25 regions was $9,933,000,
a savings of $8,833,000. Not surprisingly there were obstacles along the
way, such as initial results violating constraints on disproportional market
potential among regions and the apparent uncaring attitude of the company
toward changing the locations of current regional offices. As of the time the
case was reported (1984), VALIC had launched a 5-year phase in of the
resulting "fine-tuned" recommendations. Management appeared confident
in the projected cost savings but had decided to "go slow" in the sensitive
area of personnel relations (Gelb and Khumawala, 19841.
The deployment of ambulances throughout a city represents a totally dif-
ferent type of spatial deployment problem. The reader is referred to Brandeau
and Larson (1985) and Eaton et al. (19851. In redeploying ambulances in
Austin, Texas, Eaton reports that his $30,000 study saved the city $10.8
million over the following 7 years; ambulance first response times decreased
7 percent in the face of a 52 percent increase in demand. This example,
coupled with the earlier "urban services" cases, demonstrates that significant
returns to investment in operations research are available in the public as
well as private sectors.
CONCLUSIONS
What have we learned from our tour of OR/MS applications in services
industries and in production-related services? I would like to offer the fol-
lowing:
.
An OR/MS product can assume many forms, from a computer program
implemented in color graphics to a consultant's report, to card files, to
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135
a "smart" machine tool, to an educational "video game." The nu-
merous embodiments contribute to the field's fuzzy image.
In implementation, often unanticipated "side benefits" of an OR/MS
effort will dominate the benefits accrued in the original target area of
the work. Such side benefits can be limited to additional unanticipated
cost savings (or service enhancements), or they can extend more fun-
damentally into managerial structure and flow of information. In the
latter case the potential magnitude of the effect of OR/MS is often
accompanied by a comparably large organizational resistance to change.
· The costs of implementing OR/MS can vary enormously, from $50 per
implementation for "meals on wheels" to millions of dollars for large-
scale, multiple-site decision support systems.
· The reported benefits of OR/MS work are often one or two orders of
magnitude greater than the costs.
· In-the-field knowledge of even rudimentary properties of operations
research models (and thus of operating systems) is often lacking.
· Managers do not like to state explicitly target service levels that im-
plicitly admit to failure a certain fraction of the time (i.e., "probabil-
istically stated objectives".
· Markedly successful ORIMS efforts seemed to be accompanied by (1) top
level corporate enthusiasm and long-range commitment and
(2) involvement of operations personnel during implementation.
· Due in part to the amorphous nature of its products and the highly
technical nature of its process, as a profession OR/MS runs the risk of
being absorbed by related and more easily identifiable fields such as
computer science.
Although there are numerous OR/MS "success stories, " several of them
reported here, the overall market penetration of OR/MS in services
remains shallow. The field's limited impact to date may be due to
excessive academicism in the field, fear of technical approaches by
operating managers, need until recently to use mainframe computers,
and exclusion by many operations researchers of broader nonmathe-
matical aspects of the problem.
· Operations research offers the potential for great productivity improve-
ment in languishing services sectors, improvements often greater in
percentage terms than those typically associated with the manufacturing
.
sector.
VALUE ADDED FROM OPERATIONS RESEARCH
Is it possible to estimate a priori the "value" of any proposed OR/MS
effort? Investment in capital equipment is a familiar activity of U.S. cor-
porations. By now standard techniques exist to estimate costs and benefits
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RICHARD C. LARSON
of many "hardware" investment alternatives, including discounting cost/
benefit streams into the future, estimating time until the investment is re-
couped, and computing total (discounted) returns on investment. But con-
siderably less is known about investing in various kinds of "software"
technologies. Occasionally, as in "desktop publishing," the savings and
productivity improvements are so demonstrable that the investment is clearly
a good one. More problematic are investments in software and services that
are aimed to improve "decision making," either at high corporate levels or
at operational levels. Examples would include software/services for invest-
ment planning, assembly line balancing, airline scheduling, allocation of
marketing dollars, analyzing potential new markets, deploying work forces,
analyzing customer service satisfaction levels, and designing a new logistics
system. Many of these types of decisions are based on intuition and methods
derived from "years of experience."
When I contacted them in relation to this paper, OR/MS practitioners and
researchers were doubtful that any formal mechanism could be devised.
According to Y. Sheffi fa well-known logistics specialist and coauthor of a
case reported for Marshalls, Inc. (Carlisle et al., 197,
Estimation of cost/benefit: the burden is on me and a project champion in the or-
ganization.... Mostly, no formal analysis is undertaken as decision maker in the
organization gets finally convinced by hand waving. No specific time is used to
recoup costs. The potential has to be enormous-otherwise the project is not done.
In other words, the benefit/cost ratio has to be (an implied value of) 20-200 for
people to feel comfortable (Y. Sheffi, private communication, October 19871.
The emphasis on large benefit/cost ratios was repeated by others. Ac-
cording to Amedeo Odoni (a recognized expert in OR/MS as applied to
airport planning),
In my experience I have not really encountered any formal mechanisms for evaluating
the costs and benefits of an OR study. The reason may be that the benefits are usually
of a different order of magnitude than the costs (e.g., in a $100K study of an airport's
layout, one may "save" $25+ million, a real example). Airport benefits are also
often difficult to quantify in dollar terms (Amedeo Odoni, private communication,
November 1987~.
According to John Bartholdi (coauthor of the "meals-on-wheels" project),
In my consulting experience costs/benefits must be clear and large before a client
undertakes action. tHe or she] expects immediate payback (or at least within 1 year)
and wants insignificant risk. Future is not discounted, since action not taken unless
improvement will be enormous (John Bartholdi, private communication, November
1987~.
There appear to be some settings in which "scientific" a posterior) eval-
uation of an OR/MS product is possible. For instance, in logistics, if the
concern is solely transportation cost reduction, one can analyze the decisions
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137
of truck dispatchers with and without the OR/MS technology to estimate
savings. This was apparently the approach taken in the celebrated Air Prod-
ucts case. According to the principal author of the work, Marshall Fisher,
with regard to evaluating the benefits,
Air Products was quite thorough and methodical in this regard and developed a model
of how distribution costs related to various parameters of the distribution operation,
such as location and volumes of customer demands during a particular time period.
This model was used to predict what costs would have been in the future if the
vehicle schedule system had not been introduced, and therefore to provide a bench-
mark against which to assess improvements (Marshall Fisher, private communication,
December 1987~.
But after-the-fact evaluation still does not answer a manager's question
regarding investment in OR/MS technology prior to demonstrated beneficial
results. I particularly like what Warren Powell, an expert in logistics, has
to say about this problem area:
For some companies it (the decision to invest in OR MS) is a pure cost/benefit
decision, using very conservative estimates of cost savings as benefits. For other
companies the decision is driven by one or two individuals "with a vision" that a
model is critical to success. Operations people generally fall in the first group,
marketing/finance types in the second. The person with a vision is critical to imple-
mentation.
In reality, success usually depends on making someone's life easier. Rigorously
documenting savings is rare (e.g., numbers prepared for the Edelman Award are
generally not reliable). Service and profit benefits are virtually impossible to quantify,
because side-by-side analyses with and without a model are never available.
It is most tempting to evaluate the value of a model in terms of how much money
it saves each year. To be sure, it is a useful and often important exercise to at least
try to estimate the economic impacts of a model.... The current emphasis on cost
numbers is having the result that (a) only implementations at big companies which
may yield substantial cost savings are important; (b) traditionally conservative people
in operations, who often will acknowledge only savings they can rigorously verify,
are to be avoided; and (c) traditional applications to operations, which yield direct
cost savings are preferred over richer applications to improve pricing, marketing,
customer service, or financial planning with notoriously intangible benefits (Warren
Powell, private communication, November 1987~.
It may be that no satisfactory formal mechanism will ever be devised for
deciding before-the-fact whether or not to invest in OR/MS. For certain
narrowly defined applications areas, one can simulate proposed new pro-
cedures and compare them with status quo procedures to assess potential
benefits. But the cases in this paper illustrate that the greatest potential
benefits of an information/knowledge technology such as OR/MS are orga-
nizational, affecting fundamentally the ways firms manage and operate.
Sometimes the OR/MS model serves as the catalyst for managers from dis
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RICHARD C. LARSON
parate departments within a firm to communicate; perhaps they should have
communicated before creation of the model, but the at once bald and sci-
entifically neutral assumptions of the model can focus a group's discussions
on difficult decisions. OR/MS products implemented on a day-to-day basis,
by affecting information flows and providing immediate evaluative results
of decisions, can markedly change managerial behavior.
In increasingly competitive environments one can argue that the effective
processing of data to develop decision consequential information remains for
many services industries a viable mechanism for achieving competitive ad-
vantage. For the impacts of OR/MS and related information technologies to
grow, many managers may need a new point of view regarding investment.
According to George Kozmetsky, "Managers need to understand that in-
formation, science, and technology are not free economic goods but are
assets to be used, planned, earned on, and replenished" (Kozmetsky, 1984,
p.4~.
ACKNOWLEDGMENTS
I would like to thank the MIT School of Engineering for providing support
for developing OR/MS course material (of which this paper is a part) in a
new engineering schoolwide undergraduate elective on operations research
in engineering. I would also like to thank the National Academy of Engi-
neering for supporting my very productive research assistant, Luiz F. M.
Vieira, who is a doctoral candidate in operations research at MIT. Finally,
particular thanks are due to Bruce Guile at NAE who carefully read earlier
versions of this paper and greatly contributed to its final form.
APPENDIX
A DETAILED CASE: SCHEDULING 911 OPERATORS
In 1968 the mayor of New York, John Lindsay, opened the first-in-the-
nation big-city "911 system" for responding to calls for emergency service
(police, fire, ambulance) from the public. To call the police one formerly
had to memorize seven emergency telephone numbers, one for each of seven
dispatching zones throughout the city. (And, not insignificantly, one had to
know from which dispatching zone one was calling.) The new system allowed
a caller simply to dial "911" from anywhere in the city.
Approximately 15,000 calls per day were processed by "911 operators"
located at a central dispatch and communications room of the New York
City Police Department (NYPD) in lower Manhattan. Within weeks after
opening the new facility, complaints started pouring in (by telephone, letters,
radio talk shows, and letters to the editor) that the new multimillion dollar
system that was supposed to speed processing of calls was plagued with
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OPERATIONS RESEARCH AND THE SERVICES INDUSTRIES
139
delays. One letter to the editor of the New York Times complained of calling
911 on a Saturday evening and waiting more than 25 minutes for someone
to answer the phone; eventually the caller thought he might have called the
"wrong number," so he hung up and tried again, this time getting an answer
after "only" approximately 20 minutes of waiting. These types of complaints
forced the police commissioner to put together a study team to analyze the
problem.
I was one of three members of the team, the other two being police
lieutenants trained in police planning and operations. Quickly we jointly
discovered that the hourly average volume of 911 calls varied predictably
by a factor of eight or more on a daily basis (and more when measured over
a week), while the hourly number of 91 1 telephone operators varied only by
a factor of two (with maximum deployment averaging 25 operators during
all hours of the day except the early morning period, 3:00 to 7:00 A.M.,
when the number of operators dropped to 12 or 131. In other words, hourly
deployment of operators virtually ignored predictable changes in call vol-
umes, except during the very quiet early morning hours.
We desired to develop (in 1 month) an easy-to-use scheduling procedure
that took advantage of economies of scale. Although the 911 system incor-
porated several complications not found in more standard telephone an-
swering systems, we found it acceptable as an approximation to apply Erlang's
original formulas (circa 1915) describing the operating properties of multis-
erver queues to schedule the operators.
Two interesting encounters during the implementation process, both with
a senior managing police officer, deserve mention. First, when in a formal
briefing I displayed graphically the data showing the true (deplorable) state
of affairs with regard to queue delays (with 40 percent of callers on Saturday
evenings experiencing delays greater than 30 seconds), the senior officer
declared that the data I was using were inaccurate; after all, his officer in
charge of the Communications Division had informed him that there were
few problems and that the loud public outcries were not representative of
the service levels being provided. Luckily, the two lieutenants and I had
worked together side-by-side within the Communications Division for 1 month;
when questioned by the senior officer, the lieutenants verified the accuracy
of the data.
Second, after the presentation of the data, I requested from the senior
officer his department's "performance objectives" with regard to 911 op-
erator scheduling. In particular, I requested from him two numbers, T and
P. such that operators would be scheduled so that during no hour would
more than P percent of the callers incur delays greater than T seconds. For
instance, if T and P were set at 15 seconds and 5 percent, respectively, I
would use Erlang's formulas to schedule 911 operators each hour so that no
more than 5 percent of the callers would experience delays exceeding 15
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RICHARD C. LARSON
seconds. At first the senior officer refused to give any such numbers. Then,
when pressed, he relented, announcing his department's values:
T = P = 0.00 (I. Queues with uncertainty can virtually never achieve such
perfect operation with a finite amount of resources. Ultimately, we back-
tracked and rescheduled the department's existing number of operators on a
weekly basis, achieving major reductions in delays and "time equity" in
level of service (i.e., with all hours of the week having nearly the same
delay characteristics) [see Larson (197211. Unlike many other operations
research studies, this one was implemented in its entirety within 1 month
after completion.
Prior to the study, 17 percent of all 911 calls had been delayed 15 seconds
or more (when averaged over an entire month), with terrible congestion at
predictable times (e.g., 40 percent of calls delayed more than 30 seconds
from 8:00 P.M. to midnight, Saturday evenings). After implementation of
the recommendations of the study, no hour of the week experienced more
than 5 percent of the calls having 15-second delays. The "product" of the
study consisted of seven charts or tables, each containing for a particular
day of the week the recommended number of 911 operators to assign each
hour of the day. The "cost" to the NYPD was 3 person-months of profes-
sional effort. No additional 911 operators were hired; rather the hours of
working operators were simply reassigned. If additional 911 operators had
been hired under the "old" scheduling scheme to obtain the same new
performance levels at all hours of the week, the operator pool would have
increased by approximately 50 percent.
Before leaving this case, two other reflections are in order. We found
early on in the study that nearly all the data we needed had been recorded
meticulously by a full-time officer whose only job was to place the operating
statistics into a large loose-leaf book. By 5:00 P.M. each day, he had com-
pleted the previous day's entries, inserted the final completed sheet of num-
bers, and went home. Not one decision had been influenced by the entries
in the book! As far as we could tell, virtually no one other than this recording
officer had ever looked at the numbers. This book was so complete (and
accurate) that for our work we needed only approximately 10 percent of its
entries.
To ensure implementation, the two lieutenants decided to modify the in-
centive and reward system for the captains on duty in the 911 center. The
"data recorder's" job was modified so that the first thing he or she did each
morning was to draw a large graph displaying in an hour-by-hour fashion
the previous day's performance. The name of the captain on duty for each
8-hour tour of duty was prominently displayed, as well. Each time during a
tour that one or more callers experienced a call-answering delay exceeding
30 seconds, bells would sound and lights would go on and revolve, not unlike
that in modern discotheques. Each such event was labeled a "bell." On the
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OPERATIONS RESEARCH AND THE SERVICES INDUSTRIES
141
large graph, next to each captain's name, the data recorder displayed in large
font the number of "bells" incurred on that captain's 8-hour tour of duty.
This display was one of the first things seen by the many New York citizens,
school children, and tourists who were given tours of the 911 center each
day. Needless to say, the number of bells was kept to a reasonable minimum
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Representative terms from entire chapter:
services industries
