Topic 1
Operations Management and
Operations Research
Chapter 1
•
Primary Functions of Business
•
Operations Management applies to all businesses
•
Definition of Operations Management
•
Operations Management as a Transformation process
•
How organisations have addressed growth and complexity
• Cross Function Teams
• Operations Research
•
Impact of Operations Research
•
More than just Mathematics
•
Operations Research modelling approach
PRIMARY FUNCTIONS OF A BUSINESS
• Since the advent of the industrial revolution, the world has seen a remarkable growth
in the size and complexity of organizations. The artisans’ small shops of an earlier
era have evolved into the billion-dollar corporations of today.
• An integral part of this revolutionary change has been a tremendous increase in the
division of labour and segmentation of management responsibilities in these
organizations. The results have been spectacular.
• Typically, organisations have been structured into three critical primary functions
namely, Operations, Marketing and Finance and supporting functions including
human resources, information systems, legal and accounting
• The functional areas are concerned with a particular focus of responsibility or
decision making in an organization. The marketing function is responsible for creating
demand and generating sales revenue; finance is responsible for the acquisition
and allocation of capital and the operations function is responsible for the
production of goods or services (generating supply)
• Often, supporting functions are also set up to provide staff support to the three
primary functions.
OPERATIONS MANAGEMENT
• In most countries today, their economy has dramatically shifted from the production
of goods to the production of services.
• At first glance, it may appear that service businesses and their operations don't have
much in common with manufacturing operations.
• However, a unifying feature of these operations is that both can be viewed as a
transformation process.
Figure 1 – Operations as a Productive System
OPERATIONS MANAGEMENT
• Process technology is used to convert inputs into outputs. The process technology is
the methods, procedures and equipment used to transform materials or inputs into
products or services
• In each of these service organizations, operations managers are responsible for
providing the supply of services much like their counterparts in manufacturing who
produce the supply of goods.
• The processes, principles and tools can be applied by everyone in business.
Managing the transformation process in an efficient and effective manner is the
task of the operations manager in any type of organization.
• The operations function is therefore a critical element in every business and
Operations managers have important positions in every company, in service industries as well as in manufacturing companies.
• In the private sector, operations managers take leadership roles in hotels,
restaurants, airlines, banks, and retail stores. In the government, there are
operations managers in the post office, police department, and housing department,
to name only a few.
EXAMPLES OF PRODUCTION SYSTEMS
Operations is the engine that creates profit for the enterprise and underpins the
global economy. No business can survive without it.
In manufacturing, inputs of raw materials, energy, labour, and capital are
transformed into finished goods. In service operations, these same types of inputs
are transformed into service outputs.
Figure 2 - Examples of Production Systems
DEFINITION OF OPERATIONS MANAGEMENT
• All organizations (business and nonprofit) must produce value for a customer.
Value is something customers will buy at a price they are willing to pay.
• For example, value in a pair of shoes may be shoes that are good-looking and
comfortable and will last a long time at a price that you can afford.
• Value is always defined in the customer's eyes. Generally speaking, value is the
relationship between benefits of the product or service relative to the price and
can be expressed as follows:
Value = Benefits
Price
• The difference between what a customer is willing to pay (price) and cost is
profit.
• Thus, an organization must provide value at the lowest possible cost in order to
maximize profit. This is the objective of operations: to produce value for the
customer at the lowest possible cost.
DEFINITION OF OPERATIONS MANAGEMENT
• The essence of operations management can be summarized by the following
definition:
Operations is responsible for supplying the product or service of the organization.
Operations managers provide value for the customer at the lowest cost by making
decisions for the operations function and by managing the transformation
process.
Three points in this definition deserve emphasis:
1. Decisions. The above definition refers to decision making as an important
element of operations management. Since all managers make decisions, it is
natural to focus on decision making as a central theme in operations. This decision
focus provides a basis for dividing operations into parts according to major
decision types., for example: operations management as process, quality, capacity,
and inventory.
2. Function. Operations is a major function in any organization, along with marketing
and finance. The operations function is responsible for supplying or producing
products and services for the business. Without a supply of goods or services a
DEFINITION OF OPERATIONS MANAGEMENT
3. Process. The process view not only provides a common ground for defining
service and manufacturing operations as transformation processes but is also a
powerful basis for design and analysis of operations.
Using the process view, we consider operations managers as managers of the
conversion process in the firm.
• The process view also provides important insights for the management of
productive processes in functional areas outside the operations function.
• It provides a basis for seeing an entire business as a system of interconnected
processes which makes it possible to analyze an organization and improve it from
a process point of view.
• All work, whether in finance, marketing, accounting, or other functions, is
accomplished by a process. For example, a sales office may be viewed as a
production process with inputs, transformation, and outputs. The same is true for
an accounts payable office/department and for a loan office in a bank.
OPERATIONS AS A TRANSFORMATION PROCESS
• Accordingly, in terms of the process view, operations management concepts have
applicability beyond the functional area of operations. The 3M Company, for
example, uses Six Sigma a process to improve processes throughout the firm,
including processes in human resources, accounting, finance, information systems,
and even the legal department. Process improvement is therefore not restricted to
operations.
• The process view also helps us understand how operations cannot be insulated
from changes in the environment but rather must adapt to them.
• As the Figure 3 below indicates, operations is surrounded by its environment and
constantly interacts with its internal and external environments. Interaction with the
external environment occurs through the economic, physical, social, and political
environment of operations.
• In the fast-changing world of today's global business, constant change in
operations has become essential, as a means of survival.
• The interactive nature of these relationships makes it necessary to constantly
monitor the environment and to make corresponding changes in operations when
needed.
OPERATIONS AS A TRANSFORMATION PROCESS
Figure 3 – Operations as a Transformation Process
PROBLEMS OCCURING IN ORGANISATIONS
• Along with its blessings, increasing specialization in organisations, has
created new problems that are still occurring in many organizations.
• One problem is a tendency for the many components of an organization to
grow into relatively autonomous empires with their own goals and value
systems, thereby losing sight of how their activities and objectives mesh with
those of the overall organization.
• Functional silos have developed in many businesses and impede crossfunctional decision making.
• What is best for one component frequently is detrimental to another, so the
components may end up working at cross purposes. As a result, the overall
business suffers due to an emphasis on functional prerogatives.
• A related problem is that as the complexity and specialization in an
organization increase, it becomes more and more difficult to allocate the
available resources to the various activities in a way that is most effective for
CROSS FUNCTIONAL TEAMS
• One way that organisations overcome the creation of Functional Silos is to set up
cross functional teams by integrating the business by considering the crossfunctional nature of decision making in the firm and to foster process thinking.
• The principles of process thinking can be applied to all functions. It is common
nowadays to form cross-functional management teams for new product introduction
and for day-to-day improvement.
• Every decision is cross-functional in nature. The organization where everyone just
works with people from their own function doesn't exist anymore.
• Every function therefore must be concerned not only with its own decision
responsibilities but with integrating decisions with other functions.
• Operations is a major function in every organization and can't be avoided. Every
function of the business has an important interface with operations.
CROSS FUNCTIONAL DECISION MAKING
• Many of the ideas, techniques, and principles of operations management can be
applied across the business, not just in operations. For example, all work is
accomplished through a process (or sequence of steps).
• The decisions of operations are improved when the ideas from all functions have
been considered.
• Each member of the team is generally trained in common methodologies and the
team is given responsibility for achieving its own goals.
• Some of the key cross-functional decision-making relationships between
operations decisions and the functional areas of marketing, finance/accounting,
human resources, and information systems are shown in Figure 4 below.
• A second way that organisations address growth and complexity is by applying
Operations Research methodologies to assist in decision making.
EXAMPLES OF CROSS-FUNCTION DECISION MAKING
Figure 4 – Examples of Cross-Function Decision Making
THE ORIGINS OF OPERATIONS RESEARCH
• The need to allocate scarce resources to the various activities within each
operation in an effective manner provided for the emergence of operations
research (commonly referred to as OR).
• The roots of OR can be traced back many decades, when early attempts were
made to use a scientific approach in the management of organizations.
• However, the beginning of the activity called operations research has generally
been attributed to the military services in England early in World War II, when a
group of scientists made decisions regarding the best utilisation of war material
• By developing effective methods of using the new tool of radar, these teams were
instrumental in winning the Air Battle of Britain. Through their research on how to
better manage convoy and antisubmarine operations, they also played a major
role in winning the Battle of the North Atlantic. Similar efforts assisted the Island
Campaign in the Pacific.
• When the war ended, the success of OR in the war effort spurred interest in
applying OR outside the military as well to a variety of organizations in business,
RAPID GROWTH OF OPERATIONS RESEARCH
• Two factors played a key role in the rapid growth of OR.
• One was the substantial progress that was made early in improving the
techniques of OR. After the war, many of the scientists who had participated on
OR teams or who had heard about this work were motivated to pursue research
relevant to the field. Important advancements in the state of the art resulted. A
prime example is the simplex method for solving linear programming problems,
developed by George Dantzig in 1947. Many of the standard tools of OR, such
as linear programming, dynamic programming, queueing theory, and inventory
theory, were relatively well developed before the end of the 1950s.
• A second factor that gave great impetus to the growth of the field was the
onslaught of the computer revolution. A large amount of computation is usually
required to deal most effectively with the complex problems typically considered
by OR.
• Doing computations by hand would often be out of the question. Therefore, the
development of electronic digital computers, with their ability to perform
arithmetic calculations thousands or even millions of times faster than a human
being can, was a tremendous boon to OR.
THE NATURE OF OPERATIONS RESEARCH
• A further boost came in the 1980s with the development of increasingly powerful
personal computers accompanied by good software packages for doing OR. This
brought the use of OR within the easy reach of much larger numbers of people.
• Today, literally millions of individuals have ready access to OR software.
Consequently, a whole range of computers from mainframes to laptops now are
being routinely used to solve OR problems.
• As its name implies, operations research involves “research on operations.”
• Thus, operations research is applied to problems that concern how to conduct and
coordinate the operations (i.e., the activities) within an organization.
• The nature of the organization is essentially immaterial, and, in fact, OR has been
applied extensively in such diverse areas as manufacturing, transportation,
construction, telecommunications, financial planning, health care, the military, and
public services, to name just a few. Therefore, the breadth of application is
unusually wide.
THE NATURE OF OPERATIONS RESEARCH
• The research part of the name means that operations research uses an
approach that resembles the way research is conducted in established scientific
fields.
• To a considerable extent, the scientific method is used to investigate the problem
of concern in operations. (In fact, the term management science sometimes is
used as a synonym for operations research.)
• OR is also concerned with the practical management of the organization.
Therefore, to be successful, OR must also provide positive, understandable
conclusions to the decision maker(s) when they are needed.
• Another characteristic of OR is its broad viewpoint. OR adopts an organizational
point of view. Thus, it attempts to resolve the conflicts of interest among the
components of the organization in a way that is best for the organization as a
whole. This does not imply that the study of each problem must give explicit
consideration to all aspects of the organization; rather, the objectives being
sought must be consistent with those of the overall organization.
THE NATURE OF OPERATIONS RESEARCH
• An additional characteristic is that OR frequently attempts to find a best solution
(referred to as an optimal solution) for the problem under consideration. (We say a
best instead of the best solution because there may be multiple solutions tied as
best.) Rather than simply improving the status quo, the goal is to identify a best
possible course of action. Although it must be interpreted carefully in terms of the
practical needs of management, this “search for optimality” is an important theme in
OR.
• No single individual should be expected to be an expert on all the many aspects of
OR work or the problems typically considered; this would require a group of
individuals having diverse backgrounds and skills. Therefore, when a full-fledged
OR study of a new problem is undertaken, it is usually necessary to use a team
approach. Such an OR team typically needs to include individuals who collectively
are highly trained in mathematics, statistics and probability theory, economics,
business administration, computer science, engineering and the physical sciences,
the behavioral sciences, and the special techniques of OR.
• The team also needs to have the necessary experience and variety of skills to give
appropriate consideration to the many ramifications of the problem throughout the
organization.
THE IMPACT OF OPERATIONS RESEARCH
• Operations research has had an impressive impact on improving the efficiency of
numerous organizations around the world.
• In addition, OR has made a significant contribution to increasing the productivity of
the economies of various countries.
• The impact of OR will continue to grow and is currently one of the fastest-growing
career areas for U.S. college graduates. There now are a few dozen member
countries in the International Federation of Operational Research Societies (IFORS),
with each country having a national OR society.
• There is also a high demand for Operations personnel. All businesses want to hire
bright people who can make the best decision for the business as a whole, not the
best marketing, finance or operations decision. They want employees who can see
the big picture, not a narrow perspective.
• An understanding of operations decisions is critical for all business careers since
everyone will be involved with operations decisions in a cross functional relationship.
Employees will severely limit their career if they take a narrow functional perspective.
MORE THAN JUST MATHEMATICS
• A cornerstone of OR is mathematical modeling. Quantitative techniques used for
making decisions form the main part Operations Research (OR).
• Because of the mathematical nature of OR models, there is a tendency to think that
an OR study is always rooted in mathematical analysis. However, one should not
"jump" into using mathematical models until simpler approaches have been explored.
• In some cases, one may encounter a "commonsense" solution through simple
observations. Indeed, since the human element invariably affects most decision
problems, a study of the psychology of people may be key to solving the problem.
• Three illustrations are presented here to support this argument
1. Responding to complaints of a slow elevator service in a large office building, the
situation was initially perceived as a waiting line problem that may require the use of
mathematical queuing analysis or simulation. However, after studying the behavior
of the people voicing the complaint, the psychologist on the OR team suggested
installing full-length mirrors at the entrance to the elevators. Miraculously, the complaints disappeared as people were kept occupied watching themselves and others
while waiting for the elevator.
MORE THAN JUST MATHEMATICS
2. In a study of the check-in facilities at a large British airport, a United States Canadian
consulting team used queuing theory to investigate and analyze the situation. Part of
the solution recommended the use of well-placed signs to urge passengers who were
within 20 minutes from departure time to advance to the head of the queue and
request immediate service. The solution was not successful because the passengers,
being mostly British, were "conditioned to very strict queuing behavior" and hence
were reluctant to move ahead of others waiting in the queue.
3. In a steel mill, ingots are first produced from iron ore and then used to produce
various types of steel bars and beams. The manager of the facility noticed a long
delay between the time ingots are produced and their transfer to the next phase
(where end products are manufactured). Ideally, the next phase should start soon
after the ingots leave the furnaces to reduce the reheating cost. Initially, the problem
was perceived as a line balancing situation to be resolved either by reducing the
output production of the furnaces or by increasing the capacity of the next process.
However, as part of understanding the problem, the OR team used simple charts to
summarize the output of the furnaces during the three shifts of the day and
discovered that, even though the third shift started at 11:00 P.M., most of the
production took place between the hours of 2:00 and 7:00 A.M. only. Further
investigation revealed that third-shift operators preferred to get long rest periods at
MORE THAN JUST MATHEMATICS
• The problem was solved by " leveling out" the production of ingots throughout the
shift.
• Three conclusions can be drawn from these illustrations:
1. Before embarking on sophisticated mathematical modeling, the OR team should
entertain the possibility of using “innovative" ideas to resolve the situation. The
solution of the elevator problem by installing mirrors is rooted more in the study of
human behavior than in mathematical modeling. It is also simpler and less costly
than any other recommendation a mathematical model might have produced.
Perhaps this is the reason OR teams usually include the expertise of "outsiders"
from other non mathematical fields (psychology in the case of the elevator problem).
This point was recognized and implemented by the first OR team in Britain during
World War II.
2. Solutions are rooted in people and not in technology. Any solution that does not
take human behavior into account is apt to fail. Even though the mathematical
solution of the British airport problem may have been sound, the fact that the
consulting team was not aware of the cultural difference s between the United
States and Britain (Americans and Canadians tend to be less formal) had resulted
in an unimplementable recommendation.
MORE THAN JUST MATHEMATICS
3. An OR study should never start with bias toward using a specific mathematical
model before its use can be justified. For example, because linear programming is a
successful technique, there is a tendency to use it as the tool of choice in
modeling "any" situation. Such an approach usually leads to a mathematical model
far removed from the real situation. It is thus imperative that we first analyze
available data, using the simplest techniques where possible (e.g., averages,
charts, and histograms), with the objective of finding the source of the problem.
Once the problem is defined, a decision can be made regarding the most
appropriate tool to find the solution. In the steel mill problem, simple charting of the
ingots production was all that was needed to rectify the situation.
• As a decision making tool, OR is both a science and an art. It is a science by the
virtue of embodying mathematical techniques it presents. The mathematical analysis
often represents only a relatively small part of the total effort required. And it is an art
because of the success of all the phases that precede and succeed the solution of
the mathematical model depends largely on the creativity and experience of the OR
team. Unqualified factors such as human behavior must be accounted for before a
final decision can be reached.
• An OR study is rooted in team work, where both the OR analysts and the client work
MORE THAN JUST MATHEMATICS
• The OR analysts with their expertise in modelling must be complemented with the
experience and cooperation of the client for whom the study is being carried out.
• Effective OR practice requires more than analytical competence. It also requires
among other attributes, technical judgement (e.g. when and how to use a given
technique) and skilled in communication and organizational survival.
• It is difficult to prescribe specific courses of action (similar to those dictated by the
precise theory of mathematical models) for these intangible factors.
• As such we can only offer general guidelines for the implementation of OR in
practice
OPERATIONS RESEARCH MODELING APPROACH
• The major overlapping phases of a typical OR project include the following:
1.
2.
3.
4.
5.
6.
Define the problem of interest and gather relevant data.
Formulate a mathematical model to represent the problem.
Develop a computer-based procedure for deriving solutions to the problem from
the model.
Test the model and refine it as needed.
Prepare for the ongoing application of the model as prescribed by management.
Implement.
• Of all six phases, only phase 3 dealing with model solution is best defined and
easiest to implement in an OR study because it deals with most precise
mathematical models.
• The implementation of the remaining phases is more than art than theory.
DEFINING THE PROBLEM AND GATHERING DATA
• Describing the scope of the problem under investigation should be carried out by the
entire OR team.
• Most practical problems encountered by OR teams are initially described to them in
a vague, imprecise way. The first order of business is to study the relevant
environment and develop a well-defined statement of the problem to be considered.
• This includes determining such things as the appropriate objectives, constraints on
what can be done, interrelationships between the area to be studied and other areas
of the organization, possible alternative courses of action, time limits for making a
decision, and so on.
• The end result is to identify three principle elements of the decision problem
• The description of the decision alternatives
• Determination of the objective of the study
• Specification of the limitations under which the model system operates
•
The process of problem definition is a crucial one because it greatly affects how
relevant the conclusions of the study will be. It is difficult to extract a “right” answer
GATHERING DATA
• OR teams typically spend a surprisingly large amount of time gathering relevant data
about the problem. Much data usually are needed both to gain an accurate
understanding of the problem and to provide the needed input for the mathematical
model being formulated in the next phase of the project.
• Frequently, much of the needed data will not be available when the study begins,
either because the information never has been kept or because what was kept is
outdated or in the wrong form.
• Therefore, it often is necessary to install a new computer-based management
information system to collect the necessary data on an ongoing basis and in the
needed form.
• The OR team normally needs to enlist the assistance of various other key individuals
in the organization to track down all the vital data.
• Even with this effort, much of the data may be quite “soft,” i.e., rough estimates
based only on educated guesses. Typically, an OR team will spend considerable time
trying to improve the precision of the data and then will make do with the best that
can be obtained.
FORMULATING A MATHEMATICAL MODEL
• After the decision maker’s problem is defined, the next phase is to reformulate this
problem in a form that is convenient for analysis. The conventional OR approach for
doing this is to construct a mathematical model that represents the essence of the
problem.
• Mathematical models have many advantages over a verbal description of the
problem. One advantage is that a mathematical model describes a problem much
more concisely. This tends to make the overall structure of the problem more
comprehensible, and it helps to reveal important cause-and-effect relationships.
• The first crucial step in any business model is the definition of the alternatives or
the decision variables of the problem.
• Thus, if there are n related quantifiable decisions to be made, they are represented
as decision variables (say, x1, x2, . . . , xn) whose respective values are to be
determined.
• Next the decision variables are used to construct the objective function and the
constraints of the model.
FORMULATING A MATHEMATICAL MODEL
• The objective function the appropriate measure of performance (e.g., profit) is
expressed as a mathematical function of these decision variables (for example, P =
3x1 + 2x2 + … +5xn).
• Any restrictions on the values that can be assigned to these decision variables are
also expressed mathematically, typically by means of inequalities or equations (for
example, x1 + 3x1x2 + 2x2 ≤ 10). Such mathematical expressions for the
restrictions often are called constraints.
• The constants (namely, the coefficients and right-hand sides) in the constraints and
the objective function are called the parameters of the model.
• The mathematical model might then say that the problem is to choose the values of
the decision variables so as to maximize or minimize the objective function, subject
to the specified constraints.
• With the three steps completed the OR model is usually organized in the following
general format: Maximize or minimize the objective function
Subject to
constraints
DERIVING SOLUTIONS FROM THE MODEL
• The next phase in an OR study is to develop a procedure (usually a computerbased procedure) for deriving solutions to the problem from this model.
•
It is by far the simplest of all OR phases because it in most cases it entails the
use of well defined standard optimization algorithms (systematic solution
procedures) of by using one of a number of readily available software packages.
• For experienced OR practitioners, finding a solution is the fun part, whereas the
real work comes in the preceding and following steps, including the postoptimality
analysis covered later.
• A common theme in OR is the search for an optimal, or best, solution. However,
it needs to be recognized that these solution are optimal only with respect to the
model being used.
• Since the model necessarily is an idealized rather than an exact representation of
the real problem, there cannot be any Utopian guarantee that the optimal solution
for the model will prove to be the best possible solution that could have been
implemented for the real problem.
DERIVING SOLUTIONS FROM THE MODEL
• There just could be too many imponderables and uncertainties associated with
real problems. However, if the model is well formulated and tested, the resulting
solution should tend to be a good approximation to an ideal course of action for
the real problem.
• Postoptimality analysis (analysis done after finding an optimal solution) is a very
important part of most OR studies. This analysis also is sometimes referred to as
what-if analysis because it involves addressing some questions about what
would happen to the optimal solution if different assumptions are made about
future conditions.
• What–if or Sensitivity analysis is particularly needed when the parameters of the
model cannot be estimated accurately.
• These questions often are raised by the managers who will be making the ultimate
decisions rather than by the OR team.
• This process of experimenting with changes in the model also can be very helpful
in providing understanding of the behavior of the model and increasing confidence
in its validity.
DERIVING SOLUTIONS FROM THE MODEL
• The advent of powerful spreadsheet software now has frequently given
spreadsheets a central role in conducting postoptimality analysis. One of the great
strengths of a spreadsheet is the ease with which it can be used interactively by
anyone, including managers, to see what happens to the optimal solution when
changes are made to the model.
• After the sensitive parameters are identified, special attention is given to estimating
each one more closely, or at least its range of likely values.
• One then seeks a solution that remains a particularly good one for all the various
combinations of likely values of the sensitive parameters. If the solution is
implemented on an ongoing basis, any later change in the value of a sensitive
parameter immediately signals a need to change the solution.
• In some cases, certain parameters of the model represent policy decisions (e.g.,
resource allocations). If so, there frequently is some flexibility in the values assigned
to these parameters. Perhaps some can be increased by decreasing others.
• Postoptimality analysis includes the investigation of such trade-offs.
TESTING THE MODEL
• The next Phase of testing and improving a model to increase its validity is commonly
referred to as model validation. Model validity checks whether or not the proposed
model does what it is supposed to do, that is, does the model predict adequately the
behavior of the system under study?
• Initially, the OR team should be convinced that the output of the model does not
include "surprises." In other words, does the solution make sense? Are the results
intuitively acceptable?
• It is difficult to describe how model validation is done, because the process depends
greatly on the nature of the problem being considered and the model being used.
• Since the OR team may spend months developing all the detailed pieces of the
model, it is easy to “lose the forest for the trees.” Therefore, after the details (“the
trees”) of the initial version of the model are completed, a good way to begin model
validation is to take a fresh look at the overall model, the bigger picture (“the forest”)
to check for obvious errors or oversights.
• The group doing this review preferably should include at least one individual who did
not participate in the formulation of the model.
TESTING THE MODEL
• Although this testing phase is placed later in the modelling approach, much of this
model validation work actually is conducted during the model building phase of the
study to help guide the construction of the mathematical model.
• Care must be taken to ensure that the model remains a valid representation of the
problem.
• The proper criterion for judging the validity of a model is to whether the model
predicts the relative effects of the alternative courses of action with sufficient
accuracy to permit a sound decision.
• It is not necessary that the absolute magnitude measure of performance be
approximately correct for the various alternatives, provided that their relative values
(i.e. the difference between their values) are sufficiently precise.
• Thus, all that is required is that there be a high correlation between the prediction by
the model and what would actually happen in the real world.
• To ascertain whether this requirement is satisfied, it is important to do considerable
testing and consequent modifying of the model.
TESTING THE MODEL
• Re-examining the definition of the problem and comparing it with the model may help
to reveal mistakes. It is also useful to make sure that all the mathematical
expressions are dimensionally consistent in the units used.
• Additional insight into the validity of the model can sometimes be obtained by varying
the values of the parameters and/or the decision variables and checking to see
whether the output from the model behaves in a plausible manner. This is often
especially revealing when the parameters or variables are assigned extreme values
near their maxima or minima.
• A more systematic approach to testing the model is to use a retrospective test.
When it is applicable, this test involves using historical data to reconstruct the past
and then determining how well the model and the resulting solution would have
performed if they had been used.
• Documenting the process used for model validation is important. This helps to
increase confidence in the model for subsequent users. Furthermore, if concerns
arise in the future about the model, this documentation will be helpful in diagnosing
where problems may lie.
PREPARING TO APPLY THE MODEL
• The next step is to install a well documented system for applying the model as
prescribed by management.
• This system will include:
• the model
• solution procedure (including postoptimality analysis)
• operating procedures for implementation.
• Then, even as personnel changes, the system can be called on at regular
intervals to provide a specific numerical solution.
• This system usually is computer-based. In fact, a considerable number of
computer programs often need to be used and integrated.
• Databases and management information systems may provide up-to-date input
for the model each time it is used, in which case interface programs are needed.
After a solution procedure (another program) is applied to the model, additional
computer programs may trigger the implementation of the results automatically.
PREPARING TO APPLY THE MODEL
• In other cases, an interactive computer-based system called a decision support
system can be installed to help managers use data and models to support
(rather than replace) their decision making as needed.
• The system will generate managerial reports (in the language of management)
that interpret the output of the model and its implications for application.
• In major OR studies, several months (or longer) may be required to develop, test,
and install this computer system.
• Part of this effort involves developing and implementing a process for maintaining
the system throughout its future use.
• As conditions change over time, this process should modify the computer system
(including the model) accordingly.
IMPLEMENTATION
• The last phase of an OR study is to implement the system as prescribed by
management. This phase is a critical one because it is here, and only here, that the
benefits of the study are reaped.
• Therefore, it is important for the OR team to participate in launching this phase, both
to make sure that model solutions are accurately translated to an operating procedure
and to rectify any flaws in the solutions that are then uncovered.
• The success of the implementation phase depends a great deal upon the support of
both top management and operating management. The OR team is much more likely
to gain this support if it has kept management well informed and encouraged
management’s active guidance throughout the course of the study.
• Implementation of the solution of a validated model involves the translation of the
results into operating instructions issued in understandable form to the individuals who
will administer the recommended system. The burden of this task lies primarily with
the OR team.
• Good communications help to ensure that the study accomplishes what management
wanted and so deserves implementation. They also give management a greater sense
of ownership of the study, which encourages their support for implementation.
IMPLEMENTATION
• The implementation phase involves several steps.
• First, the OR team gives operating management a careful explanation of the new
system to be adopted and how it relates to operating realities.
• Next, these two parties share the responsibility for developing the procedures
required to put this system into operation. Operating management then sees that a
detailed indoctrination is given to the personnel involved, and the new course of
action is initiated.
• If successful, the new system may be used for years to come. With this in mind, the
OR team monitors the initial experience with the course of action taken and seeks to
identify any modifications that should be made in the future.
• Throughout the entire period during which the new system is being used, it is
important to continue to obtain feedback on how well the system is working and
whether the assumptions of the model continue to be satisfied.
• When significant deviations from the original assumptions occur, the model should
be revisited to determine if any modifications should be made in the system.
IMPLEMENTATION
• The post optimality analysis done earlier can be helpful in guiding this review
process.
• Upon culmination of a study, it is appropriate for the OR team to document its
methodology clearly and accurately enough so that the work is reproducible.
• Replicability should be part of the professional ethical code of the operations
researcher. This condition is especially crucial when controversial public policy
issues are being studied.
• It should be emphasized that there are many exceptions to the “rules” prescribed in
this approach.
• By its very nature, OR requires considerable ingenuity and innovation, so it is
impossible to write down any standard procedure that should always be followed by
OR teams.
• Rather, the preceding description may be viewed as a model that roughly represents
how successful OR studies are conducted.
Operations Research involves People, Processes and
Technology