rapid advances of research methods in real problem solving, with
rapid progress of the information technology and important structural
and institutional changes that shaped a new landscape of the
corporate and economic environment towards globalization of
markets and trade. In that process the contribution that quantitative
techniques can make to management decision making is significant.
Key words: quantitative techniques, models, analysis, decision.
Introduction
In the business world, and in fact, in practically every aspect of daily
living, quantitative techniques are used to assist in decision making. In order
to work effectively in a modern business organisation, whether the
organisation is a private commercial company, a government agency, a state
industry or whatever, managers must be able to use quantitative techniques
in a confident and reliable manner. Accountants make decisions based on the
information relating to the financial state of organization. Economists make
decision based on the information relating to the economic framework in
which the organization operates. Marketing staff make decisions based on
customer response to product and design.
Personnel managers make
decisions based on the information relating to the levels of employment in
the organization, and so on. Such information is increasingly quantitative
and it is apparent that managers need a working knowledge of the
procedures and techniques appropriate for analyzing and evaluating such
information. Such analysis and certainly the business evaluation cannot be
delegated to the specialist statistician or mathematician, who, adept though
they might be at sophisticated numerical analysis will frequently have little
overall understanding of the business relevance of such analysis.
The importance of quantitative methods for managers
The quantitative methods contain two component parts, the
quantitative and method, with asymmetrical attention to the
quantitative term.
Speaking about method, interest is focused upon the so-
called Scientific Method. Science is the mastering of things of the real
world, by knowledge about the truth. The term method drives to dialogue on
methodology in science which is clouded, as the phrase scientific method is
used in two different ways. The one is very general, as a process of
improving understanding. Although vague, it is considered as a powerful
definition, since it leaves room for criticizing dogmatic clinging to beliefs
and prejudices, or appreciating careful and systematic reasoning about
empirical evidence. The other is the traditional sense, and supports that there
is a unique standard method, which is central to identity of the science.
In effect, scientific progress requires many methods, so there is not a
unique standard method, though taught as a straightforward testing
hypotheses derived from theories in order to test those theories. The more
acceptable definition of scientific method is a process by which scientists,
collectively and over time, endeavour to construct an accurate (that is
reliable, consistent and non-arbitrary) representation of the real world. The
popular hypothetic-deductive standard method is excluding consideration
of the process of discovery in science. Rather, research is defined as a
penetrating process of learning and understanding the substance of actual
things and facts, by use of different methods. The research process
incorporates formulation of a research issue and construction of a conceptual
framework, by using all available information sources.
The quantitative methods have a number of attributes, such as: they
employ measurable data to reach comparable and useful results, assume
alternative plans for achieving objectives, plan data, concerning
observations collection, configuration and elaboration by statistical and
econometric stochastic methods, check data reliability, choose appropriate
sampling method, use carefully the estimates of the parameters for
forecasting and planning purposes, etc. since they derive from ex-post data
concerning past.
In an increasingly complex business environment managers have to
grapple with a problems and issues which range from the relatively trivial to
the strategic. In such an environment the quantitative techniques have an
important role. It is obvious that life for any manager in any organization is
becoming increasingly difficult and complex. Although there are many
factors contributing to this, figure 1 illustrates some of the major pressures
making decision making increasingly problematic. Organizations find them
selves operating in an increasingly complex environment. Changes in
government policy, privatization, increasing involvement of the European
Union contribute to this complexity. At the same time, organizations face
increasing competition from both home and abroad.
Because of the increasing complexity of the business environment in
which organizations have to function, the information needs of a manager
become more complex and demanding also. The time available to a manager
to asses, analyse and react to a problem or opportunity is much reduced.
Managers and their supporting information systems need to take
fast, and hope-fully appropriate, decisions. Finally, to add to the problems,
the consequences of taking wrong decisions become more serious and costly.
Entering the wrong markets, producing the wrong products or providing
inappropriate services will have major and big consequences for
organizations.
All of this implies that anything which can help the manager of an
organization in facing up to this pressures and difficulties in the decision
making process must be seriously considered. Quantitative techniques
provide information about a situation or problem and a different way to
examining that situation that may well help. Naturally such quantitative
analysis will produce information that must be assessed and used in
conjunction with other sources. Business problem are tackled from the
quantitative perspective. The decisions that must be made lie at the centre od
the process. These will be strongly influenced by the chosen organisatons
strategy with regard to its future direction, priorities and activities.[4, pg.2]
Before reaching a decision many factors and information must be
considered. Also, techniques have potentially important role to play in
helping a decision but they are not sufficient by themselves. This is
illustrated in figure 2. A business situation must be examined from both a
quantitative and a qualitative perspective. Information and analysis from
both these perspectives need to be brought together, assessed and acted
upon.
We can define quantitative techniques like mathematical and
statistical models which are describing a diverse array of variables
relationship, and they are designed to assist managers with management
problem-solving and decision making. There are many of mathematical and
statistical techniques which can be used to help decision making by
managers of all types of business organization: large or small, private sector,
public sector, profit-oriented, manufacturing, or service sector. Statistics is
defined as the process of collecting a sample, organizing, analyzing and
interpreting data. The numeric values which represent the characteristics
analyzed in this process are also referred to as statistics. However, in
statistics we are applying numerical way of exploration, and method of
analysis and synthesis population of numerical data depend on their nature
and extrapolation purpose. [2, pg.1] When information related to a particular
group is desired, and it is impossible or impractical to obtain this
information, a sample or subset of the group is obtained and the information
of interest is determined. Collected data are the row material which by
treatment should transform into useful quantitative measures. [2, pg. 19]
The quantitative models
The transformation of data into information, also called information
analysis, was supported by management information system processes.
Adequate models help develop quantitative techniques in a business context.
Models are simplified depictions of reality and often take the form of an
equation or set of equations that describe some economic setting. In
economic theory models are deterministic.[3, pg.36] Models come in a
variety of forms in business: they are not just quantitative. A scale model
might be constructed of a new office development, a financial model may be
developed to asses the impact of budget changes on product/service delivery;
the marketing department may develop a model in terms of assessing
customer response to product changes. However, any model, no matter what
its form or purpose, has one distinctive feature: it is an attempt to represent a
situation in a simplified form. Which model will be adequate depends on
purpose of investigation and analysis. [5, pg. 104] Many operational
problems and decision making have been based on research that deals with
application of model or quantitative techniques. There are fundamentally
four reasons why quantitative techniques are used by managers:
- Models force managers to be explicit about objectives.
- Models force managers to identify and record the types of decisions
(decision variables) that influence objectives. - Models force managers to identify and record pertinent interactions
and trade-off between decision variables.
- Models force managers to record constraints (limitations) on the
values that variables may assume.
In quantitative decision-making problems, different kinds of formal
mathematical and other types of models have been implemented.
All organizations in business use many quantitative methodologies,
including network analysis, forecasting (regression, path analysis, and time
series), cost-benefit analysis, optimization (linear programming, assignment,
and transportation), sensitivity analysis, significance testing, simulation,
benchmarking, and total quality management.
Moreover, decision support systems and computers based on this
programmed techniques are increasingly being used for enhancing
organizations capabilities. Recently, there have been relatively rapid
advances in the use of large amounts of data and in the development of new
techniques for their analysis.
In some cases decision makers faced with complex problems cannot
find, and perhaps should not seek, the best possible solutions. Qualitative
analysis is based primarily on the managers judgment and experience; it
includes managers conceptual and interpersonal ability to understand that
behavioral techniques help to solve problems. Qualitative analysis is
considered more as an art than a science. If the manager has had little
experience with no routine problems, or if a problem is sufficiently complex,
then a quantitative analysis might be a very important consideration for the
managers final decision-making.
Quantitative analysis concentrates on the facts, data, or quantitative
aspects associated with problems. A managers educational and technical
knowledge of quantitative procedures help to enhance the decision-making
process. The manager who is knowledgeable in quantitative decision-making
procedures is in a much better position to compare and evaluate the
qualitative and quantitative sources of information, or ultimately, to combine
alternatives to make the best possible decisions.
At present, seat-of-the-pants, reactive managerial styles are already
on the wane, and increased emphasis is being placed on “scientific” analysis
and planning. Up-to-date experience is still invaluable, but it must be used
with greater discipline. Analysis is now more rigorous, and computers
permit more alternatives to be analyzed in greater depth. But, most
important, formal planning is being used as a basis for action, not merely for
pro forma exercises. On a higher and more conceptual level, quantitative
analysis is facilitating communication where it never existed before. When a
problem has been stated quantitatively, one can often see that it is
structurally similar to other problems (perhaps from completely different
areas) which, on the surface, appear to be quite different. And once a
common structure has been identified, insights and predictions can be
transferred from one situation to another; the quantitative approach can
actually foster communication.
Thus it is not necessary-or even desirable-for modern managers to
be skilled practitioners of quantitative analysis. But they frequently lack
even the ability to recognize the right tool or data when they see them, let
alone the ability to focus on the basic structure of a problem rather than its
situational uniqueness. Yet they must be able to do so if they are to do more
than generate elegant nonsense. Managers must learn what the various tools
are designed to do and what the limits of their capabilities are. They must be
able to understand what staff specialists are attempting to achieve by a
particular analysis and to discuss the appropriateness of alternative
procedures sensibly (which also requires the development of additional
vocabulary). They must fully understand the variables a model will and will
not consider and be able to evaluate whether the relationships among the
variables are sensible. Managers cannot use an analytical tool wisely unless
they fully comprehend the underlying assumptions, what the analysis
achieves, what compromises the model makes with reality, and how its
conclusions are to be adapted to changing circumstances and intangible
considerations. All of this requires a more thorough knowledge of operations
than of mathematics.
The decision making process
Main turning points in the pace of the use of quantitative methods
are mentioned: the scientific management revolution of the early 90s in
last century, initiated by Frederic Taylor, the so-called Keynesian
revolution, the Operational Research originated during the Second World
War, followed by post-war developments of quantitative methods for
decision-making, notably the simplex method for solving linear
programming problems and many more methodological developments. As
complexity rose, attention moved to the dynamic interface among processes
in a chain to offer a definite output. In effect, it is (re)located in the thinking
of logistics and the Supply chain management, extended more recently to the
business process re-engineering. [7, pg. 11] Processes contain activities and
are related among each other for specific ends. The processing of real
problems solving involves the following steps:
1) Identification of corporate environment and uncertain conditions
2) Existence of Independent Management Units
3) Integrated approach of actual situations
4) Implementation of Scientific Approach
Processing is primarily a matter of understanding that the new
reality is exogenously given, irreversible and one-way pace. Open-minded
cost/benefit analysis overcomes hesitation and postponement and produces
synergy effects in due course, whereas the cost of inaction may be
insuperably higher than the action now. Critical role has the timing for the
problem of competitiveness in an uncertain environment, incorporating the
probability distributions of the variables considered into the analysis.
Decision-making under uncertainty conditions is an analytic framework of
searching for:
a) Optimal strategies, as acts from all possible courses of action, choices under control of the decision maker.
b) Various possible outcomes, states of nature or events to be
identified, beyond the control of the decision maker.
c)Determination of the pay-off function by describing different combinations of acts and events and the resulting consequences, the
pay-off resulting from the i-th strategy and the j-th event. A pay-off
is a conditional value – a conditional profit, loss or, may be, a
conditional cost. In building up a pay-off matrix, the alternative
courses of action and the possible outcomes (events) must be clearly
determined.
The trade-offs among decisions under uncertainty, within
cost/benefit analysis, uses a number of basic principles, as parts of the
decision matrix:
the Laplace Principle (highest mean value or lowest average cost),the Maximin or Minimax Principle (choice of the maximum from a
set of strategies with minimum pay-offs, adopted by pessimistic
decision makers. While such a principle has the logic of ensuring
that decision makers are in the best possible position if the worst
happens, the principle does obviously ignore the potentially larger
profit contributions that can be made by other decisions);
the Maximax or Minimin Principle (choice of the maximum from
strategies with the highest pay-offs, adopted by optimistic decision
makers. In general, for this principle, decision makers determine the
maximum pay-off for each decision and then choose the largest of
these. This principle has the advantage of focusing on the best
possible outcome.);
the Hurwicz Principle (choice somewhere between the extreme
pessimism of the maximin and the extreme optimism of the
maximax principle);
the Savage Principle (choice of action that minimizes the maximum
opportunity losses from the so called regret table);
the Maximum Likelihood Principle (considering first the event that
is most likely to occur and choice of the course of action which has
the maximum conditional pay-off.);
the Bayesian Decision Rule (an extension of the optimal strategy
choice by calculation of the expected pay-offs by using posterior
probabilities, as additional information about events is acquired);
the Expectation Principle (the optimal choice represents the strategy
with the highest expected pay-offs, calculated by multiplying the
pay-off values with their respective probabilities and adding up
these products).
The choice in decision making under risk conditions depends on a
series of objective and subjective factors, to mention a few: information,
enough knowledge of technology possibilities, attitudes against risk, etc. Just
faster and cheaper data communication is not enough for gaining
competitive advantage. Decision support systems, analytical information
technology and decision trees are helpful in decision-making. The methods
for creating and analysing models, incorporating multiple scenarios and
more explicit treatment of uncertainty, involve two overlapping disciplines:
stochastic programming and a relatively new field of strategy analysis called
scenario planning.
The risks of errors in estimates and predictive power of the scientific
methods are higher in phases of structural changes to adjust in an irreversible
new world around us. Scientific methods aim at assisting the adjustment
process that is a matter of philosophy and conceptual framework e.g. the
management that serves the fundamental economic axiom, by eliminating
the misconceptions and co-ordinating effective mobilization of total
available resources.
Testing hypotheses leads to either confirmation or rejection of a
hypothesis. Theories, which cannot be tested, because, they have no
observable ramifications, do not qualify as scientific theories. If the
predictions are found to be in disagreement with new experimental results,
the theory may be discarded as a description to reality, but it may continue to
be applicable within a limited range of measurable parameters.
Conclusions
To an ever-increasing extent, modern management is adopting and
applying quantitative techniques to aid in the process of decision making.
The intelligent use of the appropriate tools can reduce an otherwise highly
complex problem to one of manageable dimensions. The collection of these
techniques has become loosely known as “decision theory,” although there
certainly is no such thing as an integrated theory of how to make decisions.
Nevertheless, one would seriously underestimate the ultimate impact these
methods are going to have if they are viewed as nothing more than a handful
of tools that are sometimes used to solve particular types of problems.
Indeed, there is a growing body of opinion that believes that the greatest
impact of the quantitative approach will not be in the area of problem
solving, but will rather be on problem formulation. It will radically alter the
way managers think about their problems-how they size them up, gain new
insights, relate them to other problems, communicate with other people
about them, and gather information for solving them. Thus quantitative
analysis could have a profound effect on the “art” of management.