Operations Research Applies Sophisticated Statistical Analysis and Mathematical Modeling to Solve an Array of Business and Organizational Problems

7/30/2019 Operations Research Applies Sophisticated Statistical Analysis and Mathematical Modeling to Solve an Array of Business and Organizational Problems

1/26

Operations research applies sophisticated statistical analysis and mathematical modeling to solve an

array of business and organizational problems, as well as improve decision-making. As the business

environment grows more complex, companies and government agencies rely on analysis to inform

decisions that were once based largely on management intuition. Originally developed by the U.S.

Department of Defense during World War II, operations research has helped many large companies

and government agencies make better decisions, boost performance and reduce risk.

Ads by Google

QuickBooks From Intuit

Intuit QuickBooks is now in India. A Must Software for SMEs. Try Free!

quickbooks.in

Simplifying Complexity

Modern challenges associated with a global economy and the growth of technology have increased

the complexity of the business environment. Modern corporations often strive to serve a global, rather

than a regional or national, customer base and face worldwide competition. By relying onsophisticated mathematical models and advanced software tools, operations research can assess all

available options facing a firm, project possible outcomes and analyze risks associated with particular

decisions. The result is more complete information on which management can make decisions and

set policy, according to the Institute for Operations Research and the Management Sciences,

INFORMS for short, a national organization of operations research professionals.

Maximizing Data

Companies collect large amounts of data but may feel overwhelmed by the volume and lack the time

or expertise to fully analyze these data, transforming them into useful information on which to base

decisions. Operations research uses advanced mathematical and statistical techniques, such as

linear programming and regression analysis, to help organizations make the most of their data,

according to INFORMS' Science of Better website. Through detailed analysis of the data, operations

research analysts can help uncover options that lead to higher profits, more-efficient operations and

less risk.

Adding Value

In its executive guide to operations research, "Seat-of-the-Pants-Less," INFORMS reports that

operations research has added value to organizations in the public and private sector alike. For

example, INFORMS reported that UPS used operations research to redesign its overnight delivery

network in such a way that saved more than $80 million between 2000 and 2002. Meanwhile, New

Haven, Connecticut, used operations research to determine the extent to which the city's needleexchange program reduced HIV infection rates.

Considerations

INFORMS outlines five signs for organizations that could benefit from operations research. These

indicators are facing complex decisions, having problems with processes, having trouble with risk, not

making the most of available data and needing to overcome stiff competition. Operations research

analysts can help organizations overcome these challenges.

Sponsored Links

Subduing Emotion
http://www.google.com/url?ct=abg&q=https://www.google.com/adsense/support/bin/request.py%3Fcontact%3Dabg_afc%26url%3Dhttp://smallbusiness.chron.com/importance-operations-research-decisionmaking-52346.html%26gl%3DIN%26hl%3Den%26client%3Dca-pub-3235755782694080%26ai0%3DCVuy7tXgfUt7oKIPIigfus4CoBcrtk8wFquKQhJIBwI23ARABIN2PkBhQ4LWNqQdg5YKAgLgOoAGu1P3QA8gBAakCNPaWlGUiUD6oAwGqBKYBT9DETtOm9PRll8MJboYa6SU_rIgju5qYx-4FL-6-HqJLcqmxbBYgjkD38z4CqfnapIs_LdgbH2q1UDMa3co7nu-Hh5eW6HTo_l8wE_aSYes9T0LWH2mrGPI3n3QzVUSFpMMJImRX1HxlbKPQ3WT6ukcTDxl4yea8kt2cgjn1567jCpDkejAkgO9M3rfmUWHWgtEZ17c0QijYpJ1YLh7kPgdCL9RQ_YgGAYAHuquCLw&usg=AFQjCNGASa_WDutLtoyUiEWgEM90wDZR_ghttp://www.google.com/url?ct=abg&q=https://www.google.com/adsense/support/bin/request.py%3Fcontact%3Dabg_afc%26url%3Dhttp://smallbusiness.chron.com/importance-operations-research-decisionmaking-52346.html%26gl%3DIN%26hl%3Den%26client%3Dca-pub-3235755782694080%26ai0%3DCVuy7tXgfUt7oKIPIigfus4CoBcrtk8wFquKQhJIBwI23ARABIN2PkBhQ4LWNqQdg5YKAgLgOoAGu1P3QA8gBAakCNPaWlGUiUD6oAwGqBKYBT9DETtOm9PRll8MJboYa6SU_rIgju5qYx-4FL-6-HqJLcqmxbBYgjkD38z4CqfnapIs_LdgbH2q1UDMa3co7nu-Hh5eW6HTo_l8wE_aSYes9T0LWH2mrGPI3n3QzVUSFpMMJImRX1HxlbKPQ3WT6ukcTDxl4yea8kt2cgjn1567jCpDkejAkgO9M3rfmUWHWgtEZ17c0QijYpJ1YLh7kPgdCL9RQ_YgGAYAHuquCLw&usg=AFQjCNGASa_WDutLtoyUiEWgEM90wDZR_ghttp://www.googleadservices.com/pagead/aclk?sa=L&ai=CVuy7tXgfUt7oKIPIigfus4CoBcrtk8wFquKQhJIBwI23ARABIN2PkBhQ4LWNqQdg5YKAgLgOoAGu1P3QA8gBAakCNPaWlGUiUD6oAwGqBKYBT9DETtOm9PRll8MJboYa6SU_rIgju5qYx-4FL-6-HqJLcqmxbBYgjkD38z4CqfnapIs_LdgbH2q1UDMa3co7nu-Hh5eW6HTo_l8wE_aSYes9T0LWH2mrGPI3n3QzVUSFpMMJImRX1HxlbKPQ3WT6ukcTDxl4yea8kt2cgjn1567jCpDkejAkgO9M3rfmUWHWgtEZ17c0QijYpJ1YLh7kPgdCL9RQ_YgGAYAHuquCLw&num=1&cid=5GjpINBYBY0r8mWzG7Kp1aEZ&sig=AOD64_0ald7lFAhCpQKEtHCD0UY-buWQpw&client=ca-pub-3235755782694080&adurl=http://pixel.everesttech.net/2419/cq%3Fev_sid%3D3%26ev_ln%3Daccounting%2520software%26ev_crx%3D39165266394%26ev_mt%3D%26ev_n%3Dd%26ev_ltx%3D%26ev_pl%3Dsmallbusiness.chron.com%26ev_pos%3Dnone%26ev_dvc%3Dc%26ev_dvm%3D%26url%3Dhttp%253A//quickbooks.in/%253Futm_source%253DGoogle_V2%2526utm_medium%253DCPC%2526utm_campaign%253DDisplay-Contextual%2526utm_term%253Daccounting%25252Bsoftware%2526utm_content%253DAccounting%25252BSoftwarehttp://www.googleadservices.com/pagead/aclk?sa=L&ai=CVuy7tXgfUt7oKIPIigfus4CoBcrtk8wFquKQhJIBwI23ARABIN2PkBhQ4LWNqQdg5YKAgLgOoAGu1P3QA8gBAakCNPaWlGUiUD6oAwGqBKYBT9DETtOm9PRll8MJboYa6SU_rIgju5qYx-4FL-6-HqJLcqmxbBYgjkD38z4CqfnapIs_LdgbH2q1UDMa3co7nu-Hh5eW6HTo_l8wE_aSYes9T0LWH2mrGPI3n3QzVUSFpMMJImRX1HxlbKPQ3WT6ukcTDxl4yea8kt2cgjn1567jCpDkejAkgO9M3rfmUWHWgtEZ17c0QijYpJ1YLh7kPgdCL9RQ_YgGAYAHuquCLw&num=1&cid=5GjpINBYBY0r8mWzG7Kp1aEZ&sig=AOD64_0ald7lFAhCpQKEtHCD0UY-buWQpw&client=ca-pub-3235755782694080&adurl=http://pixel.everesttech.net/2419/cq%3Fev_sid%3D3%26ev_ln%3Daccounting%2520software%26ev_crx%3D39165266394%26ev_mt%3D%26ev_n%3Dd%26ev_ltx%3D%26ev_pl%3Dsmallbusiness.chron.com%26ev_pos%3Dnone%26ev_dvc%3Dc%26ev_dvm%3D%26url%3Dhttp%253A//quickbooks.in/%253Futm_source%253DGoogle_V2%2526utm_medium%253DCPC%2526utm_campaign%253DDisplay-Contextual%2526utm_term%253Daccounting%25252Bsoftware%2526utm_content%253DAccounting%25252BSoftwarehttp://www.googleadservices.com/pagead/aclk?sa=L&ai=CVuy7tXgfUt7oKIPIigfus4CoBcrtk8wFquKQhJIBwI23ARABIN2PkBhQ4LWNqQdg5YKAgLgOoAGu1P3QA8gBAakCNPaWlGUiUD6oAwGqBKYBT9DETtOm9PRll8MJboYa6SU_rIgju5qYx-4FL-6-HqJLcqmxbBYgjkD38z4CqfnapIs_LdgbH2q1UDMa3co7nu-Hh5eW6HTo_l8wE_aSYes9T0LWH2mrGPI3n3QzVUSFpMMJImRX1HxlbKPQ3WT6ukcTDxl4yea8kt2cgjn1567jCpDkejAkgO9M3rfmUWHWgtEZ17c0QijYpJ1YLh7kPgdCL9RQ_YgGAYAHuquCLw&num=1&cid=5GjpINBYBY0r8mWzG7Kp1aEZ&sig=AOD64_0ald7lFAhCpQKEtHCD0UY-buWQpw&client=ca-pub-3235755782694080&adurl=http://pixel.everesttech.net/2419/cq%3Fev_sid%3D3%26ev_ln%3Daccounting%2520software%26ev_crx%3D39165266394%26ev_mt%3D%26ev_n%3Dd%26ev_ltx%3D%26ev_pl%3Dsmallbusiness.chron.com%26ev_pos%3Dnone%26ev_dvc%3Dc%26ev_dvm%3D%26url%3Dhttp%253A//quickbooks.in/%253Futm_source%253DGoogle_V2%2526utm_medium%253DCPC%2526utm_campaign%253DDisplay-Contextual%2526utm_term%253Daccounting%25252Bsoftware%2526utm_content%253DAccounting%25252BSoftwarehttp://www.googleadservices.com/pagead/aclk?sa=L&ai=CVuy7tXgfUt7oKIPIigfus4CoBcrtk8wFquKQhJIBwI23ARABIN2PkBhQ4LWNqQdg5YKAgLgOoAGu1P3QA8gBAakCNPaWlGUiUD6oAwGqBKYBT9DETtOm9PRll8MJboYa6SU_rIgju5qYx-4FL-6-HqJLcqmxbBYgjkD38z4CqfnapIs_LdgbH2q1UDMa3co7nu-Hh5eW6HTo_l8wE_aSYes9T0LWH2mrGPI3n3QzVUSFpMMJImRX1HxlbKPQ3WT6ukcTDxl4yea8kt2cgjn1567jCpDkejAkgO9M3rfmUWHWgtEZ17c0QijYpJ1YLh7kPgdCL9RQ_YgGAYAHuquCLw&num=1&cid=5GjpINBYBY0r8mWzG7Kp1aEZ&sig=AOD64_0ald7lFAhCpQKEtHCD0UY-buWQpw&client=ca-pub-3235755782694080&adurl=http://pixel.everesttech.net/2419/cq%3Fev_sid%3D3%26ev_ln%3Daccounting%2520software%26ev_crx%3D39165266394%26ev_mt%3D%26ev_n%3Dd%26ev_ltx%3D%26ev_pl%3Dsmallbusiness.chron.com%26ev_pos%3Dnone%26ev_dvc%3Dc%26ev_dvm%3D%26url%3Dhttp%253A//quickbooks.in/%253Futm_source%253DGoogle_V2%2526utm_medium%253DCPC%2526utm_campaign%253DDisplay-Contextual%2526utm_term%253Daccounting%25252Bsoftware%2526utm_content%253DAccounting%25252BSoftwarehttp://www.googleadservices.com/pagead/aclk?sa=L&ai=CVuy7tXgfUt7oKIPIigfus4CoBcrtk8wFquKQhJIBwI23ARABIN2PkBhQ4LWNqQdg5YKAgLgOoAGu1P3QA8gBAakCNPaWlGUiUD6oAwGqBKYBT9DETtOm9PRll8MJboYa6SU_rIgju5qYx-4FL-6-HqJLcqmxbBYgjkD38z4CqfnapIs_LdgbH2q1UDMa3co7nu-Hh5eW6HTo_l8wE_aSYes9T0LWH2mrGPI3n3QzVUSFpMMJImRX1HxlbKPQ3WT6ukcTDxl4yea8kt2cgjn1567jCpDkejAkgO9M3rfmUWHWgtEZ17c0QijYpJ1YLh7kPgdCL9RQ_YgGAYAHuquCLw&num=1&cid=5GjpINBYBY0r8mWzG7Kp1aEZ&sig=AOD64_0ald7lFAhCpQKEtHCD0UY-buWQpw&client=ca-pub-3235755782694080&adurl=http://pixel.everesttech.net/2419/cq%3Fev_sid%3D3%26ev_ln%3Daccounting%2520software%26ev_crx%3D39165266394%26ev_mt%3D%26ev_n%3Dd%26ev_ltx%3D%26ev_pl%3Dsmallbusiness.chron.com%26ev_pos%3Dnone%26ev_dvc%3Dc%26ev_dvm%3D%26url%3Dhttp%253A//quickbooks.in/%253Futm_source%253DGoogle_V2%2526utm_medium%253DCPC%2526utm_campaign%253DDisplay-Contextual%2526utm_term%253Daccounting%25252Bsoftware%2526utm_content%253DAccounting%25252BSoftwarehttp://www.googleadservices.com/pagead/aclk?sa=L&ai=CVuy7tXgfUt7oKIPIigfus4CoBcrtk8wFquKQhJIBwI23ARABIN2PkBhQ4LWNqQdg5YKAgLgOoAGu1P3QA8gBAakCNPaWlGUiUD6oAwGqBKYBT9DETtOm9PRll8MJboYa6SU_rIgju5qYx-4FL-6-HqJLcqmxbBYgjkD38z4CqfnapIs_LdgbH2q1UDMa3co7nu-Hh5eW6HTo_l8wE_aSYes9T0LWH2mrGPI3n3QzVUSFpMMJImRX1HxlbKPQ3WT6ukcTDxl4yea8kt2cgjn1567jCpDkejAkgO9M3rfmUWHWgtEZ17c0QijYpJ1YLh7kPgdCL9RQ_YgGAYAHuquCLw&num=1&cid=5GjpINBYBY0r8mWzG7Kp1aEZ&sig=AOD64_0ald7lFAhCpQKEtHCD0UY-buWQpw&client=ca-pub-3235755782694080&adurl=http://pixel.everesttech.net/2419/cq%3Fev_sid%3D3%26ev_ln%3Daccounting%2520software%26ev_crx%3D39165266394%26ev_mt%3D%26ev_n%3Dd%26ev_ltx%3D%26ev_pl%3Dsmallbusiness.chron.com%26ev_pos%3Dnone%26ev_dvc%3Dc%26ev_dvm%3D%26url%3Dhttp%253A//quickbooks.in/%253Futm_source%253DGoogle_V2%2526utm_medium%253DCPC%2526utm_campaign%253DDisplay-Contextual%2526utm_term%253Daccounting%25252Bsoftware%2526utm_content%253DAccounting%25252BSoftwarehttp://www.google.com/url?ct=abg&q=https://www.google.com/adsense/support/bin/request.py%3Fcontact%3Dabg_afc%26url%3Dhttp://smallbusiness.chron.com/importance-operations-research-decisionmaking-52346.html%26gl%3DIN%26hl%3Den%26client%3Dca-pub-3235755782694080%26ai0%3DCVuy7tXgfUt7oKIPIigfus4CoBcrtk8wFquKQhJIBwI23ARABIN2PkBhQ4LWNqQdg5YKAgLgOoAGu1P3QA8gBAakCNPaWlGUiUD6oAwGqBKYBT9DETtOm9PRll8MJboYa6SU_rIgju5qYx-4FL-6-HqJLcqmxbBYgjkD38z4CqfnapIs_LdgbH2q1UDMa3co7nu-Hh5eW6HTo_l8wE_aSYes9T0LWH2mrGPI3n3QzVUSFpMMJImRX1HxlbKPQ3WT6ukcTDxl4yea8kt2cgjn1567jCpDkejAkgO9M3rfmUWHWgtEZ17c0QijYpJ1YLh7kPgdCL9RQ_YgGAYAHuquCLw&usg=AFQjCNGASa_WDutLtoyUiEWgEM90wDZR_g


2/26

One of the roles of management science in decision making is to subdue human emotion.Human emotion can get in the way of decision making. For example, a person might beemotionally attached to a project that logically will not be profitable; managementscience tools can be used to identify the rational decision, which is to abandon theproject.

Evaluating Complex Situations

Often, a decision will involve a complex web of causes and effects. The human brainsimply cannot handle so much data. Management science offers methods for arrangingthis data in a way that it can be interpreted easily.

Overcoming Biases

Humans are naturally inclined to have biases. Often, people aren't even aware of thesebiases. Biases can be very simple, such as a subjective preference for the color yellowrather than the color blue. Management science removes human biases from thedecision making process.

Read more:http://www.ehow.com/facts_6804999_role-management-science-decision-

making.html#ixzz2dNSoKr7CFrom: Shanteau, J. (2001), Encyclopedia of Psychology and Behavioral Science

(3rd ed). Craighead, W. E., & Nemeroff, C. B. (Eds). NY: Wiley. (pp. 913-915).

MANAGEMENT DECISION MAKING

A major concern in management has been to understand and improve decision making. Various

approaches have been proposedby psychologists, most based on a divide-and-conquer strategy. This strategy

also labeled problem decomposition involves breaking a large decision

problem into smaller parts. The idea is not new: In a Letter to Joseph Priestly, Benjamin

Franklin was one of the first to describe a decomposition strategy.

The theoretical justification for this approach was outlined by Simon (1957) in his account of

bounded rationality. This concept says that cognitive processing limitations leave humans with

little option but to construct simplified mental models of the world. As Simon (p. 198) put it, a

person behaves rationally with respect to this model . . . (although) such behavior is not even

approximately optimal with respect to the real world.

There have been two approaches to management decision making (Huber, 1980). The first is

concerned with development and application of normative decision rules based on formal logic

derived from economics or statistics. The second involves descriptive accounts of how people
http://www.ehow.com/facts_6804999_role-management-science-decision-making.html#ixzz2dNSoKr7Chttp://www.ehow.com/facts_6804999_role-management-science-decision-making.html#ixzz2dNSoKr7Chttp://www.ehow.com/facts_6804999_role-management-science-decision-making.html#ixzz2dNSoKr7Chttp://www.ehow.com/facts_6804999_role-management-science-decision-making.html#ixzz2dNSoKr7Chttp://www.ehow.com/facts_6804999_role-management-science-decision-making.html#ixzz2dNSoKr7C


3/26

actually go about making judgments, decisions, and choices.

NORMATIVE ANALYSES

As initially outlined by von Neumann and Morgenstern (1947) in Theory of games and economic

behavior, a variety of techniques have been derived for making optimal decisions. A distinction

is often drawn between riskless (certain outcomes) choices and risky (uncertain outcomes)

choices. Two examples of each approach are outlined here.

Certain Outcomes

Multi-Attribute Utility. This approach, abbreviated MAU, applies to decisions made with moreor-less certain

outcomes. As described by Gardiner and Edwards (1975), MAU involves obtaining a utility value for each

decision alternative and then selecting the alternative with the highest

value. The utility for an alternative is derived from a weighted sum of separate part utilities for

various attributes. The MAU approach has been successfully applied to management decisions

such as new plant sitings, personnel selection, and zoning decisions.

Linear Models. Initially based on multiple-regression analyses, linear models have been used

both to prescribe and to describe judgments under certainty. A major concern has been the

weights assigned to the cue (or attribute) values. Research has shown that equal weights, or even

random weights, do as well as optimal weights in many settings. The robustness of linear models

has allowed their use in many applied tasks, such as graduate school admissions, clinical diagnosis, and medical

decision making (Goldberg, 1970).

Uncertain Outcomes

Decision-Tree Analysis. A decision tree is a graphical model that displays the sequence of decisions and the

events that comprise a (risky) sequential decision situation (Huber, 1980, p. 118). The approach involves laying

out choice alternatives, uncertain events, and outcome utilities as a

series of branches (hence the name decision tree). For each alternative, an expected value (EV)

is computed as the average outcome value over all possible events. The optimal choice is then

the alternative with the highest EV. Decision trees have been used to guide risky decision making such as

marketing strategy, plant expansion, and public policy planning.

Bayesian Networks. This approach combines elements of Bayesian probability theory, artificial


4/26

intelligence, and graphical analysis into a decision analytic tool (Breese & Heckerman, 1999).

Starting with a fully connected network, all possible cause -and-effect linkages between nodes

for a problem space are described. Through a process of pruning using computer algorithms,

the structure of the network is simplified to essential links between nodes. This results in an

enormous reduction of problem complexity. The approach is being used to diagnose computer

programming errors and to anticipate trouble spots around the world.

DESCRIPTIVE ANALYSES

Most, but not all, descriptive analyses of decision making were initially concerned with accounting for the

discrepancies between normative rules (e.g., EV) and actual behavior. For instance,

Edwards (1954) modified EV by substituting subjective probabilities for objective probabilities

and psychological utilities for payoff amounts to produce Subjectively Expected Utility (SEU).

This model has become the starting point for descriptions of risky decision behavior. However,

many other approaches have been offered by psychologists.

Social Judgment Theory (SJT)

Based on the Lens Model proposed by Brunswik (1952), Hammond (1955) developed a co mprehensive

perspective on judgment and decision making. By adapting procedures from multiple

regression, this approach combines elements of both normative and descriptive analyses into a

single framework. Central to SJT is the distinction between analytic and intuitive modes of cognition. The

approach has been used to describe decisions by highway engineers and medical doctors (Cooksey, 1996).

Information Integration Theory (IIT)

Analyses of the psychological combination rules used to combine information from multiple

sources reveals that people often average stimulus inputs when making judgments. Anderson

(1996) has shown repeatedly that an averaging rule is more descriptive than the adding or summing rule

assumed in normative models (such as MAU). Through Functional Measurement, IIT

leads to the simultaneous evaluation of processing strategy and psychological values. The IIT

approach has been applied to marketing decisions, family choices, and expert judgments (Phelps

& Shanteau, 1978).


5/26

Image Theory

As described by Beach (1990), image th eory views the decision maker as possessing three distinct but related

images, each of which comprise a particular part of his or her decision-related

knowledge (p. 3). The value image consists of the decision makers values, beliefs, and ethics

that collectively are labeled principles. The trajectory image consists of the decision makers future agenda (or

goals). And the strategic image consists of various plans that have been adopted to achieve the goals. Using

these concepts, image theory has been applied to auditing, childbearing, and political decisions.

Heuristics and Biases

Tversky and Kahneman (1974) have argued that decisions are often made using psychological

shortcuts or heuristics. For instance, the representativeness heuristic refers to a tendency to

make probability judgments based on the similarity of an event to an underlying source; the

greater the similarity, the higher the probability estimate. Although easy to do psychologically,

such heuristics often lead to biases in that releva nt information, such as base rates, may be ignored. This

approach has been used to account for suboptimal decisions in accounting, management, and marketing.

Fast and Frugal Heuristics

Simon (1957) developed bounded rationality to deal with two interloc king components: the

limitations of the human mind, and the structure of the environment in which humans operate.

For instance, satisficing (selecting the first option that meets acceptable standards) is a cogn itively simple,

but often surprisingly efficient decision strategy. These ideas have been extended

by Gigerenzer and Todd (1999) to apply to various simple fast and frugal heuristics that take

advantage of environmental constraints. These heuristics have been applied to help decision

making in medicine and forecasting.

Naturalistic Decision Making

This perspective was developed by Klein (1993) to account for on-line decision making by experts in time-

sensitive environments. In situations such as fire fighting, there is not enough time

to apply normative choice rules. Instead, experienced decision makers frequently follow a re cognition-primed

decision making strategy they identify a single course of action through pattern matching. The NDM

approach has been applied in many real-world decisions, ranging from

military commands and intelligence analysis to medical diagnosis and accounting.

Expert Decision Making

Behind much of the advances in decision research has been the need for psychologists to help


6/26

professionals make better decisions. For instance, considerable effort has been extended to understand how

clinical psychologists make decisions (Dawes, 1988). Although such analyses often reveal that experts are

biased in their decisions, there are many domains in which surprisingly

good decisions have been observed. For example, weather forecasts were reported by Stewart, et

al. (1997) to make reliable and valid short-term predictions of precipitation and temperature.

Similarly, auditors were found by Krogstad, et al. (1984) to have an effective grasp of what information to use

in assessing the accuracy of accounting statements.

CONCLUSIONS

Using both normative and descriptive approaches, there have been many successful applications

of behavioral decision theory in management, business, and other settings. In large part, these

successes reflect the importance of Ben Franklins original insight into problem decomposition:

Decision making can usually be improved by breaking a problem into parts, working on the parts

separately, and then combining them to make a final decision.

OPERATIONS RESEARCH - CONTEMPORARY ROLE IN

MANAGERIAL DECISION MAKING

Sangeeta Agrawal

1

, KR Subramanian

2

& S.Kapoor

3

1&2

Omkarananda Institute of Management and Technology, Rishikesh-249192 (Uttrakhand), India,

3

Indian Institute of Technology, Roorkee-247667, India


7/26

ABSTRACT

As the global environment becomes fiercely competitive, Operations Research has gained

significance in

applications like world-class Manufacturing systems(WCM), Lean production, Six-sigma qualitymanagement,

Benchmarking, Just-in-time (JIT) inventory techniques. The growth of global markets and the

resulting increase in

competition have highlighted the need for Operation Research. To survive and lead the todays

highly competitive

and demand driven market, pressure is on management to make economical decisions. One of the

essential

managerial skills is ability to allocate and utilize resources appropriately in the efforts of achieving

the optimal

performance efficiently. In some cases such as small-scale low complexity environment, decision

based on intuition

with minimal quantitative basis may be reasonably acceptable and practical in achieving the goal of

the

organization. However, for a large-scale system, both quantitative and qualitative (i.e. intuition,

experience,

common sense) analyses are required to make the most economical decisions. Using Operations

Research

techniques including Linear Programming, Discrete Event Simulation and Queueing Theory,

organization leaders

can make high quality decisions. Operations managers are not expected to be experts in any decision

science tools;

however, he or she must have fundamental knowledge of such tools to acquire right resources andto make the most

economically sounding decisions for the company as a whole. Present paper is an attempt to study

the importance of

Operation research and various techniques used to improve the operational efficiency of the

organization.

1. INTRODUCTION:


8/26

Operations Research (OR) is one of the popular managerial decision science tools used by profit and

nonprofit

organizations. As the global environment becomes fiercely competitive, Operations Research has

gained

significance in applications like world-class Manufacturing systems(WCM), Lean production, Six-

sigma quality

management, Benchmarking, Just-in-time (JIT) inventory techniques. The growth of global markets

and the

resulting increase in competition have highlighted the need for Operation Research. In order to be

competitive,

businesses must meet the challenges present in a global market by offering products and services

that offer good

value to their customers. Good value is a combination of low cost, high quality, rapid availability and

real time

information on these.

In order to enhance the role of operational research and speed up the process and methodologies of

different

stakeholders, they should work closely and complement each others effort. In this process, the

academicians should

take the lead in the design, development and demonstration of sustainable operational research

models. Industry

should support this initiative and accelerate the

transmission of this methodology. This would ensure wealth creation in the short term, and

sustainable development

in the long term. The government should encourage this initiative by adopting optimized responses.

Consequently,

optimized policy responses and its implementation would bring about positive changes in the socio

political and

economic environment. As a result, sustained use of operational research would be a regular feature

in the decision

making process of the government, industry and the society. Such a wide usage of operational

research models by

the government, industry and academicians would not only contribute to the discipline but alsowould contribute to


9/26

the enhanced quality of life in India. The present paper is an attempt to highlight the significance of

operation

research, different techniques used and its application in business and industry.

2. ORIGIN, HISTORY & DEVELOPMENTS

Operations Research as a new field started in the late 1930's and has grown and expanded

tremendously in the last

30 years. The British army was conducting exercises on the radar system for detecting the aircrafts.

In July 1938, the

Superintendent of Bawdsey Research Station, announced that although the exercise had

demonstrated the technical

feasibility of the radar system for detecting aircraft, its operational achievements were not up to

what was required. IJRRAS 3 (2) May 2010 Agarwal & al. Operations Research-Contemporary

Role in Managerial Decision Making

201

He therefore proposed that a crash program of research into the operational - as opposed to the

technical - aspects of

the system should begin. The term "Operational Research" was coined as a suitable description ofthis new branch of

applied science.

On 15th May 1940, with German forces advancing rapidly in France, Stanmore Research Section was

asked to

analyze a French request for ten additional fighter squadrons. They prepared graphs for Winston

Churchill (British

Prime Minister), based upon a study of current daily losses and replacement rates, indicating howrapidly such a

move would deplete fighter strength. No aircrafts were sent and most of those currently in France

were recalled.

This is held by some to be the most strategic contribution to the course of the war made by

Operations Research (as

the aircraft and pilots saved were consequently available for the successful air defense of Britain, the

Battle of


10/26

Britain). In 1941 Operational Research Section (ORS) was established in Coastal Command which was

to carry out

some of the most well-known OR work in World War II. Thus OR as a separate field of specialization

was born!

In order to make the effective and efficient decisions, managers must have fundamental

understanding of the

decision science tools utilized in developing set of recommendations to choose from. The Operations

research is

usually the mathematical treatment, analysis of a process, problem, or operation to determine its

purpose and

effectiveness and to gain maximum efficiency. The operation technique is utilized by functional

groups such as

Industrial Engineering in effort to support Operations Managers to make economically feasible

decisions on a range

of systematic challenges. The main responsibilities of operations management are to manage and

operate as

efficiently and effectively as possible with the given resources. Quantitative methods which

comprises of

Simulation, Linear and nonlinear programming, Queueing Theory and Stochastic Modeling, are well-accepted

techniques by both research and practice communities.

Functional entities such as Industrial or Systems Engineering use methodologies to provide feasible

alternatives for

operations mangers to decide on. An important component of decision-making process is verifying

and validating

alternatives, which typically involve decision makers, engineers or analysts. Growth of OperationsResearch is to a

large extent, the result of the widespread availability of computers. Most Operations Research

involves carrying out

a large number of numeric calculations and without computers this would simply not be possible.

In India, Operation Research came into existence in 1949 when an Operation Research unit was

established at

Regional Research Laboratory, Hyderabad. Also Prof. R.S.Verma set up an Operation Research teamat Defence


11/26

Science Laboratory to solve problems of store, purchase and planning. During the 1950s there was

substantial

progress in the application of Operation Research techniques for civilian activities along with a great

interest in the

professional development and education in Operation Research. Many colleges and universities

introduced

Operation Research in their curricula. They were generally schools of engineering, public

administration, business

management, applied mathematics, economics, computer science etc.

In 1953, Prof. P.C. Mahalanobis [8] established an Operation Research team in the Indian Statistical

Institute,

Calcutta to solve problems related to national planning and survey. In 1958, project scheduling

techniques: PERT

(Program Evaluation and Review Technique) and CPM (Critical Path Method) were developed as

efficient tools for

scheduling and monitoring lengthy, complex and expensive projects of that time. The real

development of Operation

Research in the national field was carried out by Prof. Mohalanobis in India when he used it in

national planning.

Operation Research is also being used in Railway, waiting or queueing problems of passengers for

tickets at booking

windows or trains queueing up in marshalling yard, waiting to be sorted out are tackled by various

Operation

Research techniques.

3. EVOLUTION OF OPERATION RESEARCH AS AN ACADEMIC DISCIPLINE

Because of this historical legacy, operational research was accepted as a legitimate management

tool in defense

research establishments and subsequently for efficient resource planning and allocation by

Government departments.

Business supported the accelerated growth of this discipline by funding real and potential

applications. Over period


12/26

of time, a symbiotic relationship between government, business and academia ensured the growth

and expansion of

the discipline for their mutual benefit. During the last 50 years, operational research has evolved as

a

multidisciplinary function involving economics, mathematics, statistics, industrial engineering and

management.

Broadly, operational research as a discipline can be classified into three distinct set of categories.

They correspond

to tools, models and methodology. Tools include ABC analysis, 80:20 rule, and Break Even Analysis.

Blending

models, optimized distribution system, portfolio optimization of assets would broadly represent

examples under the

category of models. Operational research methodology would include project management systems,

multi criteria

optimization, game theory, simulation methodology, data envelopment analysis, enterprise

resource planning

systems and conflict resolution methods [13]. The tools, models and methodology of operational

research have

found a variety of applications in different contexts. Also, several outstanding academicians havecontributed to the

development of this discipline. IJRRAS 3 (2) May 2010 Agarwal & al. Operations Research-

Contemporary Role in Managerial Decision Making

202

Most commonly used techniques and methods [3] of Operation Research, which can be freely

applied by a

progressive management in decision-making processes are: Linear Programming, Decision Models,Network

Theory, Inventory Control Models, Queuing Theory, Sequencing, Game Theory, Simulation,

Replacement theory,

Reliability, Markovian Models.

4. SIGNIFICANCE OF OPERATIONS RESEARCH


13/26

Because of Operation Researchs multidisciplinary character and application in varied fields, it has a

bright future,

provided people devoted to Operation Research study can help meet the needs of society. Some of

the problems in

the area of hospital management, energy conservation, environmental pollution, etc. have been

solved by Operation

Research specialists and this is an indication that Operation Research can also contribute towards

the improvement

in the social life and areas of global need.

The Operation Research approach is particularly useful in balancing conflicting objectives (goals orinterests) where

there are many alternative courses of action available to the decision-makers. In a theoretical sense,

the optimum

decision must be one that is best for the organization as a whole it is often called the global

optimum. A decision

that is best for one or more sections of the organization is usually called suboptimum decision.

Operation Research

attempts to resolve the conflicts of interest among various sections of the organization and seeks

the optimal solution

which may not be acceptable to one department but is in the interest of the organization as a whole.

Operation

Research is concerned with providing the decision-maker with decision aids (or rules) derived from:

i) A total system orientation,

ii) Scientific methods of investigation, and

iii) Models of reality, generally based on quantitative measurement and techniques.

Besides its use in industry, this new technique was also utilized in a number of socio-economic

problems which

came up after the war. Operation Research has come to be used in a very large number of areas

such as problems of


14/26

traffic, question of deciding a suitable fare structure for public transport, or industrial process like

ore-handling. Its

use has now extended to academic spheres, such as the problems of communication of information,

socio-economic

fields and national planning.

5. THE OPERATIONS RESEARCH APPROACH

Given that O.R. represents an integrated framework to help make decisions, it is important to have a

clear

understanding of this framework so that it can be applied to a generic problem. To achieve this, the

so-called O.R.

approach is now detailed. This approach comprises the following seven sequential steps:

(1) Orientation,

(2) Problem Definition,

(3) Data Collection,

(4) Model Formulation,

(5) Solution,

(6) Model Validation and Output Analysis, and

(7) Implementation and Monitoring.

This is illustrated in the Flow Diagram: IJRRAS 3 (2) May 2010 Agarwal & al. Operations

Research-Contemporary Role in Managerial Decision Making

203

To illustrate how the steps might be applied, consider a typical scenario where a manufacturing

company is planning

production for the upcoming month. The company makes use of numerous resources (such as labor,

production

machinery, raw materials, capital, data processing, storage space, and material handling equipment)

to make a


15/26

number of different products which compete for these resources. The products have differing profit

margins and

require different amounts of each resource. Many of the resources are limited in their availability.

Additionally,

there are other complicating factors such as uncertainty in the demand for the products, random

machine

breakdowns, and union agreements that restrict how the labor force can be used.

As an illustration of how one might conduct an operations research study to address this situation,

consider a highly

simplified instance of a production planning problem where there are two main product lines

(widgets and gizmos,

say) and three major limiting resources (A, B and C, say) for which each of the products compete.

Each product

requires varying amounts of each of the resources and the company incurs different costs (labor,

raw materials etc.)

in making the products and realizes different revenues when they are sold. The objective of the O.R.

project is to

allocate the resources to the two products in an optimal fashion.

6. TECHNIQUES USED IN OPERATION RESEARCH

Decision Analysis: Decision analysis refers to a set of quantitative methods for analyzing decisions

that use

expected utility as the criterion for identifying the preferred alternative.

Decision analysis provides tools for quantitatively analyzing decisions with uncertainty and/or

multiple conflicting

objectives, and these tools can be especially useful when there is limited directly relevant data so

that expert

judgment plays a significant role in the decision making process. It provides a systematic quantitative

approach to

making better decisions, rather than a description of how unaided decisions are made.

A general decision making process can be divided into the following steps:

1. Define the problem


16/26

2. Determine the requirements

3. Establish Goals

4. Identify alternatives

5. Define criteria IJRRAS 3 (2) May 2010 Agarwal & al. Operations Research -Contemporary Role

in Managerial Decision Making

204

6. Select a decision making tool

7. Evaluate alternatives against criteria

8. Validate solutions against problem statement

The above steps are illustrated through a Flow Diagram as given below:

Linear programming arose as a mathematical model developed during Second World War to plan

expenditures

and returns in order to reduce costs to the army and increase losses to the enemy. In Operation

Research

optimization means to find out the maximum profit and minimum loss[11] in any deal which we can

done in

Quantitative Techniques, in this we can narrowing our choices to the very best when there are

virtually

immeasurable feasible options. This is a constrained optimization technique, which optimize some

criterion within

some constraints. In Linear programming the objective function (profit, loss or return on investment)

and constraints

are linear.

Standard form of describing a linear programming problem consists of the following three parts:

maximized

e.g. maximize

e.g.


17/26

IJRRAS 3 (2) May 2010 Agarwal & al. Operations Research-Contemporary Role in Managerial

Decision Making

205

-negative variables

e.g.

7. SIMULATION

In this technique of Operations Research we can make the model of a real situation and thenperform the various

experiments on this rough sculpt. Generally it is used in uncertain conditions where we want to

conduct a real

experiment through this model to know more about different situations which we use in this

artificial model. The

actual exercise of building a simulation model reveals previously unapparent relationships and

provides a systematic

way to analyzing the situation. Marine fisheries are highly complex and stochastic. A simulation

model, therefore, is

required. Simulation-based optimization utilizes the simulation model in obtaining the objective

function values of a

particular fishing schedule. The decision support system for fishery management will assist the

government agencies

and the fishing industry to use sound data and management science techniques in making policy

decisions for

fishing activities. Transferable rights to fish have proved a reliable and effective means of creating

incentives to

conserve marine resources. By strengthening individual fishing rights under flexible quota

management systems

make a significant contribution to conserving fish stocks, to reducing excess capacity and to raising

the profitability

of the fisheries industry.


18/26

8. QUEUING THEORY

Some real time examples for this case can be customers waiting in the queue in banks or to buy

groceries in

departmental stores. The contribution of the computer here is to maintain the queue according to

the arrival time of

the event, in this case the customers, and process each event one after the other according to their

arrival time.

Simulation [11] represents the full extent of the models covering all perceivable systems which

incorporate

characteristics of a queue. We identify the unit demanding service, whether it is human orotherwise, as customer.

The unit providing service is known as the server. This terminology of customers and servers is used

in a generic

sense regardless of the nature of the physical context. Some examples are given below.

example is

the telephone exchange.

one station wait

for access to

the next.

a doctors clinic for treatment.

Queuing System is used in situations where the queue is formed (for example customers waiting for

service,

aircrafts waiting for landing, jobs waiting for processing in the computer system, etc). The objective

here is

minimizing the cost of waiting without increasing the cost of servicing. The term classical queuing

theory refer to

descriptive models of queuing systems, usually based on Markovian assumptions, in which the goal

is to derive an


19/26

explicit expression for the queue-length or waiting-time distribution (or its transform), usually in

steady state.

9. TRANSPORTATION TECHNIQUE

The origin of transportation was first presented by F.L. Hitchcock in 1941 also presented a study

entitled The

Distribution of a Product from Several sources to numerous Localities. This presentation is

considered to be the

first important contribution to the solution of transportation problems. In 1947 T.C. Koopmans

presented an

independent study, not related to Hitchcocks, and called Optimum Utilization of theTransportation System.

These two contributions helped in the development of transportation methods which involve a

number of shipping

sources and a number of destinations. The transportation problem, received this name because

many of its

applications involve determining how to optimally transport goods.

A special class of linear programming problem is Transportation Problem, where the objective is tominimize the

cost of distributing a product from a number of sources (e.g. factories) to a number of destinations

(e.g.

warehouses) while satisfying both the supply limits and the demand requirement. Because of the

special structure of

the Transportation Problem the Simplex Method of solving is unsuitable for the Transportation

Problem. The model IJRRAS 3 (2) May 2010 Agarwal & al. Operations Research-Contemporary

Role in Managerial Decision Making

206

assumes that the distributing cost on a given route is directly proportional to the number of units

distributed on that

route. Generally, the transportation model can be extended to areas other than the direct

transportation of a

commodity, including among others, inventory control, employment scheduling, and personnel

assignment. The


20/26

objective of the transportation problem is to satisfy the required quantity of goods or services at

each demand

destination, within the limited quantity of goods or services available at each supply origin, at the

minimum

transportation cost or time.

Project Management with PERT (Project Evaluation and Review Techniques) and CPM (Critical Path

method)

CPM/PERT is based on the basis that a small set of activities, which make up the longest path

through the activity

network control the entire project. If these "critical" activities could be identified and assigned to

responsible

persons, management resources could be optimally used by concentrating on the few activities

which determine the

fate of the entire project.

Non-critical activities can be rescheduled and resources for them can be reallocated flexibly,

without affecting the

whole project. Both are project management techniques, which have been created out of the need

of Western

industrial and military establishments to plan, schedule and control complex projects.

10. USEFULNESS

CPM/PERT have been useful in planning costs, scheduling manpower and machine time. CPM/PERT

can answer

the following important questions:

are the risks/ dependencies/ assumptions involved?

time?

or ahead

of schedule?

inished earlier than planned, what is the best way to do this at the leastcost?


21/26

The procedure of drawing a network is:

1. Specify the Individual Activities: From the work breakdown structure, a listing can be made of all

the activities

in the project. This listing can be used as the basis for adding sequence and duration information inlater steps

2. Determine the Sequence of the Activities: Some activities are dependent on the completion of

others. A listing

of the immediate predecessors of each activity is useful for constructing the CPM network diagram.

3. Draw the Network Diagram: Once the activities and their sequencing have been defined, the CPM

diagram can

be drawn. CPM originally was developed as an activity on node (AON) network, but some projectplanners prefer to

specify the activities on the arcs.

4. Estimate Activity Completion Time: The time required to complete each activity can be estimated

using past

experience or the estimates of knowledgeable persons. CPM is a deterministic model that does not

take into account

variation in the completion time, so only one number is used for an activity's time estimate

5. Identify the Critical Path: The critical path is the longest-duration path through the network. The

significance

of the critical path is that the activities that lie on it cannot be delayed without delaying the project.

Because of its

impact on the entire project, critical path analysis is an important aspect of project planning

The critical path can be identified by determining the four parameters for each activity. The four

parameters

are Earliest Start, Earliest Finish, Latest Finish and Latest Start.

Models and Modeling In Operations Research

A model is defined as the approximation or abstraction, maintaining only the essential elements of

the system, which

may be constructed in various forms by establishing relationships among specified variables and

parameters of the

system. A model does not, and cannot, represent every aspect of reality because of the innumerable

and changing


22/26

characteristics of the real life problems to be represented. Modeling is the essence of an Operation

Research

approach. By building model, the complexities and uncertainties of a decision-making problem can

be changed to

logical structure that is amendable to formal analysis. In short a model provides a clear structural

frame-work to the

problem for purposes of understanding and dealing with reality.

Role of Computers in Solving Operation Research Problems

The Operation Research problems are time consuming and involve tedious computations. Even a

simple problem

with few variables take a long time to solve manually and even by a hand calculator. The advent of

computers

accelerated the wide use of Operation Research techniques for solving complex business problems

faced by

managers and administrators in business and government. The automation of computational

algorithm allows IJRRAS 3 (2) May 2010 Agarwal & al. Operations Research -Contemporary Role

in Managerial Decision Making

207

decision-makers to concentrate on problems formulation and the interpretation of the solutions.

Major computer

manufacturers and vendor have developed software packages for the various computer systems

providing

computational support for problems to be solved by the application of Operation Research

techniques [9]. Some

academic departments in different universities have also produced software packages for solving

various Operation

Research problems. Computer manufacturers like IBM, CDC, Honeywell, UNIVAC, ICL, etc. have

invested

substantial amount in developing software programs for solving the optimizing, scheduling,

inventory, simulation

and other Operation Research problems. Also large scale simulations are possible only through

computers by using


23/26

GPSS software packages.

Growth of Operation Research in Different Sectors

The response of Indian industries by and large was poor in terms of accepting O.R. methods till early

sixties. After

that Operation Research activities started rapidly in this sector. Considerable effort in this direction

has been made

by National Productivity Council, National Industrial Development Corporation, Administrative Staff

College,

Hyderabad and Indian Institutes of Management, etc. Initially the industries like Hindustan Lever

Ltd., the Metal

Box Company Ltd., Union Carbide (India) Ltd., Larson and Toubro Ltd., Indian Chemical Industries

Ltd., DCM

Ltd., set up Operation Research groups to work on problems of optimization and forecasting of

market requirements

relevant to their companies [7].

The type of industries in which these techniques were applied includes Steel, Heavy Engineering,

Chemical and

Fertilizers, Textiles, Transportation & Distribution, and Electronics.

The terminology "Operations Research" is somewhat misleading, since it is not only concerned with

operations, but

has applications involving research in different areas and fields. Operations Research is the

discipline of applying

advanced analytical methods to help make better decisions. By using techniques such as

mathematical modeling to

analyze complex situations, operations research gives executives the power to make more effective

decisions and

build more productive systems.The role of operational research in the Indian context is clear. It is

not only

important, it is even critical, given the size and magnitude of the tasks ahead to transform India as a

developed

nation. In order to achieve this goals, we need a responsive and accountable government to

promote a positive


24/26

environment of OR applications. It is hoped that the Indian democracy would lead to this. It is

believed that the

globalization would further accelerate this transition.

Typical Applications of Operations Research

-Money Laundering.

aigns, Predicting customer response, and Campaign optimization.

on, Staff allocation.

mization.

Challenges in Operations Research

Due to vast quantities of data and calculation, solving optimization problems is challenging and time

consuming.

Thus, such approach towards performance improvement may or may not be economically feasible

for some

organizations. Numerous studies are conducted on development of more effective and efficient

heuristic and exact

algorithms that can solve large scale optimization problems [11].

OR is quantitative problem solving technique; hence, data plays important, if not the most

important, role in


25/26

producing high quality and executable solutions. With an organization that has data readily available

using

information system such as MRP and ERP should be able to use the required data with certain level

of integrity.

However, for a system that is highly manual, data driven decision science techniques presented her

may or may not

be the appropriate approach. With companies moving towards managing business with some form

of company-wide

information system; Linear Programming, Discrete Event Simulation and Queueing Theory will be

most suitable

and appropriate decision tools. IJRRAS 3 (2) May 2010 Agarwal &al. Operations Research-

Contemporary Role in Managerial Decision Making

208

Integrity of data depends on many factors. Information system that requires manual input of data,

unstable network

systems, unstable programs and defective hardware are some of the factors. The most important

factor that

determines high data integrity is human error when inputting data. Human errors can be minimized

through

education combined with hands-on training such as on-the-job training. Unfortunately, many

organizations tend to

focus heavily on physical system implementation and give little or no attention on education and

training.

Regardless, employees are often reprimanded for not entering the data correctly and the quality of

hardware and/or

software is questioned for poor data integrity. Sustainment is as important implementation. Anorganization can

implement the worlds greatest database, but if the personnel responsible for operating and

sustaining the system

lacks knowledge of performing his or her job, attaining and implementing the worlds greatest

system is

meaningless.


26/26

CONCLUSION:

Another name for managers is decision makers. To survive and lead the todays highly competitive

and demand

driven market, pressure is on management to make economical decisions. One of the essentialmanagerial skills is

ability to allocate and utilize resources appropriately in the efforts of achieving the optimal

performance efficiently.

In some cases such as small-scale low complexity environment, decision based on intuition with

minimal

quantitative basis may be reasonably acceptable and practical in achieving the goal of the

organization. However,

for a large-scale system, both quantitative and qualitative (i.e. intuition, experience, common sense)

analyses are

required to make the most economical decisions. Using Operations Research techniques including

Linear

Programming, Discrete Event Simulation and Queueing Theory, organization leaders can make high

quality

decisions. Operations managers are not expected to be experts in any decision science tools;

however, he or she

must have fundamental knowledge of such tools to acquire right resources and to make the most

economically

s

Documents

Operations Research Applies Sophisticated Statistical Analysis and Mathematical Modeling to Solve an Array of Business and Organizational Problems