Chapter 16 Selecting a Technique

8/12/2019 Chapter 16 Selecting a Technique

1/15

Selecting a Technique

Introduction

Throughout the previous chapters of this book, you have been exposed to numerous management

science techniques. Whether you are a manager making decisions using output generated by those

techniques or an analyst building a model, you will be faced with a key question: What technique best

fits the problem at hand? Unfortunately, we may inadvertently seek to apply a decision technique to

problem situations in which the technique may not exactly apply, perhaps because we are more familiar

with some techniques than with others.

In this chapter, we will examine the costs associated with selecting a wrong decision technique. Once

you appreciate the types of costs (and risks) involved, we introduce you to a series of steps in building amodel. These steps will provide you with some direction, assuming that you incorporate common sense

with a thorough knowledge of management science.

The cornerstone of this chapter is a model selection key. This section of the chapter provides a working

summary of the techniques presented in the book. Once you are familiar with these major techniques,

this key will guide you in selecting an appropriate technique for many specific problem situations.

Once you have selected a technique, your real task has begun. How do you design a model so that

managers will use it? This topic-implementation-is addressed in the next chapter. These two chapters,

16 and 17, should be identified as a set designed to blend the theory of the techniques chapters with

the realities of the decision maker's world.

Any decision that leads to undesirable results may be classed as a wrong decision. Dramun Forests, for

instance, could introduce a new paper food tray for use in microwave or conventional ovens. The tray

would be sold to the frozen dinner market. If the target customers perceive the tray to be inferior to the

presently used aluminum tray and subsequently do not adopt the paper tray, such an introduction

would prove to have been a wrong decision.

Although we can discuss many costs associated with the decision, we should point out two significant

facts. First, the outcome of a decision will not be known when the decision is made. Therefore, we wish

to select a model that will accurately reflect the current environment and forecast the future within

acceptable tolerances. Second, the costs of a wrong decision could extend far beyond the immediate

cash losses of the situation.

Errors, in terms of traditional statistics, have often been related to testing some prior stated hypothesis.

If we reject an hypothesis and it is actually true, we have made a type I error with an associated

probability of ex. Likewise, if we accept a false hypothesis, we have made a type II error with a

probability of p. Much important material is available about ex and 13 and their associated errors.


2/15

However, what about the statistical test used to test the hypothesis and the statistic used to measure he

population parameter? Can we' not make an error by using a t-test when we have nonparametric data?

This third type of error can be potentially more costly than either of the other two. This error focuses on

the selection of an applicable technique. An example may be shown using linear programming that

assumes that we can allocate partial units. If we want to allocate sales managers to sales territories, wemay find that linear programming results in a substantially different allocation from that of integer

programming.

What is the net effect of selecting linear programming over integer programming in this case? Offhand,

we cannot tell. But we can employ either of two methods to estimate the cost of this error. First, we

should conduct a sensitivity analysis of the situation to see how far off our model assumptions must be

before our decision is substantially changed. For instance, we may find that a linear programming

model, although technically inapplicable to the situation just outlined, results in the same allocation

over a wide variation of assumptions. If such is the case, the technique may be sufficient to generate the

required information.

Second, if we are uncertain as to which of several models to use, we should first compare the properties

of the techniques with the assumptions of the problem and the types of data available. If uncertainty as

to the best model still exists, then pilot runs using the various models should be compared. If linear

programming and integer programming provide essentially the same results in pilot runs, then the

added cost of a full run of the more appropriate technique should be compared with its added accuracy.

It may be more economical to trade off accuracy for reduced cost.

Steps in Selecting a Final Model

Deliberate steps should be followed to assure proper fit among a problem situation, the manager, and

an appropriate technique. However, we must first identify the characteristics of a good fit.

A good fit exists when:

1. the technique meets the requirements of a problem situation without exceeding theassumptions of the technique

2. the technique and its solution implications are clear to the manager3. the model solution can be provided to the manager within an appropriate time frame4. the model application is less costly than the value of the information provided

Assuming that we can apply these criteria, we can investigate the steps involved in model selection. As

the arguments are developed, you must be aware that many of the steps overlap. Also, some steps may

have to be repeated as shown in Figure 16-1. Model selection is an evolutionary, incremental decision

process in many cases.

The following steps have proven successful in many different decision situations. You may find that

specific situations call for an expansion or modification of this list. As with all guides of a general nature,


3/15

modification will be necessary in unique situations and will reflect the modeler's experience, knowledge

of techniques, and common sense.

These steps include

1. identify the key decision maker2. determine the problem solution objectives3. specify the problem4. identify readily available secondary data5. select a portfolio of applicable techniques6. compare applicable techniques with selection criteria7. present initial model to manager for commitment8. collect primary data9. develop test model10.present preliminary results to manager11.refine and revise model as necessary

Each of these steps is discussed in subsequent sections. The list is mainly an expansion of the scientific

method. The list also excludes those elements of modeling relating to implementation and actual use.

These discussions are presented in Chapter 17, Implementation.


4/15

Perhaps it is best to forget everything you know about management science techniques (for the

moment only) at this early stage of the model selection process. Why take such a drastic measure?

Primarily because you will be entering a problem situation as a scientist and you must maintain a clearly

objective mind.

A great danger exists at this initial stage of technique selection. The management scientist may feeluncomfortable with a problem outside his or her personal portfolio of models. Bear in mind, however,

that this book is designed to provide you with a philosophy for approaching problems and a structure for

building models. No management scientist is restricted only to the techniques previously presented. The

management scientist must first specify the problem and then select a technique. If the problem does

not conform to the techniques familiar to the management scientist, then he either has to seek out an

appropriate existing technique or devise a new technique. If neither of these options is available, the

management scientist must then resort to a less applicable technique and chance encounter of the costs

discussed in the previous section or not attack the problem at all.

Step 1. Identify the Key Decision Maker

We must determine the primary decision maker in the problem setting. If the decision maker is a top

executive, he or she will have different interests and influence than will a middle manager. He or she will

also have a different level of knowledge of and appreciation for management science techniques.

The analyst must, therefore, target his or her efforts to the predominant decision maker and assess that

decision maker's needs. In fact, before the analyst interviews the decision maker in step 2, he or she

should develop a profile of the decision maker reflecting significant background and his or her role in the

organization. This will enable the analyst to more accurately develop the problem.

Step 2. Determine the Problem Solution Objectives

Before we can begin to collect data, we need to know exactly what the decision maker wishes to

achieve. Throughout this book, we have repeatedly introduced actual case settings and have then

developed the information from the case before we introduced the model. By now, you should be very

familiar with this routine.

An analyst may, however, attempt to formulate his or her own objectives prematurely. The analyst must

dig into the manager's problem situation until there is some assurance that he or she can go no farther

and has found the real problem. In the case of the log offioading operation presented in Chapter 12, we

became aware that the manager wanted to provide an efficient offloading facility for log trucks arriving

from a specific geographical area. His decision criteria involved low costs for the system coupled with

efficient service.

This situation can then be transformed into a management science objective. That is, develop a system

that minimizes costs while concurrently accommodating the arriving log trucks.


5/15

Of course, this statement would have to be refined and quantified before we can complete the analysis

of the problem. However, we are, at this point, merely attempting to select a portfolio of appropriate

techniques. Once a technique is selected, the management science objective may be refined.

Step 3. Specify the Problem

We have yet to decide on a technique. We must first find out all we can, in a brief period of time, aboutthe parameters of the problem. Generally, we should seek information about the assumptions of

management, the decision limitations, the time frame of the decision, the managers involved in the

system, and the payoff criteria. An inquiry guide is illustrated in Table 16-1. Each component is treated

in more detail in the following paragraphs.

1. Assumptions .a. What assumptions can we make about the problem?b. Which are the most limiting assumptions?c. What is the basis for each assumption? Is the basis sound?d. Are the assumptions complete?e. Should someone else review the assumptions?f. What are the variables involved?

2. Decision limitations .a. Have specific alternatives already been identified? If so, will management accept

additional alternatives?

b. What are the legal restrictions?c. Is there more than one decision maker?d. Will the decision result in a policy change?e. What are the decision maker's specific objectives (optimization, etc.)?

3.

Time framea. When is the last date that the decision can be made?b. What are the deadlines for each stage of development?c. Is there any slack in the time frame?d. What is the time horizon of the problem?e. Does the manager want all cash flows discounted? If so, at what discount rate?

4. Managers involveda. Are other managers involved in the problem situation?b. Who are they and what is their role in the problem?c. Who are the primary proponents and opponents of management science in the

problem?d. What are the quantitative backgrounds of the more significant managers in the

problem?

e. What managerial problems are likely to be encountered and how can they be minimized5. Payoff criteria

a. What are the criteria used by the decision maker in making a decision?b. What are the priorities of each criterion?


6/15

c. Can some criteria be sacrificed if necessary?

A. Assumptions.The modeler must know what assumptions are being made regarding theproblem. The questions in part I of Table 16-1 must be answered. Returning again to the

waiting-line problem from Chapter 12, we can examine the costs of the system, the arrival and

service rates, and the number of trucks that are involved. Often, our first efforts at specifying

the problem are incomplete. After we have selected some initial techniques, we may examine

the assumptions of the technique and compare them ith the assumptions that we have made

about the problem. If the model assumptions are more restrictive, we must then reexamine the

problem specifications.

B. Decision Limitations. We must determine if the decision maker has already identified possiblealternatives. If so, then we must try to incorporate these alternatives into our selection of a

technique. We must also examine possible legal restrictions, such as limitations on price

decisions. Of course, we must also determine if the decision maker is attempting to optimize

some decision or if he or she is trying to incrementally improve the position of the organization.

C. Time Frame. The time frame plays three important roles in the model selection process. First, itprovides a limit on the data we collect. Second, it provides us with a clue as to the type of model

or technique we will need. Third, it gives us the relevant deadlines that must be met in the

modeling process. Pursuit of the questions listed in Table 16-1 will provide guidelines for these

areas.

D. Managers Involved. We will find, in Chapter 17, that a critical link in the management scienceprocess is the manager. We must design a model that will find favor with the manager. For

purposes of selecting a technique, we must determine who will be involved in the decision

process, what their roles will be, and what their level of management science competence is.This, then, places a limitation of the type of technique that we can select.

E. Payoff Criteria. Generally, we assume that the payoff criteria are financially oriented items suchas discounted cash flow. A difficulty arises if the manager varies from this financial orientation.

Some techniques, such as linear programming, work quite well with financial data but tend to

weaken with other types of data. Thus, we must determine the precise criteria the manager will

use to evaluate solution proposals and then select a technique that will generate the

appropriate data.

Step 4. Identify the Readily Available Secondary Data

Secondary, or already existing, data can be useful. Although this is not really required, some funds may

be saved by locating data that already exists and using this data to pretest the model. This amounts to

designing a preliminary model around existing data. Of course, the analyst will have to assess the cost

versus the value of information to determine if more extensive data collection is warranted.


7/15

The analyst must be aware that existing data may not reflect the present underlying problem. However,

at this point, we are merely trying to identify those techniques that fit the situation and that are

acceptable to the decision makers. The data must be valid for the decision to be made, and the data

must be complete enough to draw preliminary conclusions.

Generally, we should examine the goal of the decision maker and from there we can identify the basic

type of technique that would be appropriate. In other words, is the manager maximizing profits,

maximizing cash flow, or minimizing costs? This will then provide the basis for determining the variables

and the type of data needed. Normally, data relating to costs, population size, recovery ratios, and the

like are available in the organization's records, although not necessarily in the form that is required for a

particular technique.

Many problems can be solved to the manager's satisfaction without extensive primary data collection.

We are simply identifying the readily available data and then selecting a preliminary technique. If the

resulting solution is adequate for the decision to be made, no additional data collection is necessary. If,

on the other hand, the data are inadequate, then this step has, at the least, helped the analyst to

structure the problem and narrow his or her technique selection process.

Step 5. Select a Portfolio of Applicable Techniques

The stage is now set for actually selecting a technique from among those shown in Table 16-2. Several

factors influence the selection. These factors include the objective, type of data available, time horizon,

computer availability, managers involved, assumptions of the problem, postsolution analysis


8/15

Key for Selecting a Technique

At this point, however, the analyst should focus on the various techniques that will fit the

problem and the manager's needs. Care should be taken to avoid selecting a specific technique

during this step unless only one technique seems to fit. In essence, this step is a selection

process to be followed by a screening process in the next step. Any technique that seems to

apply to the situation should be considered without prejudice.

If a technique cannot be found that fits the situation, then we can follow several courses of

action. First, examine the technique that comes closest to fitting the problem. Then review the

assumptions of the technique. If some of the assumptions of the technique can be relaxed

without a significant increase in inaccuracy, a special form of the technique may be appropriate.

Second, an expert may have to be consulted. This text contains only the basic types of models.

In most initial situations, these techniques will be adequate. However, other sources, such as

educators and consultants, can provide advanced advice for special situations.

Step 6. Compare Applicable Techniques with Selection Criteria

This step requires three elements: a portfolio of techniques, a list of selection criteria, and a priority for

these criteria. We wish to select a model that best fits the situation. An example of a screening device is

shown in Table 16-3. This example is designed to fit the log offloading problem presented in Chapter 12.

We can examine the problem and determine whether waiting-line models, dynamic programming,

decision analysis, or simulation would fit. Although any of these four techniques would work, we want

to select that technique that best fits the criteria we establish.


9/15

The first column in Table 16-3 lists the various criteria that we expect our model to meet. Thus, we want

the model to consider the assumptions of the problem and also meet the manager's cost-minimization

objectives. Many other criteria could perhaps be added. These criteria would have to be established by

the analyst based on.his or her interviews with the decision maker. The analyst should also determinethe relative importance of each criterion. We could establish a rank order for the criteria and proceed

with our selection. However, in this case, we have decided to rate' each criteria on a scale from 0 to 1, 0

being the least important. Thus, we may establish high priorities for more than one criterion.

Examining Table 16-3, we find that the manager is most concerned that the technique will meet the

assumptions of the problem, will meet the manager's

objectives, will be understandable to the managers who will use the output, and can be developed

quickly. The manager is least concerned with postsolution manipulations, such as sensitivity analysis.

The manager also seems to be less concerned about the maintenance costs of the model and the time

horizon of the problem. Thus, column 2 summarizes the relative weight of each criteria.


10/15

Each of the appropriate techniques is listed along the top of the form.

Waiting-line models (column 3) can be evaluated against each criterion. We have used a ten-point scale

in this example, with a rating of 10 being perfect. The analyst has evaluated waiting lines against the

listed criteria and has entered scores in the table. A perfect fit would result in lOs in every row of a

column. This evaluation has been repeated for each technique.

Our next task is to calculate a raw score for each column. The total for column 3 is the weighted scores

of column 3. Thus, this total is the result of column vector 3 multiplied by column vector 2, resulting in a

score of 74.6.1 The scores for the remaining techniques are shown in columns 4 through 6.

We could merely compare these raw scores and select a technique. But how close have these

techniques come to fitting our requirements? To answer this question, we must calculate the selection

efficiency ratio (SER):

Therefore, waiting-line models would provide an 85.7 percent fit to our selected criteria given the scores

and weights we have selected. We can also see from Table 16-3 that simulation comes very close to

fitting our situation. Decision analysis could be used, but it would be less satisfactory given the relative

importance of our selection criteria.

This procedure is systematic and consistent. Using this procedure, we can logically select techniques

that will best fit the various criteria we establish. Now that we have decided to use either a waiting-line

model or a simulation model, what do we do next?

Step 7.Present Initial Model to the Manager for H is or Her Commitment

Now the analyst is ready to present his or her initial model to the manager. The objective is to clear the

way for further development and to get the manager's full commitment to the project. If the manager is

going to have difficulties with the technique, this is the time to find out. On the other hand, exposing the

manager to an initial model will probably stimulate his or her thoughts. This may lead to further

specification of the problem.

Another reason for this step is as an aside to the technique selection process. If we can get the manager

involved in the selection and development process, we can smooth the path for eventual

implementation of the final model.


11/15

Step 8. Develop a Test Model

With the manager's blessing, the analyst is now ready to build the model. This will involve collection of

the required primary data to satisfy the requirements of both the model and the problem. The model

will have to be debugged until it is running well. During this stage, the analyst must continuously be

wary of violations of the model assumptions and watch for changes in the structure of the problem. Ifeither of these two events occurs, the model or technique may have to be changed.

Step 9. Refine and Revise the Model as Necessary

This is a continuous step during which the analyst must relate to the problem, the technique, and the

manager. The selection process is not actually completed until a model is providing appropriate output

for management decisions. This step, therefore, melds into the material on implementation presented

in the next chapter.

Key for Selecting a Technique

This section is designed to aid the analyst in selecting appropriate techniques for specific problem

situations. It is not designed to automatically lead the analyst to the "best" technique because the

"best" technique is evaluated in terms of a complex set of criteria, such as that presented in Table 16-4.

Four steps are required to select a portfolio of techniques. This portfolio is, in turn, referred back to the

previous section of this chapter. These four steps are:

1. Specify the manager's objectives.


12/15

2. Itemize the manager's specific problem assumptions (Tables 16-5 to 16-10).

3. Review each suggested technique for appropriateness (Tables 16-11 to 16-23).

4. Narrow the portfolio (Table 16-4).

This procedure forces the analyst to focus on the problem situation and the manager's needs. Notice

that the assumptions of the techniques are not considered until late in the process. This is justifiable

because we are first trying 'to align the objectives of a technique with the objectives of a problem and

manager. Once the initial techniques are identified for appropriateness of objectives, the fit of the

technique may then be examined.

These tables can be utilized best by observing a sample problem situation. Review, briefly, the problem

described in the introduction of Chapter 12. Our first decision involves the decision maker's primary

objective. Because he wants to know the number of offloading ramps to install, we can begin to

eliminate some of the techniques.

Examine the first elimination stage from Table 16-4. We can see that the problem does not deal with

allocating or sequencing. Obviously, it does not involve inventory. It could involve specific alternatives,

but the decision maker has identified none. Our best judgment seems that we wish to "examine the

dynamic nature of the problem." This leads us to stochastic :techniques, Table 16-9.


13/15

Then we can examine the next level of the manager's needs. Although we cannot be absolutely sure, it

appears that an analytical assessment of the system costs is appropriate. This leads us to waiting-line

models, Table 16-21.

The waiting-line models summary, Table 16-21, shows clearly that our problem meets the specifications

of the model. We can be reasonably assured that this technique will fit the problem. We now must

relate the technique to the decision maker.


14/15


15/15

Chapter 16 Selecting a Technique

Documents

Transcript of Chapter 16 Selecting a Technique