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Abia Polytechnic Analytical Tools for Strategic Decision Making Essay

In your reflective writeup, take 3-5 pages to discuss the methods introduced in the class (regression, forecasting, foresight, and agile strategy). Specifically, what new insights did you gain? Which one(s) will be most useful in your career? What are potential applications for the methods in your current position? Have you begun using any of the methods outside the class?

Module 1: Decision-making
Overview
DECISIONS, DECISIONS, DECISIONS!
Why do we care about decisions?
In business (and specifically in management), our ability to
make informed decisions is the primary basis on which we
are judged. Some judgments are external (i.e., whether
our firm succeeds or fails, and to what degree it succeeds).
Other judgments are internal. Not only keeping a job, but
how far and quickly you advance is predicated on making
good and timely decisions.
Why do we care about decisions?
(cont.)
The one theme that unifies all business disciplines is
decision-making. Each area supplies tools for making
better decisions.
As an MBA student, these tools are what constitute the
most value in the program. Understanding how and when
to apply given tools is the single most important skill you will
gain throughout the program.
Types of Decisions

Programmed vs. non-programmed decisions

Routine vs. strategic decisions

Policy vs. operational decisions
Programmed vs. Non-programmed
Decisions

Programmed decisions – concerned with
routine/repetitive matters. These problems have
standard procedures.

Non-programmed decisions do not have standard
solution procedures.
Routine vs. Strategic Decsiosns

Routine decisions – relate to the general functioning of
the organization. These decisions are made at lower
levels, and require minimal evaluation and analysis.

Strategic decisions – have effect on organizational
goals, objectives, and policy. These decisions require
careful evaluation and analysis, and are made at the
top levels.
Tactical vs. Operational Decisions

Tactical decisions – pertain to policy matters. These
decisions are made at top levels.
Operational decisions – relate to day to day operations of
the business. These decisions are made at middle and
lower levels.
Examples

To determine what type(s) of decision this is, ask your self
these questions. Is this a standard situation? Is this a
situation that occurs on a regular basis? Is this a decision
appropriate for upper- or lower-level management?

You are trying to determine what product mix is
appropriate.
Examples (cont.)

Is this a standard situation? No. A product mix is not
changed until circumstances dictate such a change.

Is this a situation that occurs on a regular basis? No.
Product mix changes occur infrequently.

Is this a decision appropriate for upper- or lower-level
management? Upper-level management. Product mix
is strategic in nature, as a change affects the trajectory
of the firm.
Examples (cont.)

Therefore, this is a

1. non-programmed decision.

2. strategic decision.

3. policy decision.
Examples (cont.)

Example 2:

You are trying to find the manufacturing schedule that
will optimize use of your five production lines to fill the
seven current orders from customers.
Examples (cont.)

Is this a standard situation? Yes. Production line scheduling is
repetitive.

Is this a situation that occurs on a regular basis? Yes. This situation
occurs every production period.

Is this a decision appropriate for upper- or lower-level
management? Lower-level management. Scheduling specialists or
production managers set the schedules.
Examples (cont.)

Therefore, this is a(n)

1. programmed decision.

2. routine decision.

3. operational decision.
Sample Steps for Decision-making
Sample Steps for Decision-making
Detailed Steps

Note that this is a process in which each step builds on
the previous step.

Any mistakes in a given step are magnified in
succeeding steps.

Remember GIGO – “garbage in, garbage out”.
Problem Identification

What is the underlying issue that needs to be addressed?

Do NOT treat a symptom as a “problem” (e.g., low
revenue is a SYMPTOM.)

While the slide title uses the term “problem”, a preferred
alternative is “opportunity for improvement”. As we will
see when we discuss Appreciative Inquiry, we prefer
POSITIVE language.
Find RELEVANT Information

To make an informed decision, we must have relevant
information.

Information may be primary (collected by the decisionmaker) or secondary (obtained from another source,
such as the internet).
Identify Alternatives

Some alternatives are simple. Ex. Either we retain this
employee, or we do not. (This is a 0/1, or “binary” type
of decision.)

Some situations have more complex alternatives. Ex.
How much should we spend on advertising? How often
should we do so?

You need as many alternatives as necessary, but no
more. One of your alternatives will be your choice.
Evaluate Alternatives

Carefully consider the tradeoffs (pros and cons) across
alternatives. Every decision involves tradeoffs. Rarely is
one alternative the best in EVERY way. Ex. One supplier
may be cheaper, but their quality may be lower. You
want the supplier with the best COMBINATION of cost
and quality (i.e., the most VALUE).

Give care to both immediate and long-term
consequences. Ex. If I hire Person A, it might be the best
hire for TODAY, but not the best hire five years later.
Which is most important? Today? Or five years later?
Select the BEST Alternative

This should follow easily from your evaluation.

Do NOT waste time. Once you have identified your best
alternative, go with it.

Avoid “paralysis through analysis”.

Avoid the unwillingness to make “hard” decisions.
Take Action

Implement your decision immediately.

Use appropriate implementation tools. Examples include
Project Management, quality tools, Lean Management
tools, Strategic Doing, etc.

A decision without timely implementation is a decision
NOT to do anything. Often, those are the WORST
decisions.
Evaluate

Reflect on your decision.

Compare your consequences to what was expected.

Are you satisfied with the result? With the process?

What would you do differently next time?
1
QM 662 Notes
Dr. Doug Barrett
Contents
1.0 Decision Theory Introduction ………………………………………………………………………… 2
2.0 Regression …………………………………………………………………………………………………….. 5
2.1 Simple Linear Regression ………………………………………………………………………… 6
2.2 Multiple Regression ………………………………………………………………………………. 11
2.3 Other Model Types ……………………………………………………………………………….. 15
3.0 Forecasting ………………………………………………………………………………………………….. 18
3.1 Moving Averages ………………………………………………………………………………….. 20
3.2 Single Parameter Exponential Smoothing (SPES)…………………………………….. 21
3.3 Regression ……………………………………………………………………………………………. 23
3.4 Classical Time Series (Decomposition) ……………………………………………………. 23
4.0 Linear Programming …………………………………………………………………………………….. 26
4.1 Transportation Problems: (Special LP) …………………………………………………. 30
4.2 Assignment Problems: (Special LP)…………………………………………………….. 33
2
1.0 Decision Theory Introduction
Decision Making:
1) Future—uncertainty
2) Tradeoffs:
Non-technical
Better or best choice
Not necessarily the best choice
Linear program: unlikely to know all the coefficients
Forecasting—predicting the future
Techniques:
1) Regression
2) Forecasting
Regression
• 2 quantitative (measurable) variables
• We want to assess the relationship between them (correlation)
• Use one variable to predict the other
• Y= response (dependent) variable
• X=explanatory (independent, predictor) variable
Simple Linear Regression:
• “Method of Least Squares”
Steps:
1) Identify the problem
2) Determine applicable variable(s)
3) Collect data
4) Graph the data
5) Numerical analysis (Most appropriate technique)
6) Interpret output
7) Conclusion/decision
8) Communicate!
Scatter plot (scatter diagram)
“X vs. Y plot”
3
➢ Positive
➢ Linear
➢ Positive
➢ Nonlinear
➢ No
relationship
4
➢ Positive
➢ Nonconstant variance
➢ Heteroscedasticity
a) Outlier – unusual x, y
combination
b) High leverage case unusually large (or small)
x-value
Interpreting scatter plots:
1) Nature of relationship (+/-/neither)
2) Function of relationship (linear, nonlinear)
3) Unusual observations (Outlier, high leverage)
Ideal case: + or – and linear.
5
2.0 Regression
Regression:
• Fitting a line to the data
• Excel gives us the linear equation (equation of a line)
• Line that “best fits the data”
• ŷ= predicted y-value
Prediction equation: ŷ= a + bx
yi-ŷi= ei (residual)
ŷx0
The smaller the error (in
magnitude), the better.
yx0
Ideally, errors are close to 0.
x0
ŷ= a + bx
ŷ= predicted y-value
b = slope =
rise
run
# of units y increase
= # of units x increase
= # of units y increase as x increases by one unit
a= y-intercept = predicted y-value when x = 0
➢ What is a “good fit”?
o Small ei values
o Positive and negative ei values (If we have positive residuals, we need
negative residuals to offset)
o ei –values sum to zero.
Criterion:
-We select the line that minimizes the sum of squared residuals.
6
-Least squares regression- “ordinary least squares”- OLS
2.1 Simple Linear Regression
-Simple linear regression- one x, one y
Excel Output for Regression:
1. Regression Statistics—measures the strength of the linear relationship (association)
2. ANOVA—hypothesis test for the slope
3. Coefficients—slope and intercept values, tests for slope & intercept
Example (sales in thousands)
Output:
ŷ= 72.96 + 1.47x
usually call the first period ‘0’
For 2002, ŷ= 72.96 + 1.47(10)
= 87.66
=) ŷ = $87, 660
Interpreting a and b:
a= 72.96
=) The predicted sales for 1992 are $72, 960.
b=1.47
=) For each year progressing, the predicted increase in sales is $1,470.
7
Predicting for other periods:
-extrapolation (predicting outside the range of the x’s)
Regression Statistics:
-goodness of fit
-R & R2
(R2 –not under simple regression) (Multiple R)
R= correlation coefficient
-measures the strength of the linear associations between y and x.
-1 < R < +1 R= +1: perfect positive linear associations = -1: perfect negative linear association = 0: no linear association R= +1 R=0 {Extreme Cases} The closer you are to +1 or -1, the stronger. (Take the absolute value…) Which is stronger? -.79 +.61 8 “Correlation does not imply causation.” (Two variables can respond to a third variable.) Possible causes for a high correlation: 1. causation 2. other (“lurking”) variables Example: As the number of preachers in a city increases, alcohol consumption increases. (There is a very high positive correlation.) Why? A. A third variable (population) causes an increase in numbers of many members of vocations (such as preachers) and consumption of virtually everything (including alcohol). Note that causation may be assessed only with the use of experimental designs (and NOT regression.) R2 = Coefficient of Determination 0 < R2 < 1 or 0 < R2 < 100% R2 = proportion (or %) of variation in y explained by the regression equation (using x). Example: R2 = .96 x=) 96% of the variations in sales is explained by using the year in the regression. (4% is unexplained.) Regression Hypothesis Testing: -Test to determine if there is a statistically significant linear association. Ho: no effect β= 0 HA : some specific effect β≠ 0 9 Model Parameters: β0 = model y-intercept—estimated by a β1 = model slope—estimated by b Ho: β1 = 0 HA : β1 ≠ 0 (Slope is equal to 0) Z-tests 1. t-tests 2. F-test (ANOVA) Look at the p-value: -“significance F” for ANOVA -“p-value” for t-test -we set α = level of significance (usually α = .05) If p-value < α, we reject Ho. =) Conclude that there is a significant linear association (x is a useful linear predictor) If p-value > α, do not reject Ho.
Example: let α = .05 [ANOVA…p-value= 0.00 xj is a useful predictor
Use α = .05:
P-values:
x1 : p-val = .00 reject Ho
x2 : p-val = .03 reject Ho
Here, we reject for x1 and x2.
x1 and x2 are both useful predictors of y when used together.
If you throw out a variable and adjusted R2 goes up, then that variable was not
contributing very much.
14
Model Building:
1. Overall model is significant. (ANOVA)
2. All x-variables are significant predictors. (t-tests)
3. High R2 (close to 1)
4. High adjusted R2
5. Low MSE/standard error
6. No multicollinearity

Data Analysis
Correlation
—Correlation matrix gives you simple information quickly
When doing multiple, look at first.
If the x-variables are linearly related, then we have a condition called
multicollinearity.
– If MC exists, the correlations between at least 2 x-variables may be
high.
– If the correlation between 2 x-variables exceeds 0.7 (in absolute value),
then there is severe MC present.
– Does not affect prediction.
Ex. xa = 4×1 – 2×2 + 3×4 – 6×5
– Does strongly affect estimates of β’s (b’s)
Control variables:
—Testing Y vs. X1, X2…..Xk
-x1 thru Xr are control variables
– Xr+1 thru Xk are the “primary variables”
Example: Predict the crime rate in a county.
– Data from 67 Alabama counties
Y = crime rate
Possible x’s: poverty, inflation, education level(s), regulations,
population
[Think about the variables you have, and the ones you can get;
account for the control variables.]
Always add in limitations.
15
2.3 Other Model Types
– Y is a binary variable (cannot used the least squared regression)
– Logistic (logit), probit
– Nonlinear relationship
-Polynomial regression
Polynomial model:
Y = a1 xn+ a2xn-1 + …. + an-1 x2 + an x + c
Whatever is the highest power is the order of the polynomial.
Common cases:
n = 1 =) linear
n = 2 =) quadratic
n = 3 =) cubic
Parabola
Cubic
16
Ŷ = a1x2 + a2x + c
Y
Y1
x
x1
=
x2
X1^2
x12
Y2
x2
=
X2^2
x22

Yn

xn =

Xn^2

xn2
What powers should we try?
-n≤4
– if n > 4, we tend to “over fit” to the data.
You do not want an overfit.
17
– the “Bulge Rule”
– “lower” powers:
– nth root, i.e.
……
– reciprocal
x = x-1
– other negative powers
-1, -2, -3, …
– logarithms (ln, log10)
Residual plots:
In a multiple regression, it is not clear any
x is causing the problem.
18
3.0 Forecasting
Time Series (sequence) Data:

autocorrelation
past values of y are predictors of the current and future y-values
x = time period
– sometimes “t”
Components:
1. Trend – increase or decrease over time
2. Seasonal – patterns occurring in periods < 1 year. Seasonal component and positive trend. 19 3. Cyclical – patterns occurring in periods > 1 year.
Ex.: stock prices
4. Random (irregular) – always there; accounts for deviations from a “perfect”
pattern
Forecasting:
1. Use y w/ no x’s. Use the mean ( y ) or the median of the y’s.
2. Use x’s – regression 1, select the “best” regression.
3. Use a time-series model – select the model that includes all the present
components’ conditions.
Component(s) present:
Model:
Random
Moving average,
Single- parameter exponential smoothing (SPES)
Trend, Random
Seasonal, Trend, Random
Regression (if the trend is linear)
Classical Decomposition
20
Output interpretation:
Goodness of fit
MSE – sum of squared errors
# of applicable periods
Errors:
Error:
Actual – forecast
Yt – Ft
“# of applicable periods”
# of periods for which we have a forecast and an actual value.
Improving – can we get a lower MSE and a broader scatter of errors?
Find a model that meets all existing conditions and find the …

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