Chapter 1
Summary
TLDRThe transcript introduces quantitative business analysis, highlighting its evolution from the scientific management revolution to the modern era of computing power. It emphasizes the importance of blending qualitative and quantitative methods for effective decision-making, especially in complex, critical, new, and repetitive problems. The process involves identifying and defining problems, evaluating alternatives, and selecting the best solution using mathematical models, which are crucial for understanding and optimizing business operations.
Takeaways
- π Quantitative Business Analysis encompasses various fields like management science, operations research, decision science, and business analytics.
- π It originated from the scientific management revolution (Taylorism) in the early 20th century, aiming to improve workplace efficiency.
- π‘ Post-WWII developments in methodological understanding and computing power significantly advanced quantitative analysis.
- π The process of quantitative business analysis involves seven steps, with the first five focusing on decision-making through problem identification, alternative selection, criteria determination, evaluation, and choice.
- π Both qualitative and quantitative analyses are essential for decision-making, with qualitative aspects like experience and intuition being valuable for simpler problems.
- π’ Quantitative methods are particularly useful for complex, critical, new, and repetitive problems, providing more structured and data-driven decision-making.
- π οΈ Model development is a key component, requiring the definition of objective functions and constraints, as well as understanding controllable and uncontrollable inputs.
- π The course will focus on mathematical models, including cost, revenue, and profit models, which need to accurately represent real-world situations.
- π§ Data preparation is often the most time-consuming part of quantitative analysis, requiring careful formatting and ensuring all necessary variables are accounted for.
- π Interpreting the results of quantitative methods is crucial, with an emphasis on understanding when and how to use these methods effectively.
- π Modern computing power has reduced the need for manual calculations, shifting the focus to setting up the models and interpreting their outcomes.
Q & A
What is quantitative business analysis?
-Quantitative business analysis is the use of mathematical and statistical methods to support decision-making and problem-solving in business. It may also be referred to as management science, operations research, decision science, or business analytics.
How did quantitative business analysis originate?
-Quantitative business analysis originated with the scientific management revolution in the early 20th century, also known as Taylorism, which introduced more efficient ways of working in factories and workplaces, contributing to economic growth.
What developments made quantitative business analysis more prevalent?
-Two significant developments contributed to the prevalence of quantitative business analysis: methodological advancements in probability, regression analysis, forecasting, and linear programming, and the explosion in computing power that allows for quick data processing and decision-making.
What are the steps of problem-solving in quantitative business analysis?
-The steps of problem-solving in quantitative business analysis include identifying and defining the problem, determining the set of alternatives, establishing evaluation criteria, evaluating the alternatives, and choosing an alternative.
How does quantitative analysis complement qualitative analysis in decision-making?
-Quantitative analysis complements qualitative analysis by providing numerical data and mathematical models to support decision-making, especially for complex, critical, new, or repetitive problems. It allows for a more objective assessment alongside the subjective insights and experiences from qualitative analysis.
What types of models are discussed in the context of quantitative business analysis?
-Three types of models are discussed: iconic models, analog models, and mathematical models. The focus of the course is on developing and understanding mathematical models.
What are the components of a mathematical model in quantitative business analysis?
-A mathematical model in quantitative business analysis includes objective functions, which can be minimizing or maximizing something, and constraints, which define what is feasible and not feasible.
What are controllable and uncontrollable inputs in a mathematical model?
-Controllable inputs are the decision variables that can be manipulated, while uncontrollable inputs are factors outside of the decision-maker's control that are taken as given.
What is the importance of data preparation in quantitative analysis?
-Data preparation is crucial in quantitative analysis as it ensures the data is in the correct format and shape for analysis. It involves identifying all necessary variables, handling missing observations, and preparing the data for the model solution.
What is the role of a report or summary in quantitative analysis?
-A report or summary is used to communicate the results of quantitative analysis. It helps interpret the data and present the findings in a way that is understandable to stakeholders who may not be familiar with the technical aspects of the analysis.
What simple mathematical models are introduced in the script?
-The script introduces models of cost, revenue, and profit. These models help in understanding how to minimize costs, maximize profits, and perform break-even analysis based on given volumes and costs.
Outlines
π Introduction to Quantitative Business Analysis
This paragraph introduces the concept of quantitative business analysis, discussing its various names such as management science, operations research, decision science, and business analytics. It traces the historical roots of quantitative analysis to the scientific management revolution in the early 20th century, highlighting the role of efficiency in driving economic growth. The speaker also touches on the advancements post-World War II, including methodological developments and the rise of computing power, which have significantly enhanced the application of quantitative analysis in decision-making processes. The paragraph emphasizes the importance of blending qualitative and quantitative analysis, especially for complex, critical, new, and repetitive problems.
π’ The Process and Importance of Quantitative Analysis
The speaker delves into the steps of problem-solving and decision-making, identifying seven steps where the first five are part of the decision-making process. These steps include defining the problem, determining alternatives, evaluating criteria, evaluating alternatives, and choosing an alternative. Quantitative methods aid in this process. The paragraph underscores the significance of both qualitative and quantitative analysis in decision-making, with quantitative analysis being particularly useful for complex and repetitive problems. It also introduces the concept of model development, including defining objective functions and constraints, and the types of models such as iconic, analog, and mathematical models. The importance of data preparation and interpretation of results is stressed, with the course aiming to focus on understanding when to use specific methods and how to interpret their outcomes.
π Basic Mathematical Models for Decision Making
The final paragraph of the script introduces basic mathematical models used in decision making, such as cost, revenue, and profit models. It explains the need for an accurate mathematical representation of reality, including fixed and variable inputs, marginal costs, and the production function. The objective function could involve minimizing costs or maximizing profits given a certain volume. Break-even analysis is mentioned as a simple mathematical equation to find the volume that equates revenue and costs. The speaker expresses a hope that the course will be engaging and not too boring, and encourages students to seek out additional resources if desired.
Mindmap
Keywords
π‘Quantitative Business Analysis
π‘Management Science
π‘Operations Research
π‘Decision Science
π‘Problem Solving
π‘Decision Making
π‘Model Development
π‘Data Preparation
π‘Model Solution
π‘Cost Function
π‘Break-Even Analysis
Highlights
Quantitative business analysis is also known as management science, operations research, decision science, or business analytics.
Quantitative analysis methods are applied in various fields, including sports analytics.
The scientific management revolution in the early 20th century, also known as Taylorism, introduced quantitative analysis in the workplace.
Post-World War II saw significant methodological developments and the advent of computing power, which enhanced quantitative business analysis.
Quantitative business analysis involves a five-step process for problem-solving and decision-making.
Both qualitative and quantitative analysis are important in decision-making, with a blend of both being ideal.
Quantitative analysis is particularly useful for complex, critical, new, and repetitive problems.
Model development is central to quantitative analysis, involving the creation of mathematical models.
Objective functions in mathematical models represent the goal of minimizing or maximizing a certain variable.
Constraints in models define what is feasible and not feasible in achieving the objective.
Models can be deterministic or stochastic, with stochastic models involving variation and uncertainty.
Data preparation is a crucial and often time-consuming part of the quantitative analysis process.
Model solution requires understanding the relationship between independent and dependent variables.
Interpreting the results from quantitative methods is a key focus of the course.
The course introduces simple mathematical models for cost, revenue, and profit analysis.
Break-even analysis is a method to find the volume that equates revenue and cost.
The importance of quantitative business analysis in decision-making is emphasized throughout the course.
Transcripts
all right there here's our first
topic it's introduction to quantitative
business analysis i didn't quite have
the slideshow ready to go but now i do
here we go
so what is quantitative business
analysis it is what it sounds like
you may have heard it under different
names like management science or
operations research or decision science
or
business analytics you know in different
worlds you might hear about like sports
analytics which i'm interested in
actually teach a sports economics class
here
at pitt state where we do some of these
types of
quantitative analysis problems or
methods
to solving problems or making decisions
right and this really took off with the
scientific management revolution in the
early 20th century
sometimes called taylorism named after
oh what's his first name i've already
blanked on it
something taylor who introduced
quantitative analysis and more
quantitative
problem solving in the workplace is
really was an add-on to the industrial
revolution
uh making factories more efficient
workplaces more efficient therefore
producing more
therefore we got a lot more outputs and
a huge explosion economic growth
and so that was early 20th century a
little bit later after world war ii we
had two big developments
making quantitative business analysis
more prevalent
and those developments were the numerous
methodological
developments and the development there's
a little redundant right but
we're improving our methods we
understand probability better we
understand
regression analysis and forecasting
better we develop linear programming and
ways to solve them
and then with the explosion and
computing power we add on to that now we
can go through a lot of information in a
hurry
our computers can crunch all those
numbers quickly and we can therefore
make much quicker decisions my professor
told me
in doctor's school he's considerably
older in your retirements and
he's talking about the computers he had
to use to do his dissertation research
he was like took up a whole room he had
to put in punch cards and
you know that was a huge improvement
even then in the 70s and now we've got
you know
computers that are the size of our
phones crunching way more numbers than
those
wall or room computers did right so both
of those developments
have contributed to the explosion and
quantitative business analysis
sorry about that i had a knock on my
door and i don't ever get visitors like
that so anyway
so when we're doing quantitative
business analysis we first need to
understand what problem solving decision
making is of course you have some sort
of understanding of this already
this is just our attempt or the books
attempt at aligning it more specifically
concretely
yeah so we have the steps of problem
solving in the steps of decision making
so
the book identifies our authors identify
seven steps to problem solving
of which the first five are the decision
making process so
we need to carefully identify and define
our problem determine the set of
alternatives available for us to choose
from
determine the criteria that we'll be
using to evaluate those
alternatives then we're going to
evaluate the alternatives and then
choose
an alternative and basically the
quantitative business analysis
or quantitative methods will help us in
this five step
process right then once we've chosen the
alternative we implement it
and then we evaluate the results which
can of of course of all
involve a quantitative component to it
as well
keeping in mind here this whole time
quantitative means numbers means math
right so
we're using math to help us make
decisions basically with these first
five steps that's
what the entire course is about um
hold on a second all right so when we
make decisions then we need to use
both qualitative and quantitative
analysis or anything qualitative or
thinking
you know not numbers are taking
information that aren't
in the mathematical form and this is
very important we do not want to
downplay the qualitative side of
decision making for managers
for anyone who has to make decisions we
want to lean on our experience
want to lean our intuition gut feelings
these are really important
and typically we'd use that kind of
analysis especially for the simple
problems
but we don't necessarily put it to the
side and say it's not used at all
where quantitative analysis is right we
want to use a blend of it we use a blend
of quantitative and qualitative
but we have because of the explosion in
our our methodological
developments and computing power have
made great strides using quantitative
analysis so we don't want to downplay
the ability of quantitative analysis to
help us in making decisions either
and in particular the book the authors
are of their textbook will make the case
that
quantitative analysis is particularly
important when the problems are complex
when they're critical when they're new
and when they're repetitive right
complex problems with more information
the better right critical problems again
more information the better including
quantitative can help us make big time
decisions
when you know the the results will be
you know big time
risk or big time reward kind of thing
new problems when we
can't lean on experience as much we want
you know to more know more of the
numbers in the situation no more about
the quantitative side of it and
repetitive problems uh if you know
they're gonna
uh occur frequently it helps to be able
to just plug in some numbers
computer and let them crunch it for you
right rather than have to
develop the model each time if it's a
repetitive problem having a quantitative
method ready to go some computing
ready to go help so basically we're just
pointing out here that quantitative
analysis
is really critical it's very important
for our decision making in the workplace
and life
whatever but it's not the only one we
also want to use qualitative right
and the book will mention several
quantitative methods that we use at the
end of the chapter
but in each of them what we're doing is
developing
a model right and we're doing model
development we want to ask how well does
the model represent the real situation
the book discusses and i'll let you read
about it
which you'll need to know for your quiz
the iconic model the analog model
and the mathematical model and this
class will be about the mathematical
models
so we're developing a mathematical model
we need to first define our objective
functions and our constraints the
objective function is usually a
minimizing of something or a maximizing
of something
finding the maximum profit or the
minimum cost right there maximizing the
probability of a certain outcome
or minimizing the probability of a
certain outcome and that kind of thing
so uh we need to objective function we
also need to know the constraints you
know like what is feasible and what is
not feasible
um you know i'd say like if you could
all make a billion dollars in profit you
would but there's some constraints
involved
in and getting to that point so what are
those constraints
and we also have uncontrollable and
controllable inputs uh basically this is
pretty straightforward the controllable
ones will be the decision variables
the uncontrollable ones you have no
control over they're just taken
as a given uh they're given to you right
and then there's the models could be
deterministic
or stats stochastic sorry deterministic
models is when everything is
uncontrollable
uh the stochastic models is when there's
some variation
some controllable aspects to it
so we're basically using mathematic
models which means we need to use
data preparation we need to prepare our
data before we can crunch the numbers we
need to have our data ready to go
and it's been my experience in my short
life as a researcher that this is often
the most time consuming uh parts right
you got to get the data ready to go you
got to make sure it's
in the shape and the format you need it
you make sure you have all the variables
that you need
you know all these things because
there's no missing observations and this
can take a lot of help from a lot of
different people to get to that point
then you also need a model solution you
know for me in my regression analysis
that mean i'd outline
you know all of the x's that affect the
y all the independent variables affect
the dependent variable for example
and you set it up appropriately it's
like linear programming type model
and you get your results and then you
need to regenerate a report oftentimes
you're crunching numbers you don't just
send the numbers to your boss
a lot of times they want to see some
sort of report some sort of results
summary
which means you need to be able to
interpret the data and we'll put a big
emphasis
in this course on understanding when the
method is appropriate to use
and being able to interpret the data
rather than having you go through a
whole bunch of work
actually doing it i'm going to do a
little bit of that i'm really going to
try to emphasize you understanding when
the method is
to be used and interpreting the results
from that method because
because these days you know manually
going through these methods
isn't necessary anymore we have
computers that can do that for us right
so the key here would be knowing when to
use it how to set it up and then
how to interpret the results right and
so the book just to try to introduce you
very briefly to maybe the most simple
mathematical models
in helping you make decisions introduces
you if you haven't already covered what
you should have with your prerequisites
uh models of cost revenue and profit
basically we're trying to point out here
that you have cost function
the model function the mathematical
model needs to represent
reality so you need to have an accurate
mathematical model
you could have involved like the costs
of the inputs right so you have some
fixed inputs some variable inputs and
you get some
marginal costs as you change the outputs
or change the inputs
these functions need to reflect the
production function we'd say
in economics the mathematical rep recipe
used in producing a certain output so
you should
use a certain amount of labor certain
amount of capital
and so on raw materials those kind of
things and you get
the prices of those sorry voice cracking
a little bit and those are your costs
and usually
the objective function here would then
be given a
a certain amount of volume these losing
my voice
uh how can we minimize cost right or
given a certain amount of volume how can
we maximize
profit that kind of thing or another
thing we could do
i might get a drink of water hold on
all right sorry about that hopefully my
voice will hold the rest of the video
here right
we can do break even analysis which is
the volume that equates the revenue and
the cost so basically you're just
putting the cost side together
at the revenue side and you try to find
the volume that equates the two very
simple
mathematical equations which i have you
do a little bit in your assignments
right so
welcome to quantitative business
analysis pretty straightforward
and next week we'll dive into
probability which hopefully isn't too
new for you but hopefully somewhat new
so we're learning something new and it's
not too boring so i'm gonna try to keep
the video short
and not too many of them but that might
mean that you want more
and if that's the case there are plenty
of videos out there discussing these
very issues that other people have made
that are probably more interesting
anyway so you can find those or i can
maybe
even provide you some links to those if
you like so
welcome to the course
Browse More Related Video
What is Demand Forecasting?
SIM 1 Sistem Informasi di dalam Bisnis Global
PR2 - Introduction to Quantitative Research: Definition of Quantitative Research
Chapter 1: 1 Introduction to Managerial Economics
Engineering Design Process Overview
Practical Research 2 Lesson 1: Introduction to Quantitative Research
5.0 / 5 (0 votes)