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Models for Supply Chain Decisions - Detailed Summary of all lectures (incl. how to apply in Excel)
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Samenvatting Quantitative Analysis for Management, Global Edition, ISBN: 9781292217659 Quantitative Decision Making (BT1115)
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Models for Supply Chain Decisions, 1st LectureThe first topic is the topic of quantitative analysis. The corresponding learning goals are as follows:
1.1. Describe the quantitative analysis approach and understand how to apply it in a real
situation.
1.2. Describe the three categories of business analytics
1.3. Describe the use of modelling in quantitative analysis
1.4. Prepare a quantitative model
1.5. Use computers and spreadsheet models to perform quantitative analysis
1.6. Recognize possible problems in using quantitative analysis
1.7. Recognize implementation concerns of quantitative analysis
Mathematical tools have been use for thousands of years. Quantitative analysis can be applied to a
wide variety of problems. It is not sufficient to just know the mathematics of a technique, but you must
also understand the specific applicability of the technique, its limitations, and assumptions. Successful
use of quantitative techniques usually result in a solution that is timely, accurate, flexible, economical,
reliable, and easy to understand and use. Examples of successful quantitative analyses are:
- NBC television increased revenues by over $200 million by using quantitative analysis to
develop better sales plans for advertisers. Before this, they used to make sales plans manually
(determining time slots and how many times advertisements should be shown, etc.).
- Taco Bell saved over $150 million using forecasting and employee scheduling quantitative
analysis models.
- Continental Airlines saved over $40 million every year using quantitative analysis models to
quickly recover from weather delays and other disruptions.
But what is this quantitative analysis that we are talking about? Quantitative Analysis is a scientific
approach to managerial decision making in which raw data are processed and manipulated to produce
meaningful information.
In an analysis you will have two factors: qualitative factors and quantitative factors. Quantitative
Factors are data that can be accurately calculated. Examples of these are different investment
alternatives, interest rates, financial ratios, cash flows and rates of return, and flow of materials
through a supply chain. On the other hand, when we talk about Qualitative Factors, we talk about
factors that are more difficult to quantify but affect the decision process. Examples of the are weather,
state and federal legislation, technological breakthroughs, and the outcome of an election.
Quantitative and qualitative factors may have different roles. Decisions based on quantitative data can
be automated and therefore, quantitative data will generally aid the decision making process.
Quantitative analysis is used/important in many areas of management – Production/Operations
Management, Supply Chain Management, Business Analytics.
,Now what is business analytics? Business Analytics is a data-driven approach to decision making. It
allows for better decisions with the use of large amounts of data. Therefore, information technology is
very important. Statistical and quantitative analysis are used to analyse the data and provide useful
information. In business analytics, there are three different approaches that we can adopt:
1. Descriptive Analytics: The study and consolidation of historical data.
2. Predictive Analytics: Forecasting future outcomes based on patterns in the past data.
3. Prescriptive Analytics: The use of optimization methods.
When we talk about the quantitative analysis approach, we talk about starting from a problem
definition, then developing a model, acquiring input data for that model, developing a solution, testing
that solution, analysing the results, and implementing the results. This process can be an iterative one
since if the testing of the solution or analysing the results yield a negative outcome, you might have to
redevelop the model.
1. Defining the problem: You have to develop a clear and concise statement of the problem to
provide direction and meaning. This might be the most important and difficult step. In this
definition, you have to go beyond symptoms and you should identify true causes. Do not look
at all the problems, but rather concentrate on only a few of them – selecting the right problem
is very important. To accompany this, specific and measurable objectives may have to be
developed.
2. Developing a model: Once it is clear what your problem is, what causes the problem, and how
you can solve this problem, you have to develop a model. Models are realistic, solvable and
understandable mathematical representations of a situation. There are different types of
models:
a. Physical models where you literally make a physical version of what you want to do
b. Scale models where a model can be done in the actual size, but is actually done in
scale to the real problem/solution.
c. Schematic models where you draw things with a pencil or a pen
d. Mathematical models which is a set of mathematical relationships. These types of
models generally contain variables and parameters. Controllable/decision variables are
generally unknown (“How many items should be ordered for inventory?”). Parameters
, are known quantities that are a part of the model (“what is the cost of placing an
order?”). In order for these models to work, the required input data must be available!
3. Acquiring Input Data: After a model has been made, the input data has to be acquired.
Important in this is that the acquired data is accurate – Garbage In Garbage Out rule (if you
use garbage data in the model, you will get a garbage output of the model). Data may come
from a variety of sources such as company reports, documents, employee interviews, direct
measurement, or statistical sampling.
4. Developing a solution: Now that a model is created and you have accurate data, it is time to
develop a solution. This involves manipulating the model to arrive at the best possible
(optimal) solution. Common techniques for this are solving equations, trial and error (trying
various approaches and picking the best result), complete enumeration (trying all possible
values), or using an algorithm (a series of repeating steps to reach a solution).
5. Testing the solution: Now that you have a solution, it is time to test how accurate it is, how
sensitive it is to changes. You do not want a solution that is very susceptible to the smallest of
changes in variables. Both input data and the model should be tested for accuracy and
completeness before analysis and implementation. New data can be collected to test the
model. Results should be logical, consistent, and representative of the real situation.
6. Analysing the results: After you have tested the solution, you have to analyse the results of
your solution. This way, you can determine the implications of the solution. For example,
implementing results often requires changes in an organization and therefore, the impact of
actions/changes needs to be studied and understood before implementation. Also, a sensitivity
analysis and post-optimality analysis are performed and determine how much the results will
change if the model or input data changes. Sensitive models should be very thoroughly tested.
7. Implementations of the results: Implementation incorporates the solution into the company.
This implementation can be very difficult as people may be resistant to changes. Many
quantitative analysis efforts have failed because a good, workable solution was not properly
implemented. One thing to note is that changes occur over time, so even successful
implementations must be monitored to determine if modifications are necessary.
How can we model in the real world? Quantitative analysis models are used extensively by real
organizations to solve real problems. In the real world, quantitative analysis models can be complex,
expensive, and difficult to sell. Following the steps in the process is an important component of
success.
Now how can we develop a model? For example we take profit. A mathematical model of profit is
“profit = revenue – expenses”. In this, revenue and expenses can be expressed in different ways.
Revenue can be “(selling price per unit)(number of units sold)” where expenses can be “[fixed costs +
(variable costs per unit)(number of units sold)]”. Therefore, we get:
- Profit = sX – [f + vX]; or
- Profit = sX – F – vX; where s is the selling price per unit, v is variable cost per unit, f is fixed
costs and X is number of units sold.
So, the parameters of this model are f, v, and s as these are the input inherent in the model. The
decision variable of interest is X.
Companies are often interested in the Break-Even Point (BEP). This is X where profits are 0. Thus, we
get “0 = sX – f – vX” or “0 = (s-v)/X-f”. Solving for X, we then get X = f/(s-v). So, the BEP is
calculated as…
¿ costs
- BEP =
selling price per unit−variable cost per unit
,
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