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EXTENSIVE SUMMARY of Introduction to Management Science!
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- Nyenrode Business Universiteit (Nyenrode)
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Voorbeeld 4 van de 192 pagina's
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SUMMARY: Introduction to Management ScienceFrederick Hillier, 7e
Final Exam Part 1: Spreadsheet skills exam – Chapters 1, 2, 4, 5, 6, 10, 11
Final Exam Part 2: Written exam – Chapters 3, 4, 8, 9, 12
CH1: INTRODUCTION............................................................................................................................... 1
CH2: OVERVIEW OF THE ANALYSIS PROCESS......................................................................................... 12
CH3: CLASSIFICATION AND PREDICTION MODELS FOR PREDICTIVE ANALYTICS ................................... 32
CH4: PREDICTIVE ANALYTICS BASED ON TRADITIONAL FORECASTING METHODS ............................... 56
CH5: LINEAR PROGRAMMING: BASIC CONCEPTS.................................................................................. 74
CH6: LINEAR PROGRAMMING: FORMULATION AND APPLICATIONS .................................................... 89
CH8: WHAT-IF ANALYSIS FOR LINEAR PROGRAMMING ....................................................................... 106
CH9: NETWORK OPTIMIZATION PROBLEMS ........................................................................................ 125
CH10: INTEGER PROGRAMMING......................................................................................................... 139
CH11: NONLINEAR PROGRAMMING ................................................................................................... 150
CH12: DECISION ANALYSIS ................................................................................................................... 172
CH1: INTRODUCTION
Business analytics= The art and the science of transforming data into insights for making better
business decisions.
Three stages of analytics:
1. Descriptive analytics= Analysing data to create informative descriptions of what has
happened so far.
2. Predictive analytics= Using models to create predictions of what is likely to happen in the
future.
3. Prescriptive analytics= Using decision models, including the optimization models of
management science, to prescribe the best options for managerial decision making.
You should be able to:
1. Define the term management science.
2. Describe the nature of management science.
3. Describe mathematical models and spreadsheet models.
4. Define the term business analytics.
5. Describe the nature of business analytics.
6. Describe the three categories of business analytics.
7. Describe the relationship between management science and business analytics.
8. Identify the levels of annual savings that management science sometimes can provide to
organizations.
, 9. Identify some special features of this book.
1.1 The Nature of Management Science
Management science (MS)= A discipline that attempts to aid managerial decision making by applying
a scientific approach to managerial problems that involve quantitative factors.
Operations research (OR)= The traditional name for management science that still is widely used
outside of business schools.
Like management science, business analytics attempts to aid managerial decision making but with
particular emphasis on three types of analysis:
1. Descriptive analysis: The use of data (sometimes massive amounts) to analyse trends to
date.
2. Predictive analytics: The use of data to predict what will happen in the future.
3. Prescriptive analytics: The use of data to prescribe the best course of action.
The techniques of the management science discipline provide the firepower for prescriptive analytics
and, to a lesser extent, for predictive analytics, but not for descriptive analytics.
When business analysts employ management science and other techniques to make
recommendations to management, it is managers (not the business analysts) who make the
decisions.
A management science team will attempt to use the scientific method in conducting its study,
meaning that the team will emphasize conducting a systematic investigation that includes careful
gathering, developing and testing hypotheses about the problem (typically in the form of a
mathematical model), and then applying sound logic in the subsequent analysis.
Steps systematic investigation: defining the problem, gathering relevant data, formulating a
mathematical model, determining how to solve the model, testing and refining the model, applying
the model to develop recommendations for management, helping to implement the
recommendations adopted by management.
Mathematical model= An approximate representation of, for example, a business problem that is
expressed in terms of mathematical symbols and expressions.
REVIEW QUESTIONS:
1. When did the rapid development of the management science discipline begin?
2. What is the traditional name given to this discipline outside of business schools?
3. What does a management science study provide to managers to aid their decision making?
4. Upon which scientific fields and social sciences is management science especially based?
5. What are some quantitative factors around which many managerial problems revolve?
,1.2 What is Business Analytics?
By using a variety of innovative techniques to analyse the available data, business analytics can be
defined as the art and the science of transforming data into insight for making better business
decisions.
Business analytics draws on management science and various other quantitative decision sciences.
Big data= Refers to the era of big data we have entered in recent decades where enormous and
increasing amounts of transactional data commonly are available for analysis.
A primary focus of business analytics is on how to make the most effective use of all these data.
Three categories of business analytics:
1. Descriptive analytics: Analysing data to create informative descriptions of what has
happened so far.
- Requires dealing with massive amounts of data. Uses innovative techniques (including
algorithms) to explore data, locate and extract the data that are relevant, and identify the
interesting patterns and summary data.
- A key tool is data visualization= After exploring data to identify the insights, the goal of
data visualization is then to communicate these insights clearly and efficiently to
managers and other users through the careful selection of the most effective visual
graphics.
2. Predictive analytics: Using models to create predictions of what is likely to happen in the
future.
- Often involves applying statistical models to predict future events or trends.
- Forecasting models= Models for predicting a future quantity of some type based on the
historical pattern of that quantity.
- Computer simulation= Using a computer to simulate the operation of an entire process
or system.
3. Prescriptive analytics: Using decision models, including the optimization models of
management science, to prescribe the best options for managerial decision making.
- Involves applying decision models to the data to prescribe what should be done in the
future.
- Purpose: to guide managerial decision making, so the name decision analytics also could
be used to describe this category.
An important goal of business analytics, including especially descriptive analytics, is to track down
and connect the relevant parts of all the available data with the business problems and issues of
current interest.
The business analytics community has made great advances in developing powerful techniques for
performing predictive analytics after the turn of the century.
Classification= Using models to predict a ‘yes-or-no’ outcome (or perhaps one of a small set of
possible outcomes).
Prescriptive analytics uses powerful techniques drawn mainly from management science to prescribe
what should be done in the future.
Business analytics is sometimes referred to as data science, as well as data analytics or decision
analytics. Business intelligence is a traditional name for descriptive and predictive analytics.
, Data science= An interdisciplinary field that uses scientific methods, processes, algorithms, and
systems to extract knowledge or insights from even massive amounts of data in various forms.
The important difference between business analytics and data science is that data science is more
interdisciplinary, more based on scientific methods, more applicable to various areas in addition to
business, and more concerned with how to deal with even massive amounts of data in various forms.
Data science is based on a strong background in statistics, computer science, and relevant
technologies.
Data scientist= A common title given to highly trained practitioners of data science or business
analytics who mainly focus on the application of science to the analysis of data.
Machine learning (ML)= A technology that allows computers to learn automatically from historical
relationships and trends in the data in order to do such things as making data-driven predictions.
Artificial intelligence (AI)= The goal of artificial intelligence is to build intelligent computer programs
and machines that can simulate human thinking capability and behaviour.
Machine learning provides an ideal platform for performing artificial intelligence.
REVIEW QUESTIONS:
1. What are some quantitative decision sciences that are drawn upon by business analytics?
2. What is meant by the era of big data and what role did it play in the origin of business
analytics?
3. What does descriptive analytics involve doing?
4. What does predictive analytics involve doing?
5. What does prescriptive analytics involve doing?
6. What is data science and how does it differ from business analytics?
7. What is machine learning and how does it relate to business analytics?
8. What is artificial intelligence and how does it relate to machine learning?
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