Management Decision Making. Hadi Hosseini CS 330 David R. Cheriton School of Computer Science University of Waterloo July 14, 2011

Transcription

1 Management Decision Making Hadi Hosseini CS 330 David R. Cheriton School of Computer Science University of Waterloo July 14, 2011

2 Management decision making Decision making Spreadsheet exercise Data visualization, graphs Business analytics Automated DM Probabilistic reasoning Examples Data mining Machine learning Data mining tools Weka Example in class Conclusion Agenda

3 Management Decision Making What is MIS? A management information system (MIS) is a system that provides information needed to manage organizations effectively. Management information systems involve three primary resources: technology, information, and people. Types: Decision support systems (DSS) Human resource management Enterprise resource planning (ERP) Supply chain management (SCM) Customer relation management (CRM) Project management

4 Decision Making What is decision making? selection of a course of action among several alternative scenarios/choices Why is it important? Risk management Any managerial DM Issues: Huge amount of data Certainty vs. uncertainty Deterministic vs. indeterministic Future planning: foreseeing future outcomes

5 Process: Decision Making

6 Data, Information, Knowledge

7 Concerns Huge amount of raw data How to handle huge amount of data? How to analyze and infer? How to process the data into meaningful numbers?

8 Spreadsheet Exercise Simple excel exercise A company sales 3 different products The manager wants to place new order for each type of products The manager s task is to analyze and make decision based on previous sales history Manager wants to maximize the company s profit Of course, assuming no other factors would affect the sales line Go to spreadsheet:

9 Business Analytics Definition: BA: the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business Intelligence (BI): computer-based techniques used in identifying, extracting, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes. Types of analytics: Descriptive, Modeling, Predictive, Affinity grouping, etc. Domains: Retail Sales analytics Financial Services analytics Risk & Credit analytics Marketing analytics Collections analytics Fraud analytics Pricing analytics Telecommunications Supply Chain analytics Transportation analytics

10 Different Analysis Automated decision making Statistical/Quantitative analysis Predictive modeling Data mining Risk analysis

11 Probabilistic Reasoning Human factor Hard to consider probabilities Foreseeing future outcomes/choices AI researchers address these problems Probability theory/ decision theory Learning (machine learning/ reinforcement learning) Game theory

12 Example: Decision Scenario with Probabilities Datasoft, an electronics producer/distributor, has decided to go into production with the new BA Data-2D model computer. The task before management now is the decision of how many models to produce. The success of the endeavour depends greatly on whether their competitors are successful in duplicating the technology. They consider three investment options: high, medium and low. Based on past history and knowledge of its competitors staff and capabilities, Datasoft manages to predict that there is a 70% chance that their competitors will be successful in duplicating their technology. If the competitors are successful and Datasoft decides to sell off its inventory, they predict that there is a 60% chance of making a profit of $200,000 and a 40% chance of losing $50,000 Which investment decision will provide the maximum payoff?

13 Probabilistic Reasoning: Allais s paradox Experiment 1 Experiment 2 Gamble 1A Gamble 1B Gamble 2A Gamble 2B Winnin gs $1 million Chance Winnin gs 100% $1 million Chance Brief solution on board Winnin gs Chance 89% Nothing 89% Nothing 1% $1 $5 million 10% million 11% Winnin gs Chance Nothing 90% $5 million 10%

14 Probabilistic Reasoning: St. Petersburg Paradox you pay a fixed fee to enter a fair coin is tossed repeatedly until a tail appears, ending the game. The pot starts at 1 dollar and is doubled every time a head appears. You win whatever is in the pot after the game ends. Thus you win 1 dollar if a tail appears on the first toss, 2 dollars if a head appears on the first toss and a tail on the second, 4 dollars if a head appears on the first two tosses and a tail on the third, 8 dollars if a head appears on the first three tosses and a tail on the fourth. What would be a fair price to pay for entering the game?

15 St. Petersburg analysis Decision tree: So you should be willing to pay any amount, as it will eventually pay off. This is why we need more precise mechanism to take all future possibilities and utilities into account.

16 Risk Analysis Risk attitudes: Risk averse Risk neutral Risk taking Depending on policies/situation, managers might take any of these attitudes while decision making

17 the process of extracting patterns from large data sets by combining methods from statistics and artifi cial intelligence with databa se management. Main DM types: Classification Clustering Data Mining

18 Machine Learning Learning from large data sets or sensors Types: Supervised learning Training data set Unsupervised learning No data set available, learning by trial and error

19 Data Mining Tools SQL Server Analysis Services By Microsoft Incorporate in Excel as an Add-in Can be used by anyone Weka Free open source software Implements many different DM, ML algorithms More professional use, should have AI knowledge

20 Weka Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Example Maybe a test using Weka!

21 Quick demo Weka Example

22 Conclusion Management Decision Making Large amount of data Certainty vs. uncertainty Predicting future outcomes Reasoning Decision Making Simple analytical/summarizing tools Probabilistic reasoning such as decision trees, etc. Data mining/ Machine learning

23 Thanks Contact me if you have any questions/comments in this regard: Hadi Hosseini address: Office: DC 2537