...Decision Trees The Decision Tree module in Excel OM (and in POM for Windows) acts differently than all other modules because rather than creating a table of data it creates a graphical tree. We will use Example 3 in Chapter A5 from Heizer & Render’s Operations Management textbook for our example. After selecting the Decision Tree Module the screen will appear as in Figure 1 below. Figure 1: The Initial Decision Tree Screen Notice the Decision Tree Creation Window on the right. This is used to perform all of the work of constructing the tree. The initial screen has 1 starting node (Node 1) which can be seen in cell A6. The first step is to add branches from this node. The default setting is to Add 2 Decision branches. The type of branch is selected by the choice of “Add” buttons that is selected and the number of branches is selected by the textbox/scrollbar combination. For this example, there are three options – Purchase CAD, Hire Engineers or Do Nothing. Therefore we will change the number of branches to 3 and click on the “Add Decisions” button. This yields the screen as displayed in Figure 2. Decision Trees.doc Page 1 of 7 Figure 2: The First Three Branches At this point the data can be entered into the shaded green cells. For decision branches, the data consists of the name of the branch and possibly a profit or cost. If the branch is at the end of the tree then the profit gets entered. If the branch is not at the end then any intermediate profits or costs...
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...Value of Information in Decision Trees 19 19.1 VALUE OF INFORMATION Useful concept for Evaluating potential information-gathering activities Comparing importance of multiple uncertainties 19.2 EXPECTED VALUE OF PERFECT INFORMATION Several computational methods Flipping tree, moving an event set of branches, appropriate for any decision tree Payoff table, most appropriate only for single-stage tree (one set of uncertain outcomes with no subsequent decisions) Expected improvement All three methods start by determining Expected Value Under Uncertainty, EVUU, which is the expected value of the optimal strategy without any additional information. To use these methods, you need (a) a model of your decision problem under uncertainty with payoffs and probabilities and (b) a willingness to summarize a payoff distribution (payoffs with associated probabilities) using expected value. The methods can be modified to use certain equivalents for a decision maker who is not risk neutral. 224 Chapter 19 Value of Information in Decision Trees Expected Value of Perfect Information, Reordered Tree Figure 19.1 Structure, Cash Flows, Endpoint Values, and Probabilities 0.5 High Sales $400,000 $700,000 0.3 Medium Sales $100,000 -$300,000 $400,000 0.2 Low Sales 1 $100,000 -$200,000 Introduce Product Don't Introduce $0 $0 Figure 19.2 Rollback Expected Values 0.5 High Sales $400,000 Introduce Product $190,000 0.3 Medium Sales $100,000 0.2 Low Sales 1 $190,000 -$200...
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...0% -100,000,000 #NAME? #NAME? Test Merck Decision Tree Although a decision analysis would recommend that Merck not commit to the proposal, the company's balance sheet shows that it has significant assets to support a loss. The projected values of earning for depression and dual indications seem to be worth the risk. Weightloss does not. Not Effective 15.0% 0 Phase III: Long Term Efficacy Testing #NAME? 25.0% 0 #NAME? #NAME? Obesity Don't Test #NAME? 0 #NAME? #NAME? Dual Indications 50.0% -400,000,000 #NAME? #NAME? #NAME? #NAME? Depression: effective #NAME? 0 Indications #NAME? 15.0% -250,000,000 Conduct Depression: not effective 10.0% 0 #NAME? #NAME? #NAME? #NAME? #NAME? #NAME? #NAME? #NAME? Obesity: effective 5.0% -100,000,000 Obesiity: not effective 10.0% 0 Complete Failure 70.0% -$500,000,000.00 Phase 4: Additional Trials #NAME? 10.0% 0 Effective Do not conduct #NAME? 0 #NAME? #NAME? 0 #NAME? #NAME? Test Not Effective 5.0% 0 Phase III: Long Term Efficacy Testing #NAME? 30.0% 0 #NAME? #NAME? Both Don't Test 70.0% 0 #NAME? #NAME? #NAME? 0 #NAME? #NAME? Neither 60.0% 0 Phase II Efficacy Testing #NAME? Don't Test #NAME? -$30,000,000.00 Phase 1: Efficacy testing #NAME? #NAME? 0 #NAME? #NAME? Pass Test Fail Preclinical Analysis Test #NAME? 40.0% 0 #NAME? #NAME? Davanrik Licensing Decision Tree Cost Testing costs Launch Costs Total Projected...
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...DECISION TREE Nurse Delegation to Nursing Assistive Personnel (NAP) Step One – Assessment and Planning Are there laws and rules/regulations in place that support the delegation? [NMAC 16.12.2] Yes Is the task within the scope of practice of the delegating nurse? [NMAC 16.12.2] Yes Has there been assessment of the client’s needs? Yes No Do not delegate. No If not in the licensed nurse’s scope of practice, he/she should not delegate to NAP. Authority to delegate varies from state-to-state; licensed nurses should check the local jurisdiction’s statutes and administrative rules/regulations. No Assess client’s needs; then proceed to a consideration of delegation. Does the delegating nurse have competencies to make the delegation decisions required? Yes No Do not delegate until evidence of competency is obtained and is documented; then reconsider delegation. Does the procedure/task meet all the following recommended criteria for delegating to NAP? • • • • • • • Task/procedure is within the range of approved functions for the NAP. Task/procedure frequently recurs in daily care of client or group of clients. Task/procedure is performed according to an established sequence of steps. Task/procedure involves little or no modification from one client-care situation to another. Task/procedure may be performed with a predictable outcome. Task/procedure does not inherently involve ongoing assessment, interpretation, or decision-making which cannot be logically separated from...
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...Los cinco patrones básicos de la mayoría de las series de tiempo aplicables a la demanda son: 1. Horizontal. La fluctuación de los datos en torno de una media constante. 2. Tendencia. El incremento o decremento sistemático de la media de la serie a través del tiempo. 3. Estacional. Un patrón repetible de incrementos o decrementos de la demanda, dependiendo de la hora del día, la semana, el mes o la temporada. 4. Cíclico. Una pauta de incrementos o decrementos graduales y menos previsibles de la demanda, los cuales se presentan en el transcurso de periodos más largos (años o decenios). 5. Aleatorio. La variación imprevisible de la demanda. Antes de usar técnicas de pronóstico para el análisis de problemas de administración de operaciones, el gerente tiene que tomar tres decisiones: (1) qué va a pronosticar; (2) qué tipo de técnica de pronóstico va a usar, y (3) qué tipo de software de computación utilizará. métodos de juicio Un tipo de método cualitativo en el que las opiniones de gerentes y expertos, los resultados de las encuestas de consumidores y las estimaciones del personal de ventas se traducen en estimaciones cuantitativas. métodos causales Un tipo de método cuantitativo que utiliza datos históricos de variables independientes, como campañas de promoción, condiciones económicas y actividades de los competidores, para pronosticar la demanda. análisis de series de tiempo Es un método estadístico que depende en alto grado de datos históricos de la demanda, con...
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...Decision Trees Using TreePlan 16 16.1 TREEPLAN OVERVIEW TreePlan is a decision tree add-in for Microsoft Excel 97–2007 for Windows and Macintosh. TreePlan helps you build a decision tree diagram in an Excel worksheet using dialog boxes. Decision trees are useful for analyzing sequential decision problems under uncertainty. Your decision tree model may include various controllable alternatives (e.g., whether to introduce a new product, whether to bid on a new project) and uncontrollable uncertainties (e.g., possible demand for a product, whether you're awarded a contract), arranged in chronological order. TreePlan automatically includes formulas for summing cash flows to obtain outcome values and for calculating rollback values for determining the optimal strategy. To use TreePlan, you (1) open a new worksheet, (2) choose Tools | Decision Tree from Excel's menu, (3) select a node to change the structure of your decision tree, (4) enter branch names, cash flows, and probabilities, and (5) determine the optimal strategy from TreePlan's results. All of TreePlan’s functionality, including its built-in help, is a part of the TreePlan XLA file. There is no separate setup file or help file. When you use TreePlan on a Windows computer, it does not create any Windows Registry entries (although Excel may use such entries to keep track of its add-ins). 16.2 BUILDING A DECISION TREE IN TREEPLAN You can start TreePlan either by choosing Tools | Decision Tree from the menu bar (Excel...
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...Decision Tree Gerber Product Company A. Background of the Company: Gerber was founded in 1927 in Fremont, Michigan by Daniel Frank Gerber, owner of the Fremont Canning Company, which produced canned fruit and vegetables. At the suggestion of a pediatrician, Gerber's wife Dorothy Gerber began making hand-strained food for their seven-month-old daughter, Sally. Recognising a business opportunity, Gerber began making baby food. By 1928 he had developed five products for the market and six months later, Gerber's baby foods were distributed nationwide. The brand eventually became a major company in the baby food industry, offering more than 190 products in 80 countries, with labeling in 16 languages and controls eighty-three percent (83%) of the baby food market in the United States. In 1994 Gerber merged with Sandoz Laboratories. Two years later, Sandoz merged with CIBA-Geigy to form Novartis, one of the largest pharmaceutical companies in the world. In 2007 Gerber was sold to Nestle for $5.5 billion. In 1960 Gerber started selling its baby food in glass jars, which often found new life as household storage, especially in home workshops. Soon after, other items such as pacifiers, , baby bottles, and small baby toys were introduced. Source: https://en.wikipedia.org/wiki/Gerber_Products_Company B. Problem Encountered and Quamet Solution Used or Applied Gerber used decision tree analysis in deciding whether to continue using the plastic known as poly-vinyl...
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...www.palgrave-journals.com/thr Using decision trees to identify tourism stakeholders: The case of two Eastern North Carolina counties Erick T. Byrd* and Larry Gustke Received (in revised form): 1st May, 2006 *Department of Recreation, Tourism, and Hospitality Management, The University of North Carolina at Greensboro, PO Box 26170, Greensboro, NC 27402, USA Tel: + 1 336-334-3041; Fax: + 1 336-334-3238; E-mail: etbyrd@uncg.edu Erick T. Byrd is an Assistant Professor in the Department of Recreation, Tourism, and Hospitality Management at The University of North Carolina at Greensboro. His current research interests focus on community participation in tourism development. Larry Gustke is an Associate Professor in the Department of Parks, Recreation and Tourism Management at North Carolina State University. His current research interests focus on community tourism planning. ABSTRACT KEYWORDS: decision tree analysis, stakeholder inclusion, sustainable tourism, tourism planning their support for sustainable tourism development in their community. Tourism and Hospitality Research (2007) 7, 176–193. doi:10.1057/palgrave.thr.6050049 This paper explores stakeholder involvement in tourism planning, development, and management. For tourism planners to include stakeholders in the tourism planning process those stakeholders and their interests need to be identified. The research reported in this paper describes and applies an analytical technique that is not traditionally used...
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...development cycle model and use - profile, exploration, modeling, implementation, scoring, and validation, monitoring and building scorecards - all in high-performance visual environment. It is used by marketing, sales and risk analysts to provide business users and analysts specialist with powerful data mining solutions, scalability and complete data mining. Most of the world's leading financial services, insurance, telecommunications, retail, high technology, and healthcare organizations use Angoss predictive analytics to increase revenue, increase sales productivity and improve marketing effectiveness, while also reducing risk and cost. 2. Discuss on data preparation features provided by the product. Known for its industry, Decision Tree patent and a graphical user interface wizard driven which, Knowledge STUDIO is a modeling and predictive analysis workbench for advanced high-performance business analysts and quantitative analysts who offer a robust set of capabilities for the development and utilization of the mining model data for a variety of applications and use cases. Advanced Predictive Modeling Knowledgestudio offers thorough, progressed information digging and gauge examination for all periods of advancement models and improvement arrangement. In a high accomplishing visual analyser that furnishes the information with complex expository solid settlement and scale, between the front driven wizards helps the client in all important model developer...
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...shares in the next 48 hours or postpone the offering indefinitely. Now whether MLCM was right or not it will be judged by real option valuation. We showed the decision analysis by using both the FCF and the net cash flow. We have used three options such as a. Timing option, b. Decision Tree Analysis, and c. Option to Wait (Black Scholes Model). |1. Timing Option | We have used Timing Option to calculate the NPV if the stocks were issued immediately. Here we consider FCF in the three methods. Here, we assume 30% probability for high demand, 40% for average and 30% for low demand. We calculated the net annual cash flow for each scenario and then calculated the expected NPV for the issuance. |Demand |Probability |Annual FCF |E(NPV) | |High |30% |142400.97 |686590.51 | |Average |40% |109539.20 |526734.24 | |Low |30% |76677.44 |366877.96 | If Spiegel issued the stocks immediately the Expected net present value gained by the company would be $526734.24 thousand. |2. Decision Tree Analysis | Here we consider three types of demand a. high, b. average, and c. low. Then we calculated the average cash flow for each scenario...
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...MGMT 530 Week 5 Case Analysis Labadee Decision To Buy This material Click below link http://www.uoptutors.com/mgmt-530/mgmt-530-week-5-case-analysis-labadee-decision In January 2010, the island nation of Haiti was devastated by an earthquake. Royal Caribbean International, a major cruise line, owns a private beach in Haiti, which is typically a port of call on several of their Caribbean cruise itineraries. The private port, known as Labadee, is about 80 miles away from Port au Prince. The beach was unaffected by the quake. In the days following the earthquake, the company wrestled with several issues as they determined whether to continue to stop in Labadee or temporarily abandon the port of call. Their objectives would be to 1) ensure guest satisfaction; 2) protect the brand; and 3) maximize profitability. Some of the consequences they considered as they tried to determine whether the cruise line should continue to make a stop in Haiti in the midst of this crisis are as follows. Will cruise passengers be interested in relaxing on a beach when hundreds of thousands are homeless and hungry just 80 miles away? Could this impact new reservations or cause people to cancel? Based on research and consulting with others, you believe there will be minimal impact. Because the community near the beach depends financially on the cruise line for income, would suspending the stop in Haiti make the country worse off? Based on your analysis, there is a high likelihood that the area...
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...See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/296695247 USING DATA MINING TO PREDICT SECONDARY SCHOOL STUDENT ALCOHOL CONSUMPTION Dataset · February 2016 DOI: 10.13140/RG.2.1.1465.8328 READS 2,200 2 authors: Fabio Pagnotta Hossain Amran University of Camerino University of Camerino 8 PUBLICATIONS 0 CITATIONS 5 PUBLICATIONS 0 CITATIONS SEE PROFILE All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. SEE PROFILE Available from: Hossain Amran Retrieved on: 12 April 2016 USING DATA MINING TO PREDICT SECONDARY SCHOOL STUDENT ALCOHOL CONSUMPTION Fabio Pagnotta Mat:-093579 Mohammad Amran Hossain Mat:-093192 Department of Computer science, University of Camerino Advanced Database In this project, we use a data set about Portuguese student on two courses ( Mathematics and Portuguese ) which was collected and analysed by Paulo Cortez and Alice Silva, University of Minho,Portugal. Our work intends to approach student addiction on alcohol in secondary level using business intelligence (BI) and Data Mining (DM) techniques. The result shows that a good predictive accuracy can be achieved, provided that addiction of alcohol can impact to the student performance. In addition,the result also provides the correlation between alcohol usage and the social, gender and study time attributes...
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...on Student Academic Performances investigating the effect of students socio-economic/family background on students academic performance in tertiary institutions using decision tree algorithms A. B. Adeyemo (Ph.D)1 and S. O. Kuyoro (M.Sc.)2 Department of Computer Science, University of Ibadan, Ibadan, Nigeria Abstract The causes of the difference in the academic performance of students in tertiary institutions has for a long time been the focus of study among higher education managers, parents, government and researchers. The cause of this differential can be due to intellective, non-intellective factors or both. From studies investigating student performance and related problems it has been determined that academic success is dependent on many factors such as; grades and achievements, personality and expectations, and academic environments. This work uses data mining techniques to investigate the effect of socio-economic or family background on the performance of students using the data from one of the Nigerian tertiary institutions as case study. The analysis was carried out using Decision Tree algorithms. The data comprised of two hundred forty (240) records of students. The academic performance of students was measured by the students’ first year cumulative grade point average (CGPA). Various Decision Tree algorithms were investigated and the algorithm which best models the data was used to generate rule sets which can be used to analyze the effect of the socio-economic background...
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...LEIDO| MAGSUMBOL| MALLILLIN I TITLE PROJECT PROPOSAL on CBC’s Business Initiatives II OBJECTIVE Primary objective is to come with a decision whether we would focus our business initiatives to capitalize on Account Officers (AO) to boost more sales or will just utilize on the current sales force and deliberately cross-sell product lines. Secondary objective is to come up with target for the following year in: o Total CASA (Current Accounts/Saving Accounts) o Total Loan Portfolio o Total Approved Credit Facilities III SOURCE & COMPANY BACKGROUND China Banking Corporation (CBC) is the first privately-owned local commercial bank in the Philippines. It was incorporated on July 20, 1920 and started its operations on August 16, 1920. Dee C. Chuan leads a group of top Chinese-Filipino businessmen to establish China Bank, the first privately-owned commercial bank in the Philippines. CBC Binondo Business Center is formerly the head office. Today, it is now the extension H.O. since the main H.O. is now located at Paseo Roxas, Makati City. Binondo is primarily populated by ethnic Chinese living in the Philippines.The district is the centre of commerce and trade for all types of businesses run by Filipino-Chinese merchants. Binondo was already a hub of Chinese commerce before the first Spanish colonisers arrived in 1521. Over the years, China Bank has grown and expanded, becoming a universal bank in 1991 and one of the most trusted banks in the industry today. An Account Officers is...
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...Case Problem #2 Steeley Associates, Inc. a property development firm, property development firm purchased an old house near the town square in Concord Falls, where State University is located. The old house was built in the mid-1800s, and Steeley Associates restored it. For almost a decade, Steeley has leased it to the university for academic office space. The house is located on a wide lawn and has become a town landmark. However, in 2003, the lease with the university expired, and Steeley Associates decided to build high-density student apartments on the site, using all the open space. The community was outraged and objected to the town council. The legal counsel for the town spoke with a representative from Steeley and hinted that if Steeley requested a permit, the town would probably reject it. Steeley had reviewed the town building code and felt confident that its plan was within the guidelines, but that did not necessarily mean that it could win a lawsuit against the town to force the town to grant a permit. The principals of Steeley Associates held a series of meetings to review their alternatives. They decided that they had three options: they would request the permit, they could sell the property, or they could request a permit for a low-density office building, which the town had indicated it would not fight. Regarding the last two options, if they sell the house and property, they think they can get $900,000. If they build a new office building, their...
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