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# Exam1StudyGuide.pdf MGT 303

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This 8 page Study Guide was uploaded by Gabriela Pallicer on Thursday February 5, 2015. The Study Guide belongs to MGT 303 at University of Miami taught by Fang Fang in Spring2015. Since its upload, it has received 132 views. For similar materials see Operations Management in Business at University of Miami.

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Date Created: 02/05/15

MGT 303 Exam 1 Notes Chapters 3 4 Module A Chapter 3 Project Management Project Management 1 2 3 Planning this phase includes goal setting de ning the project and team organization Scheduling this phase relates people money and supplies to speci c activities and relates activities to each other Controlling here the rm monitors resources costs quality and budgets also revises or changes plans and shifts resources to meet time and cost demands Project Planning Project organization developed to make sure existing programs continue to run smoothly on a daytoday basis while new projects are successfully completed 0 Works best when 1 Work can be de ned with a speci c goal and deadline 2 The job is unique or somewhat unfamiliar to the existing organization 3 The work contains complex interrelated tasks requiring specialized skills 4 The project is temporary but critical to the organization 5 The project cuts across organizational lines Project manager receive high visibility in a rm and are responsible for making sure that all necessary activities are nished in proper sequence and on time the project comes in within budget the project meets its quality goals and the people assigned to the project receive the motivation direction and information needed to do theirjobs Work Breakdown Structure WBS de nes the project by dividing it into its major subcomponents which are then subdivided into more detailed components and nally into a set of activities and their related costs 0 Level 1 Project 2 Major tasks in the project 3 Subtasks in major tasks 4 Activities to be completed Project Scheduling involves sequencing and allotting time to tall project activities Gannt Charts lowcost means of helping managers make sure that activities are planned order of performance is documented activity time estimates are recorded and overall project time is developed 0 Purpose of scheduling 1 4 Shows the relationship of each activity to others and to whole project 2 Identi es the precedence relationships among activities 3 Encourages the setting of realistic time and cost estimates for each activity Helps make better use of people money and material resources by identifying critical bottlenecks in the project Project Controlling Project Management Techniques PERT and CPM Program evaluation and review technique PERRT 0 Critical path method CPM PERT and CPM both follow six basic steps 1 2 3 4 5 6 De ne the project and prepare the work breakdown structure Develop the relationships among the activities decide which activities must precede and which must follow Draw the network connecting all the activities Assign time andor cost estimates to each activity Compute the longest time path through the network critical path Use the network to help plan schedule monitor and control the project 0 Critical path represents the tasks that will delay the entire project if they are not completed on time Network Diagrams and Approaches 0 Activity on node AON a network diagram in which nodes designate activities 0 Activity on arrow AOA a network diagram in which arrows designate activities Determining the Project Schedule 0 Critical path analysis process that helps determine a project schedule 0 0 Critical path longest time path through the network To nd critical path calculate starting and ending times for each activity 393 Earliest start ES earliest time at which an activity can start assuming all predecessors have been completed 393 Earliest nish EF earliest time at which an activity can be nished 393 Latest start LS latest time at which an activity can start so as to not delay the completion time of the entire project 393 Latest nish LF latest time by which an activity has to nish so as to not delay the completion time of the entire project Forward pass ES and EF are determined during this begins with rst activity 0 Earliest Start Time Rule before an activity can start all its immediate predecessors must be nished if an activity has multiple predecessors its ES is the maximum of all EF values of its predecessors o Earliest Finish Rule the EF time of an activity is the sum of its ES and its activity time EFESActivity time Backward Pass begins with the last activity in the project determine LF value followed by its LS value 0 Late Finish Time Rule before an activity can start all its immediate predecessors must be nished oz If an activity is an immediate predecessor forjust one single activity its LF equals the LS of the activity that immediately follows it oz If an activity is an immediate predecessor to more than one activity its LF values 0 Latest Start Time Rue LS of an activity is the difference of its LF and its activity time LSLFActivity Slack Time the length of time an activity can be delayed without delaying the entire project SIackLSES or SlackLFEF o The activities with zero slack are called the critical activities and are said to be on the critical path 0 Critical path 1 Starts at the rst activity in the projects 2 Terminates at the last activity in the project 3 Includes only critical activities Variability in Activity Times Critical path method CPM assumes we know a xed time estimate for each activity and there is no variability in activity times PERT uses a probability distribution for activity times to allow for variability more realistic approach since there is always variability in project completion times There are three required time estimates 1 Optimistic time a time when everything goes according to plan 2 Pessimistic time b time assuming very unfavorable conditions 3 Most like time m most realistic time Estimates follow a beta distribution 0 Expected time ta4mb6 o Variance of times vba06quot2 0 Standard deviation square root of variance Probability of Project Completion Project variance is computed by summing the variances of the critical activities Project variance Zvariances of activities on critical path Project standard deviation square root of project variance PERT makes two assumptions 1 Total project completion times follows a normal probability distribution after nding standard deviation look to normal distribution table to nd the probability percentage 2 Activity times are statistically independent Zdue date expected date of completionstandard deviation Due date expected completion time Zstandard deviation Project Controlling Not uncommon for the project to be behind schedule or for the completion time to be moved forward Project Crashing shortening the duration of the project Normal time original planned time to complete an activity Crash time shortened time to complete an activity Normal cost cost incurred when completing an activity at normal time Crash cost cost incurred when completing the activity at the crash time Factors to consider when crashing a project 0 The amount by which an activity is crashed is permissible 0 Taken together the shortened activity durations will enable us to nish the project by the due date 0 The total cost of crashing is as small as possible Steps in Project Crashing 1 Compute the crash cost per time period crash cost normal costnormal time crash time 2 Using current activity times nd the critical path and identify the critical activities Chapter 4 Forecasting Forecasting Process of predicting future events Underlying basis of all business decisions 0 Production 0 Inventory 0 Personnel 0 Facilities Forecasts are seldom perfect 0 Most techniques assume an underlying stability in the system 0 Product family and aggregated forecasts are more accurate than individual product forecasts Forecasting Time Horizons Shortrange forecast up to one year generally less than 3 months Mediumrange forecast three months to three years Longrange forecast three or more years Shortterm forecasting usually tends to be more accurate than longerterm forecasts Mediumlong range forecasts deal with more comprehensive issues and support management decisions regarding planning and products plants and processes Forecasting Approaches Qualitative Methods 0 Used when situation is vague and little data exists 0 lnvolve intuition experience 0 Quantitative Methods 0 Used when situation is stable and historical data exists 0 lnvolves mathematical techniques Time Series Forecasting Time series models use historical demand data to predict the future size of demand and recognize trends and seasonal patterns 0 Predictions based exclusively on previous observed values 0 Prevalent tool in Operations Management 0 Time series components 0 Horizontal uctuation of data around a constant mean oz Na39ive approach assumes demand in next period is the same as demand in most recent period 393 Moving average series of arithmetic means 393 Exponential smoothing form of weighted moving average oz Trend projection 0 Trend systematic increase or decrease in the mean of the series over time o Cyclical less predictable gradual increases or decreases over longer periods of time ex years decades 0 Moving average method moving average Zdemand in previous n periodsn o Weighted moving average used when some trend might be present weights based on experience and intuition o Weighted moving average Zweight for period ndemand in period nZ weights o Exponential smoothing form of weighted moving average requires smoothing constant and involves little record keeping of past data 0 New forecast last period s forecast qlast period s actual demand last period s forecast 0 Choosing a objective is to obtain the most accurate forecast no matter the technique 0 Common Measures of Error 0 Mean Absolute Deviation MAD ZActual Forecastn 0 Mean Squared Error MSE ZForecast Errorsquot2n 0 Mean Absolute Percent Error MAPE ZActual ForecastActualn 0 Trend projections tting a trend line to historical data points to project into the medium longrange 0 Linear trend line is a special linear regression model 0 Trends can be found using the least squares technique 0 Least Squares Method yabx 0 b ny an Z xquot2nxquot2 o3 aybx SeasonalvaNanns The multiplicative seasonal model can adjust trend data for seasonal variations in demand 0 Seasonal index an index used to adjust for seasonal variation 0 Seasonal forecast initial forecastseasonal index 0 Seasonal index average demand in a season average demand Module A Decision Theory Decisions Making 0 Fundamentals 0 Alternative course of action or strategy that may be chosen by the decision maker 0 Events state of nature occurrence or situation over which the decision maker has little or no control of o Environments a Decision making under certainty event is known b Decision making under uncertainty complete uncertainty as to which event may occur c Decision making under risk several events may occur each has a probability of occurring Commonly used payoffs Pro t revenue cost Sales volume Return on investment pro tinvestment Revenue Operating cost Uncertainty Decision rules 1 Maximax 0 Find the alternative that maximizes the maximum outcome for every alternative 0 Pick the outcome with the maximum number highest possible gains 0 Optimistic approach 2 Maximin 0 Find the alternative that maximizes the minimum outcome of every alternative 0 Pick the outcome with the minimum number 0 Least possible loss 0 Pessimistic approach 3 Equally likely 0 Find the alternative with the highest average outcome 0 Assumes each state of nature is equally likely to occur Risk each possible state of nature has an assumed probability States of nature are mutually exclusive Probabilities must sum to one Determine expected monetary value EMV for each value 0 EMVPayoff of 1st state of natureProbability of 1st state of naturePayoff of 2nCI state of natureProbability of 2nCI state of natureand so on till last state of nature 0 Expected value of perfect information EVPI expected value with perfect information maximum EMV Decision Trees Information in decision tables can be displayed as decision trees Graphic display of decision process that indicates decision alternatives events and their respective probabilities and payoffs for each combination of decision alternative events Appropriate for showing sequential decisions Square symbol decision node from which one of several alternatives may be selected decision maker has control Circle symbol an event nod out of which one event will occur decision makes has no control over this event Process 1 De ne the problem 5 Structure or draw decision tree 3 4 Assign probabilities to the events Estimate payoffs for each possible combination of decision alternatives Solve the problem by working backward through the tree computing Key aspects 0 O O O 0 Time ows from left to right Analyze from right to left go backwards Final nodes in the tree have associated value Value of an event node is the expected value of its endpoints EMV Value of a decision node is the highest value of its endpoints

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