WGU MBA Decision Analysis - The Entire Course

WGU MBA Decision Analysis - The Entire Course

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DA.Task1.2.3

DA.Task1-Output

Assignment Tool
Data
COSTS	Machine 1	Machine 2	Machine 3	Machine 4
Job 1	10	12	10	11
Job 2	11	9	11	11
Job 3	9	8	11	9
Job 4	10	8	9	10
Assignments
Shipments	Machine 1	Machine 2	Machine 3	Machine 4	Row Total
Job 1	1				1
Job 2		1			1
Job 3				1	1
Job 4			1		1
Column Total	1	1	1	1	4
Total Cost	37

DA_Task4.PPX

This is the decision tree for Shuzworld’s decision about opening a new store.  According to the case study, the stand-alone store would see profits of $700,000 if there was a favorable market and losses of $400,000 if the market was unfavorable.  The mall store would see profits of $300,000 with a favorable market and losses of $50,000 if there was an unfavorable market.  There is a 50/50 probability on both of these choices for favorable/unfavorable market.  Not opening a store would see no profits nor losses.

However, the company is considering purchasing market research, which could have a positive impact or a negative impact on the market.  They could also decide to not purchase market research, which would have no impact on the market.  Purchasing market research would cost $20,000, and it has a 60% chance of showing a favorable market and a 40% chance of showing an unfavorable one.  If the survey is positive, the chances of having a favorable market go up to 70%, and if it is negative, the chances of having an unfavorable market increase to 80%.

Entering this data into POM for Windows, the choice to make becomes obvious. The highest EMV (Expected Monetary Value) is with the decision to conduct the market research study.  Following that decision to the next decision, the next highest EMV is with the stand-alone store.  The best “chance” actions along that line are that Shuzworld will have positive survey results and have a favorable market. 

This data was calculated using POM for Windows.  The Decision Analysis Module was chosen, with the Graphical Decision Tree being selected.  This was the proper decision tool because it allowed for a visual representation ofShuzworld’s choices.  It clearly labels the options, and displayed the recommended path in blue.  This decision tool was also able to calculate Expected Monetary Values based on either maximizing profits or minimizing costs.  Since the information given in the case study was for profitability, maximizing profits was the option chosen.

The decision tree was designed based on the figures that I received about whether or not to build a stand-alone store, a mall store, or whether the company should do nothing in this market based on the projected figures.  The decision tool’s analytical attributes reveal that with a 50% probability of an unfavorable and favorable markets, or states of nature, we can use this tool to help analyze whether or not to proceed with a proposed project.  Once we know the numbers from the states of natures, we can determine the expected monetary value, or EMV for the state of nature. This is determined by multiplying the probability by one state of nature, plus the product of the probability by the other state of nature. For the Auburn Stand Alone project, the probability is 50% and the favorable market is $700,000. The unfavorable market is a loss of $40,000. So the EMV is (.5)(700,000) + (.5)(-40,000) which is $320,000.  The Auburn Mall Store’s EMV would be (.5)(300,000) + (.5)(-50,000) which is $125,000. The EMV of doing nothing is, of course 0.  This was all based on doing no survey. The top portion of the tree is conducting a $20,000 dollar market survey and watching those probabilities change based on the new data. The best decision is to chose the project that produces the highest EMV, in this case, it is my recommendation conduct a market survey and chose the Auburn Stand Alone store. Based on the survey results, the probabilities have changed based on the outcomes. All of these changes contribute to the changes in the EMV which is what we are analyzing to determine which store to proceed with. I chose the decision analysis tool in POM for Windows because it allowed for the most manipulation of the data to produce the desired outcome. Two things that should be considered when determining the site for the new store should be the states of nature, and what their expected monetary values are. It also lays information out in a graphical form which makes it easy to interpret.

Using the decision tree helped me to make decisions by using large amounts of data to interpret the data most efficiently. When you have to make a decision and there are various outcomes and different probabilities and inputs, I felt that it was best to use the decision tree. This software calculates the EMV as you input probability and profit or loss. This tool in POM can be helpful when information is needed quickly and without calculating this information by hand. When deciding on where to build a new location, there are environmental factors that must come in to play as well as profitability. One major thing I considered was the size and cost of the building and how cash flow is very important to the success of the business. It is also important to consider whether to lease or buy a site, how much building materials will cost, and how much this will affect net cash flow.  For example, will the building appreciate in value? What is the net present value of the building in 10 years? There are so many things to consider. Another thing to consider is how the city government regulates zoning laws. Is there a piece of equipment that is too large for the building being considered? Will there be a back door to allow for shipments and if so is there enough room for the driver to get back there? Is there a regulation on how long the driver may be parked in that spot based upon traffic? What color do the lines on the ground have to be in your city to signify handicap? There are a multitude of things to analyze making the decision analysis tool the most appropriate tool to use in this scenario.

Two important factors to consider when evaluating locations are the proximity of the store to its market and the proximity of its store to suppliers (Heizer & Render, 2011).

For a company like Shuzworld, it is going to be vital to their success that they are located next to their market.  With the number of shoe stores in existence, many customers are likely to go to whichever one is closest.  Shuzworld needs to ensure that they have tapped into the shoe-buying market by being as close to it as possible.  Placing their store in a location close to customers would be much wiser than building a store far away from a town.  As can be seen in the map on the slide, the recommended stand-alone store along Route 20 is the closest option to the Auburn market.  Their proximity to the market will be good if Shuzworld accepts the stand-alone store recommendation.

The proximity of a store to its suppliers is also important.  In the case of Shuzworld, the suppliers are the Shuzworldproduction facilities and warehouses.  It is unknown how close these facilities are to the proposed Auburn location.  However, the company will need to ensure that the costs of shipping shoes to the new store will not whittle away at the profits.  Transportation costs can be quite high if the store is located too far away from the supplier.  

There are two project techniques that Shuzworld can use for the upgrade of their store in Bellevue: PERT (Project Evaluation and Review Technique) and CPM (Critical Path Method).

PERT and CPM are very similar, because they both use the same six basic steps.  These steps are:

  1.  Define the project and identify the breakdown of the work.

  2.  Determine the relationship between activities.

  3.  Draw out the “network” that connects all those activities.

  4.  Determine and assign both time and cost to each activity.

  5.  Determine the critical path (discussed on next slide).

  6.  Use all this information to help plan and control the project.

The difference between PERT and CPM is that CPM assumes that one time estimate will be correct.  PERT uses three different time estimates for each activity, which gives calculations for standard values and standard deviations. The PERT techniques are used in the presenter's notes on the following slide (Heizer & Render, 2011).

The critical path is the longest path through a project’s network of activities.  PERT is the appropriate decision tool to use for determining the critical path, because there are three different time estimates gives for each activity.  Shuzworld’scritical path for the upgrades to their Bellevue store is shown above.  This critical path shows the longest amount of time it will take for the project to be completed, which is 108.2 days.  Activity D is not part of that critical path because it has slack of 19.27 days and will not delay the project if the activity is delayed.   

The company needs to know how many days to plan on if they want a 95% likelihood that they will finish the project within that time frame.  This problem can be solved with the following equation:  Due Date = Expected Completion Time + (appropriate area under the normal curve x project standard deviation).  In order to solve this, the Normal Table in Appendix 1 has to be consulted to determine the value of Z (area under the normal curve) (Heizer & Render, 2011).  The closest Z-value to 95% without going over is 1.64.  This number is then plugged into the equation.  The due date is equal to 108.2 + (1.64 x 8.46), which gives an answer of 122.07.  This means that Shuzworld needs to plan for 123 days if they want a 95% likelihood of finishing the project within the time frame.

It is possible for Shuzworld to crash this time in order to meet their grand opening.  If they crash the entire project, the crash time is 63.2 days, with a cost of $12166.67.  However, the company only needs to crash by five days. To crash by 5 days, the company should crash Activity I, for a total cost of $2,500 ($500 per day).  Although Activity D has the same crash costs, it cannot be crashed because it is not on the critical path.  Crashing Activity I would be the best way forShuzworld to ensure that their store is ready for the grand re-opening in time.

The critical path is the longest path through a project’s network of activities.  PERT is the appropriate decision tool to use for determining the critical path, because there are three different time estimates gives for each activity.  Shuzworld’scritical path for the upgrades to their Bellevue store is shown above.  This critical path shows the longest amount of time it will take for the project to be completed, which is 108.2 days.  Activity D is not part of that critical path because it has slack of 19.27 days and will not delay the project if the activity is delayed.   

The company needs to know how many days to plan on if they want a 95% likelihood that they will finish the project within that time frame.  This problem can be solved with the following equation:  Due Date = Expected Completion Time + (appropriate area under the normal curve x project standard deviation).  In order to solve this, the Normal Table in Appendix 1 has to be consulted to determine the value of Z (area under the normal curve) (Heizer & Render, 2011).  The closest Z-value to 95% without going over is 1.64.  This number is then plugged into the equation.  The due date is equal to 108.2 + (1.64 x 8.46), which gives an answer of 122.07.  This means that Shuzworld needs to plan for 123 days if they want a 95% likelihood of finishing the project within the time frame.

It is possible for Shuzworld to crash this time in order to meet their grand opening.  If they crash the entire project, the crash time is 63.2 days, with a cost of $12166.67.  However, the company only needs to crash by five days. To crash by 5 days, the company should crash Activity I, for a total cost of $2,500 ($500 per day).  Although Activity D has the same crash costs, it cannot be crashed because it is not on the critical path.  Crashing Activity I would be the best way forShuzworld to ensure that their store is ready for the grand re-opening in time.

Shuzworld has been having difficulties with their reordering/restocking at their Baltimore store.  Their daily demand for cases of shoes runs between seven and twelve cases.  When placing an order, there are varying lead times, anywhere from one to three days.  The company has provided the frequency of case demand for a period of 200 days.  They have also provided a frequency of lead times for a period of 40 days.  Shuzworld would like to know a technique that they can test and see if reordering 30 cases of shoes whenever the inventory reaches 12 or lower will be effective or not. 

This decision can be made using a Monte Carlo simulation.  A Monte Carlo simulation is one that is uses random numbers in the evaluation, because many factors are based on chance (Heizer & Render, 2011). 

A simulation of the Baltimore store shows that this store has an average demand of 10.5 cases per day.  It would be wise to remember that this average demand is based on random numbers that provide simulated demands.

The Monte Carlo simulation shows how the Baltimore store will be able to handle their inventory if they reorder 30 cases of shoes whenever that inventory drops to twelve or below.  Days 1-5 do very well, with no lost sales.  Day 3 ends the day with only one case of shoes in their inventory, which cuts it pretty close.  However, the big problem is on Day 6.  The store had an ending inventory of 9 cases on Day 5, so they reordered.  The simulation gave them a lead time of 2 days.  This was not soon enough, and on Day 6, the store shows a zero ending inventory, with three lost sales.  Although not shown on the simulation, Day 7 will also see lost sales, because the shipment will not be received until Day 8.

This one simulation shows that reordering 30 cases will not work for the store when the inventory gets to twelve cases or below.  It should once again be noted that this is just one simulation.  A company should ideally run this simulation numerous times with random numbers, in order to get a more accurate picture (Heizer & Render, 2011).  It could be discovered that reordering 30 cases when inventory hits twelve actually would work, and that this simulation just received a very pessimistic set of random numbers.  It would be wise for the Monte Carlo simulation to be ran numerous times before the Baltimore store manager changes his reordering plan.