JGT - Task 4

Andy Gonzalez

Decision Analysis tools

 The case study involves a decision tree and the decision making process for 1) opening a new stand-alone store or 2) opening a mall store in two alternative scenarios a) a favorable market or b) unfavorable market or 3) deciding not to open any store..

CONTINUED.

 State of Nature 1: In the stand-alone store profit expected is $700,000 if market is favorable and loss of $400,000 if market is unfavorable.  State of Nature 2: In the mall store profit anticipated is around 3lac USD in case of favorable market and loss of fifty thousand USD in case of unfavorable market.

 State of Nature 3: If no store is opened there would no profit or loss.

Deciding on Buying a Market Research Survey

 To decide on the options the company has to decide on whether or not to go in for purchase of a market research survey which involves a layout of $20,000 (decision tree - top portion) and might make either a positive or a negative repercussion on the market conditions.  A no purchase situation would have no market impact.

Decision based on POM for Windows

 The previous data if used with the Production & Operation Management Tool for Windows and if the highest EMV or the Expected Monetary Value factor is selected, gives the following results:  If a market research study is decided on and the criteria of highest EMV is selected then in a best “chance” scenario of action Shuzworld will have a favorable market and also a positive survey result.

Continued..

 The Decision Analytical Module together with Graphic Decision Tree was selected as it was thought to be the most suitable decision tool as the choices for Shuzworld could then be visually observed.  It would also label the best option and display the path recommended in a blue color.  The design of the decision tree was based on figures from the 3 scenarios described in the introduction and also depended on projected operating data. The tool helped to decide whether or not to pursue with a particular course of action. The analytical attributes of the tool reveal there is a 50:50 chance for encountering favorable or unfavorable market conditions.

EMV - Auburn Stand Alone Store: (No survey)

Probability Factor: 50% or 0.5

SON Favorable market: Profit $ 700000 SON Unfavorable market: Loss $ 40000 EMV Calculation: (0.5) x ( +$700000) + (0.5) x ( - $40000) = $320000

EMV - Auburn Mall Store: (No survey)

Assumptions: Probability Factor: 50% or 0.5

SON Favorable market: Profit $ 300000 SON Unfavorable market: Loss $ 50000 EMV Calculation: (0.5) x ( +$300000) + (0.5) x ( - $50000) = $125000

Factors involved in making the decision

 At the top of the decision tree is the market survey portion costing $20000. The probability factors keep changing based on newer data inputs.  Choosing the project with the highest EMV is the correct decision and according to me carrying out a survey of the market and choosing a stand-alone store model for Auburn is the best decision.

Factors involved in making the decision

Choosing the Location

 The two main factors in selection of the location for the main store were the EMV and the SON. The graphical output made for easier interpretation.

 Environmental factors as well as the profitability aspects, size and the cost of a building, the cash flow are also important for business success. Whether the site is to be purchased or leased is also an important consideration as this will have an influence on the cash flow. The appreciation in future value of the building will have implications on the cash flow.

Continued..

 Shuzworld can make effective use of both PERT and CPM for upgrade of the Bellvue store.

PERT & CPM analysis in POM Tool:

 There is a lot of similarity between PERT & CPM as both depend on 6 basic steps given below: 1. Project definition and identifying work breakdown.

2. Determining relationships between various activities.

3.

“network” drawing connecting all the activities.

4. Assigning and Determining time and cost 5. Fixing the critical path 6. Using all the above information for planning and controlling the project.



PERT & CPM

CPM and PERT differ in the premises that CPM assumes a one time calculated estimate as correct whereas PERT takes into account 3 different estimates of time for every activity whereby the standard value together with the standard deviation can be calculated.  Critical path is defined as the longest sequence of the series of activities in the project planning which are essential to complete a project within a particular timeframe. An activity in the critical path is preceded by an activity which if not completed will hold up the project.

 As PERT makes use of 3 differing time estimates for every activity it is a better choice for making an effective decision.

Crashing Activities

 A crash time calculation gives 63.2 days and a cost factor of $ 12166.67 for the entire project. However if Activity 1 crashes by 5 days costing $ 2,500 @ $500 per day the reopening of the store can be on schedule.

 Shuzworld can crash this time element for enabling it to open the project in time.

Crashing Activities

 Cost minimization  25 batches of shoes  25 batches of sneakers  Total minimized production cost of $87500

minimize Contrite 1 Constraint 2 Constraint 3 Solution Y1

2000 1 0 1 25

Y2

1500 0 1 1 25 >= >= >=

RHS

25 10 50 87500

Dual

-500 0 -1500

Crashing Activities

 Cost maximization  Tassel loafers-120 batches  Penny loafers-160 batches  $288 000 total maximized profit.

Maximize Constant 1 Constant 2 Constant 3 Constant 4 Solution X1

800 2 8 1 0 120

X2

1200 6 4 0 1 160 <= <= >= >=

RHS

1200 1600 50 100 288000

dual

160 60 0 0

Reordering and Restocking at Shuzworld Baltimore Store

 Baltimore store of the group has varying lead times ranging from 1 to 3 days. There is a provision of 200 days for a frequency of case demand. Shuzworld has a daily demand which is of the level ranging from 7 to 12 cases.  While placing an order the lead time varies from 1 to 3 days. A frequency of lead time for 40 days has been provided as a cushion. The company would like to find a way to test if by reordering 30 cases of shoes when the inventory level goes down to 12 or lower will be effective. .

Inventory Management

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Da y

0 0 30 0 0 0 30 0 30 0 0 0 30 30 0 0 0 0 30 0

Receiv ed unit Beginni ng inventor y

30 21 42 31 21 9 30 22 41 30 21 12 32 55 45 38 31 24 43 36

Deman d Ending inventor y Lost sales Orde r

9 9 11 10 12 12 8 11 11 9 9 10 7 10 7 7 7 11 7 9 21 12 31 21 9 0 22 11 30 21 12 2 25 45 38 31 24 13 36 27 3 N Y N N Y Y N N Y N N Y N N Y Y N N N N

Lead time

0 1 0 0 0 1 0 0 2 0 0 1 0 0 2 2 0 0 0 0

Inventory Management

 Day 6 clearly illustrates that 30 orders are not enough to cover for the demand. In order to be able to eliminate the loss on day is need to increase the orders from 30 to at least 50.

 The Monte Carlo technique using random numbers can help in this situation as many of the factors are on the basis of chance.

Inventory Management

 From a simulation of the Baltimore Store a demand of 10.5 cases a day can be arrived at. This average demand has a basis on a random number generation while providing the demand volumes in a simulated model.  This analysis of lead times and inventory levels leads us to conclude that it is not efficient to have a reordering level of 30 cases when the inventory goes below 12. Ideally a simulation has to be run a number of times with random numbers to have greater accuracy in predictions.

Human resources strategy and philosophies

 The simulation by the Monte Carlo method needs to be run a few time more by the store manager at Baltimore in order to get a more accurate plan for the re-ordering level.  The foremost human resource strategy required for the employee efficiency improvement is the need of employee training in the operational segment of Shezworld. A thorough training on new operational segments of Shezworld can also enhance the productivity of employees

References

 Matthews, R. L., & Marzec, P. E. (2012). Social capital, a theory for operations management: a systematic review of the evidence.

International Journal Of Production Research

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50

(24), 7081-7099. doi:10.1080/00207543.2011.617395

 Ellington, B. E., Formby, S. K., Dave, D. S., & Miller, S. E. (2014). How Operations Management Topics Support US Students' Career Goals.

IUP Journal Of Operations Management

,

13

(3), 7-17.

 Joglekar, N., & Lévesque, M. (2013). The Role of Operations Management Across the Entrepreneurial Value Chain.

Production & Operations Management

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22

(6), 1321-1335. doi:10.1111/j.1937-5956.2012.01416.x