ALEX VATANI FINAL PROJECT RC COLEMAN

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R.C. COLEMAN CASE STUDY A Project by Alex Vatani The following report contains a brief overview of what project management and business analytics are and how they utilized the modern workplace. Also included is the complete solution to the R.C. Coleman project management problem, with detailed explanations as to how the problem is formulated, solved, and analyzed. Bus 190 Winter 2016 San Jose State University Dr. Yudhi Ahuja

Transcript of ALEX VATANI FINAL PROJECT RC COLEMAN

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R.C. COLEMAN CASE STUDY

A Project by Alex Vatani

The following report contains a brief overview of what project management and business analytics are and how they utilized the modern workplace. Also included is the complete solution to the R.C. Coleman project management problem, with detailed explanations as to how the problem is formulated, solved, and analyzed.

Bus 190 Winter 2016 San Jose State University Dr. Yudhi Ahuja

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Table of Contents:

What is Project Management? ----------------------------------------------------- 3What is Business Analytics? ------------------------------------------------------- 5Problem Summary ------------------------------------------------------------------- 6Managerial Report

Answer to Question 1 -------------------------------------------------------- 8Flow Chart ------------------------------------------------------------- 9Slack Table------------------------------------------------------------- 10Final Calculation------------------------------------------------------- 11

Answer to Question 2 -------------------------------------------------------- 12Answer to Question 3

Revised Crash Table & Decision Variables ----------------------- 14Objective Function & Constraints ---------------------------------- 15Constraints Explanation & Standard Solution--------------------- 16Optimal Crashing Decisions Chart --------------------------------- 16Revised Flow Chart Including Crashing Recommendations --- 17Revised Slack Table Including Crashing Recommendations -- 18

Blank Excel Format for Problem ------------------------------------------ 19Solved Excel ------------------------------------------------------------------ 20Answer Report --------------------------------------------------------------- 21Sensitivity Report ------------------------------------------------------------ 23Attached Power Point Slides ----------------------------------------------- 24

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What is Project Management?

Project Management is the application of processes, methods, knowledge, skills and experience to achieve the project objectives. A project is a unique endeavor, undertaken to achieve planned objectives, which could be defined in terms of outputs, outcomes or benefits.

PERT/CPMProgram Evaluation and Review Technique (PERT) and Critical Path Method (CPM) help managers to plan the timing of projects involving sequential activities. PERT/CPM charts identify the time required to complete the activities in a project, and the order of the steps.

Network Diagram (Flow Chart)An illustration of relationships between activities in a given project. Nodes and arcs are shown in the network diagram to represent activities and preceding relationships. Several flow charts were used in the formulation of our attached case solution

Critical PathThis is the shortest possible time that a project can be completed, with all activities finished. Activities on the critical path must be completed, and cannot be crashed or shortened.

SlackThe amount of time an activity can be delayed without affecting the completion time of a project. The critical path will not have any slack. A slack table can be constructed after completing a network diagram.

CrashingCrashing occurs when you shorten activity time. The process of the project is sped up and resources are added to the activity. Not all activities can be crashed.

Advertising and MarketingAmazon advertises online by using cookies. When amazon markets their ads, they have

to create an ad that’s well designed that will attract a certain amount of views. They want something that is clicked based. Using project scheduling, these have different traits. In order to track all these different traits and characteristics, Amazon needs large data-bases and IT to help keep track of its customers and what they want.

Whether that be books for school, music, kindle books, movies, or TV shows Amazon wants to see what attracts customers. Amazon advertises on Google and other like sites to reach their desired audience.

Amazon Echo will help track a lot of customer data for the future. Five or ten years down the line, Amazon will be able to look back at all this data to see what has worked. Amazon will be able to look at Echo to find out if an advertisement was a hit, or a failure. They have that data to see what the general population wants; and what they are finding is that the population always wants something better

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International BusinessGoogle has used its business to branch out across many markets. Google now has offices

in over 60 countries worldwide. They use search engines to allow users to find unlimited amounts of information, which is stored on giant database servers. Google can use these servers and information to help get customers the products and information that they want. Now with Google Plus, Google has more live data to use. They can use this information to find out about more cultures and demographics and use that information to reach new customers. With the enormous amount of influence that google has been able to gain over the international community, it can now also influence political policies in other countries. An example of this can be seen in China. When China requested that Google censor information, Google pulled out of China and stopped doing business.

FinanceOnly recently people have been able to pay with smartphones through services such as

Apple Pay. Due to it’s complexity many factors are considered, in particular privacy issues. So much information and data will be kept on the phones. What if this information is lost, how will the company be able to protect people from identity theft? The solution would be to help create a software to help protect the users. You can use a thumbprint or a pin code to identify the person for access. It essentially results up as an ATM card on your phone. They are starting to see this is a trend towards what we are moving towards. Since society as a whole want to move towards this direction, since technology has changed in the last fifty years. With the added security of the touch ID or passcode, users will be more likely to use apple pay. It is also more convenient as you can just scan your phone instead of pulling out your credit card. Other Financial services are using data to find out what customers want. They are using big data to help segment their markets, which allows them to offer more relevant and sophisticated offers. Many companies are trying to take advantage of financing opportunities, and will continue to offer new ways to reach their customers, and will continue to create new services like Apple and Google Pay for easy access.

Human ResourcesBig Data Analysis is used for recruiting in several companies. It can cost a lot of

money to put up postings and specific jobs will cost more than others for advertising.

How can a company hire more people? First, they must figure out what they need, and how much – calculations that can be solved using excel solver. Companies must find a way to reach out to these via online such as LinkedIn, Facebook, and onsite recruiters on college campuses, and other recruiting sites such as monster. By using social media and new recruiting data bases, these companies will be able to find the best employees to help with project management.

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Business Analytics:

Business Analytics is the study of data through statistical and operations analysis, the formation of predictive models, application of optimization techniques and the communication of these results to customers, business partners and colleague executives.

SportsBusiness analytics is growing in the world of sports. Businesses can now use analytics to help it determine predictive behavior into fan preferences. This ranges from apps that allow you to find parking, short bathroom lines, and order food straight to your seat. You can now have access to instant replays, and view close ups. After the game, the app allows you to find the fastest way home.

Wearable technologies have also developed to help track data. These devices have helped athletes train better, and have reduced injuries. They help keep proper diets, and ensure that individuals are getting enough sleep. Some of these technologies are Google glass, the Apple Watch, and the Fit bit. These devices use apps, and can save data to help individuals perform at their best. Coaches and players are personally affected due to the growing world of analytics. Teams can now choose the best performing coaches, and can also help choose the most effective teams. Data can help teams make better decisions that can help determine the outcome of the game.

Weather

Weather has always held a huge influence over how the economy. Since three to four percent of the economy is weather sensitive, weather effects are real and must not be ignored. Companies like Facebook and other purchase data companies go beyond forecasting, and are now shaping consumer demand. High speed data now allows live forecasting, and allows users to make decisions in the moment.

Analytics in the world of weather can help protect people from dangers and disasters. There are a lot of different forecasts available, and a lot of data to help give you a warning in advance to get to safety. Weather data combined with social data can help keep citizens safe.

Utilities can vary in demand depending on what the weather is like. Eighty percent of all outages in the United States are due to weather. Analytics can help utility companies like PG&E determine and forecasts outages, and allow them to deploy solar and other alternative energy sources to fight outages. They will be more likely to predict where outages might occur, allowing them to quickly and effectively restore service.

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R. C. Coleman R. C. Coleman distributes a variety of food products that are sold through grocery store and supermarket outlets. The company receives orders directly from the individual outlets, with a typical order requesting the delivery of several cases of anywhere from 20 to 50 different products. Under the company’s current warehouse operation, warehouse clerks dispatch order-picking personnel to fill each order and have the goods moved to the warehouse ship- ping area. Because of the high labor costs and relatively low productivity of hand order- picking, management has decided to automate the warehouse operation by installing a computer-controlled order-picking system, along with a conveyor system for moving goods from storage to the warehouse shipping area.

R. C. Coleman’s director of material management has been named the project manager in charge of the automated warehouse system. After consulting with members of the engineering staff and warehouse management personnel, the director compiled a list of activities associated with the project. The optimistic, most probable, and pessimistic times (in weeks) have also been provided for each activity.

Activity Description Immediate Predecessor

A Determine equipment needs -B Obtain vendor proposals -C Select vendor A,BD Order system CE Design new warehouse layout CF Design warehouse EG Design computer interface CH Interface computer D,F,GI Install system D,FJ Train system operators HK Test system I, J

Activity Optimistic (a) Most Probable (m) Pessimistic (b) A 4 6 8B 6 8 16C 2 4 6D 8 10 24E 7 10 13F 4 6 8G 4 6 20H 4 6 8I 4 6 14J 3 4 5K 2 4 6

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Managerial Report

Develop a report that presents the activity schedule and expected project completion time for the warehouse expansion project. Include a project network in the report. In addition, take into consideration the following issues:

1. R. C. Coleman’s top management established a required 40-week completion time for the project. Can this completion time be achieved? Include probability information in your discussion. What recommendations do you have if the 40-week completion time is required?

2. Suppose that management requests that activity times be shortened to provide an 80% chance of meeting the 40-week completion time. If the variance in the project completion time is the same as you found in part (1), how much should the expected project completion time be shortened to achieve the goal of an 80% chance of completion within 40 weeks?

3. Using the expected activity times as the normal times and the following crashing in- formation, determine the activity crashing decisions and revised activity schedule for the warehouse expansion project:

Activity Crashed Activity Time (weeks)

Normal Cost Crashed Cost

A 4 $1,000 $1,900B 7 $1,000 $1,800C 2 $1,500 $2,700D 8 $2,000 $3,200E 7 $5,000 $8,000F 4 $3,000 $4,100G 5 $8,000 $10,250H 4 $5,000 $6,400I 4 $10,000 $12,400J 3 $4,000 $4,400K 3 $5,000 $5,500

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Answer to Question 1:

The very first step approaching a problem of this sort, is to get a good understanding of the process’ required in the project and how they depend on each other. By interpreting the “Immediate Predecessor” section on very first table provided in the problem we can draw this flow chart; the chart visual represents task dependence throughout the project. We will expand on this chart again later.

Secondly, expected time (weeks) for each activity and its variance are calculated using the following formulas which were provided in the book.

Formulas:a = Optimistic Expected Time = (a + 4m + b)/6m = Most Probableb = Pessimistic Variance = [(b-a)/6]2

By plugging the values (a, m, b) given in the second table provided in the problem into the two formulas above, we are able to create the following table.

For example: Expected time of Activity A = (4 + 4*6 + 8)/6 = 36/6 = 6Variance of Activity A = [(8-4)/6]2 = (4/6)2 = .44

Activity Expected Time (weeks) VarianceA 6 .44B 9 2.78C 4 .44D 12 7.11E 10 1.00F 6 .44G 8 7.11H 6 .44I 7 2.78J 4 .11

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K 4 .44

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R.C. Coleman Flow

Chart

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Using the data obtained from the improved flow chart on the previous page, we are able to create the following slack table:

Activity Earliest Start Latest Start Earliest Finish Latest Finish Slack Critical ActivityA 0 3 6 9 3 NoB 0 0 9 9 0 YesC 9 9 13 13 0 YesD 13 17 25 29 4 NoE 13 13 23 23 0 YesF 23 23 29 29 0 YesG 13 21 21 29 8 NoH 29 29 35 35 0 YesI 29 32 36 39 3 NoJ 35 35 39 39 0 YesK 39 39 43 43 0 Yes

Slack is the difference between the “Latest Start” and “Earliest Start” or the difference between the “Latest Finish” and the “Earliest Finish”.

For example: Slack for activity A = 3 – 0 = 3 or 9 – 6 = 3

Activities with 0 slack are activities that lie on the critical path. Thus we can deduce that the critical path for RC Coleman is:

B + C + E + F + H + J + K.

By adding together, the expected weeks for each activity on the critical path we find that the expected completion time of the project is 43 weeks:

9 + 4 + 10 + 6 + 6 + 4 + 4 = 43 weeks

By adding together, the variance values (table located at bottom of page 3 of this) of activities on the critical path we found the sum variance of the critical path is 5.67

2.78 + .44 + 1.00 + .44 + .44 + .11 + .44 = 5.65

We solve for the Z score under the assumption that the project must be completed in 40 weeks.

Z Score = (Proposed Completion Time – Expected Completion Time) / (Sum Variance on Critical Path)1/2

Z Score = (40-43) / (5.65) ½ = -1.26

Lastly we find our Z score value on the Normal Distribution Chart. This can be seen below.

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The Z Score of -1.26 is .1038; We can thus conclude that RC Coleman has approximately a 10.4 % chance of completing the project in 40 weeks.

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Answer to Question 2:

Because the question states that the expected project completion time must be shortened to achieve the goal of an 80% completion chance in 40 weeks, we are able to find the Z Score Value of 80% on our Normal Distribution Chart. We find a Z Value of .84 – this correlates with its percentage value of .7995; .7995, is the closest percentage value on the Normal Distribution to 80%, and is therefore used. This step can be viewed below:

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We now can simply use the previous formula backwards to solve for the new expected completion time. Note that the proposed completion time and sum variance on the critical path remain unchanged at 40 and 5.65 respectively. These calculations can be seen below:

Z Score = (Proposed Completion Time – Expected Completion Time) / (Sum Variance on Critical Path)1/2

.84 = (40 – Expected Completion Time) / (5.65)1/2

2 = 40 – Expected Completion Time

Thus

38 Weeks = Expected Completion Time to complete with 80% confidence within 40 weeks.

Due to the fact our original Expected Completion Time is 43 weeks, we must crash a total of 5 times to reach our new Expected Completion Time of 38 weeks. 43 weeks – 38 weeks = 5 crashes needed.

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Answer to Question 3:

Using the expected activity times as the normal times and the following crashing in- formation, determine the activity crashing decisions and revised activity schedule for the warehouse expansion project:

All of the following data in the chart below has been given to our project team except Mi (maximum reduction time) and Ki (crash cost per unit time) which are calculated with the following formulas:

Mi = Normal Time – Crash Time

Ki = Crash Cost – Normal Cost / Mi

Activity Crashed Activity Time (weeks)

Normal Cost Crashed Cost Mi Ki

A 4 $1,000 $1,900 2 450B 7 $1,000 $1,800 2 400C 2 $1,500 $2,700 2 600D 8 $2,000 $3,200 4 300E 7 $5,000 $8,000 3 1000F 4 $3,000 $4,100 2 550G 5 $8,000 $10,250 3 750H 4 $5,000 $6,400 2 700I 4 $10,000 $12,400 3 800J 3 $4,000 $4,400 1 400K 3 $5,000 $5,500 1 500

With this information we are able to construct the linear programming model:

Decision Variables: Let,

YA = number of weeks’ activity A is crashed. XA = Finish time for activity A

YB = number of weeks’ activity B is crashed. XB = Finish time for activity B

YC = number of weeks’ activity C is crashed. XC = Finish time for activity C

YD = number of weeks’ activity D is crashed. XD = Finish time for activity D

YE = number of weeks’ activity E is crashed. XE = Finish time for activity E

YF = number of weeks’ activity F is crashed. XF = Finish time for activity F

YG = number of weeks’ activity G is crashed. XG = Finish time for activity G

YH = number of weeks’ activity H is crashed. XH = Finish time for activity H

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YI = number of weeks’ activity I is crashed. XI = Finish time for activity I

YJ = number of weeks’ activity J is crashed. XJ = Finish time for activity J

YK = number of weeks’ activity K is crashed. XK = Finish time for activity K

Objective Function:

Min 450YA + 400YB + 600YC + 300YD + 1000YE + 550YF + 750YG + 700YH + 800YI + 400YJ + 500YK

St.

XA + YA ≥ 6 XK ≤ 38

XB+YB ≥ 9 YA ≤ 2

XC+YC–XA ≥ 4 YB ≤ 2

XC+YC–XB ≥ 4 YC ≤ 2

XD + YD – XC ≥ 12 YD ≤ 4

XE + YE – XC ≥ 10 YE ≤ 3

XF + YF – XE ≥ 6 YF≤2

XG+YG–XC ≥ 8 YG≤3

XH+YH–XF ≥ 6 YH≤2

XH+YH–XG ≥ 6 YI≤3

XI + YI – XD ≥ 7 YJ≤1

XI + YI – XF ≥ 7 YK≤1

XJ + YJ – XH ≥ 4 ALL XI,YI ≥ 0

XK + YK – XI ≥ 4

XK + YK – XJ ≥ 4

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The objective function the sum of each activities crash time multiplied by its crash cost per unit time. The data used can be found on the crash table.

The constraints on the left hand side of the page represent each activities dependence on the activity that precedes it.

For example: Constraint XK + YK – XJ ≥ 4

XK = Potential finish time for Activity K + These values must added to find the find the fastest time with crashingYK = Potential number of weeks crashed for Activity K- The potential finish time of the constrained activities predecessor’ is then subtractedXJ = Potential finish time for Activity J≥4 = This value is simply the original expected weeks for the respective Activity, in this case K

The constraints on the right hand side of the previous page represent the requirement that the potential crash times for each activity are limited to their respective maximum reduction time. These values can be found on the Crash Table two pages’ prior under the Mi column.

Only after solving the objective function with its respective constraints using the excel solver, are we able to find the optimum amount of weeks to crash to complete the project in 38 weeks for the cheapest cost. The excel solution that I have used is attached in the appendix of this report.

The Standard Solution: (400) *2 +(550) *1+(400) *1+(500) *1 = 2250

The optimal crashing decisions are as follows:

Crash Activity Weeks Crashed Cost

B 2 $800F 1 $550J 1 $400K 1 $500

Total: $2250

With the above crashing data an improved flow diagram can be constructed indicating the lesser expected completion times for crashed activities B, F, J, and K. The expected weeks for each of the “crashed activities” are subtracted by the weeks crashed found on the chart directly above.

The flow chart incorporating these changes (crashes) can be seen on the following page:

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R.C. Coleman Flow

Chart (Crashing Included)

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Using the data obtained from the improved flow chart (crashing included) on the previous page, we are able to create the following “optimized” slack table:

Activity Earliest Start Latest Start Earliest Finish Latest Finish Slack Critical ActivityA 0 1 6 7 1 NoB 0 0 7 7 0 YesC 7 7 11 11 0 YesD 11 14 23 26 3 NoE 11 11 21 21 0 YesF 21 21 26 26 0 YesG 11 18 19 26 7 NoH 26 26 32 32 0 YesI 26 28 33 35 2 NoJ 32 32 35 35 0 YesK 35 35 38 43 0 Yes

Conclusion:

The excel solver is able to compute the optimum crashing values in order to minimize cost while allowing the project to finish early. However, often the what the data suggests may not be the best change when it comes to practical applications. R.C. Coleman is crashing Activity B or “Obtain Vendor Proposals” by two weeks. Logistically I don’t see any future problems that could arise from obtaining a proposal too soon. Designing, Training Workers, and Testing are all sped up by one week only; A one-week decrease for each of these does not seem to be substantial enough to present serious problems either. However, crashing decisions may alter the variance in the project completion time. By re-defining optimistic, most probable, and pessimistic times for crashed activities B, F, J, and K, a revised variance in the project completion time can be found. Using this result, a revised probability of a 40-week completion time can be computed and used as the future template for that process.

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Excel Sheet Before Solver:

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