Appshop Case Analysis

Cover Letter The Appshop Inc case is based on the evaluation of the various alternatives available for the company while charging its client for execution of a project. Mr. Clark, Director, Central Region Appshop Inc had to make a decision on either accepting any one of the prices suggested by the client or participate in the bidding process. The case involves using Monte Carlo Simulation and Triangle Distribution to figure out the best possible option for Appshop Inc. Executive Summary Appshop Inc was a privately held, independent full-service Oracle consulting, applications and outsourcing company with revenues of $ 25 million. Mr. Eric Clark, Director, Central Region Appshop Inc was responsible for growing the company’s client base, selling additional services and supporting the existing client base. Mr. Clark had recently concluded a successful implementation of Oracle financial for one of its clients Dallas office. The client pleased with Appshop’s performance had requested Mr. Clark to implement the similar application across the company’s (client) offices across the globe and come out with a project cost for this implementation. Mr. Clark with his team of consultants outlined the scope, plan and the timeline for implementation of the project for the client. Appshop would have to put in 1000 hours of work per month for the next 24 months, which would cost Appshop $ 140 per hour. Based on these findings, Mr. Clark proposed $ 175,000 per month for 24 months for implementing the entire project. However, the client requested Appshop to lower the prices and gave two alternative prices. Appshop could either accept $ 155,000 per month for 24 months or $ 125,000 per month for 24 months along with a bonus of $1. 5 million post satisfying certain criteria, the probability of which was 0. 7. In case, Appshop did not accept the two alternate prices suggested by the client, then the client would go for a bidding process. The company winning the bid would receive the revenue bid amount and a gain share reward. The reward would be based on the saving that the company would realize upon implementation of the project. Based on previous work undertaken for the client, Appshop estimated the savings for the client to be a maximum of $12. 8 million, a minimum of $ 3. 2 million and a most likely saving of $ 5. 6 million. Appshop for implementing the project, proposed to quote $ 150,000 for the bidding. The Appshop team estimated a 45 per cent chance of winning the bid at this price. Post Monte Carlo Simulation with Triangle distribution, the revenue realized was $ 3. 8 million as shown in Appendix 1. On analyzing the three alternatives available to Appshop Inc, the decision should be based on giving equal importance to the maximum revenue that can be realized and the risk associated with it. The first alternative would generate revenue of $3,543,765 and all of which is risk free, however this alternative gives the least revenue. The second alternative would generate revenue of $ 3,751,919. 5; however, there is a risk of 0. 7 per cent associated with winning the bonus. The third alternative, the bidding process, generates the highest revenue of $3. 8 million; but, there is only a 45 per cent chance of winning the bid. Since the difference in revenue realized by exploring alternative two and three is miniscule, the decision now will be made on the alternative, which has a higher prob ability of occurring. The risk associated with alternative two is lower than the risk associated with alternative three, therefore, we would recommend going ahead with the second alternative. Analysis and Execution of the case Appshop Inc had calculated that for implementation of the project, they would have to put in 1000 hours of work per month for the next 24 months. This would cost Appshop $ 140 per hour. Therefore, Appshop proposed $ 175,000 per month for 24 months. However, the client rejected this offer and proposed two alternatives. Alternative 1 was $ 155,000 per month for 24 months and Alternative 2 was $125,000 per month for the next 24 months along with a bonus component of $1. 5 million. However, the bonus was based on meeting the multiple benchmarks set across various parameters. Appshop estimated the probability of receiving the bonus to be 0. 7. Analysis of Alternatives Proposed By the Client To make comparisons, we need to calculate the present value of each of the amount that Appshop would receive from the client. The present value annuity factor would be = (1/r – 1/r (1+r) ^24), the discount rate is . 5 per cent/month. Thus, the annuity factor calculated comes out to be 22. 563. Analyzing Alternative 1: $ 155,000 per month for 24 months With this amount, the client would pay = 155,000 x 22. 863 = $3,543,765. This amount is far below than the one proposed by Appshop of $3,948,525($175,000 x 22. 563). Analyzing Alternative 2: $ 125,000 per month for 24 months plus a $1. 5 million bonus. The probability of Appshop receiving this bonus based on their calculations was 0. 7. With this amount, the client would pay = 125,000 x 22. 563 = $2,820,375. To calculate the bonus, we need to firstly find the present value of $1. 5 million and multiply that with the probability of winning. The present value of $ 1. 5 million is = $1,330,778. 50. We now multiply this amount by 0. 7, the probability factor = $931, 5 44. 50 Therefore, the total amount that Appshop would receive from exploring this alternative two would be = 2,820,375+ 9 31,544. 950 = $ 3,751,919. 95. This amount is also lower than the one proposed by Appshop of $ 3,948,525 ($175,000 x 22. 563). We now explore alternate 3. Analysis of the Bidding Alternatives Analyzing Alternative 3: The company winning the bid would receive the revenue bid a mount and a gain share reward based. The reward would be based on the saving that the company would realize upon implementation of the project. The table below shows the saving and the bonus associated with it. Savings |Winning bidders share of saving | |< $4 million |0 | |$4 million upto $6 million|20 percent of excess above $6 million | |$4 million upto $6 million|$400,000 plus 40 percent of excess above $6 million | |> $8 million |$1. 2 million plus 60 percent of excess above $8 million| Based on previous work undertaken for the client, Appshop estimated the savings for the client to be a maximum of $12. million, a minimum of $ 3. 2 million and a most likely saving of $ 5. 6 million. Appshop for implementing the project, proposed to quote $ 150,000 for the bidding. The Appshop team estimated a 45 per cent chance of winning the bid at this price. We would use the Monte Carlo Simulation with Triangle Distribution [see Appendices] to find the revenue that Appshop would receive post bidding at the $ 150,000. The total revenue that Appshop would receive on winning the bid would be a total of the revenue bid and the share of the saving. Appendix 2 & 4 show the histogram for total cost and gain share based on the Monte Carlo simulation. The simulation also gives us a value of $ 3. 8 million, which is what Appshop would receive if it participates in the bidding process (ref appendix 1). This amount of $ 3. 8 million is generated by taking into consideration the probability of winning and the various profit sharing model devised by the client. Conclusion As we compare the present value of the revenues realized by alternative one, two and three, it is clear that alternative three is the best option in terms of revenue. Option one gives present value revenue of $3,543,765, which is the lowest as compared to the other two alternatives. Alternative two with revenue of $$ 3,751,919. 95 an alternative three with revenue of $ 3. 8 million have nearly the same value. However, there is only a 45 per cent probability of realizing alternative three (bidding process), whereas in alternative two, the probability of receiving the bonus is 0. 7. Therefore, considering the revenue and the risk associated with it, alternative 2 is the best choice for Appshop Inc to go ahead with.