Superior Grain Inc - Essay Example

?Nova Southeastern H. Wayne Huizenga School of Business & Entrepreneurship  Assignment for QNT 5040 - Business Modeling Submitted to: Dr. Thomas Griffin Submitted by: Kelly Espinosa (Student's number) (Address) (Work phone number) (Home phone number) Date of Submission: February 15, 2013 Title of Assignment: Superior Grain Elevator, Inc. Case Analysis CERTIFICATION OF AUTHORSHIP: I certify that I am the author of this paper and that any assistance I received in its preparation is fully acknowledged and disclosed in the paper. I have also cited any sources from which I used data, ideas or words, either quoted directly or paraphrased. I also certify that this paper was prepared by me specifically for this course. Student's Signature: _______Kelly Espinosa_________________ ______Diana De Castro______________ __ _ Elsa Santos _______________ _______Marie Jean ___________________ _ Lesan Thomas_ ___________ _ Courtney Waite_ _________ ***************************************************************** Instructor's Grade on Assignment: Instructor's Comments: TITLE OF RUBRIC: Case Analysis (Page 1 of 2) Course: QNT 5040 LEARNING OUTCOME/S: (see syllabus) Date: 12.15. 2013 PURPOSE: To facilitate effective decision making under uncertain conditions by quantifying risk. Name of Student: Kelly Espinosa, Diana De Castro, Elsa Santos, Marie Jean, Lesan Thomas, Courtney Waite VALIDITY: Best practices in Monte Carlo simulation. Name of Faculty: Nova Southeastern University COMPANION DOCUMENTS: Assignment and format instructions, Case Earning maximum points in each box in ‘PROFICIENT’ column and / or points in columns to the right of ‘PROFICIENT’ meets standard. > Performance Criteria Basic Developing Proficient Accomplished Exemplary Score Identify the problem Does not identify the problem, or does not identify the right problem. (0 pts) Identifies symptoms (5 pts) Identifies some elements of the problem. (10 pts) Substantially identifies the problem. (12 pt) Effectively and succinctly identifies the problem. (15 pts) Describes assumptions and methods Does not describe assumptions and methods used (0 pts) Does not precisely describe the assumptions and methods used (3 pts) Somewhat describes assumptions and methods used (7 pts) Substantially describes assumptions and methods used (8 pts) Effectively describes assumptions and methods used (10 pts) Calculate statistics using a spreadsheet Does not calculate appropriate statistics using a spreadsheet and/or does not provide evidence of calculations (0 pt) Calculates appropriate statistics using a spreadsheet (most answers are not correct) (13 pts) Calculates appropriate statistics using a spreadsheet (not all answers are correct) (21 pts) Calculates appropriate statistics using a spreadsheet (most answers are correct) (25 pts) Effectively calculates statistics using a spreadsheet (almost all answers are correct) (30 pts) Explain implications of output of statistical analysis Does not explain implications of output of statistical analysis (0 pt) Partially explains implications of output of statistical analysis (3pts) Somewhat explains implications of output of statistical analysis (7 pts) Substantially explains implications of output of statistical analysis (8 pts) Effectively explains implications of output of statistical analysis (10 pts) . . . continued . . . TITLE OF RUBRIC: Case Analysis, cont. (Page 2 of 2) Course: QNT 5040 LEARNING OUTCOME/S: (see syllabus) Date: 02.15. 2013 PURPOSE: To facilitate effective decision making under uncertain conditions by quantifying risk. Name of Student: Kelly Espinosa, Diana De Castro, Elsa Santos, Marie Jean, Lesan Thomas, Courtney Waite VALIDITY: Best practices in Monte Carlo simulation. Name of Faculty: Nova Southeastern University COMPANION DOCUMENTS: Assignment and format instructions, Case Earning maximum points in each box in ‘PROFICIENT’ column and / or points in columns to the right of ‘PROFICIENT’ meets standard. > Performance Criteria Basic Developing Proficient Accomplished Exemplary Score Generates solutions based on analysis and context Does not generate appropriate solutions based on analysis and context. (0 pt) Generates solutions (does not justify conclusions). (7 pts) Partially: *generates and justifies solutions based on analysis and context; and *justifies conclusions. (15 pts) Substantially: *generates and justifies solutions based on analysis and context; and *justifies conclusions. (17 pts) Effectively: *generates and justifies solutions based on analysis and context; and *justifies conclusions. (20 pts) Uses prescribed format (including cover sheet and grading rubric) and writing style (language, grammar, punctuation, and spelling) Does not use prescribed format and writing style (0 pt) May use prescribed format OR writing style (only one) (3 pts) Generally uses prescribed format and writing style (7 pts) Substantially uses prescribed format and writing style (8 pts) Effectively uses prescribed format and writing style (10 pts) Uses APA format (APA Style Manual 6.0) Does not provide references. (0 pt) Does not apply APA style to references. (1pts) Partially applies APA style to references. (3 pts) Substantially applies APA style to references. (4 pts) Effectively applies APA style to all references. Optimal quality and quantity of citations. (5 pts) OVERALL GRADE (100 total possible points): % Comments: __________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ Management Report for Superior Grain Elevator, Inc. Kelly Espinosa, Diana De Castro, Elsa Santos, Marie Jean, Lesan Thomas, Courtney Waite Nova Southeastern University QNT 5050 Business Modeling Professor: Dr. Thomas Griffin Executive Summary This report was created for Superior Grain Elevator Inc., as requested by the management. The company operates a port facility in Thunder Bay, Ontario, with two wharves for the operation of loading ships. Varying arrival times were causing ships to anchor while awaiting berth, resulting in a demurrage charge of $2,000 per day. Based on the analysis of the historical data, we build a model to map the interval arrival times (IAT) and the loading times (LT), in order to calculate the possible waiting time of two and three wharves. The Canadian Government and Poland had negotiated a new contract for the next year and the expected number of ships would increase from 115 to 135 within the same 8-month period. The concern was whether the increased volume from allotted shipments under a new contract between the Canadian Government and Poland would make building a third wharf a profitable investment. Historical data for IAT and LT, probability distribution using @Risk were used to identify past behavior and predict possible future outcome. After a careful review of the report, it is evident that the expected mean of the possible savings would come from the demurrage costs and according to the financial analysis that shows a negative NPV indicates that the project must be rejected. Background The two wharfs at Superior Grain Elevator Inc. are operating at capacity, and ships that have to anchor in the lake while they wait to be loaded are costing the company $2000 per day. The wait time is partially due to inconsistent arrival times and the variation of loading time of the ships. A steady stream of arrivals is ideal; however, there is no way to predict or control the arrival times of the ships, because this is dependent on the lockage transfer times in the Seaway. The new owners of Superior Grain have long considered building a third wharf, and already purchased the waterfront to do so. However, the company was not receiving the volume of shipments necessary to realize a 20% return on investments the new owners desired. A new five-year contract between the Canadian Government and Poland, allotted Superior Grain with twenty shipments with Poland for each of the five years. This report is designed to determine whether construction of the third wharf will deliver the savings in the demurrage charge required to make the investment profitable. Arrivals and loading times from the last shipping season’s operation with the two wharves were evaluated and compared with future probable arrival and loading times using a model with three wharves. Demurrage costs and waiting times for a model with the three wharfs were computed as well as the annual savings in terms of costs. Problem Will the savings in demurrage provided by the allocated twenty shipments to Poland economically justify the building of a third wharf? Analysis A simulation model for Superior Grain Elevator was built using @Risk 6 to assess the economic benefits of adding a third wharf. In order to model the data of the upcoming year, the historical data of the interval arrival times (IAT) and loading times (LT) was used. The last shipping season under the new contract has increased the number of shipments by 20 per year. The descriptive statistics and histograms were computed using Microsoft Office Excel 2007. The histogram of the IAT has a long right tail because of the positive skewness. The coefficient of the variation is close to 100% shows that it was not a normal distribution. For the first input variable, the original mean was 2 times the number of ships divided by 135 equal 1.7 or (115 x 2 =130/135=1.7). For the logistic model, we used 135 ships with a similar IAT, and then we used @Risk Expon (1.7), which randomize the numbers giving them the same behavior and feature to that of the historical data. Figure 1: Descriptive Statistics of IAT IAT Mean 2.020322581 Standard Error 0.350226189 Median 1.35 Mode 0.93 Standard Deviation 1.949976895 Sample Variance 3.802409892 Kurtosis 1.531119969 Skewness 1.429775387 Range 7.54 Minimum 0.02 Maximum 7.56 Sum 62.63 Count 31 Confidence Level (95.0%) 0.7152573 Figure 2. Histogram of the IAT The secound input variable is the LT. We tried the best distribution from the data using the minimum and the maximum (2 and 3days), and between them the possibilities are the same. Therefore, Risk Uniform was used to simulate the LT and to be sure of our decision we used the @Risk distribution fitting. Figure 3: Descriptive Statistics of LT Descriptive Statistics LT Mean 2.499032258 Standard Error 0.04854076 Median 2.53 Mode 2.18 Standard Deviation 0.270263511 Sample Variance 0.073042366 Kurtosis -1.26203341 Skewness -0.129822301 Range 0.85 Minimum 2.06 Maximum 2.91 Sum 77.47 Count 31 Confidence Level (95.0%) 0.099133456 Figure 4: Histogram of the LT The third input variable we calculate is the current clock time which actually cumulates the IAT. The @Risk was used for each of the ships in order to assign each one to its appropriate wharf and to avoid any logistic problems. To choose the best wharf we used the wharf out time which is the last ship out from that specific wharf, and the earliest available wharf was chosen. In order to determine the waiting time, we used the current clock time and the wharf out time. If the current clock time is lower than the wharf out time, then the waiting time would be the difference between the two. Knowing the waiting time, the demurrage costs could be calculated by multiplying the waiting time by $2,000. To find the benefit of the investment, we compared the two demurrage costs (with 2 and 3 wharves) and the difference would be the benefit of the investment, which was the cost savings to building the third wharf. After we signed the cost savings as @Risk output, we ran the simulation, which showed a mean of $339,678.00, the cost savings that are used as cash flow for five years. Then, we used the 20% expected return as a discount rate. After the calculation, if the NPV is zero or positive we could continue with the project and with a negative NPV the project must be rejected. Simulation Report CF0 (Cost of Investment of 3rd wharf) -$1,500,000.00 CF1 year 1 (mean) $339,678.00 CF2year 2 $339,678.00 CF3year 3 $339,678.00 CF4year 4 $339,678.00 CF5year 5 $339,678.00 Expected return 20% NPV -$403,462.37 If less than 0, do not take project IRR, 5 years 4.29% Conclusion & Recommendations Based on the analysis of the historical data and projected data for the upcoming year, it would be a mistake for Superior Grain to rely on the volume of shipments allotted from the Polish contract to cover the costs of building the third wharf with the expectation of 20% profit. It is apparent from the statistics that the addition of the third wharf would decrease the demurrage charge; however, there is a negative net present value (NPV) that indicates the company should not build the third wharf. Even though, there is a negative NPV for the company, according to our five-year analysis, Superior Grain Elevator could still consider building the third wharf. Because of the 3 factors detailed below, Superior Grain Manager, Mike Armstrong, needs to consider building the third wharf. 1- By building the third wharf the waiting time will be dramatically decreased, and customer satisfaction will also increase. 2- After the contract is over the number of ships will go back to 115 with the savings of more than $100,000 per year. 3- There is always a possibility within the next 2 years for the company to get a new contract which it will be prepared to handle. Therefore by building the third wharf the company will be able to benefit from it. References Grant, E. L., & Leavenworth, R. S. (1996). Statistical quality control (7th ed.). Boston: WCB/McGraw-Hill. Kros, J. F. (2008). Spreadsheet modeling for business decisions. New York: McGraw-Hill/Irwin. Lee, A., Lee, J., & Lee, C. (2000). Statistics for Business and Financial Economics (vol. 1). World Scientific. Robinson, S. (2004). Simulation: The practice of model development and use. John Wiley & Sons. Appendix 1 Read More