final case study QNT 5040 superior grain elevator (1)
final case study QNT 5040 superior grain elevator (1)
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Date Created: 11/15/15
TITLE OF RUBRIC: Case Analysis (Page 1 of 2) Course: QNT 5040 LEARNING OUTCOME/S: (see syllabus) Date: PURPOSE: To facilitate effective decision making Name of Student: under uncertain conditions by quantifying risk. VALIDITY: Best practices in Monte Carlo simulation. Name of Faculty: 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. <<<<<<<<<< less quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . more quality >>>>>>>>>> Performanc Basic Developing Proficient Accomplished Exemplary Score e Criteria Does not Identifies Identifies some Substantially Effectively Identify the identify the symptoms elements of the identifies the and succinctly problem, or does problem. problem. identifies the problem not identify the problem. right problem. (0 pts) (5 pts) (10 pts) (12 pt) (15 pts) Does not Does not Somewhat Substantially Effectively Describes describe precisely describes describes describes assumptions assumptions and describe the assumptions assumptions and assumptions methods used assumptions and and methods methods used and methods and methods methods used used used (0 pts) (3 pts) (7 pts) (8 pts) (10 pts) Does not Calculates Calculates Calculates Effectively Calculate calculate appropriate appropriate appropriate calculates appropriate statistics using a statistics using statistics using a statistics using statistics statistics using a spreadsheet a spreadsheet spreadsheet a spreadsheet using a spreadsheet (most answers (not all answers (most answers (almost all spreadsheet and/or are not are correct) are correct) answers are does not provide correct) correct) evidence of calculations (0 pt) (13 pts) (21 pts) (25 pts) (30 pts) Does not explain Partially Somewhat Substantially Effectively Explain implications of explains explains explains explains output of implications of implications of implications of implications implications statistical output of output of output of of of analysis statistical statistical statistical output of output of analysis analysis analysis statistical statistical analysis analysis (0 pt) (3pts) (7 pts) (8 pts) (10 pts) Earning maximum points in each box in ‘PROFICIENT’ column and / or points in columns to the right of ‘PROFICIENT’ meets standard. <<<<<<<<<< less quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . more quality >>>>>>>>>> Performanc e Criteria Basic Developing Proficient Accomplished Exemplary Score Does not Generates Partially: Substantially: Effectively: Generates solutions generate solutions (does *generates and *generates and *generates based on appropriate not justify justifies justifies solutions and justifies solutions based conclusions). solutions based based on analysis solutions analysis and on analysis and on analysis and and context; and based on context context. context; and *justifies analysis and *justifies conclusions. context; and conclusions. *justifies conclusions. (0 pt) (7 pts) (15 pts) (17 pts) (20 pts) Does not use May use Generally uses Substantially Effectively Uses prescribed prescribed prescribed prescribed uses prescribed uses format format and format OR format and format and prescribed writing style writing style writing style writing style format and (including (only one) writing style cover sheet and grading (0 pt) (3 pts) (7 pts) (10 pts) rubric) and (8 pts) writing style (language, grammar, punctuation, and spelling) Uses APA Does not provide Does not apply Partially Substantially Effectively references. APA style to applies APA applies APA applies APA format references. style to style to style to all (APA Style Manual 6.0) references. references. references. Optimal quality and quantity of citations. (0 pt) (1pts) (3 pts) (4 pts) (5 pts) OVERALL GRADE (100 total possible points): % Comments: ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ Case Study Report Superior Grain Elevator, Inc. Executive Summary Superior Grain Elevator, Inc. is a company dedicated to storing and loading grain from local silos into freighters and is responsible for sending millions of tons of grain to the Eastern part of Canada and overseas. In average, Superior handles 3.5 ships per week but arrival times are sometimes variable which forces some ships to anchor while both wharfs are busy, producing a demurrage cost of $2,000 per day and ship. The company has recently learned that a new contract has been placed that will greatly increase the volume of ships coming into the facility, Based on estimated data generated by a random sample using a Monte Carlo Simulation with the @Risk excel Addin a simulation testing was conducted in order to analyze the amount of savings when building an extra wharf. After analyzing the results of the exponential distribution, it was determined that the savings have a probability from 25% to 75% of being between $210,868.78 and $392,034.02 a year, with a median value of $283,131.77, hence, Superior Grain Elevator is advised not to build it because savings are too low and economic benefits will not offset the cost of investment nor will the owners receive the expected ROI. Background Superior Grain Elevator was located in Thunder Bay Port, Canada’s third busiest port and very important for the shipment of grain. Operations at this port were centered between railroads and the Seaway; The Seaway was closed during several months for the winter season, but the rest of the year Superior Grain operated and loaded ships with its 14 giant elevators and 2 wharfs, sending grain to many parts of Eastern Canada and around the world. Although the Company tried to have the ships arriving at the port at a steady steam they were not constant and the arrival times were variable. When the two wharfs were busy and a ship arrived, it had to anchor to wait for the wharf to be available, this coincidence made Superior Grain pay a demurrage charge of $2000 per day. Later on, the manager of the port facilities learned that the Canadian Government negotiated a fiveyear agreement of 8 million tons of grain deal with Poland; therefore, Superior Grain Elevator had been assigned with twenty more shipments to Poland each year during the 5 years of the contract. Because of this the company must accurately forecast new interarrival and loading times, compute demurrage cost and determine if building a third wharf would benefit them economically. Problem Would building a third wharf maximize Superior Grain Elevator’s profit by saving them more money than the costs of investment and give the new owners the expected 20% ROI? Analysis To accurately analyze and give an answer to the problem, a Monte Carlo Simulation Model was used; this model randomly generates variables using probability distributions to imitate a process that has uncertain input and output variables. Simulation modeling is used to forecast what will most likely occur in the real world when limited information is available. The variables used in this case are said to be uncertain because nowadays too many factors could affect a ship’s schedule of arriving and/or its loading times (climatic changes or natural disasters, political problems, newly established elevator companies in the port, or any other factor) This made Monte Carlo Simulation the best tool for dealing with this particular situation. When building the simulation an initial fix seed of 1234 was used and such simulation was made with 5,000 iterations, this resulted in several random possible InterArrival and Loading Times that helped analyze and view the data from different situations or scenarios. The simulation was made using the @Risk excel addin, after running it a distribution fitting was made that showed that the best fit was an Exponential distribution, this was shown on the graph where it was obvious that this was the most precise because it withhold the biggest amount of data. Also the exponential distribution is the best when working with cases where times between events and waiting times are the main factor; hence this case study analysis was based on this model. (Taboga. M, 2010) The parameters used in the model were the following: Inter Arrival Times (IAT), Loading Times (LT), Waiting Times, Demurrage Costs, and Savings. The IAT were calculated with the @Risk Adinn using the average InterArrival time (1.70) calculated from the sample taken in a month’s time (between May and June) as the main parameter. On the other hand the parameters used to calculate the LT were 2 and 3, which are the minimum and maximum loading days respectively. For the waiting time the calculation was made by doing an equation using the logic function “IF” which states: If the arrival time is smaller than the minimum time in which either two wharfs is available, the waiting time will be zero. Otherwise it will be that minimum value when either two wharfs become available (=IF(Arrival time of ship I (Current clock time<MIN(Clock time when 1st wharf is ready for next ship: Clock time when 2nd wharf is ready for next ship),MIN(Clock time when 1st wharf is ready for next ship: Clock time when 2nd wharf is ready for next ship) Arrival time of ship I (Current clock time,0)) Demurrage cost is $2,000 (the fee that has to be paid when a ship drops its anchor) times the waiting time. (=$2,000*Wait time for ship I) And last, Savings is defined by the difference of the total demurrage costs with two wharfs and the total demurrage costs with three wharfs. The following table displays the summary statistics of the data obtained by running the above mentioned simulation of the possible savings amount: Table 1 Data Summary Summary Statistics for Savings Demurrage Costs SAVINGS Measure Scenario 1: Scenario 2: (3 Wharfs) 2 Wharfs 3 Wharfs Mean $382,515.49 $54,387.14 $328,128.36 Median $333,699.64 $48,807.47 $283,131.77 Mode $321,671.72 $37,090.92 $212,046.11 Standard $203,373.98 $27,926.00 $182,358.82 Deviation th 25 Percentile $249,676.45 $34,627.87 $210,868.78 th 75 Percentile $458,598.24 $67,909.34 $392,034.02 Skew 2.31 1.49 2.54 Source: @Risk, Microsoft Excel, 2014 To better understand these results, it’s important to remember the initial expectations of the outcome of this project; where it was said that in order to justify the costs of building a third wharf, the new owners think it would be prudent to have at least a 20% ROI, which means that building a third wharf should generate savings higher or equal to $360,00 a year, or at least generate the costs of the investment; hence the third wharf should produce savings of at least $300,00 a year. The first scenario the company is dealing with is: having to work with the only two wharfs it currently has and, with them handle the increase of ships it will receive during this 5 year period. Like the company has a probability from 25% to 75% of having demurrage costs that vary between $249,676.45 and $458,598.24 with a median value of $333,699.64 (as indicated in table 1) with a 40.3% chance of having $300,000 demurrage costs. The other scenario is building a third wharf to work with more ships at a time, speed the process and not have to spend as much in demurrage costs with the increase on the number of ships coming in. This would give them a probability from 25% to 75% of having demurrage costs between $34,627.00 and $67,909.34, a median value of $48,807.47 (these values are a lot less than the ones of the first scenario see table 1) On the other hand, it is important to highlight that the savings have a probability from 25% to 75% of being between $210,868.78 and $392,034.02 a year, with a median value of $283,131.77, which is less than the investment cost of building a third wharf ($300,000 per year) and a lot less than the expected ROI of 20% ($360 per year). This means there is approximately 55% chance of losing or spending more money than saving it because there is a 55% probability of having savings equal or less than $300,000 and only a 30.5% approximate chance of reaching the expected 20% ROI. Finally, when analyzing both scenarios it was important to take a look at the skewness which has a positive value; this confirms the tendency of the histogram of being skewed to the left (See appendix). Conclusion & Recommendations From the analysis of the simulation it is important to take into consideration the savings, which were expected to be higher than $300,000. And even though the demurrage costs with three wharfs would be a lot less than with only two, the amount of money invested to build it, will not be offset by the economic benefits or savings it will bring. Building the third wharf is not a cost effective alternative for Superior Grain Elevator Inc. unless the demurrage savings increases to $360,000, which is the amount needed per season in order to get to the company’s expected goal of 20% ROI. Although Superior Grain is advised not to build another wharf, we do advise them to analyze their current processes to find any possible delays that could be affecting the loading times in order to optimize it, for example, a better and more efficient maintenance of the machines used for loading the ships, invest in new and more advance technology that could enhance the processes, or even find more effective techniques to deal faster with each ship. Also we advise that they analyze their communication systems in order to find a more efficient channel and/or gadget (satellite communication, GPS systems, tracking devices, applications, etc.) that will help them predict and deal correctly with the variations in the interarrival times. Bibliography Bell, P. (1998). Superior Grain Elevator, Inc. Ivey Management Services. Version: (A) 20100128. Taboga, M. (2010). Lectures on probability and statistics. Retrieved February 3, 2014 from http://www.statlect.com/ucdexp1.htm @Risk, Risk Analysis and Simulations Add in Tool for Microsoft Excel. (2010) Ithaca, NY: Palisades Corporation. Appendix Appendix 1 – Excel file el_Setupgroupproject! Andrea Mari Fabi.xlsx
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