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Contractor Profit & Loss Forecast

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Comes with one PDF document (59 pages), one unsolved Excel spreadsheet (Contractor P&L Forecast Unsolved.xlsx), one solved Excel spreadsheet (Contractor P&L Forecast Solved.xlsx) and 12 videos with 75′ 30″ of training.

Description

Contractor Profit & Loss Forecast

This simple  profit and loss forecast allows the novice user to be introduced to @RISK.  It shows the major components on how to  define, and execute a Monte Carlo simulation and then generate and interpret graphs on its results.

Joe runs a small contracting business repairing residential exteriors.  He and his two cousins working for him mainly contract on painting jobs, roofs repairs and landscaping.  On average, he is able to service 8 monthly contracts during non-winter months and 4 monthly contracts during winter months, December to February. If there is an increase in demand, he can contract additional manpower at a higher, yet variable, hourly rate.

Preparing next year’s plan, he is considering the possibility of hiring a third fixed employee on payroll.  This would allow him to increase his offer and eventually be able to service an average of 10 contracts during non-winter months. By doing this, he would be able to increase his total annual revenue by servicing more customers and decrease the expensive variable cost when he needs to subcontract additional manpower.  However, during winter, he would be tolled by a higher fixed cost, probably making him run on losses during three consecutive months.

On the other hand, he is also considering downsizing his operations by excluding one of his cousins out of his payroll.  With a downsized operation, he could only be able to handle an average of 7 service contracts per month on non-winter months. This would lighten up his fixed cost burden during the low activity winter months. He would be able to hire any required flexible manpower at a per hour basis. Evidently, this option could be less profitable but more secure and flexible

Any model in @RISK should agree in general to the following step-by-step process: Develop a deterministic model, insert @RISK input distributions, define output variables, insert correlations, define configuration settings, execute a simulation, generate charts and reports, and then finally interpret and present results.  This example goes through the whole process.

Additional information

Application

@RISK

Collaborator

Fernando Hernandez

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