Week 6_ Agbato Oluwabusayo_Determining the most economic investment _Week 17 _SeeGod Meregini

Problem Recognition/Evaluation

In my company, one of the services rendered by my department involves purchasing pumps for various company applications. I was leading a group of purchasing team in evaluating which pump to purchase that would result in an overall economic savings for the company.

For this specific purchase, the selected pump would only be utilized for one year and would have no market value at the end of the year.

Development of Feasible Alternatives/Solutions

Examination of three feasible investments is used based on total life-cycle costs.

In carrying out this analysis, an assumption is that the engineering economic analysis is present economy studies because comparison is done for only one year hence the time value of money is not a factor.

Alternative 1-

Purchasing Pump A

Alternative 2 –

Purchasing Pump B

Alternative 3-

Purchasing Pump C

Probable Outcomes of Alternatives / Solutions.

A number of meetings were held with various key stakeholders in the purchasing departments and financial divisions within the company including various vendors in order to know the technical and commercial details of the pumps.

Costs used are factored but have same equivalence with original for the purpose of showing the final result.

Key Technical/Commercial Parameters	Pump A	Pump B	Pump C
Purchase price	$166,000	$168,500	$170,000
Annual Maintenance	$8,000	$9,500	$10,500
Efficiency	70%	80%	90%

Selection Criteria in determining the solution

1. The total annual cost of owning and operating should be the most economical.

2. The selected pump should have higher operability.

Analysis and Comparison of the alternatives

The analysis was based on the fact that electric power costs $0.08 per kWh and that the pump would operate at 4,500 hours per year. Also, the pumps to be purchased are capable of delivering 100 hp.

With the knowledge that 1hp = 0.746KW, calculations for each alternatives are given below.

Alternative 1- Purchasing Pump A

Annual expense electric power =(100hp/0.7)(0.746kW/hp)($0.08/kWh)(4,500 hours/yr) = $38,365.68.

Maintenance costs = $8,000

Purchase Price = $ 166,000

Total annual cost of owning and operating = $212,366

Alternative 2 – Purchasing Pump B

Annual expense electric power =(100hp/0.8)(0.746kW/hp)($0.08/kWh)(4,500 hours/yr) = $33,570.

Maintenance costs = $9,500

Purchase Price = $168,500

Total annual cost of owning and operating = $211,570

Alternative 3- Purchasing Pump C

Annual expense electric power =(100hp/0.9)(0.746kW/hp)($0.08/kWh)(4,500 hours/yr) = $29,840.

Maintenance costs = $10,500

Purchase Price = $170,000

Total annual cost of owning and operating = $210,340

Selection of Preferred Alternative

Based on the above analysis and criteria, I recommend ALTERNATIVE 3.

· This alternative satisfies criteria 1 because it has the lowest total annual cost.

· Using only the annual energy expense (green background)

o Alternative 3 has 22% reduction in cost compared to Alternative 1

o Alternative 3 has 11% cost reduction compared to Alternative 2

· It also satisfies criteria 2 because it is more energy efficient having an efficiency of about 90% hence has the capacity to produce more output.

Performance Monitoring/Post Evaluation

This would be monitored by constantly evaluating:

· Its reliability over the coming months during its life.

· Any reduced or additional costs that might be incurred due to other risks like new technology associated with design in the higher efficient pump.

References

Sullivan, W. G., Wicks, E.M., & Koelling, C.P. (2009). Cost Concepts and Design Economics. In M.J. Horton (Ed.), Engineering economy (15^th ed.) (chapter 2) (pp. 20 - 25). New Jersey, NJ: Pearson Education, Inc.

United States Government Accountability Office (2009, March). Cost Risk and Uncertainty. GAO Cost Estimating and Assessment Guide. Best Practices for Developing and Managing Capital Program Costs. (chapter 14) (pp.160).Washington, DC: GAO.

AACE International Education Board. (2006). Risk Management. In J.K.Hollmann (Ed), Total cost management framework – A process for applying the Skills & knowledge of cost engineering (1st ed) (chapter 7.6.1) (pp.159-160). Morgantown, West Virginia: AACE International.

W7_Abiola Ojo_Risk Analysis for Readington School

Problem Statement

As part of the process for making a final investment decision for my school business, I had to conduct a risk analysis on the project uncertainties in other to understand the possible deviations from the expected outcome. Risks are typically defined as negative events, such as losing money on a venture or a storm creating large insurance claims.

Alternatives or Methods

In evaluating the project risks, three quantitative methods were used namely: Deterministic Analysis, Monte Carlo Simulation, and Tornado Diagram.

1. Deterministic Analysis: This method uses single-point estimates of risk by assigning values for discrete scenarios to see what the outcome might be in each. In this study, three different outcome were examined: worst case, best case, and most likely case. The uncertainty parameters analyzed in this study were: initial capital, operating expenditure, number of students enrolled, school fees, and loan interest rate. For each of these parameters, values were given for three cases as shown in Fig 1. These values are based on a combination of historic data and actual market prices. Outcomes analyzed were NPV and IRR.

Fig 1

2. Monte Carlo Simulation: In this method, values are sampled at random from the input probability distributions. Each set of samples is called an iteration, and the resulting outcome from that sample is recorded. Monte Carlo simulation does this hundreds or thousands of times, and the result is a probability distribution of possible outcomes. The resulting data is plot in a chart showing the different outcomes and their chances of occurrence. In this study, the variables: initial capital, operating expenditure, number of students enrolled, school fees, and loan interest rate were used to calculate the outcome which is yearly profit. Random values were generated for each of the variables from the range shown in Fig 1. The simulation was done on a spreadsheet with 5,000 iterations.

3. Tornado Diagram: This graphically displays the result of single-factor sensitivity analysis.  This lets one evaluate the risk associated with the uncertainty in each of the variables that affect the outcome. Single-factor analysis means that we measure the effect on the outcome of each factor, one at a time, while holding the others at their nominal (or base) value. Each factor is adjusted between the specified minimum (or low) and maximum (or high) values while recording the value of the outcome.  It then plots the resulting data in a bar chart. The uncertainty in the parameter associated with the largest bar, the one at the top of the chart, has the maximum impact on the result, with each successive lower bar having a lesser impact. In this study, the effect of the following parameters on the NPV was evaluated: initial capital, CAPEX depreciation, operating expenditure, number of students enrolled, school fees, and loan interest rate. The ranges shown in Fig 1 were used.

Outcome of the Alternatives

1. Deterministic Analysis:

Fig 2

2. Monte Carlo Simulation:

Fig 3

3. Tornado Diagram:

Fig 4

Selection Criteria

The following objectives were set for the analysis:

• Deterministic Analysis: NPV & IRR should be positive for all three cases
• Monte Carlo Simulation: Yearly profit should be positive between 20% to 40% range of the cumulative probability curve.
• Tornado Diagram: NPV should remain positive for both the high and low range for all the variables

Analysis of Alternatives

1. Deterministic Analysis:

The results of the Deterministic Analysis in fig 2 shows that NPV and IRR is positive for both the ‘Best Case’ and ‘Most Likely Case’ while NPV and IRR are negative for the ‘Worst Case’.

2. Monte Carlo Simulation:

The result of the Monte Carlo Simulation in fig 3 shows that within the 20% to 40% range of cumulative probability curve, the yearly profit remains positive ranging between N6MM and N14MM per annum.

3. Tornado Diagram:

The Tornado diagram in fig 4 shows the effect of six parameters on the NPV.  The result shows that “CAPEX depreciation per annum” has the maximum impact on NPV, while “loan interest” has the least impact. It also shows that the NPV remains positive for both the high and low range for all the variables.

Alternative Selected or Decision

Based on the analysis above, most of the three objectives or criteria were met except that for the deterministic analysis, NPV and IRR were negative for the ‘Worst Case’ scenario. Base on this analysis, a decision was made  to move the project forward using the ‘Most Likely’ estimates as the project base case, however strict fiscal policies will be put in place to ensure that the project does not get to the ‘Worst Case’ scenario.

Performance Monitoring

1.     Ensure strict fiscal policies so that project does not get to the ‘Worst Case’ scenario.

2.     Actual CAPEX and operating cost shall be closely monitored and controlled so as not to exceed the worst case estimates in order to realize positive NPV’s and IRR.

3.     Effort will be made to minimize the yearly CAPEX depreciation as reasonably possible because it has the greatest impact on NPV.

References

1.     Engineering Economy, 14th Edition. William G. Sullivan, Elin M. Wicks, C. Patrick Koelling, Chapter 1, Page 27 & Chapter 12, Page 515-567

2.     Skills & Knowledge of Cost Engineering, AACE International, 5^th Edition Revised, Chapters 27 & 31

3.     Project Management Using Earned Value, Humpreys & Associates, Chapter 3