W20_Hari_Technical Lessons


In the realm of Total Cost Management, there are many skills and tools which are developed to guide and assist Project Owners/Asset Managers to track and manage projects. Particularly two aspects were relevant and provide better technique to improve current practices. In this blog, brief description of these 2 methods will be shown for general reference.

  1. Early and Late Dates in S-Curve


    During scheduling, early dates and late dates are calculated to show free float for each activity and then to determine total float for entire project.


    In execution phase, S-curves are used for project tracking and controlling. S-curves are an important project management tool. They allow the progress of a project to be tracked visually over time, and form a historical record of what has happened to date. Analyses of S-curves allow project managers to quickly identify project growth, slippage, and potential problems that could adversely impact the project if no remedial action is taken.


    The current practice is using early dates only as BCWS in S-curve, which leads to many alerts and delay trigger although the actual progress is ahead of late dates. This removes the function of S-curve as accurate progress tracking tool and generates false alarms.


    By incorporating late dates into the S-curves, visibility is improved for tracking actual progress in relation to both early dates and late dates. The example below shows the application of ED (shown as PV) and LD (shown as LV) in S-curve. Future project planning and controlling should engage ED and LD in S-curve to give visual representation of project status.




  2. Earned Schedule into EVM

The EVM technique is extensively used in government and private projects internationally, and is described by numerous standards, e.g. AS 2006, ANSI 1998. By employing EVM, the project manager can get a snapshot of the project status in terms of cost and schedule; and obtain performance metrics that guide Project Managers to take corrective actions.

However EVM does have limitations. While EVM is excellent in quantitatively expressing and analyzing project cost performance, the success has not extended to schedule performance. Main reason for gap in schedule performance results in using unit of cost rather than time resulting in SV catching up to zero at the end of project, regardless of time delays. Also comparison with time based network diagram (e.g. critical path) is difficult.

The concept of Earned Schedule as extension of EVM addressed the gaps mentioned above. ES measures unit of time, thus accurately capturing the schedule index and variance through the project life cycle. The figure below shows the additional parameter, i.e. SV(t) which is schedule variance in number of week, which is different from traditional EVM SV parameter that gives variance in unit of cost. The variation of SV(t) throughout project lifecycle is shown at the bottom. While SV is back to zero (not reflecting 5 wks delay of completion), the SV(t) shows exactly the duration delay at each time interval until finish.


In addition to SV(t), there are also other parameters introduced in ES method, which compliments EVM. The list of ES parameters are shown below against corresponding parameters in EVM. The calculation methodology of status index, future work estimates and prediction are same as EVM, but done in time domain. This gives rise to new matrix of performance indicators which are helpful to Project Managers.


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[1] Czarnigowska, A., Joskowski, P., Biruk, S. (2011). Project Performance Reporting And Prediction: Extension of Earned Value Management. Retrieved from:

W19_Hari_Performance Dashboard


Problem Statement

In managing multiple projects, a dashboard is necessary to give status summary in brief and to trigger early warning alert. At present, the SPI vs CPI chart is used to monitor the performance of each project. While the chart shows the key PMB of the projects, it does not give an insight to other aspects of each project which have impact on overall project performance.

Problem Statement: To explore graphical tool as dashboard for managing multiple projects


Development of Feasible Alternatives

  1. Using SPI vs CPI chart (continue current method)
  2. Using Radar Chart


Evaluation of Alternatives

Option 1: The resulting SPI vs CPI chart is shown in Figure 1. From the chart, it is clearly visible that Project C is in jeopardy (significantly delayed in terms of schedule and warrants highest attention). Project A is also in trouble and requires recovery action; while Project B is within +/- 10% of schedule but 20% over the cost estimate.

Figure 1: SPI vs CPI Chart


Option 2: The radar chart was developed to depict 6 key dimensions of project performance, as shown in Figure 2. The 6 dimensions are:

  1. SPI (schedule)
  2. CPI (cost)
  3. Quality Index
  4. Project Documentation
  5. Process Compliance
  6. Safety Index

The figure below clearly shows that Project B is the worst in overall performance, and urgent improvement is required in terms of schedule, quality, process compliance and cost. This project has passed the contractual dateline and liable for penalty. Further analysis reveals that resources competency; supervision and compliance are weak, leading to frequent rework and repairs due to poor quality and process incompliance. This has caused delays in overall schedule and also increase in cost due to unnecessary reworks and repairs. Immediate action is required to recover the remaining work and drive towards project closure.

Project C is in jeopardy in terms of schedule but its performance in terms of other 5 aspects are better than average. Detail review shows that Project C has just started 2 weeks ago and still has limited resources (only 35% of positions budgeted in Project HR plan have been filled). This has delayed the project ramp up which was planned, thus giving low SPI. In this case, the hiring should be accelerated by introducing premium compensation/rate for competency resources since project cost is still below budget (CPI is 1.1).

Project A which was within +/- 10% of SPI; actually has areas of improvement required for cost, project documentation and process compliance.

Figure 2: Radar chart of project performance


Selection Criteria and Selection of Preferred Alternative

The selection was done based on visibility of key project aspect performances. Radar chart (option 2) was selected due to holistic overview provided to measure performance of each project. The radar chart can be configured to represent “danger” and “warning” zones as shown in Figure 3. Moving forward, radar chart will be used as dashboard for multi project tracking.

Figure 3: Radar Chart with different zones


Performance Monitoring and Post Evaluation of Results

Radar charts have more information compared to basic SPI vs CPI charts. The examples given above can be extended to include more dimensions, depending on nature of project and level of depth required. In the dashboard, it will be useful to show total % completion of scope (as text label) thus giving reviewer an idea of the current project phase. Further refinement should be made as required.


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  1. NASA (April 2000). PDRI: Project Definition Rating Index. Retrieved from:

W18_Hari_Applying 3D WBS To Telecommunication RAN Rollout

3-Dimensional (3D) Work Breakdown Structure for Telecommunication RAN Projects by Hari Kumar Sellappan


Problem Statement

In telecommunication RAN projects, the number of geographical work locations are typically more than 1000 sites, giving rise to a large GBS. In each location there are multiple products which are required (typically acquisition, civil works, telco installation and optimization); these are named as PBS. Correspondingly each product has its own activities breakdown, which is structured as ABS. The selected WBS will determine how the cost or schedule is rolled up, and later updated and controlled. Thus, the challenge is to define the most optimal WBS which can be used for cost estimation, scheduling and project controlling during execution.

Problem Statement: Selection of most optimal WBS for RAN Network Rollout



Development of Feasible Alternatives

Several options available as shown below in table 1. Option B is the default WBS used in past projects.

Table 1: Feasible options for WBS



Evaluation of Alternatives

Option A: The WBS structure of is shown in Figure 1. PBS forms Level 1, and GBS is Level 2. Within each GBS (locations), there will be the corresponding ABS, which forms WBS Level 3.

Figure 1: WBS of Option A



Option B: The WBS structure of is shown in Figure 2. GBS forms Level 1, and PBS is Level 2. Within each PBS, there will be the corresponding ABS, which forms WBS Level 3.

Figure 2: WBS of Option B


Option C: A 3-dimensional model of WBS is presented in Figure 3, where PBS forms X-axis, ABS forms Y-Axis and GBS forms Z-Axis. In such model we can visualize the building block as 2-D model of 1 particular location (PBS and ABS), then replicate the same in the third dimension for other locations within the project scope (GBS).


Figure 3: WBS 3D Model for Option C



Selection Criteria and Comparison of Alternatives

Evaluation of each WBS option was done based on 3 main criteria below:

  1. Flexible Rollup of Cost and Schedule
  2. Optimal Work Team (at sub-region across discipline)
  3. Best visibility and flexibility for reporting progress, project controlling and analysis

The comparisons are listed in table 2.

Table 2: Comparison of WBS 3 options



Selection of Preferred Alternative

Option C was selected since it meets the criteria and will be employed in subsequent RAN projects for better visibility, cost estimation & control, schedule planning, progress tracking and analysis. The 3D WBS model is very suitable for RAN cell sites projects since it involves similar PBS and ABS, but at large number of locations (GBS). The concept of 3D WBS to be employed in future RAN rollout projects are shown in Figure 4 below.

Figure 4: WBS 3D Model for RAN Rollout


Performance Monitoring and Post Evaluation of Results

While the use of 3D WBS has many advantages, it also requires strong project controls team to develop complex coding structures for each activities and work packages. The 3D WBS model is not widely used and requires training of project team members to change their paradigm. Mapping to OBS for accountability generates a 4th dimension matrix. As such, comprehensive training to project controls team and related project teams is critical to avoid error and to realize the full potential offered by 3D WBS model. Furthermore, existing financial and project management tools will require minor modification to accommodate the 3-D WBS model.


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  1. Taylor, M. (2009). How to develop Work Breakdown Structures. Retrieved from:

W17_Hari_Sensitivity Analysis


**Disclaimer: all value and cost used in this blog are NOT from actual data and used merely for purpose of illustrating BEP & Sensitivity Analysis Only.


Problem Statement

Decision was made in W15 Blog to proceed with in-building system proposal based on BEP Analysis (https://aacecasablanca.wordpress.com/2012/05/09/w15_hari_break-even-analysis/). However, the cost, revenue and interest rate values used are estimates of perceived future based on current knowledge. There is a high chance of any of the values used (investment, revenue, cost & interest rates) could be different from actual, thus changing the economic feasibility of the project.

Problem statement:

  1. To determine the profitability of the proposal if the estimated values used are subject to varying % changes (for Investment, Revenue, Cost and Interest Rate)
  2. To define the min/max value of each parameter which leads to decision reversal (project is economically not feasible)


BEP Analysis Using Most Likely Values

Using the parameters and most likely values shown in Table 1, we can evaluate the Present Worth of the project at MARR of 2% per month (24% p.a.). Study period used will be 48 months which will be estimated end of life of the equipment deployed.


Table 1: Investment Cost and Monthly Nett Cash Flow (in IDR)


PW (2%, 48months) = I + (P/A,2%,48) * (R-E-S)

PW (2%, 48months) = 465,000,000 + (P/A,2%,48) * (17,950,000)

PW (2%, 48months) = 465,000,000 + 30.6731 * (17,950,000)

PW (2%, 48months) = 85,582,496 (economically feasible)


Methodology (Tools) Used and Results

The methodology used is sensitivity analysis using Spider Plot to capture the varying effects of investment, revenue, cost and interest rate values (as % change from original estimate).

First the factors impacting the PW calculation are identified as below:

  1. Investment, I (one time Capex)
  2. Revenue, R (monthly)
  3. Electricity Cost, E (monthly)
  4. Spectrum Fee, S (monthly)
  5. MARR (Interest Rate), i%

Next, the PW is calculated for variability of each factor in incremental % change (from -100% to +100%). The results obtained was translated into a spider plot shown in Figure 1 below.

Figure 1: Spider Plot for In Building Project PW


Sensitivity Analysis

The spider plot shows the degree of sensitivity of the PW to each of the 5 factors involved in calculation. Summary of sensitivity analysis:

  1. PW is insensitive to E (electricity cost) and S (spectrum cost) as slope is relatively flat and does not lead to negative PW.
  2. PW is highly sensitive to I (investment), R (Revenue) and MARR (i%), steep slopes and leading to negative PW


Further analysis shows several decision reversal limits which causes project PW < 0 (economically not feasible) as below:

  1. Investment, I > IDR 550,582,000 (i.e. I > 18.5% from original estimate). This condition is less probable to happen considering that the actual BOQ and price quotations have been secured for deployment of in-building solution. Thus this limit of decision reversal is less likely to happen except in cases of force majeure. Mitigation can be done by signing fix price contract with vendor with limited incentive/contingency.
  2. Monthly Revenue, R < IDR 17,209,000 (i.e. R < -14.0.% from original estimate). This condition has medium risk due to high dependency on overall sales strategy covering marketing engagement, product choices, employee tariff plans, customer service and subscriber satisfaction. Thus, the revenue estimates must be done in detail with specific sales strategy and then monitored constantly to ensure the limit of decision reversal is NOT CROSSED.
  3. MARR, i% > 2.8% per month (MARR> 43% from original estimate) . This condition can be mitigated if the capital borrowing cost can be fixed for the duration of 4 years at 24% p.a. Based on past experience and current macro economic condition, fix borrowing cost at 24% p.a. is very likely, so posing a minimal risk to the project economic analysis.

Figure 2 below shows the decision reversal limits (points) discussed above.

Figure 2: Decision Reversal Limits (points)



In conclusion, the sensitivity analysis shows that I, R and MARR i% values significantly affect the economic measure of merit, thus estimates used must be highly accurate and reliable. Each of the factor has decision reversal point (PW<0 limit) which must be considered.

However, the risk of I and MARR varying significantly can be mitigated at the beginning, so both factor contribute to minimal risk at current situation. This leaves MONTHLY REVENUE (R) as the key factor for this project profitability and sustainability.

If revenue projected (monthly) is below IDR 17,209,000 then the project will be deemed not economically feasible (PW<0). So accurate Monthly Revenue estimate must be employed by historical data and with matching sales strategy. In addition, monthly revenue range estimate (e.g. 90% confidence interval) is also useful before engaging this project. If lower limit of 90% confidence interval fall within 0% to -14%, then there is small risk of project profitability going into red. Else, deeper analysis and consideration should be conducted using probabilistic risk analysis before engaging the project.


Performance Monitoring

Monthly monitoring of the revenue and cost shall be conducted to establish the variance from initial estimates. This will track the changes in the PW trajectory over time and give insight to what actions must be taken if project falls into non-profitable mode.


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[1] Sullivan, W.G., Wicks, E.M., Koelling, C.P. (2009). Engineering Economy, Fifteenth edition. Pearson International Edition, 2012, Chapter 9, pages 380-394.

[2] Gutierrez, P.H. and Dalsted, N.L. Colorado State University Extension. (Aug 29, 2011). Break-Even Method of Investment Analysis. Retrieved from: http://www.ext.colostate.edu/pubs/farmmgt/03759.html

[3] Vlahos, K. (2000). Financial Modeling & Risk Analysis. Retrieved from: http://faculty.london.edu/kvlahos/dra/ppp/RiskAnalysis.ppt

W15_Hari_Break-even Analysis

Break-Even Analysis for In building System By Hari Kumar Sellappan

**Disclaimer: all value and cost used in this blog are NOT from actual data and used merely for purpose of illustrating BEP Analysis

Opportunity Statement

There was a sales opportunity presented to my team for analysis where a corporate customer has offer to promote our services to their employees and in return we have to enhance our mobile broadband services in their premises. The location was a 6 storey office building with 620 employees. Considering the revenue potential and cost involved, an economic analysis was required before making decision to accept the proposal.

Feasible Alternatives

  1. Deploy in-building system (Accept the Proposal)
  2. Do Nothing (Refuse the Proposal)

Analysis and Comparison of Alternatives

The deployment of in-building system will have a fixed cost of infra development, telco equipment cost and distributed antenna system at the beginning. During operations, there will be monthly cost for utility and spectrum fee. All these cost will have to be considered against a monthly sales revenue projection. Table 1 below shows the Investment Cost and Monthly Nett Cash Flow

Table 1: Investment Cost and Monthly Nett Cash Flow (in IDR)

“Breakeven Analysis” was used to calculate the breakeven point (in months) of the proposal. MARR of 24% p.a. was used, translates into 2% per month.

To obtain BEP (X months), the following equation was determined:

  • Investment Cost, I = Monthly Nett Cash Flow [P/A, 2%, X)
  • 465,000,000 = 17,950,000 [P/A, 2%, X]
  • [P/A, 2%, X] = 25.91
  • Looking up the discrete compounding table, value of BEP, X = 37 months

Criteria of Selection and Selection of Preferred Alternative

The selection criteria of possible alternatives are based on the BEP (months). As rule of thumb for commercial revenue generating projects, the BEP is expected below 4 years. Since alternative 1 (deploying in-building system) gives BEP of 37 months, the proposal will be accepted and this project will be engaged.

Performance Monitoring

Further analysis is required to determine sensitivity of monthly cost and estimated revenue towards the BEP. And yearly evaluation is required to ensure the projected cost and revenue estimated is fairly accurate and adapted for similar analysis for other situations.

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[1] Sullivan, W.G., Wicks, E.M., Koelling, C.P. (2009). Engineering Economy, Fifteenth edition. Pearson International Edition, 2012, Chapter 9, pages 380-394.

[2] Gutierrez, P.H. and Dalsted, N.L. Colorado State University Extension. (Aug 29, 2011). Break-Even Method of Investment Analysis. Retrieved from: http://www.ext.colostate.edu/pubs/farmmgt/03759.html

[3] Berry, T. Breakeven Analysis. Retrieved from: http://articles.bplans.com/writing-a-business-plan/break-even-analysis/131