W19_TRI_ EPCI Schedule Analysis using Simulation Approach

1.      Problem recognition, definition and evaluation

After conducted EPCI cost analysis, the schedule of EPCI is needed to be determined for the next tender. Although the schedule is not part of tender criteria whether it’s failed or success. The firmed schedule is required for contractors to bid EPCI price. In the last tender, a bidder proposed 30 months for executing project in complete phase.

2.      Development of the feasible alternative

The simulation approach using @risk was selected to conduct the schedule analysis. The input of simulation was original schedule, bidder proposal in the last tender and third independent consultant.

3.      Development of the outcome

Triangular distribution was used since it more closely reflects the variation in typical project data. In the triangular distribution, a straight line relationship is assumed between the minimum value, up to the most likely/moderate value, and from the moderate value down to the maximum value.

 

• Minimum is the value based on analysis of the best case scenario, which means the shortest time duration (at which the probability is almost zero)

• Most likely is the value based on realistic expectations or realistic efforts for the given task element (at which the probability is greatest)

• Maximum is the value based on analysis of the worst case scenario, which means the longest time duration (at which the probability is zero)

The Monte Carlo simulation was used to generate with total 10,000 iterations and 5% limiter of two tails for schedule.
  

4.      Selection of criteria

Peterson et al (2005) stated that one of the misapplications in conducting risk analysis is assigning probability distributions to every one of hundreds of line items, just because the software enables the modeler to do so. This will create a highly inaccurate analysis that shows far more certainty in the outcome than is realistic. These will be avoided by only assigned probability distribution to critical tasks. The Criticality Index is able to identify tasks that are likely to cause delays to the project. By monitoring tasks with a high Criticality Index a project is less likely to be late.

The criticality test generated 53 out of 311 tasks that have equal to or more than 47% criticality index as shown in Figure 1. The three-point estimates given for other tasks besides these 53 tasks was removed, and then re-run the simulation was required.

Figure 1. Criticality Index

5.      Analysis

The final result of schedule simulation is shown in Figure 2, and summary of the statistic is shown in Figure 3.

Figure 2. Simulated EPCI Schedule

Figure 3. Summary Statistic of Simulated EPCI Schedule

6.      Selection of alternative

According to Figure 1 and Figure 2 above, 28 months was selected as the optimized schedule since it represented the 75% confidence level that was used as a threshold to determine the baseline schedule.

7.      Performance monitoring and post-evaluation of results

Although the confidence level was not so high, the baseline schedule shall be proposed to management to get approval. However, a 75% confidence level should be informed to them and might be upgraded to higher confidence level to leverage the possibility of successful tender if there were no economic constraints in this project. Otherwise, a negotiation with prospective bidders must be held to mitigate project delay, and to avoid over submitted-price due to longer or shorter project duration.

 

References:

·  Asmoro, Trian H. and Autie, M.P. (2012, May 16). Balancing Project Schedule & Cost on Sour Gas Development Project Case Study (pp. 9). Doha, Qatar: the SPE International Production and Operations Conference and Exhibition
·  Palisade Decision Tools. (2005). @RISK 4.1 for Project. Newfield, NY USA: Palisade Corporation
· Peterson, S.K et al. (2005). Risk and Uncertainty Management – Best Practices and Misapplications for Cost and Schedule Estimates. Dallas Texas, USA:  SPE Annual Technical Conference and Exhibition