The Problem
A mid-sized chemical company was targeting four different regions as potential sites for its new production plant. The company asked IPA for guidance during the front-end loading (FEL) 1 phase to help identify the best opportunity based on regional risks.
IPA’s Solution
IPA provided baseline data to support the company’s broader risk matrix analysis, using three approaches to address key questions and concerns:
- Historical Cost & Schedule Outcomes by Region
IPA provided distributions of historical cost and schedule outcomes for each of the four regions, given a similar production plant with industry average cost and schedule targets. Relative cost forecasts were generated for each region using IPA’s regional factor data. The prevalent contracting strategies for the regions were also evaluated and differences in performance reported when significant. - Event-Based Risk
A heat map of event-based risks was generated to compare the four regions. The evaluated risks included construction labor shortages, engineering labor shortages, unanticipated wage changes, labor strikes, price changes, and weather events. The frequencies and expected values of these events specific to the region were calculated and reported. The risk relative to the global average was used as the basis to assemble the heat map. - Regional Risks vs. General Project Risks
One particular region was subject to high variance in project outcomes. The client requested a deeper analysis of the risks experienced by projects in that region to determine if the risks were increased due to factors specific to the region or if they were general project risks independent of region. The analysis identified several issues that led to gaps in practices that led to increases in the effects of the regional risks. These lessons learned then provided a basis for reducing risks on subsequent projects in the region.
Delivery and Client Use of the Results
The client used the risk and cost and schedule performance data to compare and contrast potential locations and ultimately inform the business decision-making process.