India has become a key provider of outsourced engineering serices for companies around the world. Larsen & Toubro Limited (L&T) is a major engineering and manufacturing company in the Indian private sector. The L&T Institute of Project Management conducts education and research in project management, and used @RISK to help engineering outsourcing firms to pinpoint risks that might impede project success.
While the Indian engineering outsourcing industry has proven itself in the global playing field, the complexity of software applications has grown along with customer expectations–greatly increasing the type of risks that could significantly impact the growth and reputation of the engineering outsourcing industry. These risks include service provider's inability to use the latest technologies due to proprietary reasons or other companies having an unseen competitive advantage.
Cultural mismatches or lack of synergy between expectations of sales and delivery teams can also lead to uncertainty, as can personal interactions and processes, organisational structures of service providers and customers, trust between teams, continuous on-boarding and attrition of personnel, etc.. Furthermore, gaps in technical understanding and alignment of project objectives across outsourcing and customer teams also add to uncertainty.
All of these factors can impact project scope and delivery schedules that can greatly impact customer satisfaction. Thus, the L&T Institue of Project Management conducted a study using @RISK to find a methodology that would allow these companies to best manage these risks.
“We chose @RISK for its ease of use and compatibility with Microsoft Excel,” said Dr Chakradhar Iyyunni, Deputy General Manager and Faculty at the L&T Institute of Project Management. “@RISK imports all the analysis into Excel, which means that we can use all the formulae in the software alongside all the @RISK features – a powerful combination for statistical analysis.”
Using a three-point estimation (optimistic, most likely and pessimistic), the team was able to construct probability distributions in @RISK to represent the uncertainty.
The analysis showed that at a 90% confidence level, an engineering project team could complete the work in 887 man-hours as if the project was being undertaken for the first time. For a 95% confidence level, the man-hours required would be 902 hours. This estimate could be used for repeat projects. 107 of these 902 hours could be used for contingency and a partial use of these hours would likely be acceptable to the client at the time of pre-project approval for billing. However, for a 99% confidence level, the team would require 925 hours to complete the project including 130 hours for contingency and a partial use of these hours would be acceptable to the client (pre-project approval) for billing as a trade-off to adhering to a stricter schedule.
This study concluded that this kind of three-point estimation using Monte Carlo simulation was a better way of creating robust project delivery schedules as opposed to a detailed risk analysis exercise, especially for short duration projects that are the norm in the engineering outsourcing industry.