The Larsen & Toubro Limited (L&T) Institute of Project Management, a centre of excellence in project management, has developed a methodology to enable service providers in the Indian engineering outsourcing industry to determine risks that impact their ability to accurately define project scope and schedule. The L&T Institute used @RISK, Palisade’s Monte Carlo simulation tool, for this study. Using this approach, service providers can accurately determine project scope, schedules and therefore delivery at the start of their customer projects.
Over the last 20 years, the Indian engineering outsourcing industry has established itself as a provider of reliable services. However during this time, the complexity of software technologies has grown along with customer expectations – greatly increasing the risks related to successful project development and execution, which left unchecked could significantly impact the growth and reputation of the engineering outsourcing industry.
Aside from the market uncertainties emanating from political, economic, social, regulatory and environmental factors, there are many sources of uncertainty that affect service providers in the engineering outsourcing industry. They include technology-induced risk factors; and relationships between engineering outsourcing teams and customers due to a cultural mismatch or lack of synergy between expectations of sales and delivery teams. People interaction and processes also create uncertainty caused by personal aspects of knowledge workers (e.g. their drivers and motivations), organisational structures of service providers and customers, trust between teams, continuous on-boarding and attrition of personnel and the like. Furthermore, gaps in technical understanding and alignment of project objectives across outsourcing and customer teams also add to uncertainty.
These uncertainties give rise to a combination of project scope and schedule risks during execution that greatly impact the successful delivery of customer assignments and in turn the customer satisfaction delivered by engineering outsourcing service providers.
Using @RISK as its solution of choice for quantitative statistical modelling, the Institute, set up a study based on a typical outsourced design project of 800 hours and included 15 key activities that are characteristic of such an assignment – ranging from panel, process and layout studies; fixture design; review of design with simulation and client teams, design quality control, preparation for assembly through to final quality control. For each of these activities, the study considered the impact of eight key risk triggers such as lack of clarity on project requirements from customers, skill resource availability, environmental risks and technology.
“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) for each of the 15 activities, the team was able to construct probability distributions in @RISK to represent the uncertainty around each activity. They then conducted simulations consisting of 15000 iterations each as part of the experiment. This means the team was able to examine 15000 different scenarios with the click of a mouse.
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. This is also a more vigorous way of deriving risk schedules. For instance, it takes into consideration the risk perception and control available to the teams, which are mostly seen as ad hoc methods in other risk analysis methodologies. Furthermore, this approach would not put additional burden on delivery teams to conduct detailed risk analysis.
Based on the success of this experiment, the Institute is looking to use @RISK to develop an approach that helps outsourcing project teams to develop risk mitigation strategies.
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