Maximizing the Success of Your Capital Projects using a Mathematical Approach
Capital project portfolio planning – the work of selecting and scheduling projects to best achieve organizational goals – is a critical part of capital project management. Selecting and scheduling projects is notoriously difficult because both tasks are highly interdependent; the projects you select impact the schedule, and the schedule impacts which projects you can feasibly select! The work gets even more challenging after considering real-world requirements such as availability of funding, labor capacity, physical space, and time. Balancing all these competing factors is difficult to get right, and many organizations struggle to develop their portfolio plans in any systematic way.
The key to solving the capital project portfolio problem is to utilize its highly interdependent nature as an asset rather than a complicating factor to be avoided. In this presentation for Project Controls Expo, Enstoa’s Michael Matosin and Michael Goggin illustrate a mathematical model commonly used in logistics and financial management that allows an organization to identify the best possible capital portfolio plan. The model does this using a mathematical approach specifically designed to handle problems with complex webs of dependencies. “By combining domain knowledge and years of capital portfolio experience with a mathematical approach designed for some of the most complex logistical tasks in the world, Enstoa has built a smarter solution for portfolio planning,” Michael Matosin shares.
Want to learn more about how you can use this mathematical approach to optimize your capital project portfolio?
Speaker Bios
Michael Matosin is the lead data scientist and machine learning developer at Enstoa. Michael leverages the most up-to-date machine learning techniques to inform key business decisions and drive organizational value. A significant focus of his is acting as a trusted advisor for organizations taking their first steps into machine learning and AI.
Michael Goggin is an experienced software engineer and project controls expert, Michael applies systems engineering principles, cost control and project management principles to design and configure large, complex software implementations. At Enstoa, he is responsible for working closely with clients to understand their current and future needs and design solutions to support and grow their organization.
Capital project portfolio planning – the work of selecting and scheduling projects to best achieve organizational goals – is a critical part of capital project management. Selecting and scheduling projects is notoriously difficult because both tasks are highly interdependent; the projects you select impact the schedule, and the schedule impacts which projects you can feasibly select! The work gets even more challenging after considering real-world requirements such as availability of funding, labor capacity, physical space, and time. Balancing all these competing factors is difficult to get right, and many organizations struggle to develop their portfolio plans in any systematic way.
The key to solving the capital project portfolio problem is to utilize its highly interdependent nature as an asset rather than a complicating factor to be avoided. In this presentation for Project Controls Expo, Enstoa’s Michael Matosin and Michael Goggin illustrate a mathematical model commonly used in logistics and financial management that allows an organization to identify the best possible capital portfolio plan. The model does this using a mathematical approach specifically designed to handle problems with complex webs of dependencies. “By combining domain knowledge and years of capital portfolio experience with a mathematical approach designed for some of the most complex logistical tasks in the world, Enstoa has built a smarter solution for portfolio planning,” Michael Matosin shares.
Want to learn more about how you can use this mathematical approach to optimize your capital project portfolio?
Speaker Bios
Michael Matosin is the lead data scientist and machine learning developer at Enstoa. Michael leverages the most up-to-date machine learning techniques to inform key business decisions and drive organizational value. A significant focus of his is acting as a trusted advisor for organizations taking their first steps into machine learning and AI.
Michael Goggin is an experienced software engineer and project controls expert, Michael applies systems engineering principles, cost control and project management principles to design and configure large, complex software implementations. At Enstoa, he is responsible for working closely with clients to understand their current and future needs and design solutions to support and grow their organization.
