Is a Mathematical Approach to Portfolio Planning Really Possible in Construction?
Vice President, Enstoa
If you work for a large company, chances are it has multiple construction and infrastructure projects in play throughout the year. It has, in other words, a capital projects portfolio—a collection of resource-intensive activities that it’s doing or considering doing.
At enterprise-level organizations, there can be hundreds of projects in this portfolio at any given time. Some of these are related to the growth plans of the company—such as opening new locations or production facilities—while others may be more about repairing existing assets, like replacing an old set of pipes in a building. Another type of capital project involves improvements, like adding a suite of solar arrays to the top of every one of the company’s warehouses to cut the company’s energy costs. A final type relates to regulatory requirements, such as the infrastructure needed to introduce a new product safety feature the government requires by a certain year.
Traditionally, the executives who comprise the planning team are making the decisions about which projects get done first. They may use a few spreadsheets here and there, but primarily decisions are based on the instincts and experience of those team members. Ego can be a factor too, such as when various different pet projects are pushed to the top of the list in order to raise the profile of a particular department or leader. This is where some concern starts to enter the equation. Will such projects really add as much shareholder value as something else would have?
Times Are Changing
There’s no substitute for gut and seniority, of course, but more boards and CEOs are realizing that when a project portfolio reaches a certain level—whether through sheer number of projects or the size of those projects or both—having an objective tool in place that can support the leadership team’s decision making process is a critical resource.
Why? Well, capital projects are complex and resource intensive, so risk and the likelihood of overrun is high. Missing the opportunity to save millions of dollars’ worth of cost can mean the difference between black and red on the annual balance sheet. A mathematical approach to portfolio planning accounts for factors that other methods just can’t consider. Primarily, it considers the relationships between projects. It pinpoints where dramatic reductions in cost and risk can be achieved through smart sequencing of projects. It maximizes business value across the organization’s investments and accounts for all the variables in play—time, resources, and financials.
At biotech and life science organizations, mathematically-driven decision making is already in heavy use among research and development teams. Just like construction, R&D is expensive and has a lot of variables attached. Many large tech and military organizations use mathematically-driven decision making too.
Another sector that has portfolio sequencing down to a science is the energy industry. Drilling is a heavily resource-intensive operation that involves some of the largest, heaviest machinery in the world and massive, globally distributed teams to operate it. The most successful energy companies, therefore, plan their project sequencing using as many variables as possible to make schedules tight and actionable. Things are often planned down to the minute, in fact. The larger a capital project portfolio is at an AEC organization, the more likely it is that planning it in this kind of precise, objective way will be worth your while.
In fact, whenever there’s a lot of complexity and risk, a numerically-driven decision support system is considered a vital tool because it helps teams determine the right path forward.
What’s the Best Approach for the Built Environment?
There are a number of different ways to take a more quantitative approach to project planning and sequencing. There are purely financial ways, which among the companies using any kind of decision support at all tends to be the most commonly used method in construction today. This approach ignores time and resource-related factors, though, so it doesn’t tend to generate maximum return on investment.
There’s a weighted factor scoring method, which is based on the work of a decision committee. Each member considers an array of different factors, then estimates the importance of each according to his or her own judgment and experience. This is a more holistic approach than considering financial factors alone, but it’s still inherently subjective.
Finally, there’s the optimization model method, which typically uses some form of mathematical programming. This approach can take into account a wide array of variables and see which set of them and/or sequence delivers the maximum benefit. Artificial intelligence tools would fall under this category. AI can determine which of hundreds of projects will add the most value based on a wide array of and in which sequence they should be done.
Building Up to It
There are already a number of AI-based tools that can provide decision making support, but in order to use them, most AEC organizations will have to take the interim step of becoming more data-centric, data savvy, and willing to share information across departmental silos. One of the biggest benefits of an objective approach to portfolio planning, in fact, is that it gets valuable information out of documents and makes it more useful to the organization as a whole.
Recently, at one large development firm, the management team at one office discovered that another part of the firm was using an entirely different software platform for project management. They’d simply never informed anyone outside their location. When the various teams started to realize this, the matter actually had to be escalated to the legal department for resolution.
Too often in construction, even within the same organization data is not shared and is overly protected for arbitrary or territorial reasons. Moving to more transparent, single-source-of-truth style systems and models is what tends to drive the most value for organizations these days. It’s also the approach that makes a mathematical approach to portfolio optimization possible.
The benefits of doing this go beyond just determining the most valuable sequence of projects, by the way. We estimate that some of the companies embracing a more data-driven approach will be able to do the same number of projects with just ten percent of their current overhead—leaving them free to focus on the creative, value-added side of their work.
On the facilities management side of things, having a more data-centric approach means organizations can start to tie into external data systems of weather and financial information, which can help flag potentially anomalous events, prevent loss of life, and major unforeseen expenses. Imagine if AI, for instance, had been able to tell the management team at Bellevue Hospital that there was an 85% chance the basement would flood before Hurricane Sandy hit New York City in 2012. The hospital might have been able to evacuate its 736 patients when the power was still on and the circumstances, much safer.
At most AEC organizations, doing all this will require a period of change. The organizations that really embrace this change, though, will not only gain a competitive edge in the form of smarter, more value-added portfolio sequencing, they’ll also set themselves up to significantly reduce overhead and boost the organization’s decision-making capabilities as a whole.
If you'd like to talk to us about establishing a data-centric approach to portfolio planning, get in touch.
If you work for a large company, chances are it has multiple construction and infrastructure projects in play throughout the year. It has, in other words, a capital projects portfolio—a collection of resource-intensive activities that it’s doing or considering doing.
At enterprise-level organizations, there can be hundreds of projects in this portfolio at any given time. Some of these are related to the growth plans of the company—such as opening new locations or production facilities—while others may be more about repairing existing assets, like replacing an old set of pipes in a building. Another type of capital project involves improvements, like adding a suite of solar arrays to the top of every one of the company’s warehouses to cut the company’s energy costs. A final type relates to regulatory requirements, such as the infrastructure needed to introduce a new product safety feature the government requires by a certain year.
Traditionally, the executives who comprise the planning team are making the decisions about which projects get done first. They may use a few spreadsheets here and there, but primarily decisions are based on the instincts and experience of those team members. Ego can be a factor too, such as when various different pet projects are pushed to the top of the list in order to raise the profile of a particular department or leader. This is where some concern starts to enter the equation. Will such projects really add as much shareholder value as something else would have?
Times Are Changing
There’s no substitute for gut and seniority, of course, but more boards and CEOs are realizing that when a project portfolio reaches a certain level—whether through sheer number of projects or the size of those projects or both—having an objective tool in place that can support the leadership team’s decision making process is a critical resource.
Why? Well, capital projects are complex and resource intensive, so risk and the likelihood of overrun is high. Missing the opportunity to save millions of dollars’ worth of cost can mean the difference between black and red on the annual balance sheet. A mathematical approach to portfolio planning accounts for factors that other methods just can’t consider. Primarily, it considers the relationships between projects. It pinpoints where dramatic reductions in cost and risk can be achieved through smart sequencing of projects. It maximizes business value across the organization’s investments and accounts for all the variables in play—time, resources, and financials.
At biotech and life science organizations, mathematically-driven decision making is already in heavy use among research and development teams. Just like construction, R&D is expensive and has a lot of variables attached. Many large tech and military organizations use mathematically-driven decision making too.
Another sector that has portfolio sequencing down to a science is the energy industry. Drilling is a heavily resource-intensive operation that involves some of the largest, heaviest machinery in the world and massive, globally distributed teams to operate it. The most successful energy companies, therefore, plan their project sequencing using as many variables as possible to make schedules tight and actionable. Things are often planned down to the minute, in fact. The larger a capital project portfolio is at an AEC organization, the more likely it is that planning it in this kind of precise, objective way will be worth your while.
In fact, whenever there’s a lot of complexity and risk, a numerically-driven decision support system is considered a vital tool because it helps teams determine the right path forward.
What’s the Best Approach for the Built Environment?
There are a number of different ways to take a more quantitative approach to project planning and sequencing. There are purely financial ways, which among the companies using any kind of decision support at all tends to be the most commonly used method in construction today. This approach ignores time and resource-related factors, though, so it doesn’t tend to generate maximum return on investment.
There’s a weighted factor scoring method, which is based on the work of a decision committee. Each member considers an array of different factors, then estimates the importance of each according to his or her own judgment and experience. This is a more holistic approach than considering financial factors alone, but it’s still inherently subjective.
Finally, there’s the optimization model method, which typically uses some form of mathematical programming. This approach can take into account a wide array of variables and see which set of them and/or sequence delivers the maximum benefit. Artificial intelligence tools would fall under this category. AI can determine which of hundreds of projects will add the most value based on a wide array of and in which sequence they should be done.
Building Up to It
There are already a number of AI-based tools that can provide decision making support, but in order to use them, most AEC organizations will have to take the interim step of becoming more data-centric, data savvy, and willing to share information across departmental silos. One of the biggest benefits of an objective approach to portfolio planning, in fact, is that it gets valuable information out of documents and makes it more useful to the organization as a whole.
Recently, at one large development firm, the management team at one office discovered that another part of the firm was using an entirely different software platform for project management. They’d simply never informed anyone outside their location. When the various teams started to realize this, the matter actually had to be escalated to the legal department for resolution.
Too often in construction, even within the same organization data is not shared and is overly protected for arbitrary or territorial reasons. Moving to more transparent, single-source-of-truth style systems and models is what tends to drive the most value for organizations these days. It’s also the approach that makes a mathematical approach to portfolio optimization possible.
The benefits of doing this go beyond just determining the most valuable sequence of projects, by the way. We estimate that some of the companies embracing a more data-driven approach will be able to do the same number of projects with just ten percent of their current overhead—leaving them free to focus on the creative, value-added side of their work.
On the facilities management side of things, having a more data-centric approach means organizations can start to tie into external data systems of weather and financial information, which can help flag potentially anomalous events, prevent loss of life, and major unforeseen expenses. Imagine if AI, for instance, had been able to tell the management team at Bellevue Hospital that there was an 85% chance the basement would flood before Hurricane Sandy hit New York City in 2012. The hospital might have been able to evacuate its 736 patients when the power was still on and the circumstances, much safer.
At most AEC organizations, doing all this will require a period of change. The organizations that really embrace this change, though, will not only gain a competitive edge in the form of smarter, more value-added portfolio sequencing, they’ll also set themselves up to significantly reduce overhead and boost the organization’s decision-making capabilities as a whole.
If you'd like to talk to us about establishing a data-centric approach to portfolio planning, get in touch.
