by Sascha Chandler, Integrated Infrastructure Partner – Infrastructure Risk and Controls, PwC and Alastair Pearson, Integrated Infrastructure Partner – Infrastructure Data and Analytics, PwC.
Intelligent risk identification and modeling will be key to successfully completing infrastructure projects in times of high demand and significant headwinds.
COVID-related shutdowns, geopolitical uncertainty and extreme weather events have slowed productivity and led to flow effects such as increased input costs, shortage of skilled labor and ultimately , higher levels of delivery risk for infrastructure projects.
Many large, complex publicly funded projects have already been delayed and over budget, and there will no doubt be many more projects to move to delivery that will experience significant cost and time pressures.
Steadying the ship and setting a course is critical to the successful completion of these projects, as infrastructure projects stimulate the economy and job opportunities and bring many other important benefits to our communities.
With rising inflation and budget and resource constraints, managing the critical risks of our large infrastructure projects will be key to managing costs and bringing projects as close to budget as possible, or understanding the trade-offs that may be needed.
To identify, assess and mitigate risk, the infrastructure sector will need a disciplined and well-planned mix of:
- Using advanced analytics to help provide insights from the increasingly large and complex datasets produced by infrastructure projects and assets, to help make more informed decisions
- Strong governance, controls and assurance applied to planning, cost estimating, risk forecasting, procurement and contracting
- Industry collaboration enabling sharing of risk, cost and performance data
Smart data-driven insights
The field of advanced data analytics has taken a leap forward in the past decade and many industry sectors are recognizing the benefits. Data elements related to risk, cost (eg, timesheets, claims, material inputs, etc.), interfaces, and delivery performance are readily available in any well-controlled infrastructure project. Technologies such as optical character recognition, high-powered computing, Internet of Things devices, and high-speed data networks help ingest, normalize, and prepare this data for integration with risk modeling. sophisticated and “what if” scenario analysis.
This data and the insights it generates are also valuable inputs for “digital twin” models, which are now frequently used when modeling multi-faceted infrastructure projects. Digital twins help assess the full value of major public investments and commissioned assets so that they are fit for purpose and achieve targeted community outcomes.
Increased computing power at lower cost now gives us the ability to build much larger and more complex digital twins that can analyze data and “what if” scenarios at a level of detail never seen before. This will continue to evolve over the next decade with the advent of new advances in quantum computing, which will revolutionize digital twins.
Broader and deeper datasets allow more to be done and reveal more. Patterns and themes can be identified in data by exploring the correlations between data sets and the overlay of those correlations over time horizons. These models and themes can then be used in Monte Carlo simulations to help predict the impact of various aspects of risk on individual projects, project portfolios, or the industry as a whole. As the data set increases in depth, breadth, and history, the power of the analysis increases, as does its value in predicting risk.
Applying the advanced analytical techniques of artificial intelligence and machine learning to these broader and deeper datasets gives us greater potential to move into more sophisticated predictions of outcomes based on historical patterns and contextual information. With enough data, predictions can be converted into prescriptive actions, automatically suggesting the best potential course of action following a disruption or unforeseen event in the development of an infrastructure project.
If applied across the industry, advanced data analytics can provide the insights needed to determine the best balance of risk sharing across the industry and could inform infrastructure strategy and government policy. A shared and consistent understanding of risk between the public and private sectors would surely lead to better collaboration on delivery and, ultimately, reduced delivery risk.
Strong governance and controls
The governance, control and assurance frameworks currently used for major infrastructure projects have been developed to provide oversight of public investments and increase stakeholder confidence. These frameworks can greatly benefit from project-specific, data-driven analytics.
Insurance practices can move from a boilerplate and retrospective approach to one that is forward-looking and risk-informed. Trained predictive models informed by a rich set of historical performance and risk data can help quickly identify if the delivery may blow up, enabling proactive and targeted intervention to quickly correct the course of the project.
When forward-looking information is available at an industry-wide or portfolio level, it can be used for benchmarking across projects and sectors – thus providing impetus to information-driven continuous improvement activities such as such as targeted staff training, policy and process refinement, project-to-project knowledge sharing, procurement processes, and health and safety practices. This information is also valuable input into community consultation and reporting on progress.
Collaboration with industry
Collecting data from the many parts of the infrastructure ecosystem is perhaps the biggest hurdle to building deep and broad datasets of historical and current risk and performance. Yet concerns about “at source” data formatting and commercial data sensitivity can and should be overcome.
There is no need to migrate all projects to consistent project management platforms – that would be too much of a challenge. Instead, the heavy lifting of data staging and normalization can be done centrally by a portfolio management office or, in the case of industry-wide data collection , by state or federal infrastructure monitoring agencies.
The following factors will be critical to industry collaboration:
- The benefits of the project and the industry will need to be carefully considered so that they can be communicated succinctly. Recognition of mutual benefits will help foster collaborative behaviors
- Expectations for data sharing should be established in supply contracts.
- The intended use of the data and its relevance for generating insights and avoiding risks must be understood and communicated and be central to the design of the platform
- The value of information must be understood by industry participants who can collectively help inform design and expected outcomes
- Collaboration will be required between big data and risk modeling specialists to ensure a well-managed presentation of the design, construction and implementation of functionality within reliable and secure platforms.
The enabling technologies for intelligent, data-driven risk identification and modeling are available, the market has appetite, and the benefits to industry and ultimately the community are many. With strong and persistent sponsorship to coordinate all moving parties and engage the industry, the vision of better infrastructure risk management and efficient, on-budget delivery of the infrastructure pipeline can become a reality. .