One of the most difficult aspects of oil and gas pipeline construction, is the preconstruction and project planning phase. Gone are the days of designing and building a pipeline system based purely on a blueprint, engineering skill and vendor relationships. Data and analytics are now deeply entrenched into the planning and execution phases of any build. And even though the use of analytics has only emerged over the last ten years, the ability to leverage this data to forecast or predict jobs and outcomes based on a number of factors is quickly evolving as the “next big thing” for data use.
How Predictive Data Impacts Pipeline Design/Build
The ability to arrive at better forecasted outcomes helps create more accurate budgeting. Even though pipeline design/build professionals relied on pre-existing data, supply prices and other market factors, many of these builds still went over budget. When you combine this inability to accurately forecast projects along with the unpredictable market and supply-side prices, the recipe for budgeting disaster is illuminated and very real. Rising labor costs create a pile-on effect that further prevents the right level of profitability for organizations.
Predictive modeling allows pipeline design/build organizations to create future business insights with a significant degree of accuracy. With the help of sophisticated data analytics tools and modeling, these firms can now use past and current project utilization data to reliably forecast budget trends milliseconds, days, or years into the future.
Predictive modeling and analytics tools are expected to reach approximately $10.95 billion by 2022, growing at a compound annual growth rate (CAGR) of around 21 percent through 2022, according to Zion Market Research.
Using Historical Patterns to Forecast Future Decisions
Predictive modeling can be extremely beneficial to helping pipeline design/build firms leverage past and present data when making critical business decisions. Tech savvy firms today are using this information to do everything from identify inventory and purchase history patterns, increase efficiencies with vendor partners, and review past jobs in order to improve the customer experience with future builds and projects.
The problem with only using current data is that there are too many outside factors and variables that prevent it from maintaining its accuracy throughout the duration of the job. In some cases, projects are flush with cost overages by the time ground is broken on a new build. Many of these unknown outside factors include fluctuating material and labor costs, vendor relationships and agreements that shift, customer change orders, as well as economic factors such as trade/tariff regulations, interest rates and other policy mandates that could affect the bottom line.
Approximately half of the design/build organizations polled in an online survey commissioned by Merit Mile in January found that companies believe they can be anywhere from 25 to 50 percent more efficient and accurate in their decision-making.
Why Predictive Data Increases Decision Accuracy
There are a few reasons why today’s predictive modeling data is more accurate than what organizations had access to in years past. Primarily, predictive modeling is based on actual, empirical data and macroeconomic insights from historical outputs and present-day models. It is far more elaborate than the forecasts based on theory that were used in legacy business operations.
Empirical data is based on “evidence” derived from previous cost data and other criteria that have proven themselves in actual real-world scenarios. These data outputs are then formulated into precision-based models and scenarios that offer visibility into accurate forecasting techniques organizations and data scientists today use to arrive at certain economic conclusions in their decision making.
Increased Insight Creates a Competitive Advantage
From these conclusions, pipeline design/build organizations can concoct better timelines and budget estimates with tentacles that span out into labor pools, inventory and supply chains, vendor partner relationships and customer order estimating. In fact, customer data is an increasing source of insight that is being utilized for predictive modeling. Fifty percent of companies polled said identifying and improving more personalized customer traits and characteristics is the leading motivator behind the use of predictive modeling for their organizations.
Adoption and internal buy-in of predictive modeling technologies will be instrumental for the expansion of data analytics. With a clear path to see how predictive modeling can positively impact the bottom line, 37 percent of companies surveyed by Merit Mile said the CEO and COO are typically the primary drivers for adoption.
This top-down approach will enable more firms to incorporate and grow their predictive modeling initiatives for a greater competitive advantage.
John Sternal is director of Market Insights for Merit Mile Research, a division of integrated communications company, Merit Mile. For more information visit MeritMile.com.