The petrochemicals industry has a long history of operational and productivity improvements gained through superior process engineering and increased scale of operating assets. Over the past two years, we have seen how advanced analytics and machine learning can spur a step change in operational and financial performance.
The results of pilot projects in petrochemicals have been impressive. In operations, this could be improving yield in crackers, increasing throughput in polymerization units, or improving reliability in compressors and heat exchangers. On the commercial side, this could mean improved pricing through better integration of data on market changes or microsegmentation. These value-creation levers are possible because of the availability of an immense amount of data and improvements in processing power.
In this article, we describe the key elements required to start, accelerate, or scale up the use of advanced analytics in the petrochemicals industry: leadership commitment, high-impact initial use cases, relevant analytics capabilities, and a road map defining a systematic analytics approach. Delivering impact and scale in analytics requires companies to apply all of these elements. In our experience, employing advanced analytics could increase a petrochemicals company’s EBITDA by as much as 20 percent. And solutions are mature enough for petrochemicals companies to embrace their use immediately.
A familiarity with data
There have been significant technological advances in digital and analytics in recent years. The generation, collection, and storage of data have never been so cost-effective; at the same time, computational power is reaching unprecedented levels and at lower costs. Petrochemicals companies already possess significant amounts of data. Building expertise in data collection and analysis can create two areas of strength for petrochemicals players seeking to capture the benefits of advanced analytics:
- Substantial amounts of data, collected from analyzers and sensors and stored in historian databases. Regular calibration of the devices ensures the quality of available data. This facilitates development of use cases and implementation with minimal new investment in data infrastructure.
- Engineers and operators who are well versed in the language of data and optimization through the use of process-control technologies.
Advanced process-control approaches that employ algorithms to stabilize operations are already widely used in the industry. These methods also generate a lot of data. The availability of high-frequency, high-quality data and a track record of productivity improvement efforts offer petrochemicals facilities—even older plants—the opportunity to use advanced analytical methods to capture significant value.
However, maximizing the benefits requires more than a focus on data assets; it also demands a broad organizational effort. Historically, control systems have been the domain of vendors rather than of petrochemicals companies developing optimization approaches by themselves. Implementing advanced analytics, from supply chain to operations and commercial processes, requires a concerted effort among operators, engineers, and other teams in the organization.
Where analytics can add value in petrochemicals
We categorize value-adding use cases into four main areas: profit per hour, asset reliability, value chains, and sales performance.
Increasing profit per hour
Companies can improve site-level profit per hour by optimizing yield, throughput, and energy efficiency. Depending on the use case, typical improvements range from a 5 to 7 percent rise in throughput to an increase in the yield, selectivity, and conversion of certain processes by 1 to 2 percent. These use cases can also lead to a 3 to 5 percent reduction in fuel gas, steam, and electricity consumption.
Raising asset reliability
Advanced analytics can generate substantial improvement in the reliability of critical equipment such as in-line extruders or compressors. For example, the potential of predictive maintenance has long attracted attention in the petrochemicals industry, although it cannot be applied to every piece of equipment in a plant. Depending on where advanced analytics is applied, we have observed increases of 0.5 to 1.0 percent in machinery uptime, or a 1.0 to 2.0 percent reduction in maintenance costs. These improvements may seem small, but the efficiencies or savings they generate go straight to the bottom line.
Optimizing value chains
Petrochemicals companies manage a network of interconnected plants with many product exchanges. Optimizing these networks has proved difficult. Now, with greater data availability and more sophisticated advanced-analytics approaches, petrochemicals companies can better carry out planning activities, allowing them to optimize overall value in their systems. This could be in the form of traditional linear-programming implementations similar to what refineries do or more advanced predictive models for intermediate products-related decisions.
Improving sales performance
Companies can raise sales performance by using customer- and transaction-specific data to carry out microsegmentations, demand and price forecasting, and granular performance tracking. Customized and dynamic pricing is an important lever to improve value in commercial applications.
Investing in just a few analytics use cases through packaged solutions in isolated parts of the value chain may not produce significant benefits and sustainable value. Successful companies develop a portfolio of use cases, often employing a common approach (see sidebar “Designing a cracker furnace optimization model”).
In our experience, value maximization is possible only through a carefully designed and rigorously implemented program touching every part of the organization, with a strong emphasis on capability building and change management. Examples of potential impact through analytics in petrochemicals are presented in the exhibit.
Over the past two years, we have seen how advanced analytics and machine learning can spur a step change in productivity and financial performance.
Rolling out an advanced-analytics program
Four elements are needed to start, accelerate, and scale up the use of advanced analytics in petrochemicals (see sidebar “How Turkey’s Petkim incorporated advanced analytics into its operations”).
Obtaining leadership commitment
Implementing a successful advanced-analytics program without leadership commitment is difficult. Such commitment means the required resources will be made available to pursue an at-scale implementation rather than just introducing a few use cases. The latter could result in significantly less value capture.
Petrochemicals companies are mostly older institutions. Their workforces, while skilled, tend to fall back on standard methodologies to manage day-to-day activities. As a result, leadership commitment becomes a critical enabler of workforce desire to embrace new methodologies in daily work.
Petrochemicals companies will be dependent on external resources if they don’t develop their own analytics capabilities.
Getting started with high-impact use cases
If the advanced-analytics program does not start with a few high-impact use cases, it will be hard to convince people in the organization to scale it up. The program might even grind to a halt. Therefore, showing value early with signature implementations is key for building both internal and shareholder support.
Developing internal analytics capabilities
Petrochemicals companies will be dependent on external resources if they don’t develop their own analytics capabilities. Packaged solutions developed and deployed by vendors may not always align with the priorities of a company. For example, these solutions aren’t necessarily customized and therefore may not address the specific problems of an individual company. As a result, the use cases with the most impact may be missed.
There are two potential approaches to developing internal capabilities. In one, executives create a center of excellence staffed by data scientists who may have limited knowledge of petrochemicals operations or commercial activities. They would team up with focus-area experts to develop value-creating use cases.
In a second approach, the organization trains all key professionals in data science. This enables all of them to develop their own use cases. While the first approach would be faster to have impact and more practical, the second one delivers more value—but it would require more time and resources.
There is no single right approach for all companies. To get results quickly, establishing a center of excellence is often the best way forward. However, to transform an organization, upskilling at scale may be the better answer.
Designing a road map to impact
Petrochemicals companies need an organization-wide road map that lays out a systematic approach to analytics. The absence of a road map could result in misaligned priorities, missed value capture areas, and an incomplete transformation to an analytics mindset.
Finally, to successfully deliver impact and scale in analytics, companies need to put all four elements in place. Failure to do so could result in the development of local initiatives but no real momentum because of lack of resources; conceptual discussions but little or no traction due to lack of evidence; slow movement due to reliance on external support; and initial successes but no organization-wide scale-up.
Advanced analytics and machine learning have begun to make inroads into the petrochemicals industry. The technology is at a point that companies can confidently act to integrate it and capture value. Companies that move now to build advanced analytics into their organizations could create an enduring competitive advantage.