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How Advanced Analytics May Help the Petrochemical Industry

Analytics in Petrochemicals

The petrochemical industry has a rich history of improving operations and productivity because of better process engineering and larger operating asset scales. Watching over the last two years how machine learning and advanced analytics can enhance performance and significantly improve performance and financial outcomes.

Petrochemical pilot project results have been remarkable. It can include enhancing the output of crackers, the capacity of polymerization machines, or the reliability of compressor & heat exchangers in processes. On the business front, this can entail more accurate product costs through greater micro-segmentation or data integration on market developments. These valuation levers are practical because of the abundance of data and improvements in computing capacity.

This article highlights the essential components needed to launch, scale up, or expand advanced statistics in the petrochemicals industry, including strong leadership, initial high use cases, relevant and crucial analytic tools, and well road map outlining a systematic analytics approach. Applying these components is necessary for businesses to deliver analytics with impact and scale.

According to our observations, applying advanced analytics might raise a petrochemicals company’s EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) by up to 20%. Additionally, the solutions are developed enough for petrochemical companies to adopt them immediately.

Understanding of data

In recent years, digital and analytics technology have made massive progress. Data gathering, collecting, and storage have never been more affordable, and processing power is advancing to previously unheard-of heights while becoming more accessible. Significant volumes of data are already in the possession of petrochemical companies. Players in the petrochemical industry looking to benefit from advanced analytics can develop two strong points by becoming experts in data collecting and analysis:

  • Large volumes of information are gathered from sensors and analyzers and kept in historical databases. The accuracy of the data is ensured by routine device calibration. It enables use case creation and execution with little additional investment in data infrastructure.
  • Engineers & operators who are fluent in understanding the language of data and processing techniques and can optimize systems.

In the industry, advanced process-control methods that use algorithms to stabilize processes are already widely deployed. There is a lot of information and data produced by these techniques. Petrochemical facilities, even older plants, can have the chance to apply cutting-edge analytical techniques to capture substantial value due to the availability of high-frequency, high-quality data and a record of productivity improvement efforts.

However, leveraging the effects requires a broad organizational commitment and focus on data assets. Control systems have always been the domain of suppliers rather than petrochemical companies establishing optimization methods independently. Engineers, operators, and other teams within the company must work together to implement advanced analytics across the firm, including from supply chain to operational and commercial processes.

Where analytics can benefit petrochemical manufacturing

Value-adding use cases are categorized under four key titles: Hourly revenue, asset reliability, supply chains, and sales performance.

Increasing hourly revenue

Businesses can increase site-level revenue per hour by maximizing yield, productivity, and energy efficiency, maximizing output, productivity, and energy efficiency, and companies can increase site-level profit per hour. Typical increases often range from a 5–7% increase in capacity to a 1–2% increase in output, selection, and conversion of specific processes, depending on the application case. Additionally, fuel, steam, and electricity usage can be reduced by 3 to 5 percent due to these use cases.

Increased asset reliability

Necessary hardware like the in-line extrusion process or compressors can become significantly more reliable thanks to advanced analytics. Predictive maintenance, for instance, has long drawn interest in the petrochemicals sector, even though it cannot be used on every piece of machinery in a facility. We have seen increases in machinery uptime of 0.5 to 1.0 % or a reduction in maintenance expenditures of 1.0 to 2.0 % depending on where data technologies are used. Although these enhancements may appear minor, the efficiency or savings they produce directly affect the bottom line.

Enhancing supply chains

Companies that manage petrochemicals oversee a network of connected plants with several product exchanges. These networks have proven challenging to optimize. Petrochemicals firms can now good pick out strategic planning, optimizing total value in their systems, thanks to increased available data and more modern advanced analytics methodologies. This could take the shape of more sophisticated prediction models for decisions involving intermediate products or more conventional linear-programming implementations akin to those used in refineries.

Improving sales performance

The company can increase sales performance by utilizing customer and transaction-specific data for micro segmentations, demand and pricing forecasting, and detailed performance monitoring. Customized & dynamic costing is vital for increasing value in business applications.

Investing in a small number of analytics use cases using packaged solutions in discrete stages of the value chain won’t result in significant gains and long-term value. Successful companies build a portfolio of use cases, frequently utilizing many industry-standard techniques.

Value maximization can only be accomplished through a well-planned and thoroughly carried out program affecting every aspect of the company, with a strong focus on capability development and change management.

Launching a program for advanced analytics

Four components are required to expedite and scale up the application of advanced statistics in petrochemicals.

Gaining leadership commitment

Without the support of the leadership, complex analytics programs are challenging to implement successfully. With such a commitment, it will be possible to proceed with a scale execution compared to only offering a few use cases. The latter might lead to a significant reduction in value capture.

Most petrochemical businesses are established organizations. Even if their workforces are experienced, they frequently rely on tried-and-true management techniques to handle daily operations. As a result, the workforce’s motivation to adopt new approaches in their daily work is critically enabled by leadership commitment.

Companies in the petrochemical industry will be reliant on outside resources if they don’t build their analytical capabilities.

Starting with high-impact use cases

It will be challenging to convince customers within the business to grow the advanced analytics program if it does not begin with just a few high-impact use cases. Even worse, the program can sputter to a halt. Therefore, the key to gaining support from internal and external shareholders is demonstrating value early on through signature implementations.

Establishing in-house analytics capabilities

Petrochemical businesses will rely on outside resources if they don’t have analytics capabilities. Packaged solutions created and implemented by suppliers might not match a company’s priorities. For instance, these solutions might not necessarily be tailored to a particular company’s challenges. Therefore, they might not do so. The most critical use cases can consequently go unnoticed as a result.

  • There are two possible approaches to developing internal skills. In one, leaders establish an excellence centre managed by data scientists who might have no experience with commercial or operational aspects of petrochemicals. They would collaborate with specialists in the focus area to create use cases that make value.
  • The organization gives data science training to all crucial professionals in a second strategy. They can all create their use case as a result of this. The second strategy gives more excellent value but would take more time and resources, whereas the first strategy would have an impact faster and be more realistic.

No single approach is known that works for all companies. Building an excellence center is frequently the best line of action for achieving outcomes rapidly. However, scaling upskilling may be the most effective strategy to restructure a company.

Designing a strategy to have an impact

Companies in the petrochemical industry require an organizational road plan that outlines a systematic approach to analytics. Lack of a road map may lead to misplaced priorities, missing opportunities for value capture, and a short shift to an analytical culture.

Finally, businesses must implement all four components to achieve impact & scale in analytics properly. Failure may lead to the expansion of initiatives with little to no real momentum because of resource limitations, conceptual discussions that have little to no traction because of a lack of data, a slow transition to relying on outside support, and initial successes without organization-wide scale-up.


Big data and machine learning are gaining traction in the petrochemicals sector. Companies can confidently move to integrate the technology and derive benefits at this phase. Companies that take immediate action to integrate advanced analytics into their structures may develop a sustainable competitive advantage.

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