Global Chemical Key Stats
Improved chemical catalyst lifespan up to 40%
increase in revenue via predictive analytics
Leveraging Alteryx to go from “Gut Feeling” to Data-Driven
As the largest petrochemical company in Thailand and third largest in Asia, GC is comprised of diversified and comprehensive petrochemical businesses, including manufacturing and distribution of upstream, intermediate, and downstream petrochemical products. These products can be converted into other chemical products and serve as basic feedstock for downstream industries such as packaging, apparel, communications and electronic equipment, electrical appliances, vehicles, construction materials, engineering-based plastics, agricultural equipment, and much more. These products are not only part of our everyday lives – they also improve the way people live.
With a constant focus on innovation and technology, GC realized the need to shift to a more data-driven organization. With 70% of the business being focused on engineers and chemical plants, data and analytics leaders saw the starting blocks of a transformation within plant operations.
Unlike a chemical reaction which typically behaves like a line of falling dominoes, engineers relied on piecing together data from different suppliers which inherently carried complex variables.
GC focused on the following three dimensions in its digital transformation:
- Business transformation: An approach to enhance business unit performance by embedding digital strategy into action. Addressing impact levers, such as advanced analytics for commercial, operational, and planning optimization; large-scale process automation, and digitization called “Digital Use Case”
- Technology transformation: Leveraged IT infrastructure to extract the performance potential from new technologies, with measurable acceleration of growth, productivity, and capital efficiency
- People transformation and upskilling: The key enablers of driving digital success are people, and GC strongly focused on developing employee skills and mindset for digital disruption readiness
Reasons GC Chose Alteryx:
Changing the Status Quo
For a chemical reaction to occur at speed and scale within a controlled environment, a catalyst is often introduced into the formula. Catalysts also have the unique characteristic of causing a chemical reaction to happen in a different way. When it came to changing the status quo, the team at GC wanted to move away from doing things the way they’ve always been done.
“We often changed our chemical catalysts at known intervals, simply because that’s how we’ve always done it” said Chatsuda Kanjanarat, SVP Transformation Excellence, GC. “We looked to Alteryx for the tools and expertise to change that.”
Alteryx served as the catalyst for change at GC, starting with its team of engineers, who make up 70% of the entire organization. As they began leveraging Alteryx, they sought out a solution to the arduous task of maintaining and swapping catalysts every five years. By being able to gather the necessary data and analyze it in Alteryx, GC engineers discovered they could extend the life of their catalysts by up to 40%. The result of this discovery allows for GC engineers to effectively save business resources while still producing high quality products.
“Creating better chemistry”
After working with Alteryx automation for over a year, GC’s team of engineers have begun to leverage the insights powered by Alteryx to drive organization-wide advantages. Olefin and phenol production, GC’s area of expertise, require large amounts of chemical feedstock. Chemical feedstocks are raw, untreated petroleum oil materials. Using their learnings from over the year and Alteryx’s predictive suite of tools, engineers have been able to predict their chemical output, or “yield” as it is more commonly known in the scientific community, to make better feedstock purchasing decisions.
The process was as follows: Leverage sales, production, pricing data, and plant conditions from disparate systems like CRM’s and ERP’s to create a better algorithm that is user-friendly and allows for advanced analytics reskilling and upskilling, and provides deeper insights. The objective was to create a model to predict the price of phenol, which is an organic compound used as an intermediate for industrial synthesis. The team embarked on a year-long proof-of-concept period encompassing:
- Data training
Leveraging 19 parameters overall, the team evolved from utilizing one Python-driven Linear Regression Model to building an Alteryx driven Random Forest model which allows them to leverage up to 50 parameters with a high confidence interval above 0.95 and an error rate of 2.6%.
Prior to Alteryx, feedstock was purchased when needed and not in a strategic manner. The team now knows when it is best to purchase feedstock and how much, predict their yield, and drive value back to the business while doing so in an innovative and environmentally friendly way. This new process has driven $1.5M to the phenol businesses’ bottom-line.
“Alteryx makes predictive analytics and the use of machine learning more accessible and agile by switching between many different methods and algorithms with a simple switch icon. You can do this directly on the platform instead of spending time coding an algorithm that does it.”
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