{"id":29739,"date":"2018-11-14T10:25:17","date_gmt":"2018-11-14T15:25:17","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/machine-learning-in-the-chemicals-industry-lyondellbasell\/"},"modified":"2018-11-14T10:25:17","modified_gmt":"2018-11-14T15:25:17","slug":"machine-learning-in-the-chemicals-industry-lyondellbasell","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-in-the-chemicals-industry-lyondellbasell\/","title":{"rendered":"Machine learning in the Chemicals industry: Lyondellbasell"},"content":{"rendered":"

Lyondellbasell (LYB) is an integrated Chemicals company, with significant market positions in all major organic materials (example products include plastics and color dyes) and across the value chain from R&D to manufacturing to sales. As the chemicals industry begins to adopt digital innovations, LYB is exceptionally situated to benefit from advances in machine learning; though they have yet to make significant investments in the space, this report contends that LYB\u2019s current status as the most efficient asset operator and it\u2019s access to wide and deep datasets uniquely positions it to improve both its business processes and its product development capabilities.<\/p>\n

The value of machine learning in Chemicals<\/strong><\/span><\/p>\n

Machine learning has applications across the entire Chemicals value chain, shown below (simplified):<\/p>\n

Exhibit 1: <\/strong>Simplified Chemicals value chain<\/p>\n

\"\"<\/a><\/p>\n

R&D<\/strong>: Chemicals companies rely on their R&D organizations to develop a wide array of product innovations, from big (synthesizing new\/novel plastic polymers) to small (developing new colors to meet customer needs). By deploying machine learning technology to ever expanding datasets, companies can discover new combinations of materials and paints more rapidly.<\/p>\n

Feedstock selection<\/strong>: With their newer plants that can accept multiple feedstock sources (e.g., naptha vs. propane vs. ethane), Chemicals companies face constant optimization decisions. As each feedstock produces a different proportion of end products, managers rely on accurate assessment and projections of both input and output prices. Machine learning can support appropriate modelling of commodity supply and demand.<\/p>\n

Manufacturing<\/strong>: Like other firms in the industrial sector, Chemicals companies are using machine learning to improve their production processes. Leveraging massive operational datasets and IOT technology across a number of plants, these companies can use machine learning for predictive maintenance (i.e., proactively predict when a machine might breakdown to schedule maintenance) and optimization (i.e., recommend optimal temperatures, pressures, etc. to improve output).<\/p>\n

Sales \/ Customer service<\/strong>: At the end of the value chain, companies field a high volume of technical questions from their customers. As in other industries, machine learning can be used to create virtual assistants that can provide quick assistance to customers.<\/p>\n

As the Chemicals industry is highly commoditized, the benefits that machine learning brings in terms of efficiency and costs reduction can create significant competitive advantages. A number of LYB\u2019s competitors have started to adopt solutions across the value chain.<\/p>\n

Exhibit 2<\/strong>: Competitor solutions<\/p>\n\n\n\n\n\n\n
R&D<\/strong><\/td>\nDOW\/1Qbit<\/strong>[1]<\/a>: Dow Chemical began a collaboration in 2Q2017 to use quantum computing and machine learning to discover new useful materials and optimize existing formulations<\/td>\n<\/tr>\n
Feedstock selection<\/strong><\/td>\nShell<\/strong>[2]<\/a>: Shell\u2019s Downstream division is using AI to predict supply\/demand for oil (and it\u2019s chemical derivatives) and recommending appropriate refining mixes<\/td>\n<\/tr>\n
Manufacturing<\/strong><\/td>\nBASF<\/strong>[3]<\/a>: The German company has partnered with Schneider electric for predictive asset analytics<\/td>\n<\/tr>\n
Sales\/customer service<\/strong><\/td>\nShell<\/strong>[4]<\/a>: Shell has deployed its virtual assistant technology across 151 countries to answer questions around technical properties of products<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

LYB\u2019s positioning in Machine Learning<\/strong><\/span><\/p>\n

LYB is notably behind on adoption of the technology (at least publically). There are no mention of artificial intelligence or machine learning in their annual report or in their previous 3 earnings calls. However, LYB\u2019s operational decisions has potentially made it uniquely positioned to take advantage of this megatrend:<\/p>\n