{"id":2681,"date":"2023-02-21T14:10:25","date_gmt":"2023-02-21T14:10:25","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/speakers\/bart-maciszewski-copy\/"},"modified":"2023-02-21T14:14:42","modified_gmt":"2023-02-21T14:14:42","slug":"sreekanth-rajagopalan","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/speakers\/sreekanth-rajagopalan\/","title":{"rendered":"Sreekanth Rajagopalan"},"content":{"rendered":"\n
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Sreekanth Rajagopalan<\/h1>\n

Associate Research Scientist at Dow Chemical Company<\/span><\/h1>\n

Sreekanth Rajagopalan is an Associate Research Scientist in the Machine Learning, Optimization, and Statistics team in Dow\u2019s Research & Development organization. In his role, Sree develops purpose-built analytics solutions in the areas of manufacturing operations, supply chain, and sustainability with a focus on systems thinking and enterprise-wide optimization. Prior to joining Dow, Sree obtained a PhD in Chemical Engineering from Carnegie Mellon University in 2018 with a background in stochastic programming and global optimization. Sree is passionate about problem solving using math modeling and software development. He is active in peer-to-peer learning forums and governance teams on MLOps and DevOps for Data Science at Dow.<\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n

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Track: Manufacturing<\/h6> <\/div>\n<\/div>\n\n
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An Enterprise Decision-making Platform for Managing Maintenance Turnarounds in Process Industries<\/h2>\n

Maintenance turnarounds of manufacturing facilities are planned shutdowns of the facilities that can be several weeks long. Besides enabling safety and regulatory compliance activities and asset renewal to be conducted, a turnaround provides a window to complete reliability improvement and capital projects before resuming production. The petroleum refining industry is estimated to spend $4 billion across 550 turnaround projects in 2022 in the US. In addition to direct spending on turnarounds, a facility incurs an indirect cost from lost revenue during the shutdown time that can further be amplified in a dynamic market. For a company the size of Dow in terms of asset base and integration, turnaround planning is a complex problem offering several levers and trade-offs. In this talk, we will present how Dow has developed an ecosystem of mixed-integer linear programming models over the years to enable data-driven decision-making for planning turnarounds across the enterprise.<\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n","protected":false},"excerpt":{"rendered":"

Sreekanth Rajagopalan Sreekanth Rajagopalan Associate Research Scientist at Dow Chemical Company Sreekanth Rajagopalan is an Associate Research Scientist in the Machine Learning, Optimization, and Statistics team in Dow\u2019s Research & Development organization. In his role, Sree develops purpose-built analytics solutions in the areas of manufacturing operations, supply chain, and sustainability with a focus on systems […]<\/p>\n","protected":false},"author":1001094,"featured_media":2211,"parent":712,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"class_list":["post-2681","page","type-page","status-publish","has-post-thumbnail","hentry","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"acf":[],"yoast_head":"\nSreekanth Rajagopalan - 2023 INFORMS Business Analytics Conference<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/speakers\/sreekanth-rajagopalan\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Sreekanth Rajagopalan\" \/>\n<meta property=\"og:description\" content=\"Sreekanth Rajagopalan Sreekanth Rajagopalan Associate Research Scientist at Dow Chemical Company Sreekanth Rajagopalan is an Associate Research Scientist in the Machine Learning, Optimization, and Statistics team in Dow\u2019s Research & Development organization. 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In his role, Sree develops purpose-built analytics solutions in the areas of manufacturing operations, supply chain, and sustainability with a focus on systems thinking and enterprise-wide optimization. Prior to joining Dow, Sree obtained a PhD in Chemical Engineering from Carnegie Mellon University in 2018 with a background in stochastic programming and global optimization. Sree is passionate about problem solving using math modeling and software development. He is active in peer-to-peer learning forums and governance teams on MLOps and DevOps for Data Science at Dow.<\/p>\n<h6>Track: Manufacturing<\/h6>\n<h2>An Enterprise Decision-making Platform for Managing Maintenance Turnarounds in Process Industries<\/h2> <p>Maintenance turnarounds of manufacturing facilities are planned shutdowns of the facilities that can be several weeks long. Besides enabling safety and regulatory compliance activities and asset renewal to be conducted, a turnaround provides a window to complete reliability improvement and capital projects before resuming production. The petroleum refining industry is estimated to spend $4 billion across 550 turnaround projects in 2022 in the US. In addition to direct spending on turnarounds, a facility incurs an indirect cost from lost revenue during the shutdown time that can further be amplified in a dynamic market. For a company the size of Dow in terms of asset base and integration, turnaround planning is a complex problem offering several levers and trade-offs. In this talk, we will present how Dow has developed an ecosystem of mixed-integer linear programming models over the years to enable data-driven decision-making for planning turnarounds across the enterprise.<\/p>","_links":{"self":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/wp-json\/wp\/v2\/pages\/2681","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/wp-json\/wp\/v2\/users\/1001094"}],"replies":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/wp-json\/wp\/v2\/comments?post=2681"}],"version-history":[{"count":5,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/wp-json\/wp\/v2\/pages\/2681\/revisions"}],"predecessor-version":[{"id":2687,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/wp-json\/wp\/v2\/pages\/2681\/revisions\/2687"}],"up":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/wp-json\/wp\/v2\/pages\/712"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/wp-json\/wp\/v2\/media\/2211"}],"wp:attachment":[{"href":"https:\/\/meetings.informs.org\/wordpress\/analytics2023\/wp-json\/wp\/v2\/media?parent=2681"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}