{"id":8612,"date":"2026-03-27T13:27:00","date_gmt":"2026-03-27T18:27:00","guid":{"rendered":"https:\/\/www.mhisolutionsmag.com\/?p=8612"},"modified":"2026-03-30T07:31:06","modified_gmt":"2026-03-30T12:31:06","slug":"the-future-of-reliability","status":"publish","type":"post","link":"https:\/\/www.mhisolutionsmag.com\/index.php\/2026\/03\/27\/the-future-of-reliability\/","title":{"rendered":"The Future of Reliability\u2014in the AI Age"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full is-resized\"><a href=\"https:\/\/www.mhisolutionsmag.com\/wp-content\/uploads\/2026\/03\/101-01.jpg\"><img loading=\"lazy\" decoding=\"async\" width=\"311\" height=\"105\" src=\"https:\/\/www.mhisolutionsmag.com\/wp-content\/uploads\/2026\/03\/101-01.jpg\" alt=\"\" class=\"wp-image-8614\" style=\"width:190px;height:auto\" srcset=\"https:\/\/www.mhisolutionsmag.com\/wp-content\/uploads\/2026\/03\/101-01.jpg 311w, https:\/\/www.mhisolutionsmag.com\/wp-content\/uploads\/2026\/03\/101-01-300x101.jpg 300w\" sizes=\"auto, (max-width: 311px) 100vw, 311px\" \/><\/a><\/figure>\n\n\n<div class=\"htmlBody article_div\">\n<figure class=\"graphic\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright\" src=\"https:\/\/www.mhisolutionsmag.com\/wp-content\/uploads\/2026\/03\/101.png\" alt=\"christian dow\" width=\"103\" height=\"103\" \/><br \/>BY <span class=\"author-name\">CHRISTIAN DOW<\/span>, EVP OF INDUSTRY LEADERSHIP, MHI<\/figure>\n<p><strong>THERE IS NO<\/strong> doubt about it, the dawn of the artificial intelligence (AI) age is no longer approaching. It has arrived\u2014in full force.<\/p>\n<p>As a result, end users must understand how predictive analytics and digital twins are influencing fundamental, traditional reliability practices. After all, they\u2019re jointly strengthening reliability practices throughout the entire material handling industry.<\/p>\n<p>How so? According to Kushagra Thakur, senior product manager, Forge APM, at MHI member Honeywell, they simultaneously add precision, context and foresight. In turn, distribution centers (DCs) and warehouses have greater visibility than ever before, enabling more accurate decision\u2011making.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.mhisolutionsmag.com\/wp-content\/uploads\/2026\/03\/101-02.jpg\" alt=\"the future of reliability in the ai age\" \/><\/p>\n<p>&#8220;Predictive analytics detect microscopic shifts in vibration, temperature and electrical patterns long before failure symptoms appear,&#8221; Thakur said. &#8220;Meanwhile, digital twins extend this by simulating asset behavior under different loads and operating conditions.&#8221;<\/p>\n<p>While reflecting on predictive analytics\u2019 chief goals, Nathan Hibbs, material handling and intralogistics industry business development manager at MHI member Beckhoff Automation LLC, believes two especially stand out. First, it will detect any equipment degradation before a catastrophic failure ever occurs. And second, it will ensure DC and warehouse managers\u2019 equipment components have the most usable life possible.<\/p>\n<p>&#8220;In an ideal situation, this can enhance maintenance practices by allowing end users to reduce the amount of spare parts they need to keep on the shelf,&#8221; Hibbs said. &#8220;Predictive analytics should provide them enough warning to procure the components and schedule planned downtime.&#8221;<\/p>\n<p>On the other hand, digital twins will enable end users to prepare for unexpected equipment failures (and even predict them ahead of time). Through digital twins, they can stress\u2011test their entire equipment systems in simulated environments or test the impact of scheduled maintenance on the whole system. By doing so, they can eliminate unintended consequences that might otherwise affect an entire facility rather than just one section.<\/p>\n<p>&#8220;Automated systems also constantly feed real\u2011time data into a digital twin to validate and compare the real world versus expected values, which can inform and improve end users\u2019 predictive maintenance strategies,&#8221; Hibbs added.<\/p>\n<p>Over the coming years, the material handling industry will continue to move toward creating a digital simulation world within its systems, too. These systems typically comprise various pieces of equipment from different providers that work together in a facility, enabling many functions.<\/p>\n<p>Through this digital simulation world, design trade\u2011offs and predictions will occur. Modeling, via digital twins, will be a key component of the upcoming digital simulation, according to Mark Arisman, manager of business development, control products, at MHI member NORD Drivesystems.<\/p>\n<p>&#8220;Once we operate like this in the simulation realm, a feedback loop of real data and analytics will lead to more accurate modeling,&#8221; Arisman said. &#8220;This should result in more reliable systems\u2014because we\u2019ve already modeled and analyzed them in the digital world first\u2014and large productivity gains in design.&#8221;<\/p>\n<p>By combining predictive analytics and digital twins, DCs and warehouses will not only minimize downtime but also increase profits. According to Hibbs, they\u2019ll reduce their spare parts, damaged and scrapped products, and any collateral damage their peripheral components may have otherwise experienced due to previous catastrophic failures.<\/p>\n<p>Additionally, material handling systems as a whole will be simplified. In particular, end users will be able to model and predict behaviors more accurately than before. And, of equal importance, they\u2019ll likely notice reliability gains, according to Arisman. These gains will occur in two main ways: enhanced maintenance and diminished downtime.<\/p>\n<p>&#8220;They\u2019ll elevate several core practices too, including condition monitoring through higher fidelity and earlier detection. Failure mode and effects analysis (FMEA) will also be elevated as new failure modes and early indicators will be uncovered,&#8221; Thakur said. &#8220;Reliability\u2011centered maintenance (RCM)\u2014by matching maintenance actions more accurately to real conditions\u2014and root cause analysis, via data correlation at scale, will be elevated as well.&#8221;<\/p>\n<p>Simply put, according to Thakur, predictive analytics and digital twins aren\u2019t replacing each of these core practices. Instead, they\u2019re enhancing them, especially in terms of accuracy and impact.<\/p>\n<p>Regarding timing, it really depends on who you ask. Some industry professionals like Thakur believe the implementation of predictive analytics and digital twins has already begun. In fact, he has witnessed many material handling operations actively adopting predictive tools.<\/p>\n<div class=\"author-bio\">\n<div class=\"content\">\n<p><a href=\"https:\/\/www.mhisolutions-digital.com\/mhiq\/library\/item\/0226_volume_14__issue_2\/4330448\/\"><em>Click here to read the full article.<\/em><\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n<h6 class=\"wp-block-heading\"><a href=\"http:\/\/ISTOCK.COM\/\">ISTOCK.COM<\/a>\/BLACKJACK3D<\/h6>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>BY CHRISTIAN DOW, EVP OF INDUSTRY LEADERSHIP, MHI THERE IS NO doubt about it, the dawn of the artificial intelligence (AI) age is no longer approaching. It has arrived\u2014in full force. As a result, end users must understand how predictive analytics and digital twins are influencing fundamental, traditional reliability practices. After all, they\u2019re jointly strengthening &#8230;<\/p>\n","protected":false},"author":40,"featured_media":8615,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4,10,13,1],"tags":[1272,803,442,32,1902,1907,796,4113,466],"class_list":["post-8612","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-departments","category-industry-trends","category-technology","category-uncategorized","tag-digital-twin","tag-digital-twins","tag-mhi-solutions","tag-mhi-solutions-community","tag-mhi-solutions-magazine","tag-mhisolutionsmag","tag-predictive-analytics","tag-reliability","tag-supply-chain"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>The Future of Reliability\u2014in the AI Age - MHI Solutions<\/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:\/\/www.mhisolutions-digital.com\/mhiq\/library\/item\/0226_volume_14__issue_2\/4330448\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Future of Reliability\u2014in the AI Age - MHI Solutions\" \/>\n<meta property=\"og:description\" content=\"BY CHRISTIAN DOW, EVP OF INDUSTRY LEADERSHIP, MHI THERE IS NO doubt about it, the dawn of the artificial intelligence (AI) age is no longer approaching. 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