Which companies successfully deploy machine intelligence (MI) and data analytics for manufacturing and operations? Why are those leading adopters so ahead – and what can others learn from them?
MIT Machine Intelligence for Manufacturing and Operations (MIMO) and McKinsey & Company have the answer, the first of its kind disclosed. Harvard Business Review Article. This piece describes how MIMO and McKinsey partnered for a comprehensive 100-company survey to explain how high-performing companies successfully use machine learning techniques (and where others can improve). .
Created by the MIT Leaders for Global Operations (LGO) program, MIMO is a research and educational program designed to promote industrial competitiveness by accelerating the deployment and understanding of machine intelligence. The goal is to “find the shortest path from data to impact”, says managing director Bruce Lawler SM ’92.
As such, the McKinsey Project encapsulates MIMO’s mission to dismantle effective machine-learning use. The survey studied companies across different sectors, examining their use of digital, data analytics and Mi technology; Goals (from efficiency to customer experience to environmental impact); and tracking. Respondents were drawn from the extensive networks of MIT and McKinsey.
“This study is probably the most comprehensive that anyone has done in space: 100 companies and 21 performance indicators,” says Vijay D’Silva SM ’92, a senior partner at McKinsey & Company who collaborated with MIMO on the project.
Overall, those who benefited the most from digital technologies had stronger governance, deployment, participation, Mi-trained staff and data availability. They spent up to 60 percent more on machine learning than their competitors.
One standout company is biopharmaceutical giant Amgen, which uses deep learning image-enhancement to maximize the efficiency of visual inspection systems. This technology pays off by increasing particle detection by 70 percent and reducing the need for manual inspection. AJ Tan PhD ’19, MBA ’21, SM ’21 played a key role in this effort: he wrote his LGO thesis about the project, winning last year’s best thesis award upon graduation.
Lawler says Tan’s work exemplifies MIMO’s mission to bridge the gap between machine learning and manufacturing before it’s too late.
“We saw a need to bring these powerful new technologies into manufacturing more quickly. Over the next 20 to 30 years, we are going to add 3 billion more people to the world, and they want the lifestyle that you and I enjoy. They usually require things to be manufactured. How can we be better at turning natural resources into human welfare? One of the big vehicles for doing this is manufacturing, and one of the latest tools is AI and machine learning is,” he says.
For the survey, MIMO released a 30-page playbook to each company, analyzing how they compared against other companies in a variety of categories and metrics, from strategy to governance and data execution. This will help them in areas of opportunity or target areas to invest. Lawler expects this to be a longitudinal study each year with a wider scope and playbook — a vast but impressive undertaking with LGO brainpower as the driving engine.
“MIT was extremely important and important to the piece of work and was a wonderful partner to us. We had talented MIT students on the team who jointly with McKinsey did most of the analysis, resulting in improved quality of work,” D. Silva says.
This collaborative approach is central to MIMO’s vision as an information convener and partner for the private sector. The goal is “an effective change in industries that achieves not only technical goals, but business goals and societal goals as well,” says Duane Boning, director of engineering faculty at MIT LGO and faculty chief at MIMO.
This fusion of research and collaboration is the logical next step for LGO, he says, as it has always been at the forefront of problem-solving for global operations. Machine learning is certainly the latest big knowledge gap for many businesses, but not the first, and MIMO can teach companies how to apply it.
,[I liken] It was 30 years ago when LGO started, when it was all about lean manufacturing principles. About 15 years ago, it was the idea of a supply chain. This prompted us to think – not only for the benefit of our LGO students, but for the benefit of the industry more broadly – to understand, facilitate this major change, to conduct research and make connections in other real research activities. To achieve that, we need some effort to catalyze it,” says Boning. [MIMO’s] Genuine Enthusiasm: What are some ideas that work? What are the working methods? Which technologies work? And LGO students are, in some sense, the perfect vehicle for finding some of them. ,
