Historically, people thought if they hire a data scientist, that would be enough. And second, they took a multidimensional approach. So I think a key takeaway from the study is the laser focus of the leaders on winning use cases. The bottom 50 percent did not have this type of focus. But close behind those were quite a few others in terms of inventory optimization, or process improvement, some early warning systems, cycle time reduction, or supply chain optimization. Those are in forecasting, transportation, logistics and predictive maintenance, as I mentioned. Google Podcasts Apple Podcasts Spotify Stitcher RSSĭaphne Luchtenberg: That’s amazing, right? Even while philosophically execs have bought into the idea of machine learning, if we get down to brass tacks, there are real examples of where it’s been helpful in the context of efficiency and in operations.īruce Lawler: There are quite a few different use cases where the leaders focus. Finally, Amgen uses visual inspection to look at filled syringes, and they were able to cut false rejects by 60 percent. They were able to reduce energy consumption by about 1 percent, which doesn’t sound like a lot, but you realize they generate enough energy for 20 million households. They looked at their power plants and the overall efficiency, what they call the heat rate. Another example is Vistra, an energy generation company. They worked with Frito-Lay and they saved a million pounds of product. At Wayfair, for example, they use machine intelligence to optimize shipping, and they reduced their logistics cost by 7.5 percent, which in margin business is huge.Ī predictive maintenance company called Augury worked with Colgate-Palmolive to use predictive maintenance, and they saved 2.8 million tubes of toothpaste. And what we saw was that there really is a two- to threefold difference across every major operational indicator, and some examples of success stories came out. What was particularly important was it could define success and failure in many cases in some industries.ĭaphne Luchtenberg: Bruce, a lot of people have had false starts, right? And we hear about bots and machine learning based on data analytics, but where did you and the team see practical examples where they were really starting to add value?īruce Lawler: We looked at over 100 companies in the study itself, and then we did deep-dive interviews with quite a few of them. Based on the interviews and the surveys, we can now map out the journeys that companies should take or could take in accelerating progress in this space. What we really wanted to do was get a firsthand account across as many companies as we could find to drive both success and struggle across a fairly large weight of companies. So we launched this research to try and address the question. We started by looking at the literature and saw a lot of what companies could do or a point of view of what they should be doing in this space, but we didn’t really find a lot on what actually was working for the leaders and what wasn’t working for the rest. And it was really hard to tell why that was happening. But at the same time, we saw many companies struggle while others succeeded. It was clear that we saw a rising level of attention paid to the topic. Vijay D’Silva: Over the past few years, we’ve had conversations with dozens and dozens of companies on the topic of automation and machine intelligence, and something came out of it.
#Smart unfolder driver
What was the main driver behind the partnership and why did we commission the research? To discuss this and more, I’m joined by the authors, Vijay D’Silva, senior partner emeritus at McKinsey, and Bruce Lawler, managing director for MIT’s MIMO. The following is an edited version of their conversation.ĭaphne Luchtenberg: Earlier this year, McKinsey and MIT’s Machine Intelligence for Manufacturing and Operations studied 100 companies and sectors from automotive to mining. In this episode of McKinsey Talks Operations, Bruce Lawler, managing director for the Massachusetts Institute of Technology’s (MIT) Machine Intelligence for Manufacturing and Operations (MIMO) program, and Vijay D’Silva, senior partner emeritus at McKinsey, speak with McKinsey’s Daphne Luchtenberg about how companies across industries and sizes can learn from leaders and integrate analytics and data to improve their operations.
But why are some companies doing better than others? How do companies identify where to get started based on their digital journeys?
#Smart unfolder full
Despite the recent and significant advances in machine intelligence, the full scale of the opportunity is just beginning to unfold. Making good use of data and analytics will not be done in any single bold move but through multiple coordinated actions.
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