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MIT Management Executive Education
Published: Wednesday, May 24, 2017 - 11:00 (MIT Sloan Executive Education: Cambridge, MA) -- The use of internet of things (IoT) technologies in manufacturing—known as the industrial internet of things (IIoT)—has been heralded as a way to improve operational efficiency by correcting inefficiencies and identifying problems sooner. Ultimately, this will also lead to more rapid supply chains and greatly enhanced customer satisfaction. In many ways, the IIoT stands to revolutionize manufacturing, from behemoth organizations down to mom-and-pop manufacturing shops. This is why IIoT is often referred to as the “4th Industrial Revolution.” However, before any facility, plant, or organization starts down the path of implementing IIoT, there are a number of factors to examine and consider. John Carrier, a senior lecturer of system dynamics at MIT Sloan, detailed these steps in the recent MIT Sloan Executive Education webinar, The 7 System Principles You Need to Know Before Implementing IIoT. Carrier takes an even deeper dive into system dynamics as applied to the industrial internet in his new Executive Education program, Implementing Industry 4.0: Leading Change in Manufacturing and Operations. The seven principles Carrier presents in his webinar can serve as a checklist of what to assess, analyze, and potentially adjust before implementing any aspect of IIoT. He likens this process to preparing to move to a new home. If you take the time to assess, examine, and identify which belongings are worth keeping and which should be tossed, your new home will have less clutter and contain more items of value, instead of just “stuff.” In theory, your move should also take less time and money. In the words of Kurt Lewin, the late German social psychologist, “If you want to understand a system, try and change it.” Carrier also points out that implementing IIoT can help expose the “hidden factories” within manufacturing operations—a chaotic mix of unstable and ad hoc procedures and norms. If you are considering the leap into digitized or more data-driven manufacturing, take stock of these seven systems principles first: View the replay of Carrier’s webinar to learn more about these principles and how you can use them—in concert with new technology—to make your own system more intelligent. Quality Digest does not charge readers for its content. We believe that industry news is important for you to do your job, and Quality Digest supports businesses of all types. However, someone has to pay for this content. And that’s where advertising comes in. Most people consider ads a nuisance, but they do serve a useful function besides allowing media companies to stay afloat. They keep you aware of new products and services relevant to your industry. All ads in Quality Digest apply directly to products and services that most of our readers need. You won’t see automobile or health supplement ads. So please consider turning off your ad blocker for our site. Thanks, MIT Management Executive Education’s nondegree executive programs are led by senior MIT Sloan faculty and provide business professionals from around the world with a targeted and flexible means to advance their career development goals and position their organizations for future growth. MIT’s cutting-edge leadership training includes more than 40 short courses, executive certificates, online courses, custom programs for organizations, and its flagship program, the five-week Advanced Management Program. Seven Systems Principles You Need to Know Before Implementing IIoT
If you want to understand a system, try and change it
If you want to understand a system, try and change it
1. Observability: Can we see where we’re going?
2. Controllability: How will we get the value from this opportunity?
3. Stability and chaos: Can we use IIoT to minimize complexity and variability?
4. Metrics: Are we managing the right metric?
5. Feedback: Are we learning or just repeating the same mistakes?
6. Variation: What is causing variability in our system and how can IIot help?
7. The data is not the system: What are we going to do with the data we collect?
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