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Toward smart production: Machine intelligence in business operations

A detailed study of machine intelligence in industrial and manufacturing operations, published by McKinsey, reveals the surprisingly different paths companies can take. But a group of leaders shares similar characteristics.


McKinsey, in collaboration with the Massachusetts Institute of Technology’s (MIT’s) Machine Intelligence for Manufacturing and Operations (MIMO) program, interviewed 100 North American companies across the manufacturing sector about Machine Intelligence technologies and the adoption of those within their organisation. They found that


Leading companies are using MI technologies to move the needle on a broad set of performance indicators, achieving three or four times the impact of average players.


Throughout the interviews, McKinsey found that many of the leading companies reached their level of excellence by following four steps:

  1. Identify the organization’s North Star and conduct a visioning exercise that describes an ideal version of its operations in three or four years’ time. This will require looking past legacy infrastructure and skill constraints. It will probably involve borrowing best practices from other sectors too. For instance, Vistaprint organized its early efforts around on-time delivery as a paramount objective.

  2. Conduct an honest assessment of the organization’s starting point across dimensions. Companies often overindex on a few dimensions during early successes but find the momentum difficult to maintain without an operating-model change. Many companies find that an objective, external diagnostic is the pragmatic choice in understanding where they really stand versus their competitors—and, more importantly, versus their potential.

  3. Scope out a rough transition plan, accounting for barriers to change, critical infrastructure (such as data migration to the cloud), and assigning realistic medium-term targets. Most leaders in our survey started by using data to make decisions using tools simpler than MI. As they built maturity and familiarity with their data, they could move to more advanced techniques.

  4. Find a handful of use cases to gain early momentum while at the same time scoping out (and potentially self-funding) the necessary changes in infrastructure, talent, and related supports. The healthcare company mentioned earlier invested in use cases that had applicability across its production network, helping it build momentum by engaging a broader user base.


Read and download the study here.


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