Human + machine: A new era of
automation in manufacturing
New
technologies are opening a new era in automation for manufacturers—one in which
humans and machines will increasingly work side by side.
Over
the past two decades, automation in manufacturing has been transforming
factory floors, the nature of manufacturing employment, and the economics of
many manufacturing sectors. Today, we are on the cusp of a new automation era:
rapid advances in robotics, artificial intelligence, and machine learning are
enabling machines to match or outperform humans in a range of work activities,
including ones requiring cognitive capabilities. Industry executives—those
whose companies have already embraced automation, those who are just getting
started, and those who have not yet begun fully reckoning with the implications
of this new automation age—need to consider the following three fundamental
perspectives: what automation is making possible with current
technology and is likely to make possible as the technology
continues to evolve; what factors besides technical feasibility to
consider when making decisions about automation; and how to begin thinking
about where—and how much—to automate in order to best capture value
from automation over the long term.
How manufacturing
work—and manufacturing workforces—could change
To
understand the scope of possible automation in the manufacturing sector as a
whole, we conducted a study of manufacturing work in 46 countries in both the
developed and developing worlds, covering about 80 percent of the global
workforce. Our data and analysis show that as of 2015, 478 billion of the 749
billion working hours (64 percent) spent on manufacturing-related activities
globally were automatable with currently demonstrated technology.These
478 billion working hours represent the labor equivalent of 236 million out of
372 million full-time employees—$2.7 trillion out of $5.1 trillion of
labor—that could be eliminated or repurposed, assuming that demonstrated
technologies are adapted for use in individual cases and then adopted. These
figures suggest that, even though manufacturing is one of the most highly
automated industries globally, there is still significant automation potential
within the four walls of manufacturing sites, as well as in related functional
areas such as supply chain and procurement. As McKinsey research has shown,
manufacturing is second, among industry sectors, only to accommodation and food
services in terms of automation potential.
We
emphasize that the potential for automation described above is created by
adapting and integrating currently demonstrated technologies. Moreover, it is notable that
recent technological advances have overcome many of the traditional limitations
of robotics and automation. A new generation of robots that are more flexible and
versatile, and cost far less, than those used in many manufacturing
environments today can be “trained” by frontline staff to perform tasks
previously thought to be too difficult for machines—tasks such as picking and
packing irregularly spaced objects, and resolving wiring conflicts in
large-scale projects in, for example, the aerospace industry. Artificial
intelligence is also making major strides that are increasing the potential for
automating work activities in many industries: in one recent test, for example,
computers were able to read lips far more accurately than professionals.
Our
study also looked at the automation potential for specific types of activities
and jobs within the manufacturing sector. We found that 87 percent of the hours
spent on activities performed by workers in production occupations are
automatable—the most of any manufacturing occupation. Even among other
occupations in manufacturing (for example, engineering, maintenance, materials
movement, management, and administration), however, there is still significant
opportunity, with approximately 45 percent of these working hours automatable
as well.
When
comparing various subsectors within manufacturing, we see a wide variation of
automation potential that can be explained partly by the nature of the
activities themselves, and partly by differences in the skills levels required
of workers and in the technological complexity of the manufactured product:
·
Low-skill labor/low product complexity.
Apparel/fashion/luxury
(82 percent of hours worked are automatable), agriculture processing (80
percent), food (76 percent), beverages (69 percent). The predominance of
repetitive, low-skilled activities in this group makes it highly susceptible to
automation.
·
Medium-skill labor/moderate product complexity.
Furniture (70
percent), basic materials (72 percent), chemicals (69 percent), medical devices
(60 percent), pharmaceuticals (68 percent), auto/assembly (64 percent), electric
power and natural gas (53 percent), and oil and gas (49 percent).
·
High-skill labor/high product complexity.
Aerospace and
defense (52 percent), advanced electronics (50 percent), high tech (49
percent), and telecom (43 percent).
As
for the monetary value of the automatable labor in various manufacturing
subsectors, the differences can be up to threefold, depending on the mix of
labor in a given subsector ($27,000 per year in apparel/fashion/luxury,
compared with $75,000 per year in oil and gas). Comparing the groupings listed
above, on average we see a 1.6-fold increase in wages per hour automatable
increase going from low- to high-skill/complexity, and a 1.4-fold increase
going from low- to medium-skill/complexity.
Finally,
we find that even though technical automation potential does not vary greatly
across the global economy, the fact that 81 percent of the world’s automatable
manufacturing hours and 49 percent of automatable labor value reside in
developing countries means that an upswing in automation in the developing
world could have significant global impact. Considering that 68 percent of the
automatable manufacturing hours in the developing world (and 62 percent of
automatable labor value) are in China and India alone, we see potential for
major automation-driven disruption in India and China, although how long that
could take will depend, in part, on the speed with which the costs of
automation solutions fall to below wage levels in these countries. A radical
shift toward automation in India and China could have major employment
implications in both countries and would also inject a substantial boost to
economic growth there.
What to automate:
Factors to consider
Technical
feasibility is, of course, a necessary precondition for automating a given work
activity or set of activities. Yet it is far from the only factor companies
need to take into account when deciding what and how to automate. A second
factor to consider is the cost of developing and deploying both the hardware and
the software for automation. The cost of labor and related supply-and-demand
dynamics represent a third factor: if workers are in abundant supply and
significantly less expensive than automation, this could be a decisive argument
against it—or for automating only to a limited degree. For example, an
automotive supplier in India has found that after introducing low-cost
automation of a few steps on its production line—which reduced staffing levels
from 17 to 8—its costs are now equivalent to those for a Japanese company
running the same kind of production line with a higher degree of automation and
a staffing level of only two.
A
fourth factor to consider is the benefits beyond labor substitution, including
higher levels of output, better quality, and fewer errors. While it is tempting for a
manufacturer to view automation primarily as a labor-savings lever, these other
benefits are often larger than those of reducing labor costs. Automation
options should be considered and evaluated using a clear strategy focused on
reducing the total cost of operations. We find that companies typically use
automation to address a number of opportunities, including increasing throughput
and productivity, eliminating variation and improving quality, improving
agility and ensuring flexibility, and improving safety and ergonomics.
In
addition to technical feasibility, cost of hardware and software, labor supply
and demand, and benefits beyond labor substitution, a fifth factor to be taken
into account in deciding whether and where to automate is regulatory and
social-acceptance issues, such as the degree to which machines are acceptable
in any particular setting, especially where they will interact with humans. The
potential for automation to take hold in a given sector or occupation reflects
a subtle interplay among all five of the factors we have listed and the
trade-offs among them.
Capturing long-term
value from automation
The
ultimate goal for manufacturers as they weigh the various factors described
above is to capture as much long-term value as possible from automation. How to
go about achieving this depends, in part, on how far along the spectrum of
automation maturity a given manufacturer is. We see this spectrum as having
four stages:
·
Low maturity. There is limited infrastructure
for employing automation—for example, lack of robotics, sensors, and
data-collection systems.
·
Mid-maturity. There is significant automation
infrastructure in place but it uses only a fraction of the potential—for
example, many sensors are installed but the majority of data are not utilized;
numerous data-capture systems lack interconnectedness; programming optimizes
local processes but not global value streams.
·
High maturity. There is full utilization of
traditional automation infrastructure on the manufacturing floor, but not
employment of cutting-edge automation technology and realization of potential
of automating managerial, support-function, and back-office tasks.
·
Best-in-class. Full potential of automation is
captured with latest technology across all spectrums of the operation.
Evaluating
a manufacturer’s operations along this spectrum of automation maturity can help
determine what kind of approach will best help to capture full long-term
impact. For example, lower-maturity operations will benefit more from “clean
sheeting,” while more mature operations can focus on fully utilizing their
already robust automation infrastructure to get to best-in-class.
Wherever
a given company is on the maturity spectrum, it is essential to keep the focus
on value creation. To help diagnose where automation could most profitably be
applied to improve performance, business leaders may want to conduct a thorough
inventory of their organization’s activities and create a heat map of where
automation potential is high. Business processes shown to have activities with
high automation potential can then be reimagined under scenarios where they
take full advantage of automation technologies (rather than mechanically
attempting to automate individual activities using current processes). Finally,
the feasibility and benefits of these automation-enabled process
transformations can be used to prioritize which processes to transform using
automation technologies. Such an approach can help ensure that automation
investments deliver maximum impact for the enterprise.
By
Michael Chui, Katy George, James Manyika, and Mehdi Miremadi
september
2017
http://www.mckinsey.com/business-functions/operations/our-insights/human-plus-machine-a-new-era-of-automation-in-manufacturing?cid=other-eml-alt-mip-mck-oth-1709&hlkid=655b57f3741f4527a54b1e671397e27a&hctky=1627601&hdpid=6c08ac7b-fc68-4601-b925-f5278b13d295
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