The MLC’s first survey of AI and machine learning in manufacturing reveals growing experimentation with the technologies and a sober view of their effect on jobs. By David R. Brousell
Opinions about the impact of artificial intelligence today range from the apocalyptic to the miraculous. Media darling Elon Musk of Tesla, for example, thinks AI is an “existential threat” to human civilization. Oracle CEO Mark Hurd believes a battle between the United States and China for “AI supremacy” will have important consequences for the global economy. And Ginny Rometty, IBM’s CEO, is convinced that AI has the power to transform industries in positive ways.
Whatever your view of AI, a term coined in 1955 by the computer scientist John McCarthy, the technology is at the forefront of discussions throughout society today, leading a debate about the future of work, jobs, and even what it means to be human. And as the manufacturing industry transitions to the digital era, AI is being viewed as central to leveraging the vast amounts of data that factories and plants will generate to do everything from improving operational efficiency to creating new, competitive advantages.
“Industrial AI can give the Fourth Industrial Revolution a huge boost and take Industrie 4.0 and similar initiatives to the next level,” said Roland Busch, Chief Operating Officer, CTO, and Member of the Managing Board of Siemens AG, in an article posted on the World Economic Forum’s website in January.
In an attempt to separate the hype from the reality of AI, and to take the measure of where AI and its cousin machine learning stand in manufacturing today, the Manufacturing Leadership Council undertook its first ever survey on manufacturers’ attitudes, plans, projects, and expectations with the technology earlier this year.
Chief among the survey’s findings is that, despite the hype, the 64-year old concept is at an early stage in most manufacturing companies. And while many companies expect AI to displace significant percentages of their workforces, they also anticipate that many of the displaced workers will be retrained for other roles in their companies, undercutting the notion that AI will inevitably lead to a vast wasteland of unemployed people. Moreover, a majority believes that while AI and machine learning are significant, they will not be transformative for the manufacturing industry.
PART 1: CHALLENGES TO AI ADOPTION
1 65% See Workforce
Changes Stemming from AI
Q: What percentage of your current workforce headcount do you expect will be replaced or removed by 2025 as a result of AI adoption?
2 But 60% Also See
Retraining for Those Displaced
Q: What percentage of the workforce displaced by AI adoption do you expect to be retrained for other roles in your company by 2025?
A majority of survey takers believes that AI and machine learning are significant but will not have a transformative impact on the industry.
3 Top 5 Challenges to AI
Q: What do you see as the biggest challenges to AI adoption in your organization today?
4 A Majority Sees AI as Significant But Not Transformative
Q: Ultimately, how significant an impact will AI and Machine Learning have on the manufacturing industry in the future?
Small Projects the Norm
Digging deeper into what the survey data reveals about the status of AI and machine learning adoption, at an overall corporate level, 20% of respondents indicated that they are experimenting with a range of small-scale pilot projects in their companies and another 12% said single projects have been implemented. The largest group, 40%, are either in the stage of developing awareness of the technology, conducting research, or defining a roadmap (Q10).
The good news is that, over the next two years, survey respondents expect AI and machine learning investments to increase, in some cases substantially. More than 30% of respondents said they anticipate spending increases of between one and 10% in that timeframe, while 22% said 10-25%, and 14% indicated an increase of 25 to 50%.
At a departmental or functional level, manufacturing and production, with 60% of respondents indicating they have begun the adoption of AI, are the leading areas for the technology at present. Supply chain follows, at 30%, and research and development comes in third at 28%. But many other areas of the enterprise, from sales and marketing to quality operations, are also getting involved (Q11).
On the factory floor itself, 24% of survey respondents said they are implementing AI and machine learning on a single-project basis, while 48% are still going down the awareness, research, and roadmap trail (Q12). And among the application areas being addressed, process improvement, production planning, and preventative maintenance are getting the most attention.
PART 2: AI STRATEGY & ORGANIZATION
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5 Few Have a Formal AI Strategy Today
Q: How would you characterize your company’s approach to AI and Machine Learning today?
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6 AI Importance May Rise Dramatically
Q: How important do you think AI and Machine Learning is to your company in terms of business impact today, and how important will it be in in 2 years?
A Lack of Formality
As companies proceed with pockets of AI and machine learning activity, they are doing so largely on an informal basis, suggesting experimentation with single or pilot projects to address a specific need or opportunity. Only 12.5% of survey respondents said their companies have a formal plan and strategy in place for the adoption and use of AI and machine learning technologies today (Q5).
But as knowledge of and experience with the technology matures, and as the number of applications increase, the informality will inevitably give way to more structure. And this shift could come in relatively short order as the perceived importance of AI and machine learning grows.
Interestingly, the survey suggests that a possible inflection point in that perception could come in the next couple of years. Today, only 12.5% of survey takers attach a “high importance” to the business impact of the technologies, but over the next two years, this group grows to 41%, a shift, should it occur, that would amount to a dramatic change in attitude (Q6).
Before that happens, though, manufacturers will need to work out some process issues as well as grow their own knowledge bases about the technologies. Right now, for example, fewer than one-third of respondents say their companies have a dedicated budget for AI and machine learning technologies (Q9). And just under 11% say they have a high level of confidence that their companies have the internal expertise to successfully manage and support deployment of the technologies. About 20% of survey respondents say that their software providers function as the primary source of support on AI and machine learning projects today, while just 16% say an in-house AI development team fulfills that important role (Q14).
Process Improvement Focus
As might be expected at this stage of adoption, many of the anticipated benefits of AI and machine learning tend to center around improving existing processes. Just over 52% of survey respondents identify predictive insights and better decision making, for example, as “high potential” benefits of the adoption of AI and machine learning technologies.
Cost savings, at 45% of the sample, and better planning, at 43%, come in fourth and fifth in terms of having high potential. But respondents also seem to be thinking broadly about the possible business impact of the technologies. Nearly 48% selected increased competitive advantage arising from the technologies as a potential benefit (Q16).
Respondents’ assessments of potential benefits in specific functional areas also tend to focus around process improvements. In production operations, for example, the top three expected benefits are increased uptime of factory assets, production process innovation, and improved predictive maintenance of plant floor equipment (Q17). And in their supply chain operations, survey takers cited better planning, more predictive insights, and increased agility as their most desired improvements (Q18).
But before they can truly understand the effectiveness of AI and machine learning technologies in any area of their organizations, manufacturers will have to get better at measuring them. Right now, nearly 48% of respondents said they do not have metrics established to measure the impact of the technologies; encouragingly, 38% said they do.
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7 Manufacturing, IT in AI Driver’s Seat
Q: Who is in charge of AI and Machine Learning efforts in your organization?
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8 Much Headroom for Growth of Internal AI Expertise
Q: What level of confidence do you have that your company has the internal expertise to successfully manage and support AI and Machine Learning deployment?
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9 Fewer Than One-Third
Have an AI Budget
Q: Does a dedicated budget exist within your company for AI and Machine Learning technologies, training, and education?
PART 3: STATUS OF AI ADOPTION
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10 Awareness Building, Pilots Characterize AI Status Today
for Performance Assessment
Q: What is the overall progress level for AI adoption at your company? (Check all that apply)
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11 Production Leads Corporate
Functions in AI Adoption
for Performance Assessment
Q: Which of the following corporate functions has begun the adoption of AI? (Check all that apply)
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12 Nearly One-Quarter Implementing AI Projects in Factories
Q: What is the progress level of AI adoption in your plants and factories?
Characteristic of the early stage most manufacturers are at with AI, few companies have a formal strategy in place for the technology.
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13 Process Improvement,
Planning Key AI Factory Applications
Q: What are the key application areas for AI and Machine Learning technologies in your plants and factories? (Check all that apply)
Adoption Challenges Abound
Among the most significant and provocative challenges attending AI and machine learning are the effects that these technologies may have on the workforce. There is little question that there will indeed be an impact, perhaps even a dramatic one. But people in manufacturing, who have had to cope with skills shortages and the problem of unfilled job for many years, may have a perspective on the issue that is markedly different from those outside the industry who fear a dark future for the human race.
A powerful majority of survey respondents, 65%, does indeed believe that AI adoption will result in workforce headcount level changes by 2025. That number breaks down to 39% saying the impact will translate to a one to five percent replacement or reduction of their current workforces in the next six years. Another 18% expect the impact to range between five and 10% and nearly seven percent see a 10 to 20% impact. Fully one quarter see no impact at all (Q1).
But a noteworthy percentage of respondents, 60%, expect that those displaced will be retrained for other jobs. That number breaks down at about 18% expecting that one to five percent of those displaced will find other jobs, another 18% anticipating five to 10%, , nearly seven percent expecting an offset of 10-20%, and about 16% foreseeing 20% or more being retrained (Q2).
In addition to the workforce issue, there are a number of other significant challenges associated with the adoption of AI and machine learning technologies.
Chief among these are understanding the technologies, at 67% of respondents; understanding the business case for them, at nearly 56%; and data issues, at 53%. The need to upgrade legacy technology systems in order to use AI and machine learning, cited by nearly 49%, is also a substantial challenge for many companies (Q3).
And on the critical question of what impact overall AI and machine learning will have on the manufacturing industry in the future, an interesting but not unusual schism has occurred in the survey data. A majority, 53%, say that AI and machine learning, while significant, will not add up to a force so powerful as to transform what they do. On the other side of the isle, 39% do indeed see AI as not only a game changer for their companies, but also amounting to a new era of technology affecting the business (Q4).
MLC surveys on the impact of Manufacturing 4.0 have revealed a similar dynamic. Several years ago, survey data was pretty much evenly split between those who thought M4.0 was significant but not transformative and those who thought it was truly a game-changer for the industry. But those numbers have slowly shifted over the years toward the more imaginative view as experience and knowledge have developed about the potential of digitization.
Could a similar route be traveled by AI?
The Road Ahead
The answer to that question will, of course, come with the passage of time, but, in the interim, those manufacturers who are trying to educate themselves about the technology, undertaking research, and even engaging in some pilot projects would be well advised to move ahead deliberately and with a sense of urgency.
Artificial intelligence is a force to be reckoned with. It will come at manufacturing from many directions and affect many functions within the manufacturing enterprise. AI will be part of many different types of application software products, to ERP and supply chain systems, to quality and maintenance systems, and customer-facing systems. It has the potential to be a pervasive influence on those systems, the processes supported by them, and job functions and roles. It could, as Roland Busch of Siemens said, take manufacturing to a new and better level. It could also cause unwanted disruption.
But it is not a force unto itself. People can and should remain in conscious control of deciding when to use it and how much to use of it. Like any technology, and certainly as we have learned with social media, technology can be used wisely or not so wisely.
The decision rests with us.M
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14 Manufacturers Tap Broad
Array of AI Expertise
Q:What is the primary source of support for the development of AI & Machine Learning competencies in your organization?
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15 Growth in AI, Machine Learning Investments Foreseen
Q: What level of increase in AI and Machine Learning investment do you plan, or expect to see, in your
manufacturing operations over the next 2 years?
PART 4: BENEFITS OF AI ADOPTION
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16 Top 5 Potential Benefits Foreseen
Q: How would you assess the potential benefits of AI adoption for your overall business? (% of those indicating high potential benefit)
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17 Top 3 Production Benefits Expected
Q: How would you assess the potential benefits of AI adoption for your production operations? (% of those
indicating high potential benefit)
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18 Top 3 Supply Chain Benefits Desired
Q: How would you assess the potential benefits of AI adoption for your supply chain? (% of those indicating high potential benefit)
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19 Most Lack Metrics on AI Effectiveness
Q: Do you use a specific set of metrics to measure
the effectiveness/impact of your AI & Machine
Learning deployments?
Opinions about the impact of artificial intelligence today range from the apocalyptic to the miraculous. Media darling Elon Musk of Tesla, for example, thinks AI is an “existential threat” to human civilization. Oracle CEO Mark Hurd believes a battle between the United States and China for “AI supremacy” will have important consequences for the global economy. And Ginny Rometty, IBM’s CEO, is convinced that AI has the power to transform industries in positive ways.
Whatever your view of AI, a term coined in 1955 by the computer scientist John McCarthy, the technology is at the forefront of discussions throughout society today, leading a debate about the future of work, jobs, and even what it means to be human. And as the manufacturing industry transitions to the digital era, AI is being viewed as central to leveraging the vast amounts of data that factories and plants will generate to do everything from improving operational efficiency to creating new, competitive advantages.
“Industrial AI can give the Fourth Industrial Revolution a huge boost and take Industrie 4.0 and similar initiatives to the next level,” said Roland Busch, Chief Operating Officer, CTO, and Member of the Managing Board of Siemens AG, in an article posted on the World Economic Forum’s website in January.
In an attempt to separate the hype from the reality of AI, and to take the measure of where AI and its cousin machine learning stand in manufacturing today, the Manufacturing Leadership Council undertook its first ever survey on manufacturers’ attitudes, plans, projects, and expectations with the technology earlier this year.
Chief among the survey’s findings is that, despite the hype, the 64-year old concept is at an early stage in most manufacturing companies. And while many companies expect AI to displace significant percentages of their workforces, they also anticipate that many of the displaced workers will be retrained for other roles in their companies, undercutting the notion that AI will inevitably lead to a vast wasteland of unemployed people. Moreover, a majority believes that while AI and machine learning are significant, they will not be transformative for the manufacturing industry.
Small Projects the Norm
Digging deeper into what the survey data reveals about the status of AI and machine learning adoption, at an overall corporate level, 20% of respondents indicated that they are experimenting with a range of small-scale pilot projects in their companies and another 12% said single projects have been implemented. The largest group, 40%, are either in the stage of developing awareness of the technology, conducting research, or defining a roadmap (Q10).
The good news is that, over the next two years, survey respondents expect AI and machine learning investments to increase, in some cases substantially. More than 30% of respondents said they anticipate spending increases of between one and 10% in that timeframe, while 22% said 10-25%, and 14% indicated an increase of 25 to 50%.
At a departmental or functional level, manufacturing and production, with 60% of respondents indicating they have begun the adoption of AI, are the leading areas for the technology at present. Supply chain follows, at 30%, and research and development comes in third at 28%. But many other areas of the enterprise, from sales and marketing to quality operations, are also getting involved (Q11).
On the factory floor itself, 24% of survey respondents said they are implementing AI and machine learning on a single-project basis, while 48% are still going down the awareness, research, and roadmap trail (Q12). And among the application areas being addressed, process improvement, production planning, and preventative maintenance are getting the most attention.
A Lack of Formality
As companies proceed with pockets of AI and machine learning activity, they are doing so largely on an informal basis, suggesting experimentation with single or pilot projects to address a specific need or opportunity. Only 12.5% of survey respondents said their companies have a formal plan and strategy in place for the adoption and use of AI and machine learning technologies today (Q5).
But as knowledge of and experience with the technology matures, and as the number of applications increase, the informality will inevitably give way to more structure. And this shift could come in relatively short order as the perceived importance of AI and machine learning grows.
Interestingly, the survey suggests that a possible inflection point in that perception could come in the next couple of years. Today, only 12.5% of survey takers attach a “high importance” to the business impact of the technologies, but over the next two years, this group grows to 41%, a shift, should it occur, that would amount to a dramatic change in attitude (Q6).
Before that happens, though, manufacturers will need to work out some process issues as well as grow their own knowledge bases about the technologies. Right now, for example, fewer than one-third of respondents say their companies have a dedicated budget for AI and machine learning technologies (Q9). And just under 11% say they have a high level of confidence that their companies have the internal expertise to successfully manage and support deployment of the technologies. About 20% of survey respondents say that their software providers function as the primary source of support on AI and machine learning projects today, while just 16% say an in-house AI development team fulfills that important role (Q14).
Process Improvement Focus
As might be expected at this stage of adoption, many of the anticipated benefits of AI and machine learning tend to center around improving existing processes. Just over 52% of survey respondents identify predictive insights and better decision making, for example, as “high potential” benefits of the adoption of AI and machine learning technologies.
Cost savings, at 45% of the sample, and better planning, at 43%, come in fourth and fifth in terms of having high potential. But respondents also seem to be thinking broadly about the possible business impact of the technologies. Nearly 48% selected increased competitive advantage arising from the technologies as a potential benefit (Q16).
Respondents’ assessments of potential benefits in specific functional areas also tend to focus around process improvements. In production operations, for example, the top three expected benefits are increased uptime of factory assets, production process innovation, and improved predictive maintenance of plant floor equipment (Q17). And in their supply chain operations, survey takers cited better planning, more predictive insights, and increased agility as their most desired improvements (Q18).
But before they can truly understand the effectiveness of AI and machine learning technologies in any area of their organizations, manufacturers will have to get better at measuring them. Right now, nearly 48% of respondents said they do not have metrics established to measure the impact of the technologies; encouragingly, 38% said they do.
Adoption Challenges Abound
Among the most significant and provocative challenges attending AI and machine learning are the effects that these technologies may have on the workforce. There is little question that there will indeed be an impact, perhaps even a dramatic one. But people in manufacturing, who have had to cope with skills shortages and the problem of unfilled job for many years, may have a perspective on the issue that is markedly different from those outside the industry who fear a dark future for the human race.
A powerful majority of survey respondents, 65%, does indeed believe that AI adoption will result in workforce headcount level changes by 2025. That number breaks down to 39% saying the impact will translate to a one to five percent replacement or reduction of their current workforces in the next six years. Another 18% expect the impact to range between five and 10% and nearly seven percent see a 10 to 20% impact. Fully one quarter see no impact at all (Q1).
But a noteworthy percentage of respondents, 60%, expect that those displaced will be retrained for other jobs. That number breaks down at about 18% expecting that one to five percent of those displaced will find other jobs, another 18% anticipating five to 10%, , nearly seven percent expecting an offset of 10-20%, and about 16% foreseeing 20% or more being retrained (Q2).
In addition to the workforce issue, there are a number of other significant challenges associated with the adoption of AI and machine learning technologies.
Chief among these are understanding the technologies, at 67% of respondents; understanding the business case for them, at nearly 56%; and data issues, at 53%. The need to upgrade legacy technology systems in order to use AI and machine learning, cited by nearly 49%, is also a substantial challenge for many companies (Q3).
And on the critical question of what impact overall AI and machine learning will have on the manufacturing industry in the future, an interesting but not unusual schism has occurred in the survey data. A majority, 53%, say that AI and machine learning, while significant, will not add up to a force so powerful as to transform what they do. On the other side of the isle, 39% do indeed see AI as not only a game changer for their companies, but also amounting to a new era of technology affecting the business (Q4).
MLC surveys on the impact of Manufacturing 4.0 have revealed a similar dynamic. Several years ago, survey data was pretty much evenly split between those who thought M4.0 was significant but not transformative and those who thought it was truly a game-changer for the industry. But those numbers have slowly shifted over the years toward the more imaginative view as experience and knowledge have developed about the potential of digitization.
Could a similar route be traveled by AI?
The Road Ahead
The answer to that question will, of course, come with the passage of time, but, in the interim, those manufacturers who are trying to educate themselves about the technology, undertaking research, and even engaging in some pilot projects would be well advised to move ahead deliberately and with a sense of urgency.
Artificial intelligence is a force to be reckoned with. It will come at manufacturing from many directions and affect many functions within the manufacturing enterprise. AI will be part of many different types of application software products, to ERP and supply chain systems, to quality and maintenance systems, and customer-facing systems. It has the potential to be a pervasive influence on those systems, the processes supported by them, and job functions and roles. It could, as Roland Busch of Siemens said, take manufacturing to a new and better level. It could also cause unwanted disruption.
But it is not a force unto itself. People can and should remain in conscious control of deciding when to use it and how much to use of it. Like any technology, and certainly as we have learned with social media, technology can be used wisely or not so wisely.
The decision rests with us. M
Survey development was lead by Executive Editor Paul Tate, with input from the MLC editorial team and the MLC’s Board of Governors.
Facing an urgent need to satisfy escalating customer expectations, manufacturers are pushing to elevate both the speed and the collaborative nature of innovation. By Jeff Moad
For years, manufacturers have excelled at applying their prodigious powers of innovation to achieving and sustaining operational improvement. Through the application of standard production practices and lean principals, increasingly capable plant automation, and robust supply chain optimization and practices, manufacturers have achieved lower costs, increased productivity, and more efficient use of assets, all while supporting the flow of new products to market.
That historic focus on innovation in the service of operational performance will, no doubt, continue, necessitated by global competitive pressures and enhanced by emerging Manufacturing 4.0 technologies such as advanced robotics, machine learning, and IoT on the plant floor.
Now, however, there is evidence that manufacturers are responding not only to a need to dramatically upgrade the pace and impact of innovation but also to redirect it in ways that will allow them to satisfy the soaring expectations of customers for everything from mass customization to shorter cycle times and smart products.
Manufacturers believe that M4.0 technologies such as advanced data analytics, IoT tools, and 3D printing platforms and prototyping techniques will help them accelerate innovation and better satisfy customers. And they realize that this customer-centric approach to innovation will require their company cultures and their approaches to innovation to become more collaborative.
These are some of the key findings of the Manufacturing Leadership Council’s latest research survey on Innovation in Manufacturing conducted in June of this year.
Customer Expectations Drive the Pace of Innovation
As they did last year, an overwhelming majority (84%) of manufacturers participating in the ML Council Innovation survey said the competitive importance and pace of innovation are increasing as the industry continues to embrace M4.0 digital transformation (Chart 1). Only 15% said they are seeing no change.
“Customer requirements and expectations are the most significant factors driving the growing importance and accelerating pace of innovation.”
And, while manufacturers said that M4.0 technologies are enabling them to step up the pace of innovation, the largest group by a significant margin (36%) said that customer requirements and expectations are the most significant factors driving the growing importance and accelerating pace of innovation (Chart 3). Like many members of the Manufacturing Leadership Council, these respondents no doubt are being pushed by customers for shorter turn-around times, more customized products, and new value-added service offerings, among other things.
Accompanying this need to innovate faster in response to rising customer expectations, manufacturers report a noticeable shift in terms of where they will be placing their innovation emphasis in coming years. While they report that product innovation and manufacturing process innovation will continue to be the top areas of innovation emphasis, other innovation priorities are growing much faster. Over the next five years, manufacturers said they will be significantly increasing their emphasis on business model and service innovation as well as supply chain innovation (Chart 2). This suggests that manufacturers are anticipating customer expectations for new types of value-added service offerings such as preemptive maintenance that leverage IoT data.
The largest group of manufacturers (36%) say the primary goal of their innovation efforts is to deliver new products to market, and they say that the most critical factor influencing innovation success is the presence of a strong culture of innovation among all employees (Chart 4). While the presence of visionary leadership was ranked as the second-most-important factor determining the success of innovation efforts, the largest group of manufacturers (43%) also said that the senior executive team has the most significant impact on driving innovation, followed by cross-functional teams (22%).
Part 1: Innovation Strategy and Organization
1 M4.0 Drives Importance of Innovation
Q: As the industry deepens its adoption of M4.0 and digitization, do you see the competitive importance and pace of innovation as … (check one)
2 Emphasis Shifts to Service, Supply Chain, Business Model Innovation
Q: What degree of emphasis does your company place on the following areas of innovation today, and what will be the emphasis in five years?
3 Customer Requirements
Drive the Pace of Innovation
Q: What is the most significant factor driving the importance and pace of innovation? (check one)
4 Culture and Leadership
Still Key to Innovation
Q: What do you see as the most important enabler that drives a successful innovation strategy for a manufacturing enterprise? (check one)
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5 But Most Still Lack Standard
Innovation Processes
Q: Does your company have a formal corporate-wide innovation process — including metrics and incentives – in place?
Despite manufacturers’ overwhelming belief that the pace and importance of innovation is rising, most manufacturers are still managing innovation as a coordinated set of activities but without standard, enterprise-wide processes or metrics that are directly linked to company strategy. Seventy-two percent of manufacturers said their companies do not have formal, corporate-wide innovation processes that include metrics and incentives (Chart 5). At the same time, the largest group of respondents (43%) characterized their companies’ innovation approach as informal, but coordinated initiatives, and only 36% said it was driven by formal and coordinated strategic goals. Twenty one percent said innovation efforts at their companies are ad hoc.
Perhaps as a result, most manufacturers (53%) say their companies place the greatest emphasis on innovation efforts aimed at delivering incremental improvements to products and services in the short term. Only 30% of manufacturers said their companies place a high degree of emphasis on exploring potentially game-changing ideas that would come to fruition over the long term.
M4.0 Technologies Seen Enabling Innovation
Manufacturers do expect rapidly-maturing M4.0 technologies to play key roles in enabling the accelerating pace of innovation. As they did in last year’s ML Council Innovation in Manufacturing survey, manufacturers see the greatest benefits flowing from sensors and IoT technologies, advanced analytics, and 3D printing and rapid prototyping tools (Chart 6).
Manufacturers also continue to have high expectations for Product Lifecycle Management tools and for digital design technologies that enable a digital twin approach to driving innovation.
These tools are expected to deliver a variety of benefits. Manufacturers, for example, expect the greatest benefits from IoT technologies to flow from the process improvements that they enable (Chart 7). Analytics and artificial intelligence are expected to make their greatest contribution in helping manufacturers to improve quality, and 3D printing and digital design tools will help most in enabling manufacturers to get products to market faster. Augmented and virtual reality tools, meanwhile, will help manufacturers most by enhancing ideation, respondents said.
Part 2: Manufacturing Innovation: M4.0 Technology Enablers
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6 Top Innovation Enablers: IoT, Analytics, and 3D Printing
Q: Which technology enablers do you think will have the most positive impact on your innovation performance in manufacturing over the next five years? (check top three)
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7 Technologies Expected to Streamline Processes, Reduce Costs
Q: What do you see as the top three most important benefits of using the following M4.0 technologies to help drive innovation?
Part 3: Collaborative Innovation
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8 Collaborative Innovation Still a Work in Progress
Q: Which statement best describes your company’s current level of involvement in collaborative innovation? (check one)
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9 Finding and Managing Partners Looms as a Challenge to Collaborative Innovation
Q: What do you regard as your top three challenges in seeking to achieve successful collaborative innovation? (check top three)
Manufacturers See Value in but Struggle with Collaboration
Manufacturers also believe they can improve and accelerate innovation by enhancing collaboration inside and outside their enterprises, particularly with customers. Manufacturers responding to the survey said engaging in more collaborative approaches to innovation will allow them to deliver greater new product development and operational improvements (Chart 10). At the same time, 30% of manufacturers said they see greater collaboration leading directly to greater customer-centricity. By comparison, 25% said the same in last year’s survey.
Going forward, manufacturers expect that collaborative approaches to innovation will be focused much more than today on driving customer engagement and creating product-related services (Chart 11). While 28% of manufacturers said driving customer engagement is a major focus of collaborative innovation today, 41% said it will be in five years. Similarly, just 13% said product-related services are a major focus of collaborative innovation today, but that number jumps to 20% in five years. Clearly manufacturers see a connection between more collaborative approaches to innovation and meeting escalating customer expectations.
That connection was reinforced when manufacturers were asked with which external groups their companies will engage in collaborative innovation over the next two years (Chart 12). The largest group by far, 78%, said key customers will be their greatest focus for collaborative innovation, suggesting that manufacturers are striving to understand and satisfy evolving customer demands.
Another 67% said they will have a greater focus on collaborating with technology providers, suggesting again the important role that M4.0 technologies such as IoT, artificial intelligence, and analytics will play in innovation going forward.
Despite manufacturers’ optimism about the potential for more collaborative approaches to innovation, however, the transition to collaborative cultures, processes, and organizational structures continues to be slow (Chart 8). Only 16% of manufacturers described their companies’ current approaches to innovation as highly collaborative across the enterprise. That figure was actually down from the number saying so in last year’s survey (20%).
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10 Collaboration Will Benefit Product Development, Improvement
Q: What do you see as the top three business benefits from collaborative innovation? (check top three))
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11 Customer Engagement, Service to Be Greater Focus of Collaborative Innovation
Q: What do you see as the top three areas of focus for collaborative innovation today and in five years’ time?
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12 Customers, Tech Providers Seen as More Important Collaboration Partners
Q: To what degree does your company plan to engage in collaborative innovation activities with the following external groups over the next one to two years?
The largest group of manufacturers (48%) said collaborative innovation today happens only in some areas of the company.
To the extent that manufacturers struggle with implementing collaborative innovation, the primary challenge revolves around finding the right collaboration partners and managing those relationships (Chart 9). Manufacturers also cited challenges turning ideas generated from collaboration into new products and internal reluctance to adopt externally-generated ideas.
Clearly, at some companies, the ‘not invented here’ culture still obstructs collaboration. That suggests that changing company culture will be a major prerequisite to adopting a more collaborative approach to innovation, more important even than the embrace of emerging technologies that may enhance collaboration. To that point, only 8% of respondents said they are currently using crowd-sourcing platforms to enhance collaborative innovation. Sixty-three percent said their companies have no plans to do so.
While it is clear that manufacturers face challenges in migrating to a more collaborative approach to innovation, it is certain that they will continue to strive to do so while also pushing to pick up the pace on innovation. Why? Because manufacturers are aware that innovation, besides driving down costs and driving up productivity, is a critical competitive tool allowing them to understand and quickly respond to the evolving requirements of increasingly well-informed and demanding customers. M