Survey

In Pursuit of Digital Acumen

Overwhelmingly, manufacturing leaders say a new leadership model is required for the digital age, but much preparation and learning about a new approach has yet to be done, reveals the MLC’s new Next Generation Leadership survey.   By David R. Brousell

It almost goes without saying that leadership will be the linchpin in whether manufacturing companies make a successful transition to Manufacturing 4.0. Leaders will have to establish the business goals of the transition, determine appropriate investments, and devise implementation and execution plans based on a timeline – all standard duties of a leader.

Manufacturing company executives will need to exercise many of the traditional skills and competencies associated with leadership in their journey to M4.0, the next wave of industrial progress based on digitization, but the rules of the old playbook may not be enough.

What’s coming into sharper focus, according to the Manufacturing Leadership Council’s new survey on Next Generation Leadership and the Changing Workforce, is that leaders in the M4.0 era will need to develop a new layer of competencies around the digital model. Called digital acumen, these competencies include understanding the potential of advanced technologies to create new competitive advantages, redesigning decision-making processes to leverage real-time data availability, and managing increasingly information-empowered employees in a collaborative working environment.,

These are some of the highlights of the MLC’s new leadership and workforce survey, which also looked at the state of leadership preparedness with M4.0, areas where leaders need to develop knowledge and expertise, challenges leaders face around culture and M4.0 planning, the state of unfilled jobs, and the role of automation in dealing with the workforce issue.

What M4.0 Leadership Means

What is crystal clear at this stage of the industry’s journey to M4.0 is that few of today’s leaders dispute the idea that the digital era requires a different approach and set of skills on the part of manufacturing company leadership. To underscore the point, more than 80% of the new survey’s respondents agree that they must write a new leadership playbook for the digital era (Chart 2). What this means in practice, says a strong majority of respondents, is that leaders, leveraging the powerful capabilities of advanced information and analytic technologies, must establish a fact-based, information-driven culture of decision making.

In addition, it means that leaders must develop both a deep understanding of what it means to fully integrate digital technology in company business operations and skills to orchestrate employees, customers, and business partners in a digitally-drive, collaborative business eco-system (Chart 1).

Tall orders, particularly for highly tenured executives that have relied primarily on their experience and intuition, but they are part and parcel of the overall goal of developing digital acumen – thinking digital first. The new mentality means not only understanding how to apply digital technologies to improve manufacturing, but also how to drive down decision making in the organization (Chart 3).

And, all of this needs to be accomplished as, you guessed it, the business of manufacturing runs hard on a day-to-day basis.

PART 1: DEFINING THE LEADERSHIP ROLE

1 ‘Fact-Based Culture’ Leads M4.0 Leadership Descriptions

Q: Which statement best describes what leadership means for the Manufacturing 4.0 era? (Top 3)

2 Strong Agreement on Need for Different Leadership Approach

Q: Please indicate the extent to which you agree with this statement: The emergence of the Manufacturing 4.0 era of information-driven factories will require a substantially different approach and set of skills on the part of manufacturing company leadership.

3 Digital Acumen is Key to the New Approach

Q: If you agree, which statement best characterizes the new approach and skills? (Top 3)

Today, few manufacturing executives dispute the fact that the digital era requires a different approach to and set of skills for leadership.

4 M4.0 Knowledge is Still in Ramp Up Mode

Q: What level of knowledge does your company’s executive management team have today about the concept of M4.0, its requirements, and its challenges?

The State of M4.0 Readiness

That challenge – essentially changing the wheels on the car as it travels at 60 miles per hour — helps explain how hard it is for many companies to get behind the wheel of the digital model of doing business. Today, only a fraction of survey respondents, 13%, say that M4.0 concepts, requirements, and challenges are well understood in their companies. Just over one-third say that they are superficially understood and more than one-quarter indicate they are just beginning to gather information about M4.0 (Chart 4).

But the good news is that many companies are working hard to develop the requisite knowledge. Just over 50% of survey respondents say, for example, that their company’s executive management team is somewhat prepared for M4.0, with another 10% saying they are very prepared. On the other side of the ledger, 26% indicate that their teams are not at all prepared and, in a finding that is noteworthy although not troublesome at this point, seven percent say there is resistance to M4.0 in their companies (Chart 6).

The lack of preparedness, of course, has mostly to do with that fast-moving car. Just one-third of respondents, 34.7%, say that their state of readiness is a function of being too focused on other issues. The lack of understanding about M4.0 requirements plays into the situation, too, as does the related issue, cited by 27% of respondents, of trying to understand how M4.0 applies to their specific businesses (Chart 7).

But the biggest challenge by far, say 62% of survey respondents, is changing corporate culture and the attitudes of employees toward the digital model. Not far behind is understanding the business case for M4.0 and developing a roadmap to get there (Chart 12).

As a result of where they are on the M4.0 preparation curve, many manufacturing leaders understandably express concern about their companies’ future success. In a critical finding, nearly 80% of survey respondents indicate that their companies’ future success is vulnerable due to their current level of M4.0 preparedness. Thirteen percent of respondents said they feel very vulnerable and another nearly 29% said they feel moderately vulnerable. Clearly, these leaders grasp the idea that the digital model is not optional (Chart 8).

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5 Top M4.0 Question: What’s the Business Case?

Q: What’s the most important thing your company’s executive management team wants to know about M4.0?

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6 Management is Getting Prepared for M4.0

Q: At this point in time, how prepared do you think your company’s executive management team is to undertake the journey to M4.0?

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7 ‘Other Issues’ is Chief Reason for Unpreparedness

Q: If your company’s executive management is not well prepared for M4.0, what is the most important reason for the lack of preparedness?

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8 Majority Sees Some Vulnerability Around Preparedness

Q: How vulnerable will your company’s future success be as a direct result of your company’s current level of M4.0 preparedness?

Wanted: Knowledge and Expertise

The velocity that a manufacturing company can attain to move along the preparedness curve is directly related to how fast and how well the company can build M4.0-related leadership skills and competencies and develop knowledge and expertise around new technologies.

First and foremost, say survey respondents, leaders must develop a willingness and ability to re-think the business and understand and embrace a digital model. Equally important is using computer-based analytics to make data-driven decisions. Not far behind are competencies around getting better at cross-functional integration of processes and functions – what the MLC refers to as the One Company model – and developing collaborative skills to manage flatter organizations (Chart 9).

And in terms of developing knowledge and expertise in technology areas, survey respondents placed significant emphasis around cybersecurity and advanced data analytics. Also important are digital factory techniques to link design and production processes and simulation and modeling technology used in the design phase of product development (Chart 10).

PART 2: DEVELOPING KNOWLEDGE AND EXPERTISE

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9 Embracing the Digital Model is Crucial Leadership Trait

Q: Looking ahead, what degree of importance would you assign to the following leadership skills and abilities?

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10 Cyber, Analytics Top Techs for Knowledge Development

Q: Looking ahead what degree of emphasis would
you place on the following technology areas in terms
of developing knowledge and expertise?

PART 3: STATUS OF AI ADOPTION

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11 Majority Sees Home Grown Next Generation Leaders

Q: Where do you see the next generation of leaders coming from for your company?

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12 Cultural Change is Top Challenge for Leadership

Q: In thinking about the requirements and implications of M4.0, what do you think are the most important challenges for leadership? (Rank top 3)

Despite the persistence of the workforce issue, most manufacturing companies don’t have a formal strategy to attract next-generation workers.

PART 4: WORKFORCE DEVELOPMENT AND TRANSITION

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13 Just Over One-Third Have a Strategy to Attract Next Generation Workers

Q: Does your company have a formal strategy to identify and attract next-generation workers for your factories or plants?

A Workforce in Transition

In the U.S., the problems of unfilled job openings and attracting younger people into the workforce have persisted for many years. Yet, a majority of companies, 54% according to the survey, do not have formal strategies to identify and attract next-generation workers. And among the 36% that do have a strategy, only 13% consider those strategies to be very effective (Charts 13, 14).

And so the issue of unfilled jobs across many categories of job functions drags on. Most prominent among jobs that are open for at least six months are production supervisors, quality control specialists, mechanical engineers, and process control engineers. And those that are open one year or more include cybersecurity professionals, digital design and modeling specialists, and maintenance engineers (Chart 16).

Looking ahead at the digital roles and skills that will be required, the survey offers some encouragement that manufacturers are beginning to turn their attention to understanding future needs. Almost half of survey respondents, 48%, say they have some understanding today what the digital roles and skills will be. But with only six percent indicating that these requirements are well understood, there is obviously much distance yet to be traveled before the industry as a whole has a clear picture of what the workforce of the future will look like (Chart 17).

In the meantime, given the persistence of the workforce issue, a significant number of manufacturing companies are looking at automation as at least a partial cure. Thirty-five percent of survey respondents indicate that automation and advanced technologies will help offset the difficulty in filling open jobs and another 28% expect that these technologies will actually reduce the number of workers they require (Chart 18). A small group, 11%, thinks they will require more workers in the future as a result of automation.

However this trend plays out, it’s clear that manufacturing leaders face no ordinary times. The advent of the digital era, the rush of new technologies such as artificial intelligence and collaborative robotics, and the churning demographics of the workplace have combined to erect an unprecedented challenge before manufacturing leaders.

What is the key to victory? The answer may lie within. Leaders will have to adapt to changing times, develop digital acumen, and lead differently. And that’s both the challenge and the opportunity. M

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14 But Strategies Have Been Only Somewhat Effective

Q:If yes, how effective has the strategy been?

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15 Vast Majority of Companies Have Open Jobs

Q: Does your company have open production/operations jobs today that it has been trying to fill?

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16 Many Job Roles Remain Open for Extended Periods

Q: If yes, what types of jobs and how long have they been open?

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17 A Majority Has Some Understanding of Digital Roles

Q: How well prepared do you think your company is in understanding the new digital roles and skills that you will need in the next few years?

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18 Automation Seen as Partial Cure for Open Job

Q: What impact do you think the increasing adoption of automation and advanced M4.0 technologies will have on workforce levels in your company in the future?

Survey development was lead by Executive Editor Paul Tate, with input from the MLC editorial team and the MLC’s Board of Governors.

AI in Manufacturing: Nascent, But on a Fast Track

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.

High Expectations Attend Transformative Technologies

The Manufacturing Leadership Council’s latest survey on Manufacturing 4.0-related technologies reveals that companies are expecting big things from the IIoT, analytics, 3D printing, and other new technologies. But few are approaching their adoption strategically.   By David R. Brousell


Over the next five years, manufacturers see a range of new technologies fundamentally changing they way they produce products. Whether it is the Internet of Things, 3D printing, or artificial intelligence, manufacturers expect these and other technologies to be potential game-changers in their operations.
At the same time, though, the path to realizing these changes for many manufacturers is anything but well paved. Few manufacturers have formal technology roadmaps in place today. Responsibility for devising and implementing technology strategies and their roadmaps is often diffused and unclear. And, particularly for small and medium-size companies, the capacity to evaluate and understand the potential of a growing set of new technologies emerging on the industrial scene is often lacking.
These are some of the key findings of the Manufacturing Leadership Council’s latest survey on Transformative Technologies in Manufacturing, one of the Council’s Critical Issues facing the industry. Survey respondents weighed in on the potential of new Manufacturing 4.0-related technologies, their investment plans, their challenges around adopting and using technologies, and where they think they stand in relation to competitors at this stage in the M4.0 journey.

The View in 5 Years 

Today, many manufacturing organizations are at the beginning of the learning curve of understanding and realizing the potential of new production and information technologies. This is not to be unexpected. It takes time, experimentation, and experience to leverage capabilities in a new analytics software program or production platform.
But imagination has been unleashed. Manufacturers are seeing game-changing potential over the next few years in a range of technologies that will be used on factory and plant floors.
Take the Industrial Internet of Things. Today, only 16% of survey respondents think that the IIoT, which involves IP-enabling plant floor equipment, is a game-changer in their production operations (Chart 10). But over the next five years, that number catapults to 40% expecting a fundamental impact from the technology.

Over the next two years, manufacturers are looking to invest in a mixed bag of IT-related technologies.


A similar acceleration in expectations occurs with other M4.0 technologies. Only 10% of survey respondents today believe that artificial intelligence and machine learning, for example, are having a transformative impact in their operations. Over the next five years, however, a near-tidal wave builds as the number soars to 49% of respondents expecting that AI and machine learning will be a game-changer for their companies.
This quantum leap effect is evident with 3D printing technology, Big Data and advanced analytics software, and collaborative robots, all of which will enable manufacturing operations to become more information-driven, automated and autonomous, and flexible in how they produce products.

Near-Term Purchase Plans 

Over the next two years, manufacturers are looking to invest in what has turned out to be a mixed bag of IT-related technologies, including already well-entrenched products such as enterprise resource planning (ERP) and manufacturing execution system (MES) software.
ERP and MES software have been in use in manufacturing companies for a long time, but many systems in place have aged. There is an increasing realization among these companies that, in order to move ahead with M4.0, these back-end systems need to be modernized first. As a result, the survey results indicate fairly robust investment intentions in these technologies in the years ahead.
But equal if not stronger intentions lie with some of the newer technologies (Chart 1). Artificial intelligence technology, for example, is in use in only 16% of survey respondent companies today, but over the next two years, 26% expect to invest in it, the strongest buying intention among 10 technologies examined in the survey. Not too far behind is augmented reality and virtual reality systems. Today, only 16% say they have already invested in these systems. Within the next two years, 19% say they will do so. Blockchain is another interesting example. Only 1% of survey respondents say they are using blockchain today, but 16% expect to invest in it over the next two years.

Part 1: Technology Investments and Plans

1 AI, MES Slated for Investments in Next 2 Years

Q: Please indicate your company’s investment posture for the following IT-related technologies.

2 More than One Third Plan IIoT Investments

Q: Please indicate your company’s investment posture for the following communications and networking technologies.

3 Predictive Maintenance, Machine Learning Top Production Plans

Q: Please indicate your company’s investment posture for the following production technologies.


Part 2: The Technology Assessment Process

4 Most Have Not Yet
Developed M4.0 Roadmaps

Q: Has your company developed a formal M4.0 roadmap to support transformative technology adoption?


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5 Informality Attends Most M4.0 Roadmap Approaches

Q: Which statement best describes your company’s
current approach to adopting a M4.0 Roadmap?


Within the realm of communications and networking technologies, IIoT-related technologies, now in use in 36% of respondent companies, will see significant growth as another 34% of respondents make investments in the technologies (Chart 2). And on the factory floor, predictive maintenance, IoT, machine learning, and collaborative robots garner the strongest buying intentions in the years ahead (Chart 3).
Along the way, manufacturers will be seeking competitive advantage from the technology, an edge, if found, that is always hard to sustain over time as new technologies emerge and the adoption/value cycle begins again. Right now, only about one-third of survey respondents say they are ahead of their competitors in the adoption of M4.0 transformative technologies. A nearly equal percentage says they are about equal with their competitors and 17% say they are behind (Chart 8).

The Challenges Ahead 

Best intentions aside, the technology assessment process currently in place in many companies could end up being a constraint on adopting and deriving value from many IT and operational technologies.
At this point in time, only 6% of survey respondents say they have a formal roadmap for M4.0 transformative technologies in place in their companies. Another 11% say they are working on such roadmaps, while 39% say roadmaps are under consideration but not yet started (Chart 4).
What’s apparently going on in some companies is that different groups within companies are investing in specific technologies often to deal with immediate issues. This reactive, tactical approach was cited by 70% of survey takers (Chart 5).

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6 Responsibility for M4.0 Roadmaps is Diffused

Q: Who is responsible for implementing your M4.0 roadmap/strategy?


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7 Majority Indicates Difficulty
in Vetting New Technologies

Q: Please indicate the extent to which you agree with the following statement: The accelerating pace at which new technologies are emerging is causing us to fall behind in our efforts to evaluate and understand their potential.


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8 Less Than One-Third Think They are Competitive With M4.0

Q: Where do you think your company stands in relation to its primary competitors’ adoption of transformative M4.0 technologies? (Check one)


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9 Measuring ROI, Assessing Cost/Benefit Are Top Challenges

Q: What are the most significant challenges related to adopting and using M4.0 technologies?


Part of the challenge here may be found in survey findings regarding who within manufacturing companies has responsibility for M4.0 technology strategy and road-mapping. The picture appears to show a diffusion of responsibility. For example, 26% of the survey respondents identified their CEO as the responsible executive, which certainly makes sense in smaller manufacturing companies where the CEO is wearing many hats (Chart 6).
But even more, 36%, said it is their chief operating officer or manufacturing head. Thirteen percent said their plant manager had the responsibility and 9% identified their CIO as the responsible authority.
Layered on top of this issue is the challenge of measuring the impact and effectiveness of M4.0 technologies, an issue that has cropped up in prior surveys. The most significant challenge with the technologies, say survey respondents, is in measuring the return on investment, followed by assessing cost/benefit, and migrating from or integrating with legacy systems still in place (Chart 9).
Taken as a whole, though, these challenges are pretty typical of the cycle that always surrounds the adoption of new technologies. Over time, as they get more experience with the technologies, manufacturers will get better at deriving benefits and measuring both hard and soft returns.
As they do, one challenge that has all the markings of an issue that seemingly will become more pronounced over time is the growing wave of data that is inundating companies. With now near-ubiquitous connectivity and more instruments in place for generating data from both objects and people, today’s manufacturing enterprise is hard at work to not only hold back the flood but also to manage its flow in ways that will benefit the business.
This is no easy task as the data volumes grow. The survey has revealed the dimensions of the problem. When asked how well prepared their companies are to organize, evaluate, and make decisions on the volumes of data that are or will be generated from greater connectivity of devices and equipment, only 9% of survey respondents indicated that their organizations were well prepared today. Another 44% said moderately prepared and 39% said poorly prepared (Chart 15).
This may very well be the chief challenge of our digital age. We asked for more information. Now, we are going to have to figure out what to do with it.   M

Part 3: Collaborative Innovation

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10 Strong Potential Seen for the IIoT

Q: What is your assessment of the potential of the Industrial Internet of Things IIOT), specifically IP-enabling your plant floor equipment and products, both today and in five years time?


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11 Nearly a Majority Sees 3D as a Game-Changer Ahead

Q: What is your assessment of the potential of 3D printing, both today and in five years time?


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12 Potential for Analytics Seen
as Strong in Next 5 Years

Q: What is your current assessment of the potential of Big Data/advanced analytics, both today and in five years time?


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13 More Than One-Third See Key Role for Collaborative Robots

Q: What is your current assessment of the potential of collaborative robots, both today and in five years time?


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14 More Than One-Third See Key
Role for Collaborative Robots

Q: What is your current assessment of the potential of
collaborative robots, both today and in five years time?


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15 Only a Fraction Say They Are Well Prepared for the Data Tsunami

Q: How prepared is your company to organize,
evaluate, and make decisions on the volumes of data that are or will be generated from greater connectivity of devices and equipment?


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16 44% Indicate Some Understanding of Digital Threads

Q: How well understood is the concept of a Digital Thread that connects and shares data across multiple functions in your organization?

Manufacturers Undertake a Course Correction on Innovation

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