What Robot Makers Must Learn from Dogs, Animators and Video Game Designers: A Conversation with Bruce Blumberg

DogAs robots become more ubiquitous, the interaction between humans and machines becomes more interesting. Understanding how we as people engage with robots or virtual characters is at the heart of Bruce Blumberg’s passion and mission, shaping a career that starts in the earliest days of Apple and NeXT, Inc. and moves on to creating World of Zoo, a video game that ultimately informed the user interface on the earliest collaborative robots. I recently sat down with Bruce to talk about his ideas about the evolving nature of the relationship between human and machine.

Q: What did the path you took from leading product marketing and development at Apple and Next, Inc. with Steve Jobs to working on human interaction with autonomous characters look like?

Thinking about the work I’ve done, on the whole, I’ve always been engaged in ways to make the user experience better. After getting my MBA from MIT’s Sloan School of Management, I worked at Apple – as a product manager for the Lisa, one of the first PCs with a graphical user interface, and later as the product manager for the Apple LaserWriter. I was the first employee at NeXT after the founders and while there I got interested in Rodney Brook’s work on autonomous systems. So, I returned to MIT to get my Ph.D. in Media Arts and Sciences, and taught for several years at the Media Lab.

My work at the Media Lab drew on the fields of Animal Behavior, Artificial Intelligence, Animation and Machine Learning. Interestingly enough, it all started because my son got interested in show dogs. Diving deep into the ways that dogs engage with people, I realized that every interaction – whether human-human, human-animal or human-machine – could be viewed as a conversation. That then led to looking at ways to create virtual characters based on the kinds of non-verbal cues and cognitive processes that scaffold interspecies communication. From there working on the user experience with collaborative robots was a very natural evolution.

Q: You’ve built your career in technology and seen several tipping points in the way technology makes our lives better. What are some of the things you’ve learned along the way?

Of course, working closely with Steve Jobs taught me the importance of design, user experience and the philosophy that no detail is too small. He also taught me to trust the technology: do the right thing in software and ride the cost curve down. I have also learned the power of taking ideas from one discipline and applying them in another: whether it was high-quality typography and helping to create the Desktop Publishing industry, or ideas from animal behavior informing the AI for computer characters or UX design for industrial robots. Finally, much of my work has been in developing tools for others, and here I am guided by the philosophy that simple things must be simple and complex things possible. Hopefully, all of this has allowed me to see ways to harness technology in ways to truly solve problems and improve our lives

Q: You worked at a video gaming company that paved the way for much more interactive engagement between players and the characters on the screen. What got you interested in gaming?

I’ve always loved building things, making things move and creating characters where perceptions are perceivable. They all came together in the pursuit of ways to make video games more interactive and engaging.

For me, it all goes back to that idea of a conversation. Humans, dogs and many other animals use non-verbal cues to scaffold their understanding of the interaction. For example, give a command and if the dog doesn’t understand what you want, it will cock its head to the side – who knows why they do this, but we use it as a reliable cue that they don’t understand us. Or think about a rattlesnake that communicates a warning, by coiling up and shaking its rattle before it strikes. Much of this is rooted in the physics of movement or the biology of attention but we use these cues to help construct a story about the mental state of the other, and guide our subsequent interaction. And trust me, animals are doing the same to us.

Classical animators have known this instinctively. Think about a Disney character pitching a ball: the animators focus on exaggerating the motion of the wind-up so that the audience can anticipate what comes next. The anticipation prepares the viewer’s eye and mind for what is to follow. I call this ‘playing to the amygdala’. The amygdala is a primitive, but critical, structure in the brain that makes snap decisions based on coarse sensory data and which in turn guides more sophisticated cognitive processes.

Another important understanding that came out of my work with dogs, and my reading, especially the work of Winifred Gallagher, was the importance of solicitation and attention. Dogs are “man’s best friend”, in large part because they are biased to attend to us, and most importantly they actively solicit our attention and care, and by doing so, they make us feel better about ourselves. And for dogs, it has turned out to be a terrific evolutionary strategy.

So at Blue Fang, we set out to create a video game that took these ideas and delivered on a child’s fantasy of being a zookeeper where the fun of the game was based on the child’s moment-to-moment interaction with the animals

Q: How did you translate that experience into developing the user interface for collaborative robots?

As robots are increasingly deployed in close proximity to people they need to interact with the real world. Something that computer engineers and scientists consistently underestimate is how hard it is to build technology that incorporates anticipation, expectation, and solicitation.

Here again, the concept of thinking about the interaction as a conversation between the person and the robot comes into play. Human cognitive processes lag behind immediate perception by some number of milliseconds and so getting the anticipatory clues right is critical to prepping person for what comes next. That’s in large part why the Baxter cobot we built at Rethink Robotics had a moveable screen that through its movement and images communicates to the person it is working with that it “understands” – or not – what it is being asked to do. For example, the screen would ‘nod’ as an acknowledgment of a command. The noise of the nod and the movement of the screen, seen out of the corner of the eye, was an incredibly efficient way to communicate, “yup, got that”. Not only does the interface make the robot approachable to workers on the floor, it communicates cues that are recognizable by those workers and allows for continued “conversation” that makes for a positive experience for both parties.

Q: We’re seeing and reading a lot about machines and AI taking over. Thoughts?

Rodney Brook’s blog does a very good job of covering AI cogently. Personally, I think there are a lot of misconceptions about artificial intelligence and we are overestimating the power of what’s been done to date.

More important than a level-set on the progress that’s been made though, is recognizing that no matter how great the technology might be, infrastructure can dramatically slow adoption. Technology is always adopted quickest when it doesn’t have to fit into an existing infrastructure. We have to always go back to what has to be true in order for something to work. The Segway is a great illustration of why: here was technology that was going to revolutionize the way people moved, but, cities are full of curbs and stairs and sidewalks are full of pedestrians who don’t want to share the sidewalk with a mechanized vehicle. I am very pessimistic about the rate of adoption of autonomous cars.

I think we’re better spending our time on augmented technology. These are not fully autonomous systems but are designed to collaborate with people to help them do their jobs better, increase safety and so on. Careful attention will need to be paid to transparency and extensibility: transparency so the user has confidence in the how the system works, and extensibility so that if the system only gets it 95% right as far as the user is concerned, the user can add the 5% that makes it perfect for that user. One of the challenges for deep learning is that these systems create representations that are neither transparent nor extensible.


Bruce’s journey has certainly taken some interesting twists and turns. For me, his focus on creating a user experience that’s positive and transparent and how that informs the way machines and humans work together is compelling. Is it possible for humans and machines to truly have a dialogue and move work forward through that conversation? Share your thoughts on that connection with me. Where do you find positive experiences in working with machines? Tweet me @jim_lawton.

Originally published on Forbes.

Robots: The Automation Juggernaut that Manufacturers Need

I work for a robotics company. I’m sure you can imagine the rush that comes when the latest predictions about the growth of the market peg the opportunity on the fast-track to exponential growth. After all, it’s how I make my living.

For collaborative robots (cobots), the news seems to be all good. Last fall, a report crossed my desk touting CAGR of 57+ %, resulting in a market size of $4+ billion by 2023; another reported that 150,000 cobots would be deployed worldwide by 2020. Reading these, it’s easy to understand how one might get swept up in the head-spinning optimism. Not me, though. If there’s one lesson I’ve learned in my career in the world of disruptive technology, it’s beware the hype.

That’s not at all to say that I don’t believe in the potential of cobots to transform manufacturing. I’ve written plenty about it – and more importantly – have seen our customers put cobots to work in their operations with great success. More often than not, though, there’s plenty of resistance to the disruptive innovation that cobots bring. And even after the cobots are in place and doing the job well, entrenched thinking throws up roadblocks to expansion.

What is it going to take to close the gap between the hype and what Gartner has labeled as the “trough of disillusionment” for cobots? Manufacturers know they are caught between today’s reality – where productivity is being squelched by one workforce aging out and a labor pool that doesn’t want to work in manufacturing and the pressure to move to the age of digital manufacturing – where machines operate autonomously, powered by distributed decision-making, and products find their way independently through the production process. Manufacturers know that more automation is key to breaking the deadlock between today’s status quo and the promise of Industry 4.0.

Getting more of their operations automated requires evaluating robotics vendors through a dual focus: what can you do for me today and how will you help me be ready for tomorrow? Here are some things to ask and look for to be confident that the cobot solution is one for both:

  • Who trains the cobot? A cobot should be easily trained by the people you already have in the factory. No programming. No advanced computer skills. Just show it what needs to be done by moving the “arms”, save that task in its onboard memory and put it to work.
  • How quickly can cobots be put to work? Cobots should be easy to deploy – in the existing environment, with no new fixtures or processes required. Tasks should already have been identified for the cobots to be trained on and so the time from out-of-the-box to productivity should be measured in days – not months.
  • How many tasks can one cobot do? Unlike traditional automation, where robots are hard-wired and programmed to perform a single task, cobots should be able to perform a wide range of tasks. Advanced sensors and vision systems are critical to this flexibility.
  • What kinds of tasks can cobots do? This question is a critical one, especially since the technology behind cobots is so new, most automation engineers haven’t even dreamed yet of the possibilities. Repetitive tasks that are human scale, done at human cadence are a good place to start. Choose a partner who is able to visit the operation and, on-the-spot, point out tasks that the cobot can do. With innovation, it is not enough to sell the promise.
  • How much does innovation cost me? Cobots can do more because of software and so just like our smartphones, upgrades should be free. This gives manufacturers a future-proof path to continuous automation innovation.
  • How smart is the cobot? As the digitization of manufacturing becomes more and more a reality, smart machines will be essential. The cobot should be able to manage itself on the task assigned – and monitor its own performance. It should be able to manage and monitor the machines it is interacting with and report on key metrics. It should be able to troubleshoot routine problems, resolve them and provide updates as well.

It truly is exciting to see the changes we’re going to experience as manufacturing evolves toward the digital future. And it’s all the more exciting to be a part of the market that’s going to help accelerate that journey. Tweet me @jim_lawton.

Originally published on Forbes.

MIT’S Dr. Duane Boning Talks Leadership in Digital Age of Operations

Duane-Boning-MIT-LGO_0Few things in my life have shaped who I am today, including graduating from MIT’s Leaders for Global Operations (at the time, Leaders for Manufacturing) program. So, when an opportunity arose for me to talk with Dr. Duane Boning, Co-Director, MIT Leaders for Global Operations Program, about the future of leadership in manufacturing and operations, I took it.

After all, part of the program’s stated mission is “to educate leaders to address the world’s most challenging operations and high-tech problems.” As I’ve written about here a lot, capitalizing on the next wave of innovation in manufacturing (fueled by complex advances like artificial intelligence, machine learning, and advanced analytics) is no easy feat.

Here, Dr. Boning shares his perspectives on the challenges and opportunities for developing leaders who can thrive in the digital age of operations.

What is MIT’s Leaders for Global Operations (LGO) program?

The program began about 30 years ago, with the vision of offering dual master’s degrees in management and engineering to prepare leaders for the unique challenges found in manufacturing and operations.

In collaboration with some of the world’s largest high-tech, manufacturing, pharmaceutical, energy and global supply chain industries, LGO brings together the best and brightest in academia and business. LGO Fellows apply the latest thinking in technology and management best practices to the real-world. Pushing the limits of what’s possible, MIT’s LGO Fellows tackle the most intractable problems in manufacturing and operations today and position companies for better performance tomorrow.

How has the LGO program changed or adapted to students and industry?

At the outset of the program, it was focused on manufacturing at the shop floor and plant level. We quickly learned that focusing on any one silo was not enough so, 10 years ago, we shifted to a broader focus on challenges manufacturing and operations firms face. Today, our member companies include Amazon, Verizon, and National Grid, as well as more-traditional manufacturers.

More recently, we’ve expanded the curriculum to align more closely with the leading edge of digital transformation, equipping future leaders with the necessary skills to harness advances in analytics and data-rich problem-solving. All 50 students in the incoming class now also complete intense training in programming skills and machine learning. Through this, they gain an understanding of how these will affect their chosen industry – characterized by fast-moving, data-intensive environments.

What are some of the operational challenges facing your member companies today?

You won’t be surprised to learn classic manufacturing/operations problems still exist: Quality, statistical process control, etc. In operations and supply chain, the basic principles will take you a long way, but it really is about best practices. While those have been defined, they are hard to implement and sustain – it takes knowledgeable and skilled leaders to make that happen.

The emerging problems involve more data and more-sophisticated data analytics. Machine learning is a great example because this field of computer science provides the ability to extract insights from data and learn. Though not a new field, machine learning has been advancing rapidly, and helping companies apply the technology – from predicting problems before they happen, to optimizing performance beyond what has been possible with traditional analytics, to integrating insights to enable better and faster response to market opportunities – that’s new.

The level of sophistication required to apply machine learning in manufacturing is very challenging. Take, for example, determining whether a product is good or bad: we need to use machine learning to make decisions across many levels of classification, but we may not have the same huge volume of images as are used in learning if an image is one of a dog or a cat.

How are you preparing the future leaders in manufacturing and operations for these challenges?

Well, fundamentals are key. Like I said, the problems in manufacturing and operations aren’t easy ones to solve. Reinforcing those basics and making sure best practices around them are cemented, is a major focus for us at MIT.

At a broader level, the LGO model — synthesizing management perspectives (the MBA) with deeper analytical and engineering skills (the MS in one of six engineering disciplines) – gives students a different way of thinking about their roles.

One of the most important lessons our students learn is an appreciation of management challenges. Most come to LGO with four or five years of work experience. Then through the internship model, they not only come face-to-face with an operational challenge, they learn to work through it through the management lens. That appreciation of the management challenge is essential.

At the very heart of all these challenges, we’re talking about the transformation of operations. And transformation requires change. LGO graduates understand that and appreciate what it requires to bring about change and implement it.

What’s next for the LGO program?

We talk with the COOs and CEOs of our member companies in great depth to understand what keeps them – as leaders – awake at night. One thing 100 percent of them have shared with us is that they believe talent – or the lack thereof – is a real challenge.

One of the biggest challenges of a huge transformation, like the digital age of operations, is that you need more people, not fewer. While we’re extremely proud of the 1,200+ alumni who’ve gone on from LGO to become agents of change around the world, we realize that’s a very small population. Companies need more people with a broader and deeper set of skills. So we’ve got to do something to close the gap.

To that end, MIT has developed the Principles of Manufacturing MicroMasters, a set of online courses on edX.org. This program is designed to give new talent an easy and accessible way to explore fundamental skills needed for global excellence in manufacturing and competitiveness. In this way, we hope to extend the learning and experience that defines leadership to help a vitally important industrial sector thrive.

Dr. Boning’s insight into the challenges of integrating data and analytics to drive operational excellence and his perspective on the talent gap are familiar topics here. Which do you find to be the most vexing? Are there others? Share your thoughts with me @jim_lawton.

The Role of Robots in Industry 4.0

Robotic hand using laptop, illustrationAs manufacturers around the world make strides to build out the vision of Industry 4.0, many – if not most – will embrace advanced machines to accelerate the digital transformation of their operations. The pressures remain: improve productivity, meet consumers’ expectations for customization and drive continuous product innovation while consistently lowering costs. Bottom line: manufacturers can’t ignore the thrumming beat calling for measurable and meaningful progress on Industry 4.0 in 2018.

It’s not an easy journey. In last month’s post, we called out some of the realities manufacturers face when tacking the wholesale change called for in the digitization of manufacturing. Those obstacles will not easily be overcome, and yet, there’s not a lot of patience for sitting on the sidelines – progress must be made. So, stepping onto the factory floor, what are some the biggest challenges in the transformation from analog to digital operations? In general, these are the most common

  • complex, legacy systems, many with proprietary applications,
  • huge volumes of data generated by existing equipment and
  • few resources to dedicate to deriving insight from the huge volumes of data.

It’s no wonder then that for many manufacturers, the first thing that comes to mind when thinking about connecting machines on the production floor to one another, and then ultimately to back-office systems in finance, purchasing and more is that in no short timeframe, they’ll be drowning in data without a life preserver in sight. Changing that mindset requires a model that’s built on a new paradigm for automation: one that taps the power of software to orchestrate the actions needed.

Changing the Real-world of Manufacturing with Gaming Technology

In the earliest days, automation programming was hard-wired, literally. Programmable logic controllers (PLCs) were the brains behind industrial automation, a technology that relied on cables, relay racks and highly skilled programmers using ladder logic and structured text to direct the machines. The complexity and rigidity made these solutions costly to implement and to change when change was necessary.

No more. Behavior tree technology, used in video games to define basic behaviors for objects, characters, and environments which come together to bring an interactive world to life, blends logical flows and actions seamlessly to direct robots to perform tasks. It’s all done with onboard software – and can be done by the operator who knows best what the workflow is for a task.

While behavior trees make it much easier for manufacturers to put more robots to work on tasks, this approach to enabling automation does so very much more. Starting with the behavior-based robot, manufacturers can build behavior-based work cells, then behavior-based factories. They can create intelligent portions of a line first, transition to an entire line, a collection of lines, a whole factory and then a collection of factories.

With performance, task data collection and introspection built into the design – not as an add-on or layer that sits on top of the design – manufacturers no longer will find themselves drowning in a sea of data. Instead companies will have a powerful way of making sense of the data, and more importantly, deriving value from that data. In the race to build the digital factory, manufacturers will find robots the perfect physical and cognitive partner. These machines make themselves indispensable with the ability to perform tasks, collect and analyze data on productivity, quality, and reliability and cost for themselves and other equipment in the process and make the insights available for interpretation and action. Where do you see the greatest potential for robots to contribute to the brave new world of digital manufacturing? Tweet me @jim_lawton.

Originally published on Forbes.

Industry 4.0: Harder Than We Think

Young man against cloudy sky, shielding eyes, side view, close-upI feel for manufacturers. I really do. Transforming raw materials into a finished product is hard work. There have been many advances in technology that improve productivity and efficiency of manufacturing operations, but the innovation promised in the 4th Industrial Revolution effects much more than the production and planning processes. As data-driven insights ripple through to effect action on everything from product development and sales to sourcing and delivery, organizations need to embrace wholesale change in structure, strategy, and execution. Most manufacturers are on board with the opportunity – a recent poll found that 99% agreed that the 4th Industrial Revolution will be about getting actionable insights from data. But more telling is the result that almost 60% of those interviewed reported that they are still getting to grips with the concept and are less certain about what it actually entails.

In the cold, harsh light of the dawn of Industry 4.0, there are many reasons why the factories of the future are not much more than a very futuristic vision. The changes fueled by the digitization of manufacturing are hard and manufacturers have a lot to wrestle with before they see the promise of Industry 4.0 realized.

Everyone’s Got an Opinion

Getting advice on how, what, and when to begin the transformation is a lot like having your first child. Everyone you know – and even people you don’t – are very happy to share with you the secrets they’ve learned about raising a child. And more often than not the advice from one well-intentioned friend will be in direct conflict with advice from another. Manufacturers are being bombarded constantly with ideas, insight, recommendations and proposals for implementing solutions that will revolutionize their operations. But who really knows?

Stakeholders, Stakeholders Everywhere…

…and not a common vision for what exactly this all is supposed to do and for whom. There’s not a single constituency within a manufacturing organization – production, operations, design, engineering, marketing, sales, finance – that doesn’t expect to get something from the new way of manufacturing. They’re all excited about it. But there’s really no collaboration between all these groups – everyone is off building their own piece of the pie and no one is asking about how it will work with anything else.

 Solution Swamp

Not too long ago I created a map of all the technology companies that are messaging their solutions as the answer to Industry 4.0. It looks a lot like one of those modern art pieces where there are just thousands and thousands of dots – and they don’t come together to create a beautiful image – they are just dots. How is anyone who’s got to meet delivery deadlines expected to sort all that out?

Combined, these three conditions add up to one big headache for leaders. Odds are good that every day they see a new report on the imperative for making progress toward building connected operations. They’ve told their stakeholders and shareholders they are all in. They may have built cross-functional teams to focus on the opportunity and ask for regular updates on progress. It’s all good. Until leaders can articulate in concise and specific language the reasons why and how their own company will measure the impact of Industry 4.0 on operations and customers, it’s just noise.

I’m not saying the promise will never be realized. I’m excited that it will actually happen. What I think though we all need is a long, deep breath and a little reflection on what we want to achieve with Industry 4.0, and how we’ll know we’ve achieved it.

How are you breaking through the hype to change your operations? What do you see as the most powerful result of the new model for manufacturing? Tweet me @jim_lawton.

Originally published on Forbes.

How Manufacturers Can Tackle The Labor Problem In Their Race To Industry 4.0

Elephant standing in conference room in office

There’s a lot of attention being paid to the role that technological innovation will play in transforming manufacturing. Still, even as they are being inundated with ways to achieve the Industry 4.0 vision, most manufacturers are struggling with realities that limit their ability to achieve the most basic measures of manufacturing success.

More than a vision (and a lot of promises), manufacturers need ways to overcome those challenges even as they tackle the opportunity for revolutionary change.

Problem #1: The Workforce Dilemma

There’s a lot of noise about the role manufacturing has in providing “good” jobs, but the reality is that manufacturers can’t fill the jobs they have today. We hear it over and over again from our customers and prospects – “fix my labor problem, please.” Manufacturing’s labor gap isn’t surprising when you look at the factors behind the challenge of building a reliable, productive workforce:

  • The gray tsunami: In 2016, the Bureau of Labor Statistics reported that more than half of employees in manufacturing were over the age of 45, and more than half of those were over 55. That’s a lot of people headed quickly toward retirement. The next generation of workers doesn’t want those jobs, leaving manufacturers unable to staff operations reliably.
  • The drug crisis: When it comes to a stable labor pool, the opioid crisis is hitting manufacturing hardest. At fellow Forbes contributor Lora Cecere’s Supply Chain Summit this fall, one panelist posited that up to 25% of the decline in labor force participation by the prime demographic (men ages 25-54) for these jobs is a direct result of opioid use.
  • The skills gap: In a recent post on Forbes.com, BCG analyst Justin Rose highlighted the urgent need to shift away from a workforce that has a single set of specific technical skills to cultivating one with a more broad-based set of adaptive skills that draw on abilities such as active listening and learning, critical thinking, decision making, and communication. The sticking point here is that our education and workforce training models aren’t aligned with this need.

For manufacturers, these challenges mean they don’t have what they need to run operations efficiently:

  • A reliable workforce.
  • The ability to scale up and down quickly.
  • Stable levels of productivity and quality.

Without these, any aggressive move toward the new model of a connected and intelligent factory will not amount to much more than putting lipstick on a pig.

Problem, Solved

What manufacturers need is a way to staff operations today, while they begin testing and proving the benefits of Industry 4.0. That’s where collaborative robots come into play, giving manufacturers the ability to:

  • Automate more. With robots able to move toward automating more tasks that require technical skills, like machine tending, manufacturers get 1) the assurance that operations can run efficiently, smoothly and reliably, and 2) the runway to offer more training focused on the adaptive skills required for the new models.
  • Scale up and down as business needs. Moving from one to two shifts or three shifts to one with people as the primary labor force is not easy – or desirable. Robots can work as much – or as little – as needed, and at a price point under $35,000, the ROI can still be achieved if the robot is only in use for a single shift.
  • Increase dependable levels of quality and productivity. For many tasks, repetition is the name of the game. People are simply not suited to doing the same thing over and over: Minds wander, fingers and hands become numb, and mistakes get made – or worse, someone gets hurt. Robots are built to perform the same task over and over with a guaranteed level of precision and reliability.

Toward A Brighter Future

Manufacturers are, and always have been, data-driven. So the promise of Industry 4.0 to streamline the collection, analysis and data-driven decision making most definitely appeals.

More than simply a workforce multiplier, collaborative robots can help manufacturers move toward achieving this benefit. Robots make it possible for manufacturers to get started on the things they most want today:

  • Metrics matter: Manufacturers measure everything – production counts, scrap rates, cycle time, machine downtime, and more. Having this information collected and analyzed to make decisions and changes in real-time is essential to more agile operations.
  • Freed machine data: The information exists, but most often it’s locked in proprietary systems that limit accessibility and applicability. Advances in software-driven robots are making the information visible on the robot, available for onboard analytics and easy-to-extract for operational-wide use.
  • Plug-and-play integration: A single visual view of all equipment on one screen allows for orchestrating flow changes from a central source to the equipment that can perform the changed action.

With these objectives handily met by robots, manufacturers have a foundation on which to build with confidence.

A Journey Begun

Running a manufacturing company – whether you are in the C-suite or working in day-to-operations – and navigating the transformative time we’re in is not for the faint of heart. Success will come to those who tackle the biggest challenges first – with a smart approach that takes care of today’s problem while enabling the path to tomorrow.

Tweet me @jim_lawton.

Originally published on Forbes.

Robots are Boring

Office Workers ArmyRobots are no longer the object of science fiction; they are here, capable and doing productive and valuable work in manufacturing. Collaborative robots arrived 5 years ago to kick off a revolution in automation. Safe enough to work alongside humans, capable of performing tasks in the variable and sometimes chaotic world of real-world manufacturing and easy to train, these robots drew a lot of attention from media, pundits, and educators. Manufacturers, skeptics that they are, were pragmatic, having been burned before by technology that promised breakthrough results and rapid ROI.

So, yes, it’s taken some time, but the tide has turned. Today, manufacturers are embracing robots to solve labor challenges; soon they will do more. As manufacturers take concrete steps to embrace Industry 4.0, the disruptive technology embodied in robots will drive more than efficiency and productivity improvements – it will become a vital contributor to operations that are agile, innovative and highly competitive.

Tearing Down the Barriers to Adoption

Automation historically was a massive undertaking: sizable investments, sophisticated programming expertise and months of integration for a single task to be automated. These barriers put automation far beyond the reach of the 250,000 manufacturers in the US with fewer than 500 employees.

Smart collaborative robots changed all that. These robots can

  • be used to do more than one type of task. Users tell the robot what to do (i.e. call up a task by name), put a tool in its hand (e.g. screwdriver in its gripper), and off it goes to work;
  • be on the job on day one. Manufacturers do not have to design the environment around the robot and
  • see and feel like humans, with onboard vision and the ability to feel multi-axis forces (not just one-dimensional) throughout its entire arm, allowing them to perform tasks that would otherwise be out of reach.

And all at a price point that brought the innovation mainstream.

Advanced Manufacturing Requires More

Productivity and efficiency, check. But manufacturing is becoming increasingly sophisticated. Tapping into the critical-thinking skills that people bring is essential. By freeing them up to focus on making informed decisions and problem solve, manufacturing operations become more agile, able to respond quickly to shifts in everything from performance to supply and demand.

In this model, robots play a vital role. Bringing smarts to the process, with intelligent analysis on error reporting, quality reporting, and status updates, the robots feed faster and better decision–making.

As manufacturers increasingly deploy smart, collaborative robots in an ever-broadening portfolio of tasks, in a wider range of industries, these robots are poised to become an integral asset in the world of digital manufacturing. Intelligent contributions to process improvement, quality, and more robust operations are only the beginning. Where do you see robots contributing to performance? What kinds of insights do you think robots should be able to offer? Tweet me @jim_lawton.

Originally published on Forbes.

Supply Chain 2030: Engines for Growth

summit-2017Earlier this month, I joined a select group of supply chain executives at Lora’s annual Supply Chain Summit for a few days of heady conversations about what works, what needs work and what supply chains will look like in 2030.

One ah-ha moment that is still on my mind came with what is a pretty obvious truth: Growth is what matters – on Wall Street, Main St. and around the world. Sadly, most of today’s supply chains aren’t designed to be an engine for growth, they are designed to squeeze and squeeze and squeeze costs out of the process. Sadder still, I think, is that we’ve been talking about the transition from cost-based agenda to a value-centric model focused on growth for decades. And while Lora’s annual list of Supply Chains to Admire spotlights companies that are making measurable strides, like Hershey, L’Oreal and Dollar General, the universe remains small.

Why is it so hard to build a growth-centric supply chain? For decades, supply chain teams have been measured on getting products to market at the lowest possible cost. Every new idea is challenged by the mantra: “it’ll cost you” and all too often, that’s when the conversation stops. I’m not saying that cost should not be a consideration when it comes to measuring supply chain performance – I’m just saying it should not be the first and most weighted factor in making decisions about what products to bring to market, what markets to enter and how we compete on the global stage.

Supply chain executives need to embrace the vision of a supply chain that supports growth. They then need to put information to work for them – using data and analytics to get the answers to a different, more strategic set of questions, such as:

  • How will this help us serve our customers better?
  • How can we get new products to market more quickly?
  • How can we break into new markets successfully and more quickly?
  • How can we use sensing to change in near real-time what gets built to better adapt to market demand?

Changing the lens through which supply chain performance is measured requires that supply chain executives do what Lora’s event called us all to do: Imagine. When we stop looking at the supply chain as a cost center and start treating it as an engine for growth, the possibilities are indeed, endless.

Originally published on Beet Fusion.

In the Race to Advance Manufacturing: China’s Betting on Robots

WRCIt’s been just over three years since the Chinese government announced its Made in China 2025 [MiC2025] initiative to re-boot its manufacturing sector with a focus on quality over volume. With China set to become the largest user of robots in its manufacturing operations, we are, of course, paying close attention to what’s happening in the market. There’s a lot, in fact.

In the race to apply smart, collaborative robots in manufacturing, the Chinese are all in. Since announcing MiC2025, the country’s actions have clearly reflected its vision to use robots and robotics technology to achieve its objectives. The commitment is backed by government incentives and investments that accelerate its strategy.

China’s road to robot-deployment dominance is clear:

  • Build them. It’s widely recognized that the Chinese market lags behind the rest of the world in the skills and knowledge needed to design, build and deliver robots. That’s going to change. According to the China Machinery Industry Foundation, the country plans to increase annual sales of domestically produced industrial robots to 100,000 by 2020. Earlier this year, there were an estimated 800 start-up robotics companies in China, launched to take advantage of government incentives.
  • Buy them. In 2015, China acquired 75,000 robots, nearly twice the number from 2013 and outpacing all of the European countries combined in terms of robot acquisition. It is expected to increase investment by 20 percent each year, and reach 400,000 robots acquired by 2019.
  • Put them to work. In 2016, China installed 90,000 new robots. That’s one-third of the world total and 30 percent more than the year before.

The thing is that China isn’t just showing the world’s manufacturers how to put robots to work in volume – they’re showing the world’s manufacturers how to move quickly to innovate and advance. There’s mounting evidence that, just three years in, the MiC2025 initiative to move China from the world’s leader in high-volume, low-cost goods to the leader in quality and innovation is working.

  • In 2016, the Global Innovation Index recognized China for its innovation and leadership in supercomputing, gene editing, big-data analytics, and 5G mobile technology and making it the first ever middle-income economy to break into the top 25 countries.
  • Productivity is on the rise, forecasted to grow 6-7% through 2025, outpacing Vietnam and India – rivals for volume production.
  • Quality can be found. Manufacturers in China are eager to prove they’ve got what it takes to compete globally. …and US companies are finding they can be very useful in launching new products in highly competitive markets. Case in point: Scott Colosimo, president of Cleveland Cyclewerks found the US manufacturers simply would not take him seriously when he launched his retro motorcycle. After months of being turned down, Colosimo turned to China – where he found the only questions they asked were “how many?” and “how long?” Now 10 years later, he’s proven that you can build a company with products made in China.

Of course, 2025 is still a ways off and a lot can happen. From where we sit, China is making taking the bold steps needed to step up its game when it comes to manufacturing. We’re curious to know what you’re seeing – tweet me @jim_lawton.

Think Efficiency is Enough in the New Age of Manufacturing? Think Again

cairn-1286256Not a day goes by without something crossing my screen about the transformation of manufacturing. Or the 4th Industrial Revolution. Or advanced manufacturing. Whatever we call it, we can’t ignore that something – something big – is happening in manufacturing. I have to wonder though, in all the hype about innovation – from the cloud to robots and additive technologies – if it’s not time to reboot how we measure the value of technology. The harder questions around how manufacturers can harness all this innovation for sustained advantage seem to be lost in the noise.

I believe those questions need to be top of mind for anyone in a manufacturing organization and especially for those in leadership. For decades, the focus has been on driving efficiency, increasing productivity and lowering costs. These metrics have shaped the way operations have run and, no surprise, have helped manufacturers meet shareholder expectations, but not much more. Overall, the emphasis on driving efficiency in manufacturing has resulted in less innovation and little differentiation. In the midst of an industrial revolution, what better time for some truly transformational thinking?

At What Cost the Focus on Cost-Cutting?

For decades now, leaders in manufacturing have focused on the bottom line. Labor costs too high? Send production overseas where labor is cheap. Building, maintaining and operating production facilities too costly? Outsource core production processes to a contract manufacturer. Fuel prices skyrocketing? Hand off the last mile of the supply chain to a logistics expert. All of these decisions made sense. Continually looking for ways to save money and increase efficiency will always be important, but a singular focus on those objectives isn’t going to allow you to thrive.

Time for a Foundational Shift

If focusing only on cutting costs isn’t enough, it’s time for some bold thinking when it comes to technology adoption in manufacturing. If what Peter Drucker said still stands (I for one believe it still does): what get measured gets done, smart leaders will evaluate the innovations that promise so much through a different set of lenses:

  • What are we doing to move quickly into new markets at home and abroad?
  • How does our continuous product innovation strategy stack up against the competition’s?
  • What are we doing to dynamically gain insights into what our customers want? How are we initiating action on those insights?
  • How are we speeding time-to-market? Time-to-volume?

It’s not necessarily an easy shift to make. The focus on cutting costs has been driven for so hard and so long that it is practically institutional DNA. Driving the fundamental shift requires advocacy from both the top down and the bottom up to infuse all levels of the organization. Making it happen brings CEOs and operations executives together to work from a common set of metrics toward a shared vision.

Efficient and Effective: The New Standard for Manufacturing Technology

Technology has always promised to improve efficiency – the ability to do things right. And that has always been at the heart of any case for investment. Today though, technology has to meet a higher standard: effectiveness or the ability to do the right thing. For manufacturers, having leveraged technology to optimize efficiency, it is time to focus on growth, innovation and customer-centric models to change the mindset that’s fueling the race to the bottom (line). Emphasizing the results that contribute to opportunity and potential will cultivate performance that’s built on lasting value.

Where do you see technology innovation taking manufacturing? In what ways do you see technology supporting an emphasis on effectiveness? Tweet me @jim_lawton.

Originally published in Forbes.