The Inevitability Of Software-Defined Manufacturing

From 2003 to 2006, I worked at a contract manufacturing company as a robotics engineer. I was the first software engineer hired by the company, an opportunistic hire by a visionary CEO who saw the importance of automation in manufacturing. The CEO wanted to reduce downtime in manufacturing, improve quality, and empower the folks on the factory floor to be more efficient.

That period of my career was a fascinating experience. I was coming from a Fortune 500 energy company, where I had been a database programmer working with many highly capable engineers on scaling large data models. In that environment, continuous improvement through software automation wasn’t aspirational, it was our explicit mission. I took the role in manufacturing because I wanted the opportunity to define and deploy a software roadmap from scratch. I learned a lot during that time. As successful as the company was, software didn’t really exist inside the company, aside from an arcane enterprise resource planning (ERP) system that was poorly supported and badly used. I did everything from programming robots by hacking into them (APIs in manufacturing equipment didn’t really exist at the time, and still don’t), to developing web-based workflow software, to educating employees on how to use not only the tools I built, but software such as Microsoft Excel. Along the way, I discovered these existential truths, so to speak, as they applied to manufacturing as a whole:

  • Everyone saw the benefits of automation and wanted to automate as much as they could
  • Very few people understood the role software played in automation, even at the highest levels of the company

Fast-forward 16 years and much to my astonishment, manufacturing as a whole has not progressed. In learning about Bright Machines and our opportunity space, I encountered a lot of the same problems I faced 16 years ago. In manufacturing, the bulk of inspection remains largely manual. Instead of data being collected across the factory to be analyzed, it is mostly hostage to a particular machine, or worse, not collected at all. The concept of transforming data across the factory floor into actionable information that enables building higher quality products faster is at best an ambition. From designing a product to setting up a job, there is very little automation throughout the process of building physical products. In fact, setup and deployment take weeks, sometimes months, leading to significant product delays. That’s just the beginning of the list of problems with manufacturing today. It’s a very long list indeed.

When we compare manufacturing to other industries that have not only embraced technology, but pushed its boundaries to innovate and succeed, we can’t help but wonder why this key economic pillar remains stuck in time. I posit that this is for several reasons. Manufacturing is a demand-driven industry with low margins. For most manufacturing companies, it has simply been easier to throw humans at any given problem, knowing that labor costs can be scaled up and down based on demand. At first blush, the calculation seems rational. Investing in sophisticated hardware powered by equally sophisticated software at an industrial scale carries a lot of expense, not only in upfront costs, but maintenance, ongoing upgrades, support, and so on. Then, there’s the problem of time. Customers want things manufactured quickly. Who has time to invest in equipment set up, calibrating machines, setting up networks, securing the data, etc.? Human workers, on the other hand, can be deployed on an as needed basis.

Except that things really don’t work this way anymore. Humans, rightfully so, decided they are no longer willing to work in arduous and monotonous jobs, leading to reports of “voluntary turnover rates exceeding 300%” in some parts of the world. That is an astonishing statistic. The cycle of innovation in industry has evolved and sped up so much that having the ability to not only deliver product in near-real time, but perform meaningful reactive as well as predictive data analysis is an absolute must in order to operate efficiently in manufacturing. The increasing sophistication of the products being developed require the precision of machine automation and the power of not only software, but artificial intelligence, for higher product quality and predictability.

Which brings us to today. Manufacturing is crippled by these pain points, but ill equipped to solve them, for the same two fundamental reasons I encountered 16 years ago: manufacturing companies certainly understand the value of automation but have not historically utilized software to implement automation. Manufacturing companies are, after all, not software companies. And until now, the lack of demand for software-defined manufacturing has led to few external companies that are actually positioned to deliver holistic software solutions that act as both immediate relief as well as business accelerators to manufacturing companies. Thus, we are at a critical inflection point where manufacturing as an industry is not only ripe for disruption, it is virtually begging to be disrupted in order to save itself.

So what does disruption look like in this space? In fact, what is software-defined manufacturing, really?  Is it artificially intelligent robots? Is it data platforms with state of the art business intelligence? Is it cloud-based platforms, remote deployment and troubleshooting, machine-learning driven analytics? These things definitely comprise the concept, but Software-Defined Manufacturing is really just the beginning.

Software-Defined Manufacturing will happen simply because it has to – it is the immediate cure to manufacturing’s already existing pain points. The true disruption in manufacturing will involve not disrupting manufacturing per se, but actually disrupting the very idea of software-defined manufacturing itself. And it will happen by industrializing all the technologies that make up software-defined manufacturing, deploying them as a scalable platform and delivering them to customers in a service-based model that grows and modulates with the needs of the business. True disruption is extending software-defined manufacturing to a hardware/software ecosystem, with minimal to nonexistent single points of failure, where multiple components work harmoniously with the single purpose of enabling fast, high quality delivery at lower cost; where data is assembled, collected and turned into predictive analytics, and artificial intelligence is effectively used to solve repetitive human tasks.

When will this happen? At Bright Machines, the call to innovation has been answered, and the transformation in manufacturing has already begun. For us, software-defined manufacturing is just the beginning, the building blocks of delivering an ecosystem of products that will not only disrupt but redefine an entire industry. It’s an extraordinary challenge and truly a generational opportunity. And it’s Day One of our own journey to change the world.

— Nick Ciubotariu, SVP software engineering, Bright Machines

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.