The Impact of Machine Vision In the Manufacturing Space
Q. What are the current market trends you see shaping the Machine Vision Space?
SE: Certainly the demand for machine vision technology is surging. According to AIA, the machine vision trade association, Sales of machine vision components and systems in North America grew 19 percent in the first quarter of 2018 to $709 million, which represents a new record for quarterly sales.
I think what is fundamentally driving this trend is the need to keep a lid on capital expenditures. In a manufacturing plant, for example, collecting and analyzing data from sensors in a machine to avoid an event is a lot less expensive than replacing a piece of equipment that has imbedded sensors/cameras.
Also, manufacturers are starting to ask the right questions. Waiting to fail is no longer an option nor necessary in order to understand what to address within a facility.
Q. Machine vision technology has found its way into applications inside and outside factory settings, riding a wave of progress in automation technology and growing into a sizable global industry. Your views on this trend.
SE: We’ve been helping a variety of companies re-examine their supply chains to identify where machine vision technology can improve performance. That could mean improving product quality, enhancing production scheduling, reducing spare parts inventory and eliminating risks to employee safety.
In fact, I think any environment where accidents occur is ripe for machine vision applications. Most industrial accidents occur because someone is trying to take a short-cut. Real-time video analytics can identify where—and why—those dangerous short-cuts are occurring and help management take remedial action. We all need to be invested in ensuring we do everything possible in order to reduce accidents or save a life. Further to that, the industrial insurance savings alone just might justify the expense, but the value of a safe working environment are well-documented — lower workers’ comp costs, avoiding OSHA penalties and the like.
In terms of specific industries, there’s tremendous immediate potential in any discrete manufacturing, oil and gas, or really any process manufacturing environment like pharmaceuticals where there is value in monitoring real-time sampling or mixing. Healthcare and retail are ripe for machine vision applications that can improve service delivery and identify gaps in shrink opportunities..
Q. Please elaborate on the challenges that the organizations will need to address related to Machine Vision space.
SE: One challenge, of course, is employee fear of a “Big Brother” environment or technology taking away jobs so I think one of the trends I hope we’ll be seeing is a greater emphasis on change management. You can’t just drop this technology into a job site. Employees need to see it as a valuable enhancement for both safety and productivity for their benefit. The approach that this is a “job cutting” project is flawed and quite frankly missing the point. These types of Initiative should be seen as a growth strategy and supporting your company for market leadership.
Another challenge is building a business case and knowing which opportunities are worth pursuing. Set clear business goals and then identify a few machine vision pilot projects that you can quickly test and measure with data. Perhaps adding machine vision technology can eliminate scrap on the shop floor or increase throughput in the warehouse. Only take on projects with a strong business case and focus on execution and results. In my experience, those proof-of-concept projects are essential in getting buy-in.
Q. What are the major tasks for organizational CIOs at this point in time?
SE: Because machine vision tools are introducing huge volumes of data into the IT environment, CIOs need to be well versed in cloud technology. This isn’t a case of just adding a couple of servers.
CIOs also need to look at this technology through a business lens and keep an open mind for looking at data in new ways. Let me give you an example.
We were working with a coil manufacturer and installed high-end cameras and sensors on machines in the manufacturing plant that were moving out of tolerance. Yet historically they didn’t know the tolerances had been breached as the human eye could not pick up the variance during the process. The machines themselves could not advise the operator of the variance until the variance exceeded tolerances. As in any plant, creating waste/scrap/rework is unacceptable.
Interestingly, what got the CEO excited was that with this Machine Vision technology he could pro-duce products much more reliably. In fact, he could change his company’s entire value proposition, and guarantee delivery of high quality products in a way the competition just couldn’t match, even while charging a premium. The technology investment didn’t just reduce production costs; it trans-formed the entire customer value proposition. The CIO who can see that sort of potential will be having an entirely different discussion with the rest of the management team. He or she will be-come a strategic enabler vs. a “technology provider”.
Q. Are there any unmet needs in terms of the Machine Vision space that are yet to be leveraged from the vendors?
SE: I think that by and large, the vendor community has a good sense of what the marketplace needs as of today but to the extent they can help clients figure out how to mine the value of all the new data these tools are generating, I think there may be an opportunity to add additional value.
Digital operations is no longer impossible, but inevitable
Q. The main drivers of growth in the machine vision market are the need for quality inspection and automation inside factories, the growing demand for AI and IoT integrated systems that depend on machine vision, increasing adoption of Industrial 4.0 technology that uses vision to improve the productivity of robotic automation, and government initiatives to support smart factories across the globe. What can organizations do to stay abreast of these changes?
SE: One approach I’ve seen work well is to seek advice from leaders outside your own organization, particularly leaders from other industries. What you may have thought was unreachable is actually standard operating procedure in another industry. Management will get excited when they see the success of others on similar digital transformation journeys and be more willing to let you test new hypotheses.
Q. What is your advice for budding technologists in the Machine Vision space? How do you see the evolution a few years from now with regard to disruptions and transformations within the Machine Vision space?
SE: Be willing to experiment and, as I like to say, fail fast. Test hypotheses about the impact machine vision could potentially have on a few real business problems. Sometimes the value won’t justify the investment but you’ll learn a lot in the process and be more effective in testing the next hypothesis.
You should also be thinking about machine vision technology as a major disruptor, similar to the mobile phone. What offensive strategies could you use to create a new product category? To create a new relationship with your customers? How might you use machine vision to sharpen your positioning and differentiation against the competition?
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