Building Security Systems: Ramping up Production Reliability with Intelligent Assembly Automation

February 5, 2020 | 4 min read

By Stevan Dobrasevic, Director of Product Marketing, Bright Machines

Security systems for buildings, whether for homes, small offices, or large business complexes, are in big demand. Grand View Research projects the global physical security market to reach $292 billion by 2025, growing at a 9.4% CAGR. That’s a lot of motion sensors, glass-break detection systems, door-contact monitors, security cameras, smoke detectors, alarm sirens, and control panels!

It’s also a lot of pressure on manufacturers to find ways to make these devices more efficiently – faster, at greater scale and quality, and at lower cost. Putting more humans to work in the basic assembly and testing of these products has long been the answer, but this approach has its limits.

First, manufacturing-line workers are hard to find. The National Association of Manufacturers says a record 522,000 manufacturing jobs remained open in September of 2019, while a report by The Manufacturing Institute and Deloitte projects 2.4 million unfilled openings in the 2020s. Second, human operators introduce a number of variables that can affect production, from calling in sick to switching jobs.

There’s a better way.

Different Products, Common Components

Many building security products are comprised of common components and assembly processes. For example, smoke detectors and motion sensors contain circuit boards, plastic housings, buttons, and wall mounts, each added to an individual unit through a series of discrete assembly tasks, from pick and place, to screwdriving, labeling, and testing.

For more complex products, such as security cameras and control panels, additional components and steps are added. Historically, that required hiring more people, increasing manufacturing cost while making production output, as noted above, less predictable.

In each of these scenarios, software-defined automation is proving to be a better solution, one that can handle multi-step assembly reliably and at scale.

Assembly Required:  Meet Your New Workforce

The recent success of a control-panel manufacturer highlights the virtues of intelligent assembly automation. The company had a problem – a good problem to have, and one not uncommon in this industry – its products were in demand, but it was having difficulty scaling to meet that demand.

At its current factory, it could deploy 3 shifts of 8 workers to do 7 assembly steps, completing 105 units per hour and shipping a total of 630,000 control panels per year. Not bad, but its buyers wanted more than 1,000,000 – and even hitting the 630,000 number was proving to be a struggle.

Deploying a Bright Machines Microfactory, the company turned its old math on its head and stepped up and into the next generation of assembly automation. Across the microfactory, discrete assembly tasks formerly done manually were performed automatically by robotic cells guided by intelligent software. The exact steps follow:

1. Put label on circuit board and place back cover over circuit board
2. Screw-drive back cover to secure it to front cover
3. Perform “power up” test
4. Put battery cover in place on back cover
5. Put label on back cover and place bracket over back cover
6. Perform functional test
7. Put wall mount over bracket

Today, two employees manually kit the control-panel components onto pallets that are fed into the first microfactory station. As the assembly process is completed at the seventh station, a third employee manually packages the finished product.

Any guesses on the throughput gains or economics of this automated approach?

• The microfactory produced 180 units per hour, a 71% throughput gain over the former 8-person crew, that increased output to 1.1 million units
• It drove the cost of control-panel production of the 7 assembly steps down to $0.94 per unit, a 34% savings over the human-run lines (over three shifts)

The microfactory – enhanced each step of the way by machine vision – not only performed pick & place and screwdriving tasks, but an array of functional tests that might have once seemed too difficult to automate. For example, leveraging the robotic system’s configurable software, the control-panel maker was able to teach the robotic cell to perform functional tests such as RF testing, LED color detection, and other checks on base functionality vital to verifying product quality. In the past, these human-run tests were among the assembly process’s most time consuming and error prone.

No longer.

And with demand continuing to increase, this manufacturer is betting – based on experience – that its best way to scale is to open a second microfactory.

To learn more about our capabilities in building the backbone of AI, visit Bright Machines.