Labor efficiency has become a holy grail of performance measurement in the staff operation of most large equipment, especially with staff shortages and rising costs.
Automation and remote operation are changing the way machines are designed and used, allowing for fewer skilled operators to be assigned to jobs requiring more fine-tuning.
Officials should learn how these things work, where they’re most useful, and what sort of engineering vote of confidence is really required.
Labor Efficiency as an Engineering Design Objective
Labor efficiency has gone from a consideration for operations to one of the design goals of engineering in the development of modern equipment. Instead of building machines around a constant human effort, engineers increasingly design equipment so that it results in fewer hours to produce a ton of output without compromising reliability or safety.
This is due to the increasing cost of labour, the increasing difficulty in finding it, and the increasing demands for uniform performance over long operating cycles.
From the engineering viewpoint, labour efficiency influences choices made as early as architectural systems designs, the complexity of control logic, and the planning of redundancy.
For instance, the trend toward modular and prefab components in construction is a way to eliminate as much work as possible done in an uncontrolled environment; such work is then done in a controlled factory environment, which drastically reduces on-site labor and waste.
An example of automation systems designed for minimal human interaction is those with automatic startup sequences and self-monitoring diagnostic routines tied to adaptive control and feedback loops that eliminate the need for constant manual adjustments.
Such equipment enables one worker to oversee several machines, or move from direct operation of such equipment into a higher oversight role, rather than to the direct operation of others.
Viewing labor efficiency as an engineering quantity to minimize, rather than as a profit-enabling adjunct, allows the design team to gel mechanical, electrical, and software systems around a single objective. They get productive runtime at the minimum cost of human resources.
Core Technologies Driving Equipment Automation
Equipment automation in the modern world rests on an integrated platform from control systems to sensing hardware to actuators.
Central to this are the programmable logic controllers or embedded controllers that enact predetermined logic flows based on feedback from sensing hardware. Putting it another way, CNC machines are commonplace for tasks requiring strict repeatability, even if it doesn’t require close supervision.
Another important piece of the automation puzzle involves sensor module technology. Proximity sensors, vision, lidar, and inertial measurement units provide the data to allow the automated equipment to determine where it is, what is around it, and whether it is operating as intended.
Paired with closed-loop feedback systems, sensors give the ability to ‘self-correct’ from deviation that would otherwise require manual correction.
Of course, automation relies heavily on the software that ties it all together with a capability of sequencing and fault detection, and of course need for control. More advanced systems take into account changing conditions, such as environment or load, for example, to adapt operation to changing conditions.
Extending Operator Reach in Remote Operation Technology
Remote operation technology makes effective use of labor by removing the requirement for physical presence with the operated equipment. In contrast to fully autonomous equipment, remotely operated equipment still requires operator decision-making but does so from a safe or central location, and is useful in hazardous/remote locations, and also where labor availability is limited.
Engineering implementations tend to be either direct remote equipment control systems, teleoperation with sensory feedback, or supervisory control, where operator intervention occurs largely on exception events.
There is nothing simple in developing the technical protocols for controlling all this equipment, latency during communications, reliability of signals, and fail-safe protocols in case of system failure. Reliable communications lines must be maintained, usually by using cellular or RF protocols or some hybrid, and maintaining online control in addition to providing graceful shutdowns.
For example, in commercial landscaping, a specialty job that once required six crew members with string-trimmers can now be done with a single operator using a remote-controlled robotic mower.
A specialty job done by a crew of six with string-trimmers previously can now be completed by a single operator and a remotely controlled robotic mower.
Engineering Metrics for Evaluating Labor Efficiency Gains
Quantifying the degree of labor-efficient optimization requires engineering metrics to be tied to system performance. One of the most common of these is the ratio of labor hours per operating hour, which quantifies how much human involvement is needed to keep the assets operating effectively.
Net reductions in self-loading hours often indicate successful deployment of automation and remote operation.
A second critical engineering benchmark is the operator-to-equipment ratio. Systems that implement exception-based alerting and self-monitoring typically allow for much higher ratios than may otherwise be safe or practical.
A metric that is becoming more common in evaluating autonomous and semi-autonomous vehicles is the mean time between human intervention.
Tracking these metrics over time allows engineers and fleet managers to identify bottlenecks, validate return on investment, and refine system configurations. When labor efficiency is numerically measured, then optimization is much more of a reasoned data-driven process rather than an assumption-based decision.
Designing for Reliability and Maintainability
Successful automation and remote operation initiatives are built on engineering decisions made during implementation, especially those affecting reliability and long-term maintainability.
Equipment used in the real world must be able to tolerate dust, vibration, moisture, temperature extremes, and uneven ground without degrading system performance. Automation parts that work well in labs often need to be ruggedized further for field use.
Fail-safe design is another key consideration. Automated and remotely operated equipment must fail over to safe states if sensor information goes dead or is interrupted, or if communication is lost.
This usually requires redundant sensors as well as watchdog timers and clear manual override provisions. Engineers also consider how quickly an operator or technician is able to take back planning and control if automated processes start running into trouble.
Maintenance is often different when automation is added. Rather than waiting for things to break, teams can use IoT sensors to monitor machine health, for example, if vibrational motion is not entirely as it should be, if heat is building up, and to do preventative maintenance before it breaks down.
While an automated system often has the potential to eliminate human labor from the equation, it does require technicians with advanced electrical skillsets. Designing robust systems with modular components, accessible diagnostic readouts, standardized replacement parts, etc., fosters reliable maintenance with minimum downtime and less dearly paid-for system mushiness.
Common Engineering Challenges and System Limitations
However, automation and remote operation systems do create new engineering problems. The first is over-automation. There is a point at which a system becomes overly sophisticated for the operational value it delivers.
An overreliance on sensors or unnecessarily complex control logic may simply create more points of failure.
Remote operation systems dependent on intermittent connection in unpredictable environments can be a limiting factor; for this reason, engineers must ensure safe operation even in the event of communication loss, often in the form of onboard autonomy or some sort of fail-safe behavior.
Cybersecurity is another growing concern, as remote access points introduce potential vulnerabilities if not properly secured. If a remote system has a remote operator, it must be hardened properly in case of an attack.
Finally, although automation can free employees from manual labor, it requires more people who understand what’s going on with the system and can respond to alerts.
Future Outlook
Automation and remote operation are scheduled for more intelligent and interoperable systems – access to components’ changing history and further machine learning and adaptive control makes for better-informed equipment. At the same time, messaging standardization enables a wider number of machines from disparate vendors to report to a single control hub.
Remote operation is also moving toward “supervisory” control of fleets, rather than individual vehicles, maximizing efficiency goals by increasing the amount of oversight any operator can do without increasing their cognitive load.
As these technologies mature, engineering efforts are moving further upstream, improving labor efficiency in the design rather than involving workarounds during operation.
Working Smarter, Not Harder.
Automation and remote operation redefine how labor efficiency is engineered around, rather than into, a modern machine.
As the design of equipment continues to develop, labor efficiency will eventually be designed into the machine it is controlling, making engineering insight into a lasting advantage rather than a temporary improvement.