Chapter 3: Literature Review
Process automation has transformed manufacturing in the 21st century. As digital technologies have advanced, industries have sought new ways to improve efficiency, quality control, and production flexibility through automation. This literature review examines peer-reviewed research on the impacts and implications of automation in industrial settings. The topics covered include the history of process automation, common automation technologies used today, benefits realized through automation projects, challenges to effective implementation, and how automation affects the workforce.
A brief history of process automation is necessary to understand its current applications. The earliest efforts to automate manufacturing processes date back to the late 18th century with inventions like James Watt’s steam engine and subsequent mechanization during the Industrial Revolution (McCorduck, 1979). It was not until the 1940s and 1950s that automation began integrating electro-mechanical servo controllers with programmable logic to replace human labor in repetitive tasks (Graham & Pineault, 2020). Further advances in electronics and computing power allowed for distributed control systems and computer numerical control in the 1960s-1980s, automating entire production lines for the first time (Rehg & Kraebber, 2012).
Today’s process automation is built upon decades of incremental innovations but was truly transformed by the digital revolution beginning in the 1990s. Several technologies underpin modern factory automation systems, according to Li et al. (2020). Sensor technologies like cameras, temperature probes, and scanners provide real-time monitoring of processes. Drive technologies like servo motors and actuators precisely control movement and positioning of machinery. Programmable logic controllers serve as the “brains” governing automated equipment based on pre-defined logic programs. Human-machine interfaces through visual display units and touchscreens allow operators to monitor and supervise systems. Industrial communication protocols like Ethernet, Modbus, and PROFIBUS connect machines to one another and factory-wide control systems.
Data analytics provides further intelligence for process optimization. Yoo et al. (2019) describe how cloud-based systems now collect and analyze massive amounts of sensor data from automated equipment. Predictive maintenance algorithms monitor for anomalies indicating failure risk. Quality control can be continuously adjusted based on performance trends. Simulation tools even plan future factory layouts and workflows in virtual environments before implementation. These digital technologies facilitate flexible, reconfigurable automation suited for changing production needs.
A multitude of benefits have been realized through process automation, according to industry case studies. Most directly, automation increases productivity through higher throughput, reduced waste, and minimized rework (Schwab, 2017). Quality consistency improves as machines operate with precise tolerances indefinitely without fatigue. Labor costs are reduced by replacing human operators with capital equipment, though complete workforce elimination is rare. Operational costs fall through energy efficiency gains, less material consumption per unit, and optimized maintenance routines (Rossit et al., 2020). Production flexibility expands as facilities can quickly changeover between product variants or adjust volumes up/down based on demand signals.
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