The ontology of “automation” implies a process that integrates systemic, operational and cognitive objects with quality assurance through automated processes or human control.
The ontology of automation and its segments will be described in conceptual terms.
Manual implies that the process is conditioned by the instruction individuals receive to develop their tasks.
The manual process implies that the operator makes the necessary controls and decisions to define if a process continues or not.
Manual processes are usually sustained by a strict “procedures handbook” to maximize the automation of human behavior to ensure the quality assurance process.
It is a manual process that integrates automation with a high level of human participation in the work processes.
Human intervention occurs in pre-planned intermediate stages to define if the fulfillment of the stages is accepted as valid or invalid.
Semi-automatic includes strict manual operational control processes.
It implies a systemic automation process including automated quality assurance of the system.
It is based on maximizing efficiency to sustain effectiveness. It limits the role of efficacy to the system design.
The quality assurance is defined by the operational redundancies and the self-exclusion mechanisms of the system in case problems cannot be corrected using redundancies.
An adaptive automation works with the feedback of reality to manage the changes of the system.
It implies an intelligent automation that rules the functionality of the objectives to be achieved.
It includes redundancy and self-exclusion mechanisms to ensure quality.
But its intelligent process is based on its capacity to modify the system to deal with the environment.
The Architecture of Automation
The architecture of automation defines the structures of conceptual engineering. It can also be defined by the conceptual design of the automation system.
In operational terms, the architecture of automation is integrated by systemic objects that transform energy, operational objects that seek to generate value and robots that automate the work processes to optimize the energetic algorithm.
The objective of an automation process is the generation of value to be added to the participants of a “system”.
The transformation of the energy is sustained by a knowledge bank and a quality assurance model.
It is a system that learns from the feedback to improve the work processes while it is included in robots and systemic objects.
The knowledge banks that sustain the operation are generated by the members of the system. Therefore, the system is closed although it is open if the energy equation is considered.