Automation technology researchers report progress on virtually all of the technical problems, although the degree of progress often depends on research funding, commercial demand for related products, and inclinations of researchers. The technical barriers to increased sophistication in programmable automation are largely due to the complexity of the manufacturing environment, and to the fact that many operations management processes - e. g.
, machining, scheduling, design - have not been clearly understood in a way that can easily be computerized.Neely (2002) wrote a research book on work design and manufacturing automation. Using a few case studies, he illustrates which organizational choices have to be made with respect to the division of labor. He distinguishes four types of workers the work can be allocated to i) the internal generalist, i.
e. an FMS operator, ii) an internal specialist, i. e. another
...employee, employed within the manufacturing department, iii) an external generalist, i. e. an employee, employed in another department than manufacturing, who has a variety of tasks, and iv) an external specialist, i.
e.an employee employed in a department other than manufacturing and specialized in certain tasks. The case studies presented by Chase, Aquilano and Jacobs (2001) show a variety of work allocations. This has resulted in a centralization of the control of work operations. The complexity of some tasks (e. g.
programming) makes it difficult to allocate these tasks to generalists. Furthermore, in the opinion of the management, the assignment of additional tasks (e. g. maintenance, programming, planning and scheduling) to operators may obstruct the efficient and effective use of expensive machinery.
Improved computer technology has led to automation approaches that follow the idea of artificial
intelligence. Scheduling problems might be distributed to virtual agents. Each of these agents supports a clearly defined task and has its own specific problem-solving strategies. Agents have information about the problem area they are 'responsible' for (e.
g. a group of equipment) and they can request services from other agents. The agents use simulation to determine the future consequences of their decisions.Events from the ongoing process are forwarded in real time to the agents. The agents compare the actual state of the process with the planning data, taking appropriate actions if necessary. Like the 'classical' approaches to automation, the agent-based approach also considers technology as an enabler of dynamic process control.
It is based on (quantitative) models of the production processes. The quality of these models influences efficiency and effectiveness of the automated control system.In the process of generating and comparing different scheduling solutions by computer simulation, only those variables and interactions represented by the models are considered. Effects of variables that are not represented by the models (e. g.
the human operators' motivation) are neglected. Because of this fundamental problem, even the more sophisticated technology of artificial intelligence does not guarantee technical control over highly dynamic production processes. Job destruction has been a concern associated with automation since at least as early as the 16th century.This concern was epitomized by the machine-demolishing activities of the Luddites in England in the early part of the 19th century.
The counter argument to the concern is that automation has typically created more jobs than it has made obsolete. This argument is easier to accept if one is not among those whose jobs have been made obsolete and
are unable to accommodate to the change. How automation will affect job opportunities in the future is a debatable question and one can find predictions that range from dire to rosily optimistic.
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