Improved computer technology
In the past few years, makers of all computerized equipment have become aware of a need to design systems for optimal usefulness and productivity for their human operators. There are various terms used to describe the focus of such efforts: “user friendly” qualities and “man-machine interface,” for example. In part to help market their equipment, computer manufacturers have found that there are steps they can take to improve the human factors aspects. Human factors experts argue that research and testing of the effectiveness of a product must be undertaken throughout its design cycle.
Current machine vision is in a very early stage. The range of objects that can be identified, the speed of the interpretation, and the susceptibility of the systems to lighting problems and minor variations in texture of objects are all examples of serious problems with current technology. Successful applications of current machine vision technology tend to be very specific, ad hoc solutions, often using clever “tricks” or manipulations of the manufacturing environment. As one report notes, “The vision systems of today, and those for the rest of the decade will not promise great generality.
These sorts of tricks will be an important part of the field for many years to come” (Lowson 2002). Nevertheless, many useful applications are possible with existing technology and machine vision is currently a rapidly growing field. In certain specific applications, especially very tedious tasks such as inspection of electronic circuit boards, machine vision systems can outperform humans. CAD experts report that many systems for 3-D solid modeling are available now, but they are not being used because of their large appetite for computer power, and because their capacity to link design data manufacturing equipment is inadequate.
Part (b) of this entry refers to this ability to store and manipulate design data about the physical characteristics of a part in such a way that it can be transmitted to manufacturing equipment with only minimal intermediate steps. This entry refers to modules powerful enough to allow extensive interactive testing, simulation and refinement of designs in a wide range of applications. Such systems are strongly product-dependent; while they may be near available for certain products now (e. g. , integrated circuits, certain portions of aircraft and motor vehicles), they are much less advanced in other industries and applications.
This entry denotes the “window from design to production” which would, for instance, allow designers to examine the production implications of designs choices. These includes the costs and necessary product processes, as well as the history of manufacturing similar items at the plant. Such comprehensive connections would allow much more substantial integration of CAD, CAM, and computer-based management.
Many in industry would argue that CIM is inevitably the future of operations management. Its advantages in cost, quality, flexibility, and control will, they assert, mandate its adoption. Many parts of computer-integrated manufacturing can be put together on an ad hoc basis now, prototype solutions for many of the key problems already exist. However, several key aspects of the puzzle are as yet unsolved (the development of interface standards for computerized tools, in particular), and for CIM to be practical each of its elements must be mature, versatile, and relatively easily available commercially.
CIM does not necessarily imply manufacturing without humans. In fact, one of the biggest challenges on the road to CIM is learning to use humans in effective ways, to develop machines with which humans can work effectively, and to identify the points in the production process where maintaining human involvement may enhance flexibility, responsiveness, diagnostic power, and creativity. The extent to which that effective use of people in manufacturing will develop, and the extent to which CIM will remove humans from manufacturing environments, are still open questions.
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. The possibility of job deskilling – the replacement of jobs that require skilled workers with those that do not – is a closely related concern.
Automation has had the effect of decreasing the skill requirements of some jobs to the point of making them almost intolerably boring, and it has made other jobs more interesting and satisfying. Again, as we look to the future, it is possible to find a range of predictions regarding what the long-term effects of automation on job-skill requirements in operations management will be. The increased adoption of advanced computer-controlled technology in manufacturing firms is evident.
While this study contributes additional insights into the impact of automation on operations management practices over the next decade, the total impact remains unclear. A comprehensive understanding of automation’s impact is necessary for the success of the operations management. Operations management must remain strong and viable as a key element of the business sector. The circle is closed: changes in markets and technologies have set in motion a search for new models of industrial relations practice and theory, and success in these new market and technological conditions depends on the implementation of new industrial relations models.
Increased automation raises several related issues. For instance, parts may need to be designed in such a way that they facilitate quick, inexpensive setups and greater interchangeability. In this area, too, technology has surged recently, with Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM). Another issue that must be addressed is the displacement of workers, possibly resulting in morale problems and the need for retraining. At any rate, it is clear that technological advances will continue to change the jobs that workers do in a company.
New operations systems will continue to reshape jobs to have more responsibility and decision-making, more variety, and less repetitive physical labor.
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