1970s: Computer-Integrated Manufacturing (CIM)
Since the 1970s there had been a growing trend towards the use of computer programs in manufacturing companies. Especially functions related to design and production, but also business functions should be facilitated through the use of computers.
Accordingly the CAD/CAM technology, related to the use of computer systems for design and production, was developed. CAD (computer-aided design) was created to assist in the creation, modification, analysis, and optimization of design. CAM (computer-aided manufacturing) was designed to help with the planning, control, and management of production operations. CAD/CAM technology, since the 1970s, has been applied in many industries, including machined components, electronics products, equipment design and fabrication for chemical processing.
To enable a more comprehensive use of computers in firms the  CIM (computer-integrated manufacturing) technology, which also includes applications concerning the business functions of companies, was created. CIM systems can handle order entry, cost accounting, customer billing and employee time records and payroll. The scope of CIM technology includes all activities that are concerned with production. Therefore in many ways CIM represents the highest level of automation in manufacturing.
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Neural network
A bottom-up artificial intelligence approach, a neural network is a network of many very simple processors ("units" or "neurons"), each possibly having a (small amount of) local memory. The units are connected by unidirectional communication channels ("connections"), which carry numeric data. The units operate only on their local data and on the inputs they receive via the connections. A neural network is a processing device, either an algorithm, or actual hardware, whose design was inspired by the design and functioning of animal brains and components thereof. Most neural networks have some sort of "training" rule whereby the weights of connections are adjusted on the basis of presented patterns. In other words, neural networks "learn" from examples and exhibit some structural capability for generalization.
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