Survey of university industrial relations centers, Fall, 1954.

Mode of access: Internet.

Knowledge based improvement:simulation and artificial intelligence for identifying and improving human decision-making in an operations systems

The performance of most operations systems is significantly affected by the interaction of human decision-makers. A methodology, based on the use of visual interactive simulation (VIS) and artificial intelligence (AI), is described that aims to identify and improve human decision-making in operations systems. The methodology, known as 'knowledge-based improvement' (KBI), elicits knowledge from a decision-maker via a VIS and then uses AI methods to represent decision-making. By linking the VIS and AI representation, it is possible to predict the performance of the operations system under different decision-making strategies and to search for improved strategies. The KBI methodology is applied to the decision-making surrounding unplanned maintenance operations at a Ford Motor Company engine assembly plant.

Simulation based knowledge elicitation:effect of visual representation and model parameters

Since much knowledge is tacit, eliciting knowledge is a common bottleneck during the development of knowledge-based systems. Visual interactive simulation (VIS) has been proposed as a means for eliciting experts’ decision-making by getting them to interact with a visual simulation of the real system in which they work. In order to explore the effectiveness and efficiency of VIS based knowledge elicitation, an experiment has been carried out with decision-makers in a Ford Motor Company engine assembly plant. The model properties under investigation were the level of visual representation (2-dimensional, 2½-dimensional and 3-dimensional) and the model parameter settings (unadjusted and adjusted to represent more uncommon and extreme situations). The conclusion from the experiment is that using a 2-dimensional representation with adjusted parameter settings provides the better simulation-based means for eliciting knowledge, at least for the case modelled.

Humans:the missing link in manufacturing simulation?

Computer based discrete event simulation (DES) is one of the most commonly used aids for the design of automotive manufacturing systems. However, DES tools represent machines in extensive detail, while only representing workers as simple resources. This presents a problem when modelling systems with a highly manual work content, such as an assembly line. This paper describes research at Cranfield University, in collaboration with the Ford Motor Company, founded on the assumption that human variation is the cause of a large percentage of the disparity between simulation predictions and real world performance. The research aims to improve the accuracy and reliability of simulation prediction by including models of human factors.

Modelling the behaviour of engine assembly workers

Presents a prototype modelling methodology that provides a generic approach to the creation of quantitative models of the relationships between a working environment, the direct workers and their subsequent performance. Once created for an organisation, such models can provide a prediction of how the behaviour of their workers will alter in response to changes in their working environment. The goal of this work is to improve the decision processes used in the design of the working environment. Through improving such processes, companies will gain better performance from their direct workers, and so improve business competitiveness. This paper first presents the need to model the behaviour of direct workers in manufacturing environments. To begin to address this need, a simplistic modelling framework is developed, and then this is expanded to provide a detailed modelling methodology. There then follows a description of an industrial evaluation of this methodology at Ford Motor Company. This modelling methodology has been assessed in this case study and has been found to be valid in this case. There are many challenges that this theme of research needs to address. The work described in this paper has made an important first step in this area, having gone some way to establishing a generic methodology and illustrating its potential value. Our future work will build on this foundation.

Lake Inaha 217971

This vessel was built at Lorain, Ohio in 1919 by the American Ship Building Company. Until 1930, she was owned by the U. S. Shipping Board of Washington, D. C. That year she was sold to the Ford Motor Company. In 1943, she once again was owned by the U.S. Shipping Board. In 1946, she was purchased by the Bright Star Steamship Company of Washington, Panama. Her name was changed to "Captain John." From 1951 to 1954, she was owned by Navebras of Rio de Janeiro, Brazil. Her name was then changed to "Santa Martha." In 1954, she foundered off the coast of Brazil.

Lake Belnona 217230

"Lake Belnona" was built at Saginaw, Michigan in 1918 by the Saginaw Shipbuilding Company. She was owned by the U. S. Shipping Board of Washington, D. C. until 1928 when she was scrapped at River Rouge, Michigan by the Ford Motor Company

Ford Escort GL baseline weight data. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

1980 and 1979 Ford F-150 light truck weight and manufacturing cost analysis. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

The evaluation of compliance with federal motor vehicle safety standards.

Mode of access: Internet.

Development of a motor vehicle materials historical high-volume industrial processing rates cost data bank (3500-4000 pound) full size car. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Deft driving.

Text on p. [2] of cover.

The Dearborn independent.

At head of title, 1919-April 1925: The Ford international weekly.

Lima Township (Mich.) residences

Left to right: Oak Lawn Farm Res. of Thos. S. Sears, Sec. 6, Lima Tp. Mich.; Res. of Elias Westfall, Sec. 9, Lima Tp. Mich.; Res. of Thomas Jewett, Sec 20, Lima Tp., Mich.; Res. of Richard Goodwin, sec. 9, Lima Tp. Mich.; Res. of Geo. H. Mitchell, Sec 28, Lima Tp., Mich.; Res. of Sampson Parker, Sec 24, Lima Tp. Mich. Publication information: Chicago, Ill. : Everts & Stewart, 1874.