Ergonomics Analysis for Vehicle Assembly Using Artificial Intelligence

Article excerpt

At the Sixth Annual Applied Ergonomics Conference held in Dallas, Texas, in March of 2003, the Institute of Industrial Engineers (IIE) awarded the Ergo Cup in Training and Education to Ford Motor Company for the GSPAS Ergonomics Application (Ergo Solutions 2003). Ford Motor Company has been utilizing an integrated process-planning system since 1990 to standardize the process sheet writing, create work allocations for the plant floor, and estimate labor time accurately. This system, formerly known as the Direct Labor Management System (DLMS) (Rychtyckyj 1999) is a knowledge-based system that utilizes a semantic network knowledge representation scheme. DLMS uses techniques from natural language processing, description logics, and classification-based reasoning to generate detailed plant floor assembly instructions from high-level process descriptions. This system also provides detailed estimates of the labor content that is required from these process descriptions. Techniques such as machine translation and evolutionary computation were integrated into DLMS to support knowledge base maintenance and to deploy DLMS to Ford's assembly plants that do not use English as their main language.

The process sheet is the primary vehicle for conveying vehicle assembly information from the central engineering functions to the assembly plants. It contains specific information about work instructions and describes the parts and tools required for the build process. The work that is required to build the vehicle according to the process sheet instructions is then allocated among the available personnel. Work allocation requires a precise means of measuring the labor time that is needed for any particular task. The DLMS system interprets these instructions and generates a list of detailed actions that are required to implement these instructions at the assembly-plant level. These work instructions, known as "allocatable elements," are associated with modular arrangement of predetermined time standards (MODAPTS) codes that are used to calculate the time required to perform these actions. MODAPTS codes are widely utilized as a means of measuring the body movements that are required to perform a physical action and have been accepted as a valid work measurement system (Carey et al. 2001). For example, the MODAPTS code for moving a small object with only a band is M2; utilizing the arm gives a code of M3. The MODAPTS codes are then combined to describe an entire sequence of actions. MODAPTS codes are then converted into an equivalent time required to perform that action.

Subsequently the ergonomics engineers would manually inspect the process sheets for possible ergonomic problems. Since there may be 1,000 or more process sheets for every single type of vehicle that Ford manufactures, this manual type of inspection was very labor intensive and time-consuming. An ergonomics engineer would spend upwards of two weeks manually inspecting each process build instruction for a vehicle.

To streamline this process, I and my colleagues at Ford developed a system that would automate the inspection of process sheets for ergonomics concerns. This work resulted in the development of an artificial intelligence system for ergonomic analysis within GSPAS that checks for two types of potential ergonomics issues: "red" and "warning." Process sheets that are flagged red are not sent to the assembly plants until those errors are corrected. Process sheets that are flagged with warning messages are released to the assembly plants; however, the ergonomic specialists have the opportunity to check and approve these issues through the use of a system that was built specifically for this application.

The ergonomics system was developed and deployed to production in April of 2002. Since that time, more than 1,100 process sheets with ergonomics problems were stopped by the AI system from going into production at the assembly plants. This has already resulted in a savings of more than $17,000,000 in injury costs alone, because the high risk processes never made it to the plant floor. …


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