Altair > Case Studies > Ford Enhances Manufacturing Efficiency with Altair Knowledge Studio

Ford Enhances Manufacturing Efficiency with Altair Knowledge Studio

Technology Category

Analytics & Modeling - Machine Learning
Analytics & Modeling - Predictive Analytics

Applicable Industries

Automotive
Education

Applicable Functions

Human Resources
Product Research & Development

Use Cases

Predictive Maintenance
Time Sensitive Networking

Services

Data Science Services
Training

About The Customer

Ford Motor Company is a Fortune 50 automotive company that operates plants all over the world and produces millions of vehicles every year. Quality, efficiency, and time-to-market are all critical to its profitability and sales growth. More than 30 years ago, Ford began working with Altair to support the company’s product development activities. Today, the company employs Altair software globally to support the development of Ford cars, trucks, and heavy equipment. In many production facilities, there are multiple sheet metal stamping processes available to form nested and individual parts. These include progressive, transfer and tandem press lines.

The Challenge

Sheet metal stamping is a crucial process in the automotive manufacturing industry, with a variety of tool, die, and process combinations used to create a diverse range of components. Traditionally, identifying the optimal stamping process for a specific part design has been a labor-intensive and time-consuming task, heavily reliant on the knowledge and skill level of the stamping engineer. Ford Mexico sought to address this issue by documenting successful metal stamping production runs over a five-year period. The goal was to capture in-house domain knowledge and best practices to expedite the selection of the best stamping process for future production runs. This would enable increased plant efficiency and part quality, reduction of scrap material, and the ability to rapidly train new personnel. However, the challenge lay in the growing design complexity, non-conventional material types, and numerous process combinations that could challenge even the most experienced process engineer, necessitating a labor and material intensive trial-and-error prove-out process.

The Solution

Ford Mexico approached Altair to explore the possibility of applying Altair’s machine learning and predictive analytics solution, Knowledge Studio, to support their business objectives. Using the data Ford had collected for over 3,000 stamping processes identified as being representative of future requirements, Ford’s stamping domain experts and Altair’s solution architects collaborated to develop an accurate, reliable machine learning model with Knowledge Studio. Knowledge Studio offers 15 different machine learning models allowing users to explore, select and train the model that best fits their data. Using subsets of the data, the team ran a series of tests to determine which was most effective. With an accuracy rate of over 90%, the decision tree model produced the most consistent results. The machine learning algorithm provided Ford with results that are close to 100% accurate when combined with all the other datapoints.

Operational Impact

The machine learning predictive power of Knowledge Studio proved to be highly accurate and successful in largely automating stamping process selection. By minimizing manual trial-and-error process validations and rework, more time was available for stamping process engineers to address the most difficult and complex part designs further enhancing production efficiency and business value. Overall the projected throughput increased by a factor of three and, increased FTT rates resulting in reduced rework time – all accomplished without increasing resources. In addition, the Knowledge Studio machine learning model was effective in capturing Ford’s in-house domain knowledge to support a faster learning-curve for training of new personnel.

Quantitative Benefit