This Small Business Innovation Research (SBIR) Phase I project aims to create adaptive job scheduling software for cutting room operations. Cutting rooms are used primarily for the production of sewn goods (apparel, furniture, luggage, etc) as well as many other industries. Today, cutting room managers rely on personal experience, spreadsheets, e-mails, faxes, whiteboards and often outdated rules-of-thumb to schedule material, labor and equipment for their operations. There is currently no software that provides real-time decision support to assist them in executing cutting operations with optimal resource usage. This project is configurable software that can model and optimize, in real-time, the complex variables of cutting operations across many industry segments. The software will collect live manufacturing data and perform calculations to produce job schedules that optimize equipment, labor and material usage in cutting rooms. A further innovation is the addition of adaptive control. This will use actual operational performance to provide feedback in order to fine-tune the scheduling calculations. The use of adaptive job scheduling software is expected to result in a 20% increase in cutting productivity, as measured by lower labor costs, improved machine utilization and shorter lead times.
The commercial potential of this project is to make cutting room operations more productive in order to retain U.S. manufacturing jobs. The U.S. sewn goods industries have been devastated by intense international competition that has made most domestic production non-competitive against off-shore labor rates. As a result, new innovations in sewn goods technology have focused on software that enabled manufacturers to outsource production low-cost countries instead of improvements for domestic manufacturing and protection of U.S. jobs. However, recent developments in highly engineered materials, such as body armor, require skilled labor to ensure quality standards of the finished product. These skill sets are more reliably found in domestic labor pools than in low-cost, untrained, foreign language, off-shore labor pools. Furthermore, domestic cutting operations are more likely than off-shore operations to use automated cut room equipment to increase productivity. This automated equipment can be made more efficient with effective job scheduling software. A 20% gain in productivity would help domestic manufacturers offset labor costs to maintain global competitiveness and retain an estimated 75,000 US manufacturing jobs.
in the apparel and flexible goods industries that could achieve at least 20% operational efficiency in apparel manufacturing cut room processes. We secured the assistance of two domestic apparel manufacturers as Test Beds to work with us to test the algorithms on actual operational data. Test Bed Managers reviewed their operations in detail with us and provided many data sets, however, neither Test Bed could provide all the data that our algorithms were designed to utilize. More importantly, initial analysis of the data indicated that both Test Beds were struggling with small Lot sizes. Our Job Scheduling algorithms assumed large Job sizes and focused on optimizing scheduling through the spreading and cutting equipment. The small Lot sizes meant that the Test Beds needed to optimize scheduling throughout the entire process from release of Manufacturing Order through feeding specific production lines and equipment in the sewing plants. As a solution, we devised a methodology and corresponding algorithms to consolidate Lots in larger Job sizes. We used the scheduling algorithms to calculate labor savings in spreading and cutting operations for the Consolidated Jobs versus the Planned Order output. We and the Test Bed Managers believe that our calculated savings for spreading and cutting labor of 22% in Test Bed 1 and 27% in Test Bed 2 are conservative. Although we were successful in creating algorithms that increased cutting room productivity by more than 20% we did not test the Job Scheduling algorithms but, rather, used them to validate the Job Planning algorithms that we devised for the small Lot sizes. Both our Test Beds vetted our scheduling methodology, were enthusiastic about our results and have committed to continue working with us to refine our models and test our Scheduling algorithms. For Phase II, we intend to productize our algorithms into software for the apparel market. Interface Technologies has developed Production Tracking software as the first functional module in its Manufacturing Execution Software (MES) suite. Job Planning and Scheduling is a game-changer: most cutting room applications can benefit from it but no one has developed it. We plan to develop two new software modules for this platform: Job Consolidation and Job Scheduling. The Job Consolidation Module would contain the logic that we developed and tested in this study. The Job Scheduling Module would contain the cutting and spreading equipment optimization algorithms that were initially developed for this project and that we intend to test before award of Phase II. Apparel manufacturers could choose either one or both modules depending upon their production Lot sizes.
