Operations Research at GM Selected Projects (No math, but lots of pictures!)

1. Operations Research at GM • Selected Projects • (No math, but lots of pictures!) Jeff Alden Group Manager, Manufacturing Architecture Design Manufacturing Systems Research Laboratory Warren, Michigan GM R&D 12-Mar-03 * JMA * 1

2. Lots of Analysis Opportunities in Production System Operations Research Throughput Carrier Return Loop Requirements 5 Styles 80 JPH Load Unload 80 JPH • Common Issues: • Throughput improvement • Placement of Buffers • Model Mix • Number of Workstations • Number of Carriers • Cost Drivers • Maintenance Operations • Plant Operations • Parallel Lines • System Layout • Material Handling • Allocation of Work • Vehicle Sequencing • System Design • Lot sizes • Setting Station Speeds • Plant Capacity Allocation 12-Mar-03 * JMA * 2

3. Buffer = 20 10 5 2 10 10 1 Load Weld Inspect Robogate S Weld1 S Weld2 Seal UnLoad Mean Time to Repair (min) Speed (JPH) % Scrap Mean Cycles Between Failures 100 10 8 200 7 80 8 6 150 5 6 60 4 100 40 4 3 2 50 20 2 1 0 0 0 0 Seal Seal Seal Seal Load Load Load Weld Weld Weld Load Weld Unload Unload Unload Inspect Inspect Inspect Unload Inspect S Weld1 S Weld2 S Weld1 S Weld2 S Weld1 S Weld2 S Weld1 S Weld2 Robogate Robogate Robogate Robogate Bottleneck Analysis Increase in Jobs/Hr 2 1.5 1 0.5 0 Seal Load Weld Inspect Unload S Weld2 S Weld1 Robogate Project A: Plant Improvement usingthe CMORE throughput Analysis Tool 2005 Edelman Prize winner > $2 Billion Savings over 18 years 12-Mar-03 * JMA * 3

4. Story of “Throughput analysis” in GM Plants“There’s more than just solving the analysis problem.” • Researcher spent months visiting plants (~ 1985) • Main complaint: Improvement efforts is too “hit-or-miss” • Understood the need for throughput analysis (great vision). • Hired consultants to work with GM R&D to develop an analytic throughput analysis tool • Simulation way too slow! • Serial lines with equal station speeds … start simple • Clarified definitions “buffer”, “station”, “bottleneck”, etc • Developed concept and usefulness of “breaking bottlenecks” … perhaps more powerful than the tool! • Big push to get some plants to use model … “chicken or egg” problem, also competition • Major effort to get good data at a few plants • Developed Mean Cycles Between Failures as “a failure rate measure” • Initiated some automatic data collection … many issues here (e.g., what is a good cycle time?) • Developed a throughput improvements process (TIP) … throughput analysis tool was not enough!!! • Collect Data, Analyze Bottlenecks, Team Solve Bottlenecks, Track effort & improvement, Repeat. • Lingering complaints of model failure to track throughput … problem was unequal station speeds • Consultants failed to solve new problem …. Alden did (~1989) • Convinced high management to form dedicated SWAT implementation teams … mostly from R&D • Worked with 20+ plants in many areas … mostly in body assembly • Developed the “Bottleneck Busters” Award, user’s group and newsletter • Saved many millions of dollars in one year (~1991) • Plants have TICs = throughput improvement coordinators to support TIP, Tool, Data collection. • Support via user group, EDS contracts, central simulation & IT groups, and a few researchers. 12-Mar-03 * JMA * 4

5. Electrician Tasks (E) Millwright Tasks (Mi) Pipe Fitter Tasks (P) Mechanic Tasks (Me) Welding Tasks (W) Project B: Chaining Principle: a Maintenance Work Example Worker Pools Task Types What if we trained some people to do multiple task types (i.e., cross training)? 12-Mar-03 * JMA * 5

6. Impact of Cross Training for Mean Time to Repair (MTTR) Tasks Workers Elec Mill Pipe Mech Weld Elec Mill Pipe Mech Weld Elec Mill Pipe Mech Weld MTTR = 25.0 minutes MTTR = 9.9 minutes MTTR = 7.9 minutes • Chaining provides most of the performance improvement of total cross-training with much less training effort. 12-Mar-03 * JMA * 6

7. 12-Mar-03 * JMA * 7

8. Project C: Visualization of Models and Spread Sheets a Business Case Example Full of hidden errors (approx 90% have errors) … links & equations Onerous to validate … so many cells, equations, data, ranges, etc. Poor collaboration environment … what cells do you own and how does it link in? Difficult to present the model … nice graphs of output, but hared to visualize the model 12-Mar-03 * JMA * 8

9. Business Case viewed as an Influence Diagram Enables collaborations easier … I see my part and how it links in Provide a quick high level validation aid Provide environment for visual validation (InSight) Can actually be fun! Much easier to explain the model 12-Mar-03 * JMA * 9

10. Influence Diagram No black box! Presentation Documentation Watch the movie! All in one place! InSight Programming Analysis Data Management Easy what-ifs! Point and click! Use legacy data! So we developed InSight … an Integrated Visual Systems Modeling Environment 12-Mar-03 * JMA * 10

11. Maintenance System Model PdM Monitoring Detection Window Time Length Annual Hrs Cycles Fixed Costs Dls Maintenance Heads Cost Per Predictive Maintenance Head Cost Spare Part Maintenance Annual Dls per Head per Yr PdM Dls per Part [PdM] Heads Maintenance Replacements PdM Costs Num per Yr Analysis Annual Dls Percent of Parts Spare Parts Spare Part Parts per Yr Covered by PdM Costs Percent Labor Wage Rate Dls Dls per Hr per Head PdM Annual Detection Mean Cycles Direct Labor Probability Between Failures Costs per Part MCBF Direct Labor Max Avail Hours Dls With No PM Heads Hrs per Yr Cycles Mean Cycles Between Annual Failures Spare Part Idle Time Annual Straight Time MCBF Costs Replacement Hrs per Yr Percent Overtime Hrs per Yr Cycles Dls Time of Parts Costs Hrs per Part Covered Dls by PvM Percent Overtime Parts per Premium Annual Station Repair Percent Overtime Variable Run Time Call Rate Annual (Under-time) Costs Hrs per Yr Num per Yr Station Speed Hrs per Yr Dls Profit JPH Dls PvM Num of Replacements Stations Variable Num per Yr Unit Cost Dls per Unit Throughput Start Preventive JPH Mean Time Maintenance To Recover Wait Time Annual Annual [PvM] Heads From Failure for Trades Revenues Production MTTR Hrs per Failure Units Mass Dls Station Speed Reactive Hrs Relief JPH Maintenance Percent [RM] Heads Mean Time To Recover Mean Cycles Annual From Halt Between Halts Wait Time for Spare Parts Hrs Sales Price Demand MTTR MCBF RM Repair Time Dls per Unit Units Hrs Cycles Hrs per Failure Example of using InSight for Maintenance Operations What if we moved people from reactive maintenance to predictive maintenance? 12-Mar-03 * JMA * 11

12. Visual Story-Telling of Results … like watching a Movie. 12-Mar-03 * JMA * 12

13. Properties Optimization Knowledge Gap Identification • Physical Tests Detail Vehicle Requirements Learning Objectives Property Opt Create Plans to Balance Program Resources, Cost, Timing, and Risk Which requirements need ADV? How to prioritize learnings? How to evaluate? (math & physical) What properties? (count, configuration) Road-Lab-Math • Test Requirements • Program Constraints Learning Priorities Operations Plan Optimization • Property count • Test – Property Assignment Math Validation • RLM analysis capabilities • Available test capacity forecasts • Property use performance • (idle, prep, test, move,etc) How to route properties? When & where to execute tasks? • Test resource requirements (T,$,L) Build Plans Sharing Plans Schedule Plans Current Knowledge Workload Analysis Staffing Levels • Execution issues • (delays, availability, property, etc) • Available capacity updates Execute Evaluations • Build capacity forecasts & cadence • Build costs Test Labs, PG, & Analysis Scheduling Dynamic Program Planning“Stuff Happens” Manage Shared Resources to Meet Program Timing, Cost, & Quality Imperatives Build Scheduling • Delivery Capabilities Build Delivery Information PG PPO Test Labs VSAS • Operations Plan Which evaluations to drop/add due to issues? How to update operations schedule? What is the sequence of builds? When to deliver builds? Who should execute tasks & when? Lab Scheduling Visible Schedule Build Finish/Schedule Project D: Optimizing Vehicle Performance Validation Evaluation Approach Optimization 12-Mar-03 * JMA * 13

14. Project E: Use of Probe Vehicles for Real-Time Traffic Information Data Transmission Raw data on vehicle speed, time, location Data Processing Map matching Historical data Incident detection Trip time models Automatic data collection from GPS-equipped probe vehicles Incident reports Updated traffic data Dynamic routing Current traffic info Data Dissemination 12-Mar-03 * JMA * 14

15. Schematic of Test Area N Van Dyke Mound Groesbeck Ryan Hoover Schoenherr GM Tech Center 12 Mile Rd I-696 West I-696 East 1 mile Michigan DOT loop detectors 12-Mar-03 * JMA * 15

16. 12-Mar-03 * JMA * 16

17. Average Speeds on Westbound I-696 80 70 9:45 60 50 Average Speed (miles/hour) 9:20 40 30 8:50 20 10 0 0 1 2 3 4 5 6 Groesbeck Hoover Van Dyke Mound Ryan Miles west of Groesbeck Highway Probe data MDOT loop detector data 12-Mar-03 * JMA * 17