Kenneth J. Andrews EMP Gen-X: Manufacturing Analysis What is the process?Build & test of AXIS machine for a specific Customer Who is the customer?MegaPower- product quality - install time - on-time delivery - ship what ordered - good training Installation- complete shipment - documentation - tested, working - acceptance test OK - early notification

Kenneth J. Andrews EMP Gen-X: Manufacturing Analysis – Flowchart (1) 1. Order is logged in 2. Scheduled by the Manufacturing Manager (remote board) 3. Order sent to Manufacturing Engineer 4. Wait for drawings – always 5 days late 5. Initiate system build (before designs arrive) 6. Designs are checked, mistakes noted – no direct feedback 7. Problems with designs – try to reach designer  WAIT 8. Mfg. Engineer modifies the designs (inventory-driven) 9. Supervisor takes the new designs 10. Systems are re-worked to account for actual designs 11. Parts are requested from Stores  WAIT 12. Problems during build  Mfg. Eng  Mfg. Mgr  Eng. Mgr  13. System hardware completed 14. System moved to Test

Kenneth J. Andrews EMP Gen-X: Manufacturing Analysis – Flowchart (2) 15. Chase software from Design  WAIT 16. Software arrives (late) 17. Hardware functional check – problems fixed – no feedback 18. Software check – patches for bugs – documentation? 19. No time for Acceptance Test 20. System moved to shipping dock 21. Install Coordinator advised about imminent ship

Kenneth J. Andrews EMP Gen-X: Manufacturing Analysis – Flowchart (1) 1. Order is logged in 2. Scheduled by the Manufacturing Manager (remote board) 3. Order sent to Manufacturing Engineer 4. Wait for drawings – always 5 days late 5. Initiate system build (before designs arrive) 6. Designs are checked, mistakes noted – no direct feedback 7. Problems with designs – try to reach designer  WAIT 8. Mfg. Engineer modifies the designs (inventory-driven) 9. Supervisor takes the new designs 10. Systems are re-worked to account for actual designs 11. Parts are requested from Stores  WAIT 12. Problems during build  Mfg. Eng  Mfg. Mgr  Eng. Mgr  13. System hardware completed 14. System moved to Test

Kenneth J. Andrews EMP Gen-X: Manufacturing Analysis – Flowchart (2) 15. Chase software from Design  WAIT 16. Software arrives (late) 17. Hardware functional check – problems fixed – no feedback 18. Software check – patches for bugs – documentation? 19. No time for Acceptance Test 20. System moved to shipping dock 21. Install Coordinator advised about imminent ship

Kenneth J. Andrews EMP Process Improvement What process? Customer + requirements Map current process Identify hot-spots Root-cause analysis Improvements to a) fix root causes b) meet C requirements Metrics (1-3 months) Communicate plan Implement, measure, fine-tune

Kenneth J. Andrews EMP Manufacturing Systems: EMP-5179 Module #6: Manufacturing Metrics Dr. Ken Andrews High Impact Facilitation Fall 2010

Kenneth J. Andrews EMP EMP-5179: Module #6  Sigma, Variance, SPC etc. Revisited  Factory Physics  Balanced Scorecard

Kenneth J. Andrews EMP Variability The world tends to be bell-shaped Most outcomes occur in the middle Fewer in the “tails” (lower) Fewer in the “tails” (upper) Even very rare outcomes are possible (probability > 0) Even very rare outcomes are possible (probability > 0)

Kenneth J. Andrews EMP Number of Samples Process Spread/ Variability Mean Process variability is determined by US

Kenneth J. Andrews EMP Number of Samples Specification Tolerance Mean Upper Specification Limit (USL) Lower Specification Limit (LSL) Specification tolerance is defined by the Customer

Kenneth J. Andrews EMP Tolerance Limits

Kenneth J. Andrews EMP Variation in Process Output Due to Random Causes

Kenneth J. Andrews EMP Low Process Capability

Kenneth J. Andrews EMP High Process Capability

Kenneth J. Andrews EMP We can be much more specific about process capability by measuring the process variability and comparing it directly to the required tolerance. Common measures are called Process Capability Indices (PCIs) μ= mean σ= std. deviation USL= Upper Spec. Limit LSL= Lower Spec. Limit Process Capability Indices

Kenneth J. Andrews EMP Process Capability C pk = min USL – μ 3σ μ - LSL 3σ – 20 3(2) = = – 15 3(2) = =.833

Kenneth J. Andrews EMP C pk measures “Process Capability” Good quality:defects are rare (C pk >1) μ target

Kenneth J. Andrews EMP C pk measures “Process Capability” Poor quality: defects are common (C pk <1) μ target If process limits and control limits are at the same location, C pk = 1 C pk ≥ 2 is exceptional.

Kenneth J. Andrews EMP EMP-5179: Module #6  Sigma, Variance, SPC etc. Revisited  Factory Physics  Balanced Scorecard

Kenneth J. Andrews EMP Factory Dynamics: Batch Production Consider a simple 4-station production line, where the processing time at each station is exactly 1 minute Batch Size (WIP) Cycle Time (minutes) Throughput (pieces/minute) Throughput (pieces/hour)

Kenneth J. Andrews EMP Factory Dynamics: Single-Piece Flow Consider a simple 4-station production line, where the processing time at each station is exactly 1 minute Batch Size (WIP) Cycle Time (minutes) Throughput (pieces/minute) Throughput (pieces/hour)

Kenneth J. Andrews EMP Production Throughput

Kenneth J. Andrews EMP “Decrease Inventories” A factor of variability Lower WIP = Less Throughput = Not Good

Kenneth J. Andrews EMP “Reduce Variability AND Inventories” Reduced variability Lower WIP + Reduced variability = Higher Throughput = Good

Kenneth J. Andrews EMP Self-Paced Study Review and research the following material relating to: SCV Availability Factory Physics Confirm your understanding by following the examples provided.

Kenneth J. Andrews EMP Objective Measure of Variability For example, an assembly operation with an average process time of 20 minutes and a standard deviation of 1 minute: scv = (1/20) 2 =

Kenneth J. Andrews EMP Availability Consider a workstation that operates an average of 70 hours before it must be shut down for maintenance, lasting 10 hours.

Kenneth J. Andrews EMP Optimal Maintenance Intervals? Infrequent maintenance: 70 hours on, 10 hours off Frequent maintenance: 3.5 hours on, 0.5 hours off What about variability? Isn’t that important too?

Kenneth J. Andrews EMP Optimal Maintenance Intervals?

Kenneth J. Andrews EMP Optimal Maintenance Intervals? scv = squared coefficient of variation m r = mean time to repair A = availability t 0 = original processing time

Kenneth J. Andrews EMP Optimal Maintenance Intervals? Infrequent maintenance: 70 hours on, 10 hours off Frequent maintenance: 3.5 hours on, 0.5 hours off For the same equipment availability, shorter repair times lead to lower variability i.e. they are better

Kenneth J. Andrews EMP Utilization: High or Low?  One way to improve Return on Investment (ROI) is to maximize the revenue generated by utilizing production resources to the fullest extent possible = high capacity utilization.  Is a 24/7/52 factory a good strategy?  It depends on whether you are striving for shorter cycle times  It also depends on whether you are living in a: deterministic (ideal) world = very low variability stochastic (real) world = moderate/high variability

Kenneth J. Andrews EMP Cycle Time, Utilization & Variability High Variability Low Variability 20% 50% 100% Cycle Time Capacity Utilization Moderate Variability Standard & Davis: “Running Today’s Factory”

Kenneth J. Andrews EMP Causes of Variability  Equipment downtime  Excessive set-up time  Uneven production demand  Batch material movement  Non-standard processes  Human factors  Supplier problems  Unexpected outages (e.g. power) 1.Reduce variability wherever possible throughout the production process. 2.Do not strive for 100% capacity utilization.

Kenneth J. Andrews EMP Balanced Scorecard Perspectives

Kenneth J. Andrews EMP Preparation for Next Week  Watch for new articles/links on the website  Download material for module #7