Production Scheduling and OEE Optimization Guide

OEE = Availability x Performance x Quality, where each factor is expressed as a percentage. For HWAMDA SPV5 thin-wall production, the calculation for an 8-cavity yogurt cup line running 24/7: Availability = (Planned Production Time - Downtime) / Planned Production Time. Planned production time is 24 hours x 30 days = 720 hours/month. Downtime includes: mold changes (2-4 hours per change, 4-8 changes per month = 8-32 hours), material changes (0.5-1 hour each, 8-12 per month = 4-12 hours), unplanned stops (machine faults, mold repairs = 5-20 hours), and startup/shutdown waste time (0.5-1 hour per start = 4-8 hours). Typical availability: 92-97 percent (well-managed) versus 80-90 percent (poorly managed). Performance = (Ideal Cycle Time x Total Parts) / Running Time. Ideal cycle time for 8-cavity yogurt cups: 4.0s. If actual average cycle time is 4.3s due to minor stops and speed losses: Performance = 4.0/4.3 = 93 percent. Quality = Good Parts / Total Parts. Thin-wall quality rates: 98-99.5 percent (well-optimized) versus 95-97 percent (startup or unstable process). Combined OEE: optimized line = 0.95 x 0.93 x 0.99 = 87.4 percent; unoptimized = 0.85 x 0.88 x 0.96 = 71.8 percent.

Unplanned downtime is the largest OEE loss on thin-wall production lines, accounting for 5-15 percent of available time on poorly maintained HWAMDA SPV5 machines versus less than 2 percent on well-maintained lines. The top 5 unplanned downtime causes and prevention strategies: 1. Hot runner heater failure (2-8 hours downtime): prevent by replacing heaters at 2 million shots and monitoring duty cycle trending on the INOVA controller. 2. Check ring wear causing weight variation (1-4 hours for diagnosis and replacement): prevent by replacing at 2 million shot intervals based on cushion SPC data. 3. Mold cooling blockage (1-2 hours for cleaning): prevent with quarterly descaling and monthly flow rate verification. 4. Hydraulic leak (2-6 hours for seal replacement): prevent by inspecting high-pressure connections weekly and replacing seals at annual overhaul. 5. Robot fault/cycle stop (0.5-2 hours): prevent by cleaning robot gripper mechanisms weekly and verifying sensor alignment monthly. Total preventive maintenance time budget: 4-6 hours per week scheduled during planned downtime (shift change, weekends) to prevent 10-20 hours per month of unplanned stops. ROI of preventive maintenance: USD 1,000-2,000 per month in parts and labor prevents USD 5,000-15,000 per month in lost production.

Cycle time directly impacts OEE performance rate and is the most controllable variable on HWAMDA SPV5 machines. A thin-wall cycle consists of: injection fill (0.15-0.25s), pack/hold (0.5-1.5s), cooling (1.5-3.0s), mold open (0.3-0.5s), ejection and robot handling (0.3-0.8s), and mold close (0.3-0.5s). Optimization priorities by impact: Cooling time reduction (largest component at 40-65 percent of cycle): optimize coolant temperature (reduce from 25 to 15 degrees C for PP saves 0.3-0.5s), improve cooling channel efficiency (conformal cooling saves 0.5-0.8s), use BeCu core inserts (saves 0.2-0.4s). Parallel movements via INOVA controller (saves 0.3-0.5s total): charging on fly (screw recovery during mold opening), ejection during mold opening, robot entry during ejection. Mold movement speed optimization: increase clamp speed to 1200 mm/s with proper deceleration profile to prevent impact (saves 0.1-0.2s). Hold time optimization through gate seal study: holding beyond gate freeze wastes time -- determine exact gate seal time and set hold 0.1s beyond that point. Cumulative cycle time reduction on an initial 4.5s cycle: 4.5 - 0.4 (cooling) - 0.4 (parallel movements) - 0.1 (mold speed) - 0.2 (hold time) = 3.4s, a 24 percent improvement yielding 32 percent more output per hour.

The HWAMDA INOVA controller generates data that enables systematic OEE improvement when analyzed properly. Real-time data available: cycle time per shot (resolution 0.01s), machine state (running, stopped, fault, mold change), reject count (from automated sorting), and energy consumption per cycle. Export data via OPC-UA to an MES (Manufacturing Execution System) for automated OEE calculation and reporting. Minimum viable OEE tracking: log machine state changes (start, stop, fault) with timestamps and reason codes. Pareto analysis of downtime reasons identifies the top 3 causes responsible for 60-80 percent of losses. Weekly OEE review meetings should compare: actual versus target cycle time (identify performance losses), downtime breakdown by cause (identify availability losses), and scrap rate by defect type (identify quality losses). Target setting: establish a baseline OEE over 4 weeks of normal production, then set improvement targets of 2-3 percentage points per quarter. For a 4-machine facility, increasing OEE from 78 to 85 percent (7 points over 3 quarters) generates approximately 9 percent more output from existing capacity -- equivalent to the production of half an additional machine without capital investment. Annual value of this improvement: USD 50,000-120,000 per machine depending on product value and volume.