See how Industrial Engineers apply TaktMaster Pro features to solve common time study challenges across different manufacturing scenarios. These examples demonstrate typical applications and results based on standard Industrial Engineering practices.
Assembly line running 47-second cycle time with 22 individual elements. Production struggling to maintain takt time of 42 seconds to meet customer demand. Management knew there were bottlenecks but couldn't pinpoint which specific elements were causing delays. Previous consultant spent $15,000 and recommended 'improve operator training' without identifying root causes.
Conducted 30 observations across 3 shifts using offline mode on factory floor. Control charts with capability scoring automatically flagged 3 elements with Cpk scores below 1.0 (red badges). Element-level analysis showed: Element B3 (socket installation): 8.2 seconds with high variation, Element F7 (wire routing): 6.1 seconds, tool retrieval adding 2.3 seconds, Element K4 (quality check): 4.9 seconds, excessive walking. Production planning tool tested multiple scenarios: reducing any single element wouldn't hit target, but reducing all 3 problem elements by 30% would achieve 38-second cycle time with buffer.
Moved tools closer for Element F7, added fixture for B3, relocated inspection station for K4. Production planning tested scenarios: reducing any single element wouldn't hit target, but reducing all 3 problem elements by 30% achieved 38-second cycle time with buffer.
CNC machining center producing aircraft brackets with cycle times ranging from 87 to 134 minutes for supposedly identical parts. Customer quotes unreliable because standard time estimates had 35% variation. Operators blamed 'every part is different' but engineering suspected process inconsistency, not part variation.
Conducted time studies on 15 identical parts across different operators and shifts. Used multi-device capability: started study on desktop, continued observations on tablet at machine center, synced back to desktop for analysis. Control charts revealed cycle time variation was 90% operator technique, only 10% part-specific. Value-added ratio analysis showed only 34% of cycle time was actual machining (value-added). Non-value-added time breakdown: Tool changes: 18 minutes per part (23%), Program setup/verification: 14 minutes (18%), Waiting for inspection: 12 minutes (16%), Material handling: 8 minutes (10%).
Created tool staging system, pre-verified programs, relocated inspection station. Non-value-added time breakdown showed 23% tool changes, 18% program setup, 16% waiting for inspection, 10% material handling.
Sales team quoted 2-week lead times for 5,000 units/week based on 'gut feel.' Production frequently missed commitments, causing overtime costs averaging $8,200/month. No one knew actual capacity—estimates ranged from 3,200 to 6,800 units/week depending on who you asked.
Conducted time studies on complete assembly process (12 workstations). Used production planning calculator to determine: Current cycle time: 34.2 seconds per unit, Available time: 27,000 seconds per shift (2 shifts, 5 days), Current capacity: 3,947 units/week with 3 operators per shift. Tested capacity scenarios: Scenario 1: Add 1 operator per shift → 5,263 units/week, Scenario 2: Reduce cycle time by 15% (eliminate 2 non-value elements) → 4,627 units/week, Scenario 3: Combination → 6,159 units/week. Generated professional PDF report showing takt time vs cycle time gap, bottleneck analysis, and staffing recommendations.
Scenario 1: Add 1 operator per shift → 5,263 units/week. Scenario 2: Reduce cycle time by 15% → 4,627 units/week. Scenario 3: Combination → 6,159 units/week. Report showed takt time vs cycle time gap, bottleneck analysis, staffing recommendations.
Packaging line with 6 workstations producing 180 cases/hour, well below 240 cases/hour target. Station 4 (labeling) was obvious bottleneck—always backed up, operators stressed. Previous solution: 'work faster' didn't help. Line supervisor suspected uneven work distribution but had no data to prove it.
Conducted time studies at all 6 stations simultaneously (3 engineers with phones, working offline on production floor). Exported data to Excel showing exact cycle time per station: Station 1 (unloading): 12.3 seconds, Station 2 (inspection): 9.8 seconds, Station 3 (sorting): 11.1 seconds, Station 4 (labeling): 22.7 seconds ← bottleneck, Station 5 (boxing): 10.4 seconds, Station 6 (palletizing): 13.2 seconds. Element-level breakdown of Station 4 showed 3 tasks could be redistributed: Label placement (5.2 sec) → move to Station 3, Quality stamp (3.1 sec) → move to Station 5, Remaining core labeling: 14.4 seconds.
Station times: S1: 12.3s, S2: 9.8s, S3: 11.1s, S4: 22.7s ← bottleneck, S5: 10.4s, S6: 13.2s. Redistributed tasks to balance load. Multi-engineer simultaneous timing with offline phones synced automatically.
Management knew process was 'inefficient' but couldn't quantify waste or prioritize improvements. Lean consultant recommended 'eliminate all non-value-added activities' but provided no roadmap for where to start. Team paralyzed by 47 potential improvement ideas with no data to prioritize.
Conducted time studies on 4 primary production cells. Classified each element as value-added or non-value-added during project setup. System automatically calculated value-added ratios: Cell A (cutting): 41% VA ratio (YELLOW), Cell B (forming): 28% VA ratio (RED), Cell C (welding): 58% VA ratio (YELLOW), Cell D (finishing): 33% VA ratio (RED). Focused on Cell B (worst performer). Top waste breakdown showed: Walking to tool crib: 22 seconds per cycle (15% of total time), Waiting for crane: 18 seconds (12%), Checking part orientation: 15 seconds (10%), Searching for fixtures: 12 seconds (8%), Adjusting machine settings: 9 seconds (6%).
VA ratios: Cell A: 41% (yellow), Cell B: 28% (red), Cell C: 58% (yellow), Cell D: 33% (red). Top waste: Walking to tool crib: 22s (15%), Waiting for crane: 18s (12%), Checking orientation: 15s (10%), Searching fixtures: 12s (8%), Adjusting settings: 9s (6%).
Job shop running 40+ different product configurations with no standard times. Quotes based on 'last time we made something similar' resulting in 60% of jobs under-quoted (lost money) and 40% over-quoted (lost bids). Estimator retiring in 6 months—all tribal knowledge would be lost. Needed documented standard times fast.
Created 12 project templates for common product families (brackets, enclosures, frames, etc.). Set up 6 core processes shared across products: Material prep, Primary machining, Secondary operations, Assembly, Quality inspection, Packaging/shipping. Conducted time studies on 28 different jobs over 8 weeks using smart rating suggestions (system learned typical ratings and suggested them, saving 2-3 minutes per study). Exported all studies to Excel for comparison analysis. Identified common elements across product families that could be standardized.
6 core processes: Material prep, Primary machining, Secondary operations, Assembly, Quality inspection, Packaging/shipping. Smart rating learned patterns and suggested ratings. Captured retiring estimator's knowledge in documented form. New estimator onboarded in 2 weeks using library (previously 6 months).
See how TaktMaster Pro's 9 core features can transform your time study workflow and deliver measurable results for your manufacturing operations.