A precision stainless steel tubing manufacturer running $13.7M in revenue with 30+ week lead times, 6 km of material travel per job, and no pull system. Labor productivity was 0.5 Kg/hour and the highest-volume product family had a 30–35 day cycle time driven by batch-and-queue scheduling across a multi-level facility.

A U.S. nitinol and specialty metal tubing facility producing 26 lbs/day from 30 draw cycles per shift — well below potential. Upstream lead time was 8.8 days, labor productivity sat at 0.9 lbs/hour, and significant capacity remained untapped within the existing workforce and equipment footprint.

An international guide wire manufacturing operation running 7–8 week lead times with $480K in WIP inventory and 22 operators producing at 229 pcs/month. Under GMP/FDA constraints, the site needed to improve flow, reduce inventory risk, and increase output per operator without compromising quality.

A California nitinol tube and wire drawing operation at $29M annual revenue with 5–6 week lead times on the flagship product and only 6 hollows/day upstream. Over 70% of materials processing time was non-value-added wait. The business needed an operational roadmap to scale to $50M without proportional headcount or capital growth.

Two stent manufacturing cells producing OEM product families at a negative 2% gross margin — with some individual SKUs as low as -58%. On-time delivery was 36%, output was 1.01 parts per labor hour, and lead times ranged from 19–26 days. The business was losing money on these products and needed a full operational turnaround.

A stent laser production line requiring 10 fiber lasers running at 21% downtime to hit a 5,200 parts/month target — a capital-intensive, high-maintenance model. Each laser represented ~$250K in capital. Setups consumed 16–18 changeovers per machine per month and scheduling drove frequent line changeovers.

A medical device contract manufacturer producing flagship product lines with a demand of 6,500 units per week (takt time: 19 seconds). Current state productivity was 92 parts per hour — below the 93 PPH target. Part of a 5-kaizen annual program with a documented ROI objective tied to the consulting investment.

A medical device manufacturer producing surgical inserter and delivery systems with 15–19 operators spread across a fragmented production layout — no defined cells, high operator variability, and no standard work. The engagement focused on designing a future-state cell that would dramatically reduce labor requirements and unlock revenue growth leverage.

A medical device manufacturer producing orthobiologic bone graft materials via a demineralization process. Operators walked 0.72 miles per day per lot through a fragmented layout — each lot requiring approximately 900 steps of travel through multiple departments, creating quality risk, ergonomic strain, and production variability.

A surgical robotics manufacturer assembling small machines in a scattered, ad hoc environment — no defined cell, no dedicated operators, no standard work, and no established takt time. Output was unpredictable, lead times were inconsistent, and assembly spanned sub-assembly and integration activities with no material flow design.

Building on the Small Machine cell success, the medium machine product family was being produced with shared resources, reactive scheduling, and no Kanban system — making component shortages and production stoppages common. The supply chain had no replenishment system, creating chronic “waiting on parts” delays.

A precision medical-grade tubing manufacturer running without a visual management system — no department-level metrics, no daily performance visibility, no escalation structure. Issues surfaced reactively. Leadership had no real-time view of quality, delivery, or productivity status across the value stream.

A medical tubing manufacturer’s weld strip procurement was reactive — no pull system, no visual replenishment, and 2–3 week lead times through welding for high-volume part numbers. The top 4 strip SKUs represented 48% of total consumption but were managed identically to low-volume specials, creating unnecessary lead time and scheduling complexity.

The highest-volume product family at a medical-grade tubing manufacturer was scheduled through the same shared generalist queue as all other families — no dedicated cell, no defined operator assignments, no weekly output targets. Output was inconsistent and lead times were variable, creating downstream scheduling risk for the key OEM customer relationship.

A space-grade electronics manufacturer’s Pick-and-Place circuit board assembly process ran 734 sides/year. Each kit required 45 minutes of “Get Stuff” time, 10 minutes of manual parts verification, and 6.5 hours of tape-and-reel prep — all non-value-added. Operators traveled 2,500 feet per kit. Two PNP machines were needed to keep pace with demand. 5S score was 0.

The MFG1 process — covering PNP completion through build-ready-for-functional-test — had 10 handoffs between departments and a median lead time of 24 days. The process crossed QCI, MFG, TEST, NCR, and rework loops with no standardized naming, no WIP caps, and no visibility into how much time was real work versus waiting.

Creating a new internal part required navigating two workflows: Change Requests averaging 40 days (42 unique routing steps) and Engineering Part Revisions averaging 76.5 days (76 unique steps). No two parts followed exactly the same path — variation was built in by design, creating unpredictable engineering lead times that rippled into program schedules.

An IT support team managing 25 tickets per week per person with consistent resolution times of 37–42 days — not from work complexity, but from tickets waiting to be routed, assigned, and picked up. Users submitted through multiple channels with no priority system, no SLA commitments, and no status visibility.

A defense electronics manufacturer’s recruiting process was losing nearly 3 in 10 candidates after full investment in the hiring funnel. Approval lists for requisitions, interviews, and offers were bloated. Salary identification was inconsistent, creating last-minute renegotiations. No automated pipeline reporting existed, so hiring managers had no visibility into candidate status.

A defense electronics manufacturer’s shop order production process spanned 30+ distinct work centers. Despite high technical quality, shop orders routinely piled up in queue between work centers, driving expediting and unpredictable delivery. No metric captured what percentage of lead time was real work versus waiting.

Test software development was consuming 7.6% of total program labor hours at a defense electronics manufacturer. Test script reuse was effectively 0% — every program rewrote from scratch. STD and script reworks were common, adding unplanned hours late in the program lifecycle when schedule pressure is highest.

As a defense electronics manufacturer scaled production, it began outsourcing circuit board assembly — but the process was ad hoc, rebuilt program by program. Recurring failures: material not production-ready per CM specs, flight vs. non-flight parts unseparated, traceability gaps, and kitting managed via spreadsheet. Every program rediscovered the same problems.

A commercial spaceflight manufacturer’s rocket motor assembly process spanned multiple enterprise systems with complex cross-functional handoffs across configuration management, procurement, kitting, NCR, and job closure. End-to-end process visibility was limited and lead times were inconsistent across the 2-motors-per-month production cycle.

An aerospace-grade lacing tape and wire manufacturer with a high-volume product line operating in an ad hoc, high-travel production environment. Material moved 672 feet through the facility before completing the process. No dedicated cell, no defined operator roles, and no pull system — resulting in unpredictable output and poor capacity visibility.

A high-volume defense components manufacturer producing millions of ammunition and ordnance parts annually. The plant operated as a traditional batch-and-queue job shop with high WIP, long travel distances, excessive setup times, and no formal visual management system. LSSE was brought in for what became a 10+ year continuous improvement partnership.

Automotive shock body components were manufactured across 5 scattered operations with no cell, no standard work, and no visual management. Parts traveled 1,516 feet per unit, 3.4 operators were required, 4,972 pieces of WIP sat on the floor at any time, 5S score was 0.8, and machining lead time alone was 3 weeks.

A defense part was manufactured across 3 separate locations in the plant with 731 feet of part travel per unit. WIP sat at ~2,950 pieces with no cell structure, no standard work, and a 5S score of 0.8. Operations (Impact, In-Line Wash, Pack) were disconnected with uncontrolled queues between each step.

Firearm receiver manufacturing had 117,000 pieces of WIP on the floor, 290 open jobs, and a 1st-piece inspection cycle time of 2–3+ hours. The 5S score was under 1.0. No pull system existed — jobs were released without regard to downstream capacity, creating chronic expediting and a floor full of queued inventory.

A defense manufacturer’s planning and scheduling was driven by manual part counting, uncontrolled raw material storage, and no formal pull system. With 290 open jobs on the floor and slugs traveling 943,750 ft/year, material handlers spent significant time searching and moving rather than adding value.

The Tool Crib had a 5S score of 0.0 — no visual management, no scheduling process, no standard staging, and obsolete tooling mixed throughout. Tool setup time was 31 minutes per changeover. 19 waste issues and 14 safety hazards were documented on Day 1, including chemical station non-compliance and multiple trip hazards.

Across an entire defense manufacturing facility, 24 people (material handlers, operators, setup technicians) were spending an average of 2 hours per day searching for parts, crates, and material-handling equipment. 46 staging locations existed across the plant — none standardized, none visually labeled, and none integrated with the ERP system.

Large cylinder finishing was producing 210 cylinders per 8-hour shift against a customer demand of 421 units/day — 50% of required output. The 5S score was 0.6, per-piece cost was $12.96, brush rework was running at an estimated 80%, and 25 safety/ergonomic issues and 31 waste issues were documented on Day 1.

A medium cylinder cell was producing 943 cylinders per 8-hour shift against a 2,591 units/day demand across 2 shifts — well below target. Overtime costs had reached $17,800 YTD. The press had run 46 alternate-press production shifts YTD due to machine reliability issues. 5S score was 1.0 and 24 safety hazards were documented on Day 1.

A cylinder paint line was missing 275 hooks per shift due to batching waste and uncontrolled line changeovers. WIP in the load/unload area was 31 units. Annual overtime was $70,000. The 5S score was 1.4, and 29 waste issues and 10 safety hazards were documented at the start of the event.

Thin wall cylinder production (10,000 units/month) ran 5 operators through a scattered, non-flow layout. No cell existed — machines were placed for convenience, not flow. Per-piece cost was $6.56, 5S score was 2.0, and the layout created long travel paths and operator idle time between machine cycle completions.

An automotive machining cell (PUMA lathes, Okuma mill, LYNX lathe, graining booth) was producing 333 cylinders per 8-hour shift against a weekly objective of 6,000 units — running at 75% of target. Labor cost was $1.08/piece, 5S score was 1.0, and 8 safety/ergonomic issues were documented at the start of the event.

Six production presses had no visual scheduling system — sequencing was driven by verbal priority and reactive expediting. Annual expedited freight was $30,000/year and overtime was $465,000/year. Without a master scheduling board, supervisors had no way to see upcoming demand across presses and proactively prevent late orders.

A Midwest defense plant had 9 temporary employees dedicated full-time to sorting defective parts — a symptom of uncontrolled quality at the press. A Pareto analysis revealed the top 5 part numbers drove the majority of sorting cost. The top reworked part was running a 27.7% rework rate at 4,697 pieces. 26 waste issues and 7 safety hazards were documented at the start of the event.

A full facility 5S assessment of 17 areas at a Midwest defense plant revealed scores ranging from 0.0 (Back Lube Line) to 4.0 (Shipping). The office scored 0.04, most production areas averaged between 1.0 and 2.2 — well below a sustainable lean baseline. No bilingual audit system existed for the Spanish-speaking workforce.

A firearms production line (47,014 parts/month across 6 part numbers) had a total setup time of 21 hours 15 minutes — with QC wait time alone accounting for 555 minutes (44% of total setup). QC reject rate was 30%, machine downtime was 27%, and kitting required 30–45 minutes of tote movement with no staging system. 5S score was 1.8.

A precision metal fabricator’s QA paperwork process required 22–42 minutes per job cycle with 217 feet of travel per transaction. The Red Folder scanning process sent operators walking 1,194 feet per document through 45 print/copy/file steps. QA was closing ~23 operations per day manually and Armor DIP data entry was 100% manual into the ERP system.

A fabricated metal products shipping department took 28 minutes to pre-pack a single job, with operators walking 791 feet per pack cycle. Supply outages (cardboard, pads) occurred 2–3 times per week requiring emergency vendor trips. 5S score was 0.25, shipping errors were running at 27 YTD, and the department was being considered for a costly $15,000+ QA office build-out.

An armor product family was losing money at -$420 per part (average cost $2,164 vs. sales price $1,743), generating -$196,000 in gross margin YTD on 467 units. Actual weld hours were 11.1 hrs/unit against an 8.2 hr standard — a 35% overage driven by lack of standard work, inconsistent fixturing, and no defined weld sequence.

A precision metal fabricator was losing money on its Armor family and running thin margins on Exhaust Collectors. Labor and overhead were the dominant cost drivers — 80% for Armor and 60% for Exhaust Collectors. Quality holds (especially weld-related) were creating additional cost through scrap and rework across both families.

A laser department was completing 7 jobs per day with no visual scheduling, no organized incoming queue for tooling/fixtures/overlays, and a 5S score of 0.6. Operators frequently lost time searching for overlays — a chronic recurring complaint. No prioritization system existed and operators self-sequenced without visibility to downstream demand or due dates.

The hydroform tooling area had a 5S score of 0.8. High-volume and low-volume tooling were mixed with no organization by job type or size. Rings were stored non-sequentially and specialty mixed among standard tooling. No kitting system existed — operators assembled their own setup kits from scratch before every run. 6 safety hazards were identified including incorrect heavy tooling placement.

A leading manufacturer of RV awnings and outdoor products with a high-volume product line (10’–21′ models) wanted to identify cost reduction opportunities without compromising product quality or performance — targeting the flagship awning family in a single focused kaizen event.

A necker operation producing 1,436 parts/day against a weekly demand of 10,000 units. The cell ran 19 setups in 23 working days, machine uptime was only 54%, 5S score was 0.0, and 38 safety and ergonomic issues were identified on Day 1. Labor cost was running $0.37/unit with significant overtime exposure.

A plastic injection molder running 12-week raw material lead times and 8-week manufacturing lead times. Current capacity was 475,000 units/month against a growth target of 850,000. Travel distance per order was 18.6 miles. 5S score was 1.4. The clean room cell had no defined operator roles, no takt time, and no pull system — making capacity scaling impossible without adding headcount.

A ring tumbler cell was producing 2,300 cups per 8-hour shift — below target and below potential. 5S score was 0.8, no standard work existed for operators, and cell layout was not optimized for efficient material flow. The improvement target was modest: 10% productivity gain and 50% 5S improvement.

A $500M+ regional electric grid operator managing the bulk electric grid across 15 states had no formal process improvement structure — just informal pockets of effort with no consistency or accountability. Senior leadership identified a need for enterprise-wide CI capability and engaged LSSE to design it from scratch.

Before 2013, there was no standardized process improvement training at this large electric utility. Employees had no common language for identifying waste or running improvement projects, and leadership had no framework for sponsoring or governing PI work across 15 states of operations.

A large electric utility operated with siloed processes across 14+ functional areas — operations, finance, legal, technology, HR, stakeholder engagement, and more. There was no enterprise view of how processes connected, who owned them, or where handoffs broke down — making improvement project prioritization guesswork.

With training deployed and governance in place, a large electric utility needed to show real results. Key processes — Engineering Studies workflows, employee repricing, and talent recruiting — had long cycle times, excessive handoffs, and no standard work. These were the first live improvement events in the organization’s history.

Leadership at a large electric utility tied efficiency improvements directly to employee Short-Term Incentive Plan (STIP) targets in 2015 — but had no consistent methodology for identifying, calculating, or tracking efficiency gains across business areas. Without a standard framework, divisions reported savings inconsistently and gains were unverifiable.

A $50M precision sheet metal and fabrication company recently acquired by a PE firm. The plant operated as a 100% functional batch job shop with heavy expediting, flow instability at shared constraints, and structural complexity that made profitability difficult to scale beyond current revenue levels.

A healthcare and social services organization with a chronic hiring bottleneck — average time from requisition to hire was 60–85 days, with a 187-day queue backlog. Staff turnover and patient service capacity were directly impacted. Two kaizen events were commissioned to redesign the full talent acquisition and onboarding lifecycle.