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The study examines the Mechanical Subgroups ETU assembly process at Fiat Group Automobiles’ Cassino plant, where flexibility, labor productivity, ergonomics, and material movement had to be improved without sacrificing multi-model production.

The work combined WCM technical pillars with Pareto-based Cost Deployment, Autonomous Maintenance, 5S, MUDA analysis, spaghetti diagrams, material-and-flow matrices, JIT/JIS classification, simulation, and low-cost automation.

The authors report substantial improvements in direct-labor productivity and ergonomics, together with a material-flow redesign that removed forklifts from the studied process.

This research synthesis examines the shift from function-based production toward process-based organizations, drawing on evidence from NUMMI, Japanese transplants, and related manufacturing studies.

It evaluates combinations of team problem-solving, multi-skilling, mentoring, ERP-supported information flow, Kanban, autonomation, and other high-involvement practices using factor analysis and econometric methods.

The analysis finds that coordinated bundles can be more valuable than isolated practices and that measurable productivity effects may develop over several years rather than immediately.

The study considers how an established WCM system can evolve as connected equipment, data availability, and digital manufacturing technologies become part of the production environment.

The authors map Industry 4.0 capabilities against WCM pillars and examine where digital data, connectivity, and automation can reinforce rather than replace the existing improvement system.

The case provides a structured view of how digital technologies can strengthen WCM problem identification, analysis, and execution while preserving the underlying operating discipline.

A global agricultural- and industrial-vehicle manufacturer needed new equipment that would improve operator ergonomics while meeting technical, efficiency, and standardization requirements.

The methodology joined the WCM Workplace Organization pillar with human-centered design, digital human modeling, virtual prototyping, ergonomic analysis, physical prototyping, and Industry 4.0 enabling technologies.

The case led to the design and implementation of new equipment, with reported benefits in ergonomics, process efficiency, and work standardization.

The case investigates equipment availability and reliability in a WCM environment where faster communication was needed between the production line, machine specialists, and engineers.

Mobile technology supported near-real-time collaboration and more refined causation data for Cost Deployment and maintenance-improvement decisions.

The authors report movement in production-line efficiency and conclude that connected collaboration can support WCM’s progression toward zero breakdowns.

The paper describes WCM use in a Polish mechanical-engineering factory within a global manufacturing company producing approximately 800 finished machines per year.

The implementation addresses Safety, Cost Deployment, Focused Improvement, Autonomous Activities, Professional Maintenance, Quality Control, Logistics, Early Management, People Development, and Environment.

The study reports reduced losses and improved product quality through coordinated use of WCM pillars and their associated methods.

The case addresses a small manufacturing company that needed a practical TPM structure without the cost and organizational burden of a full-scale program.

The model used plan–improve–sustain phases, 5S, Autonomous Maintenance, focused improvement, value-stream mapping, OEE tracking, checkpoint tables, and routine patrol audits.

On the pilot equipment, OEE increased from 54.23% to 66.90%. The study also reports lower setup losses, less non-value-added work, and stronger employee involvement.

The boiler plant was treated as a production bottleneck where availability, downtime, and the gap from an 85% OEE benchmark were limiting performance.

The implementation included education, 5S, Autonomous Maintenance, Planned Maintenance, Why-Why analysis, Poka-Yoke, OEE measurement, visual controls, and kaizen registers.

OEE rose from 69.87% to 78.94%, availability increased from 90.99% to 96.67%, and reported downtime fell from 82 hours to 38.35 hours.

The ring-frame section of a spinning plant was selected because equipment losses directly affected yarn production, product quality, and throughput.

The team used Pareto analysis, Why-Why Because Logical Analysis, cause-and-effect analysis, operator training, and focused kaizen against breakdowns, setup, minor stoppages, speed loss, defects, and reduced yield.

OEE increased from 75.09% to 86.02%, productivity improved by 23.93%, and defective production declined by 49.50%.

The study applies TPM inside a metal-forming operation to identify the production losses and equipment conditions limiting workstation effectiveness.

The approach evaluates availability, performance, and quality losses, then organizes improvement activity around TPM practices and workstation-level OEE measurement.

The case documents improved equipment effectiveness after the TPM interventions and provides a repeatable activity sequence for extending the work across additional stations.

An aerospace manufacturer focused on the CNC lathe with the most frequent breakdowns, where equipment unreliability was constraining productivity and capacity.

The work combined maintenance value-stream mapping, OEE analysis, TPM training, early-failure recognition, visual cues, and easier inspection access.

Productivity increased by about 22%, OEE rose from 39% to 45%, lost capacity fell from 61% to 55%, and the company reported avoiding $250,000 in equipment investment.

The study evaluates a sheeter machine on cut-size line 5 at PT RAPP, where equipment losses and the causes of reduced effectiveness needed to be identified.

The authors use OEE, six-big-loss analysis, and TPM improvement priorities to evaluate the machine and define corrective work.

The paper reports an average OEE of 82.75% for the studied machine and identifies the loss categories requiring focused improvement.

A plastic manufacturer faced low equipment effectiveness, extended setup activity, and reliability losses that could not be addressed by a single improvement tool.

The study combines TPM, SMED, process simulation, and machine-learning analysis to improve maintenance decisions, setup performance, and production stability.

The authors report OEE increasing from 61.87% to 80.86%, together with improved reliability indicators and process efficiency.

The project targeted recurring testing-equipment failures and sought a 50% reduction over three months while improving repeatability in the production test process.

The team used four years of data, Minitab, histograms, box plots, process-capability analysis, value-stream mapping, fishbone analysis, MTBF/MTTR tracking, standard work, proactive component maintenance, and automated data collection.

The case reports sustained downtime reduction and a more standardized testing process, supported by control-stage monitoring.

The study maps the Formation and Ageing processes in battery-cell manufacturing, where long lead times, movement, workstation layout, and detection activities created avoidable delay.

The work uses DMAIC, value-stream mapping, spaghetti diagrams, 5S, interviews, surveys, FMEA, control plans, kaizen events, and improvements to OCV and AC-IR testing accessories.

Reported non-value-added time declined by 18.6%, from 297.22 minutes to 241.8 minutes.

The manufacturer was experiencing a combined daily rejection rate of 5.5% across front- and rear-door rubber weather-strip production.

The project followed DMAIC and used process measurement, statistical analysis, root-cause identification, and controlled process changes to address the principal rejection modes.

The reported rejection rate declined from 5.5% to 3.08%, with corresponding improvement in process capability and sigma performance.

The project was launched to raise first-pass yield in a plunger-manufacturing line from roughly 94% toward a 99% target.

The cross-functional team used DMAIC, regression, hypothesis testing, Taguchi methods, process analysis, and beta correction to identify and control influential parameters.

The study reports first-pass yield improving from 94.86% to 99.48%, with estimated annual savings of approximately $87,000.

A Taiwanese automotive manufacturer applied Six Sigma to recurring rejection in a brushless-motor product produced through an automated process.

The study follows Define, Measure, Analyze, Improve, and Control, using statistical and root-cause tools to identify the dominant defect mechanisms and establish process controls.

The case documents a reduction in defective output and a more stable production process after the improvement and control actions were implemented.

The study investigates low efficiency in finished-goods and corrugated-board production, using machine-level OEE to locate downtime, performance, and quality losses.

The authors use DMAIC, OEE calculation, kaizen, fishbone analysis, Why-Why analysis, and 5W+1H action planning across corrugation, printing, gluing, and binding equipment.

The paper reports material improvements in waste, output, and production efficiency after the kaizen actions, with gains across both studied lines.

The manufacturer produced thousands of component types in small quantities, creating conditions where conventional high-volume Lean Six Sigma assumptions were not automatically valid.

The researchers compared a baseline process with cellular and single-work-center arrangements, measuring throughput, uptime, lead time, on-time delivery, effectiveness, and cost per part.

The study shows that different arrangements improve different performance measures and emphasizes selecting interventions based on the actual high-mix operating objective rather than applying a universal lean layout.

The case addresses the difficulty of comparing and improving additive-manufacturing operations when quality performance and sustainability effects are managed independently.

DMAIC is extended to define linked quality and sustainability measures, analyze performance gaps, prioritize improvement opportunities, and establish a control structure for future decisions.

The study provides an operational framework and case demonstration for improving both dimensions together, reducing the risk that a quality gain simply shifts cost or environmental burden elsewhere.

Returned material was moving through a slow, poorly visible process with excessive handoffs, long total lead time, and very low first-pass yield.

The project used value-stream mapping, a spaghetti diagram, root-cause analysis, simplified documentation, and visual management to establish a clearer future-state flow.

Lead time fell from more than 40 days to five days, processing time declined from 11.8 hours to 3.4 hours, and first-pass yield increased from 10% to 90%.

A food manufacturer faced long cleaning changeovers, repeated cleaning and testing, capacity constraints, and inefficient material retrieval in shipping and receiving.

The work combined changeover analysis, line balancing, value-stream mapping, tailored lean training, and a future-state action plan for production and warehouse flow.

The company reports eliminating a 50% re-clean/re-test rate, reducing main-line changeover time by 50%, and cutting order-processing time by 80%.

The research asks whether a value-stream map can be updated with machine data rather than relying only on a manually prepared snapshot of the production process.

A lab-scale manufacturing cell was modeled with a digital twin that collects machine data, supports real-time visualization, and generates an improvement aimed at non-value-added process time.

The case demonstrates the feasibility of combining digital twins with VSM to support monitoring, simulation, and more dynamic waste-reduction decisions.