Mechanical error-proofing in CNC turning industrial validation of a Poka-Yoke device for scrap reductionAbstract
Paper aims To design and validate a mechanically integrated error-proofing mechanism capable of preventing incorrect center drill positioning during setup operations in Computer Numerical Control (CNC) turning environments, reducing scrap generation, and improving process reliability.
Originality This study provides empirical industrial evidence on the implementation and quantitative validation of a mechanically integrated Poka-Yoke mechanism in CNC machining operations, preventing setup errors at the source through an intervention.
Research method An industrial case study was conducted in an automotive component production line. The research included process observation, root-cause identification of setup variability associated with center drill positioning, and the design and implementation of a mechanical error-prevention device. Operational performance was evaluated through scrap rate and Overall Equipment Effectiveness (OEE) indicators using a before-and-after approach.
Main findings Scrap was reduced from 1.18% to 0.56% (52.5%), while OEE increased from 85% to 90% (5 percentage points), indicating improvements in process stability and operational performance.
Implications for theory and practice The study contributes empirical evidence to the literature on Poka-Yoke, preventive quality engineering, and source-level error prevention, while demonstrating the applicability of low-cost integrated interventions in machining environments. Findings also suggest that such mechanisms may complement digital quality systems within hybrid quality strategies.
Keywords:
Manufacturing systems; Setup errors; Machining reliability; Operational performance; Tool positioning
1. Introduction
Current manufacturing environments are characterized by an increasing variety of products, reduced batch sizes, and high demands for dimensional accuracy and operational reliability. In these contexts, process variability and human errors related to setup can significantly affect production stability, scrap rates, and equipment effectiveness. Although quality control tools and parameter optimization techniques contribute to performance improvement (Sopelana et al., 2022; Psarommatis et al., 2020), they are generally reactive, detecting defects after they occur rather than preventing their origin. Consequently, preventive engineering approaches, oriented towards eliminating process errors before they generate non-conforming parts, have become increasingly relevant (Psarommatis & Azamfirei, 2024).
Recent contributions in quality engineering have emphasized structured quality design methodologies, such as the implementation of Design for Six Sigma within Total Quality Management (TQM) frameworks (Li et al., 2018), as well as rigorous statistical modeling approaches to evaluate the reliability impact of manufacturing defects (Shang et al., 2023), reinforcing the discipline’s strong focus on systematic and analytical defect prevention strategies (Friederich & Lazarova-Molnar, 2024).
Preventive quality engineering has increasingly emphasized the integration of structured improvement methodologies and error prevention mechanisms in manufacturing systems. Recent research in quality engineering has explored the incorporation of error-proofing concepts into advanced quality frameworks, such as the integration of Poka-Yoke into Define–Measure–Analyze–Improve–Control (DMAIC) 4.0 and the analysis of measurement systems to improve defect prevention in modern production environments (Pongboonchai-Empl et al., 2025). Similarly, risk-based approaches, such as FMEA (Failure Mode and Effects Analysis) and RPN (Risk Priority Number) techniques, have been applied to assess and mitigate failures in human-machine interaction, reinforcing the importance of proactively identifying and controlling error sources in industrial processes (Ostadi & Masouleh, 2019).
Beyond framework-driven methodologies, research has proposed classifications and efficiency assessments of error-proofing solutions to support the systematic selection of preventive mechanisms in manufacturing contexts (Antonelli & Stadnicka, 2016), while more recent studies have examined the integration of Poka-Yoke systems in smart production environments to improve operational performance (Trojanowska et al., 2023).
Despite these contributions, the literature remains predominantly focused on conceptual frameworks, statistical modeling, or digital integration strategies (Friederich & Lazarova-Molnar, 2024; Trojanowska et al., 2023). There is relatively little empirical evidence detailing the mechanical design, implementation, and quantitative industrial validation of mechanically integrated error-prevention devices specifically targeting human errors (Antonelli & Stadnicka, 2016; Paolo et al., 2022). Few studies report measurable impacts on scrap reduction and OEE resulting from physically imposed setup control mechanisms in turning environments (Martinelli et al., 2022; Velásquez et al., 2022). This gap highlights the need for experimentally validated engineering solutions that translate error prevention principles into mechanically integrated interventions with demonstrable operational impact.
Based on the identified research gap, this study addresses the following research question: How can a mechanically integrated error-proofing mechanism contribute to the reduction of setup-related defects and the improvement of operational performance in CNC turning environments?
Accordingly, this study investigates whether a low-cost mechanically integrated error-proofing mechanism can effectively prevent incorrect center drill positioning during setup operations while improving process reliability and reducing scrap generation.
It contributes to the literature in three main ways. First, it provides empirical industrial evidence on the implementation of a mechanically integrated error-proofing mechanism in CNC turning operations, an area still relatively unexplored in existing research. Second, unlike previous studies that focused predominantly on conceptual frameworks, digital monitoring systems, or inspection-based approaches, this work investigates a physically constrained error prevention mechanism designed to eliminate setup errors at the source. Third, the study quantitatively assesses the operational impact of the proposed solution through measurable industrial performance indicators, including scrap reduction and OEE, thus strengthening the practical and managerial relevance of Poka-Yoke applications in machining environments. Accordingly, the study proposes that reducing setup variability through a mechanically integrated error-prevention mechanism may contribute to lower scrap generation and improved operational performance in CNC turning environments.
Therefore, this study aims to investigate the operational impact of a mechanically integrated error-proofing mechanism designed to prevent incorrect center drill positioning during CNC lathe setup operations. The proposed solution is implemented and validated in an industrial environment using quantitative performance indicators, including scrap rate and OEE. By combining practical implementation with measurable operational outcomes, this study advances current knowledge on low-cost error-prevention mechanisms in machining systems. This study suggests that physically constrained solutions may improve process reliability in manufacturing environments.
The remainder of this paper is structured as follows: Section 2 presents the literature review, Section 3 describes the research methodology, Section 4 presents the industrial case study and Poka-Yoke implementation, Section 5 reports the results, Section 6 discusses the findings, and Section 7 concludes the study.
2. Literature review on Poka-Yoke systems
In some industrial contexts, companies prioritize sophisticated monitoring or inspection technologies that may increase system complexity and implementation costs, while simpler mechanically integrated error-prevention mechanisms remain underutilized (Paolo et al., 2022; Prasad et al., 2020; Saurin et al., 2012; Vinod et al., 2015).
Poka-Yoke refers to error-proofing mechanisms designed to prevent operational mistakes or immediately detect them before they evolve into defects or process failures. Unlike inspection-based quality approaches that identify nonconformities after production, Poka-Yoke systems aim to eliminate errors at their source through preventive and often mechanically integrated error-proofing mechanisms integrated into the production process (Pötters et al., 2018; Rahardjo et al., 2023).
Although various approaches, such as SPC (Statistical Process Control), Six Sigma, FMEA, and digital monitoring systems, contribute to quality improvement and defect reduction, many of these methods still rely on inspection activities, statistical monitoring, operator interpretation, or post-processing corrective actions (Helena et al., 2022; Fontalvo Herrera et al., 2024; Ostadi & Masouleh, 2019). In contrast, mechanically integrated Poka-Yoke systems act directly on the source of error generation, physically restricting incorrect operations before defects occur (Pötters et al., 2018; Rahardjo et al., 2023). This characteristic makes such mechanisms particularly relevant in machining environments with repetitive setup operations and high sensitivity to positioning errors (Paolo et al., 2022; Vinod et al., 2015). However, despite the growing interest in intelligent and digitally integrated systems for error prevention (Helena et al., 2022; Schmidt et al., 2023), relatively few studies provide empirical industrial validation of low-cost mechanical Poka-Yoke devices with measurable operational impacts on scrap reduction and OEE improvement (Martinelli et al., 2022; Velásquez et al., 2022).
Recent studies have further broadened the discussion on improving manufacturing performance through complementary perspectives. Schmidt et al. (2023) proposed a production system for the automotive industry integrating Industry 4.0 elements to enhance operational performance, while Helena et al. (2022) demonstrated how interoperable data extraction and smart manufacturing technologies can reduce human error and improve decision-making in CNC machining environments. Similarly, Fontalvo Herrera et al. (2024) emphasized the contribution of Six Sigma metrics to evaluating production system performance and supporting quality improvement initiatives. Together, these studies reinforce the growing emphasis on digital technologies, production systems, and statistical quality management as drivers of manufacturing excellence within contemporary manufacturing systems.
From a broader operations management perspective, recent research has also highlighted the value of integrating established continuous improvement approaches with emerging digital technologies (Luiz et al., 2025), while industrial case studies continue to demonstrate the effectiveness of Lean-based interventions in improving operational performance (Silvestre et al., 2022). However, comparatively little attention has been paid to mechanically integrated error prevention mechanisms, which physically prevent setup errors at their source in machining operations. Aiming to fill this gap, the present study investigates how a mechanically integrated Poka-Yoke device can complement existing quality improvement approaches by directly preventing human-induced setup errors.
2.1. Differences between errors and defects
It is critical to understand how errors and defects differ in various situations, from product development to service delivery. Defects can be avoided when the error is promptly identified and corrected. Making the distinction between these two concepts and approaching them appropriately is crucial to obtaining satisfactory results. Hiroyuki Hirano highlights that errors can have several origins (Franciosi et al., 2019; Hofinger, 2018; Klages & Zaeh, 2023; Nallathambi et al., 2023; Tommelein, 2019) such as human, material, methodological, or informational origins.
By recognizing the different sources of errors in production processes and collecting data on problems encountered, organizations can adopt more effective approaches in situations of non-compliance and significantly improve their processes. Hiroyuki Hirano saw the need to implement new error prevention methods and process improvements to ensure improved operational availability and customer satisfaction (Hofinger, 2018; Klages & Zaeh, 2023). If no feedback corrects or eliminates the errors described in Table 1, they can easily become defects. Producing defective parts is not only a loss for the customer but also the producer and is associated with extremely high costs, such as inspection costs, rework, and customer complaints (Hofinger, 2018; Klages & Zaeh, 2023; Sheikhalishahi et al., 2019; Torres et al., 2021).
Hofinger (2018), Sheikhalishahi et al. (2019) and Torres et al.(2021) presented structured classifications and preventive approaches covering different sources of failures. However, the proposed solutions remain predominantly generic and are not operationalized into implementation mechanisms adapted to specific industrial contexts. For example, recommendations such as continuous training or predictive maintenance may contribute to error reduction, but additional implementation strategies may be required to address context-specific operational conditions and improve practical applicability.
The most common causes associated with defects are predominantly related to processes and the product. The use of Poka-Yoke mechanisms or systems is essential for detecting errors in a timely manner. Defects can be classified as having process failures, which is when an operational or procedural failure occurs due to process errors, inaccuracies in operations, or inaccuracies. All these causes can result in a final product that does not meet established quality standards and remains unfinished. The Poka-Yoke systems, as well as the devices associated with them, become an added value for preventing errors throughout the process, thus avoiding long stops and unnecessary tasks (Kumar et al., 2022; Paolo et al., 2022; Vinod et al., 2015).
2.2. Poka-Yoke classifications
Shingo classifies Poka-Yoke systems according to the techniques and mechanisms used for error detection. Detection methods refer to the way devices identify when an error has occurred (Belu et al., 2015; Saurin et al., 2012). According to Figure 1, the first main category, contact method, is when devices encounter the part, for positioning, identifying unevenness, or ensuring sizing. The second category, the “Fixed-Value” method is when it performs counts, precise number of repetitions or movements, or automatic counting of the number of machined parts. The third category is the “Motion-Step” method which detects anomalies, failures, or delays in movement sequence operations, avoiding unnecessary movements by operators. A Poka-Yoke can be proactive when it prevents defects and is related to the inspection at source, or reactive when they do not prevent the occurrence of defects, since inspection is only carried out after the process. Poka-Yoke nature can be classified as physical, functional, or symbolic. Poka-Yoke function can be prevention if devices prevent an error before it becomes a defect, can be a control method when an error occurs, and the process is stopped and immediately corrected, and can be a warning method when the detection of an error or anomaly is reported to the operator through a warning signal, normally associated with a buzzer (audible alarm) or Andon (light signaling system), or shutdown method when an error is detected, the operation is stopped immediately (Saurin et al., 2012).
3. Research methodology
This section presents the methodological approach adopted to evaluate the implementation of a mechanically integrated error-proofing mechanism in CNC turning operations. The study was developed through an industrial case study strategy combining process observation, root cause analysis, device implementation, and operational performance evaluation using quantitative production indicators. Figure 2 summarizes the structured methodological framework adopted throughout the study, enabling a systematic evaluation of the impact of the mechanically integrated error-proofing mechanism on process reliability and operational performance.
3.1. Research design
This study adopted a single-case study methodology to evaluate the operational impact of a mechanically integrated error prevention mechanism designed to prevent setup-related errors in CNC turning operations. The case study approach is particularly appropriate for investigating contemporary phenomena in their real-world industrial context, allowing for a thorough understanding of operational processes and implementation conditions (Yin, 2025). Consequently, this methodological approach was deemed suitable for evaluating the implementation and operational impact of the proposed mechanism under real-world industrial operating conditions.
A pre-implementation and post-implementation comparative approach was used to evaluate the effectiveness of the proposed solution. The analysis compared historical production data collected before the implementation of the Poka-Yoke device with operational data obtained after its integration into the production process. The evaluation focused on assessing the impact of the proposed solution on process reliability and operational performance using quantitative industrial indicators.
The unit of analysis for this study was the setup operation associated with the positioning of the center drill in the OP10 and OP20 CNC turning operations of the Shaft X production line. The research was conducted in an industrial environment within the automotive parts sector, involving repetitive machining operations with high sensitivity to setup accuracy and process stability.
3.2. Methodological framework
The research followed a structured methodological framework composed of sequential stages, as illustrated in Figure 2. The framework combined direct process observation, root cause analysis, collaborative technical evaluation, solution development, industrial implementation, and operational performance evaluation. The use of direct observation allowed for the identification of practices related to configuration, operator interactions, and process variability under real production conditions, providing multiple sources of evidence adopted in this industrial case study (Yin, 2025). This approach enabled the systematic identification, implementation, and validation of setup-error prevention mechanisms under real industrial operating conditions.
Finally, the operational performance of the process was evaluated through a comparative analysis of production indicators before and after the implementation of the Poka-Yoke mechanism. The methodology allowed for the systematic evaluation of the effectiveness of the proposed solution in preventing errors, reducing setup-related defects, and improving operational reliability.
3.3. Data collection procedure
Data collection was carried out using historical production records, operational monitoring data, quality control reports, and direct observations performed during production activities across 14 CNC machines operating in three production shifts. The analysis considered accumulated production data throughout 2023 as the reference period and compared these results with the first two production campaigns conducted after the implementation of the Poka-Yoke device in 2024.
The analysis focused on setup operations associated with center drill positioning in the OP10 and OP20 CNC turning stages, selected due to the recurrent occurrence of positioning inconsistencies, center drill breakage, and dimensional nonconformities identified during preliminary process analyses. The research also included qualitative observations of the process involving setup procedures, operator interaction with the equipment, tool positioning practices, and production interruptions associated with setup inconsistencies.
The combination of documentary evidence, operational records, and direct observation increased the robustness of the evidence by enabling the triangulation of multiple data sources, in accordance with established principles of case study research (Yin, 2025). To strengthen the validity of the case study findings, the identified root causes were corroborated through multiple sources of evidence, including direct process observations, dimensional inspections, Coordinate Measuring Machine (CMM) measurements, and collaborative technical evaluation conducted by a cross-functional team. The effectiveness of the proposed mechanism was subsequently validated through a comparative analysis of operational performance indicators before and after implementation under real production conditions.
3.4. Performance evaluation criteria
The effectiveness of the proposed Poka-Yoke solution was evaluated using two key operational performance indicators: scrap rate and OEE. These indicators were selected because scrap directly reflects quality losses associated with setup-related defects, whereas OEE enables the assessment of broader operational impacts associated with availability, performance, and quality dimensions.
The scrap rate was used to assess the reduction in non-conforming parts associated with machining defects related to setup. OEE was used to evaluate the broader operational impact of the implemented solution, considering improvements related to machine availability, operational performance, and process quality. The comparative evaluation between the pre- and post-implementation periods enabled the identification and quantification of operational improvements associated with the adoption of the mechanically integrated error-prevention mechanism.
The following section presents the industrial context and the implementation process of the proposed mechanically integrated Poka-Yoke solution.
4. Industrial case study and Poka-Yoke implementation
This section presents the industrial application of the proposed mechanically integrated Poka-Yoke solution in a CNC turning production environment. First, the industrial context and the analyzed production system are presented, followed by the identification and analysis of the setup-related problem affecting operations OP10 (Operation 10) and OP20 (Operation 20). Subsequently, the development and implementation of the proposed error-proofing mechanism are presented, emphasizing its role in standardizing center drill positioning and improving operational stability during setup activities.
4.1. Industrial context and production system
The analyzed production system manufactures compressor shaft components used in automotive air conditioning systems. The shaft component, shown in Figure 3, is responsible for transmitting mechanical energy within the compressor system. Due to its functional importance and dimensional requirements, the machining process demands high operational precision and setup stability.
4.2. Problem identification and process analysis
The analyzed production line operates across three shifts and includes fourteen CNC lathes dedicated to shaft X machining operations. The study focused on operations OP10 and OP20, which presented recurrent setup-related inconsistencies affecting scrap generation and operational performance. The production line operated with an OEE target of 85%. Historical operational analyses performed during the diagnostic phase are presented in the Pareto chart in Figure 4, which identified setup-related losses and scrap generation as critical factors affecting operational performance.
Although in terms of OEE losses, scrap only represents the third-largest loss at 1.18%, it is important to highlight its greater impact on operational costs. Scrap represents a direct waste of raw materials. In machining environments, scrap also implies losses associated with machine occupation time, energy consumption, and tooling resources. During the last year, the OP10 and OP20 of the shaft X line accumulated a total of 1.18% of scrap, with this rejection being characterized by several defects, as shown in Figure 5.
Dimensional inspections were systematically performed during production using standardized measuring procedures and dedicated quality-control equipment, as illustrated in Figure 6. These inspections enabled the identification of recurrent dimensional deviations associated with setup inconsistencies in operations OP10 and OP20.
The flowchart presented in Figure 7 serves as a visual guide for understanding the essential steps of the shaft production process. Figure 7 summarizes the shaft production process and highlights the setup and machining stages associated with operations OP10 and OP20, where recurrent setup-related inconsistencies were identified.
Operations OP10 and OP20 involve turning, drilling, and finishing procedures requiring precise center drill positioning during setup activities. As shown in Figure 8, both operations present high sensitivity to setup consistency, particularly regarding center drill positioning consistency and drilling depth control.
Figure 9 illustrates the spindle and turret systems used in the CNC lathes. Tool installation is manually performed in the turret fixation system, making setup consistency dependent on operator intervention, and increasing susceptibility to positioning errors.
Preliminary dimensional inspections were performed after machining operations to verify conformity with specified tolerances. The measurements illustrated in Figure 10 enabled the identification of recurrent deviations associated with setup inconsistencies.
Selected samples were subsequently evaluated through CMM measurements to validate dimensional conformity and support root cause identification.
Due to the absence of automatic positioning mechanisms, center drill positioning was performed manually during setup operations. The lack of a standardized shank insertion depth generated inconsistent drill positioning, resulting in shallow or excessive drilling, center drill breakage, and dimensional nonconformities. As shown in Figure 11, variations in shank insertion directly affected drilling depth and process stability, confirming setup inconsistency as the main source of defects.
Center drill with low amount of shank into the clamp in (a), and (b) with large amount of shank. Source: Authors.
Therefore, the absence of a standardized center drill positioning mechanism was identified as the critical root cause linking setup variability, dimensional deviations, center drill breakage, and scrap generation. This diagnosis was supported by dimensional inspections, CMM analyses, and direct observations performed during setup activities, which consistently associated drill positioning variability with the observed defects.
4.3. Development of the Poka-Yoke device
The recurrent breakage of center drills caused by incorrect positioning resulted in defective parts, workflow interruptions, and setup instability. Although alternative technologies such as automatic tool changers could potentially reduce setup variability, their implementation was considered economically unfeasible due to the high acquisition and integration costs.
To ensure quality and process stability, a cross-functional team involving academic researchers, production management, engineering staff, quality specialists, technicians, and maintenance personnel was established to develop a low-cost error-prevention solution. Through brainstorming sessions and direct observations during setup activities, the team identified inconsistencies in center drill positioning and non-uniform shank insertion depth as the main causes of misalignment and drill breakage.
Several fixation alternatives, including magnetic, hydraulic, pneumatic, and mechanical concepts, were evaluated considering implementation cost, maintenance requirements, operational simplicity, and compatibility with the existing machining infrastructure. Due to their complexity and investment requirements, non-mechanical alternatives were discarded.
The mechanical concept was selected due to its lower implementation complexity, compatibility with existing equipment, absence of auxiliary systems, and capability to physically constrain setup execution. The selected concept directly addressed the identified root cause by transforming an operator-dependent setup activity into a physically constrained operation, thereby eliminating dependence on operator judgment during center drill positioning.
The resulting solution consisted of a mechanically integrated preventive Poka-Yoke device designed to standardize center drill positioning during setup operations. The device presents a cylindrical geometry incorporating a groove that physically constrains shank insertion depth into the collet, operating as a contact-based preventive mechanism.
By allowing only the predefined insertion depth, the device eliminates excessive or insufficient shank penetration, ensuring repeatable drill positioning and preventing setup-related defects and center drill breakage, as shown in Figure 12. According to the classification presented in Section 2, the proposed solution can be categorized as a contact-based, preventive, and physical Poka-Yoke mechanism.
Poka-Yoke device in (a) technical drawing; (b) showing mounting with center drill; (c) fixing the center drill. Source: Authors.
4.4. Industrial implementation
Prior to full-scale implementation, several center drill positioning tests and setup reviews were conducted to validate the applicability of the proposed device under production conditions. The tests confirmed stable and aligned center drill positioning during fixation procedures and indicated a reduction in setup adjustment time, as operators no longer needed iterative positioning corrections within the collet. In addition, the first machined parts consistently presented hole depth deviations within the specified tolerance range (±100 μm), indicating satisfactory dimensional stability during startup operations. Following validation, multiple devices were distributed across the three production shifts to support standardized implementation. The implementation covered all CNC lathes associated with OP10 and OP20 across the three production shifts.
The implementation enabled direct control of center drill penetration depth, drilling length, and hole positioning consistency. By establishing fixed shank insertion limits, the Poka-Yoke device ensured repeatable drilling conditions and reduced the occurrence of excessive or insufficient penetration depths. Consequently, the integrity of the center drills was preserved, reducing premature wear, avoiding unnecessary replacements, and minimizing damage to machined parts.
The proposed solution improved process reproducibility by standardizing center drill positioning independently of operator variability across the three production shifts. The reduction in offset corrections contributed to greater machining consistency and operational stability, particularly throughout the service life of the center drills (approximately 1100 parts). This repeatability contributed to maintaining uniform quality levels and dimensional conformity throughout production. The implementation was extended to the setup activities associated with operations OP10 and OP20 across the analyzed production environment, enabling standardized application of the proposed mechanism under real multi-shift operating conditions.
The proposed mechanism establishes a direct physical constraint between setup execution and tool positioning, converting operator-dependent adjustments into a standardized setup condition. Consequently, the solution contributed to reducing setup variability and improving the operational performance indicators evaluated in the following section. The operational impacts were subsequently evaluated through a comparative analysis between pre-implementation and post-implementation production data. The effectiveness of the implementation was assessed based on setup stability, dimensional conformity, scrap reduction, and OEE evolution.
5. Results analysis
This section presents the operational impacts of the implemented Poka-Yoke mechanism based on the comparative analysis between pre-implementation and post-implementation production campaigns. The evaluation focused on OEE evolution and scrap reduction in operations OP10 and OP20.
5.1. OEE evolution
To ensure the effectiveness of the Poka-Yoke device, a comparative analysis was conducted on the results obtained from the first two production campaigns of shaft X after the implementation of the solution, compared to the accumulated production data from 2023 used as the pre-implementation reference period. This evaluation focused on the key performance indicators established for project monitoring: OEE and the scrap rate. The implementation of the Poka-Yoke device increased OEE from 85% in the pre-implementation period to 90% during the first two production campaigns after implementation, corresponding to an improvement of 5 percentage points. These campaigns corresponded to the first available production periods after full industrial deployment of the proposed mechanism. Figure 13 illustrates the OEE evolution before and after implementation in operations OP10 and OP20.
The OEE improvement can be directly associated with the reduction in setup variability achieved through the physical constraint introduced by the Poka-Yoke device. The standardization of center drill positioning reduced iterative adjustments during setup activities, contributing to shorter changeover periods and improved equipment availability. In addition, the reduction in positioning-related defects minimized rework and process interruptions, positively affecting process quality and operational stability.
5.2. Scrap reduction
Scrap rate was used as the second operational indicator to evaluate the effectiveness of the proposed mechanism. The comparison between pre-implementation and post-implementation periods showed a reduction in rejection levels from 1.18% to 0.56%, corresponding to a reduction of 52.5%. Figure 14 indicates that defects associated with auxiliary machining point positioning were practically eliminated after implementation, while roughing and finishing defects related to positioning inconsistencies also exhibited lower frequencies. Other defect categories, including broken inserts, dents, scratches, and top-surface defects, also presented improvement tendencies, although with lower magnitude.
Pareto Diagram - Cumulated % of scrap defects in shaft X (Trial 1 & Trial 2). Source: Authors.
The reduction in scrap can be directly associated with the physical standardization imposed by the Poka-Yoke device. By eliminating variability in center drill insertion depth, the mechanism reduced dimensional deviations and prevented the occurrence of positioning-related defects during setup operations. Dimensional stability during implementation was also supported by startup inspections, which maintained hole depth deviations within ±100 μm tolerance limits.
Similar operational improvements associated with Poka-Yoke implementation have been reported in previous studies (Martinelli et al., 2022; Velásquez et al., 2022), reinforcing the practical effectiveness of preventive error-proofing mechanisms. However, unlike previous studies predominantly reporting qualitative improvements, the present study quantified the operational impact through scrap and OEE indicators.
Figure 15 illustrates the evolution of overall scrap levels before and after implementation, evidencing the reduction trend obtained during the first production campaigns. The observed reduction trend across the first production campaigns suggests improved process stabilization after implementation, although the obtained value remained slightly above the established target level. Although the rejection rate remained slightly above the established target of 0.5%, the observed reduction demonstrates the positive operational impact of the implemented mechanism.
The simultaneous improvement in OEE and scrap indicators suggests that the operational gains were not restricted to quality performance, but also contributed to broader process stabilization effects associated with setup standardization. This result suggests that setup standardization effects may propagate beyond local quality improvements and influence broader operational stability conditions. These findings are further interpreted and contrasted with previous studies in the discussion section.
5.3. Limitation of results
Although the observed results indicate positive operational impacts, the analysis was based on the first two production campaigns after implementation. Due to the industrial case study nature and the limited number of post-implementation campaigns, statistical variability analyses were not performed. The absence of statistical inference analyses is associated with the exploratory industrial validation nature of the study and the limited number of post-implementation production campaigns available for evaluation. Therefore, long-term stability and performance evolution should be confirmed through future production monitoring. The results should therefore be interpreted as industrial validation evidence rather than statistical generalization. Furthermore, the findings are limited to a single industrial environment and should therefore be interpreted considering the operational characteristics of the analyzed production system.
6. Discussion
The implementation of the mechanically integrated error-proofing mechanism contributed to reducing setup variability and improving operational performance in CNC turning operations. The observed improvements in OEE and scrap indicators suggest that physically constrained setup standardization mechanisms can positively affect process stability, quality performance, and equipment availability in machining environments. From a quality engineering perspective, the proposed mechanism shifts error management from inspection-based correction to source-level prevention. Rather than detecting defects after production, the device physically constrains setup execution and prevents incorrect center drill positioning before machining operations begin. This characteristic aligns the proposed solution with proactive quality assurance principles and reinforces the role of mechanically integrated Poka-Yoke mechanisms as preventive interventions in machining environments.
The presented findings complement recent studies that addressed improving manufacturing performance from different operational perspectives. Previous studies have demonstrated the benefits of integrating Industry 4.0 elements into production systems to enhance operational performance (Schmidt et al., 2023), the application of interoperable digital infrastructures to support CNC manufacturing and reduce human error (Helena et al., 2022), and the use of Six Sigma metrics to evaluate and monitor the quality of the production system (Fontalvo Herrera et al., 2024). The improvements observed in this study are consistent with the common goal of enhancing manufacturing performance, although they were achieved through a fundamentally different intervention strategy based on the prevention of physical errors.
Unlike most of these studies, which are mainly based on digital technologies, production system redesign, or statistical quality assessment, the proposed solution acts directly on the source of process variability, physically preventing incorrect tool placement during setup. Instead of detecting deviations after they occur or supporting decision-making through digital information, the mechanically integrated Poka-Yoke eliminates the possibility of setup errors themselves. This distinction reinforces the complementary role of physical error-prevention mechanisms within broader quality-improvement strategies.
One of the main operational effects observed after implementation was the improvement of setup consistency and the reduction of variability associated with center drill positioning. The setup process became more standardized and operationally stable, while reducing the need for iterative positioning adjustments. The consistent shank insertion depth was another issue addressed through the proposed Poka-Yoke mechanism. This control contributed to more stable and consistent positioning conditions, supporting machining precision and dimensional conformity. From an operational perspective, the observed improvement mechanism may be interpreted as a sequential effect in which physical constraint promoted setup standardization, reducing positioning variability and defect occurrence, which subsequently contributed to greater process stability and improved OEE performance.
Another important operational effect was the reduction of defective parts and broken tools. With improved positioning consistency, the occurrence of machining-related errors was reduced, contributing to lower defect generation. This improvement contributed positively to process efficiency while reducing material losses and costs associated with scrap generation.
A further operational effect was the improvement in equipment availability. By reducing setup variability and iterative positioning adjustments, machines could operate under more stable conditions and with fewer operational interruptions. Operational interruptions and setup correction requirements were reduced, contributing to more stable production conditions and overall productivity improvement.
Although few studies have reported mechanically integrated Poka-Yoke implementations in CNC machining environments, the present study contributes to the discussion regarding the balance between digital quality systems and low-cost engineering interventions. While recent studies have increasingly explored smart manufacturing, digital monitoring, and AI-assisted quality systems, the obtained results indicate that physically integrated preventive mechanisms may still provide substantial operational improvements with lower implementation complexity and investment requirements. However, previous case studies reported in the literature suggest operational and competitive benefits associated with Poka-Yoke implementation.
The findings obtained in the present study are consistent with these contributions, particularly regarding defect prevention and operational improvement. Nevertheless, the current study differs by focusing on a mechanically integrated setup-error prevention mechanism specifically developed for CNC machining environments. Unlike inspection-based or digitally integrated approaches, the proposed solution acts directly on the source of setup variability through physical constraint.
Martinelli et al. (2022) and Velásquez et al. (2022) presented studies with the greatest similarity to the present study. Martinelli et al. (2022) integrated Poka-Yoke with deep learning in assembly environments and reported productivity improvements. In contrast, the present study focused on machining operations and evaluated performance through OEE and scrap indicators. Velásquez et al. (2022) reported qualitative operational improvements associated with reduced adjustments and improved process quality. Similarly, the present study observed operational gains associated with setup standardization; however, unlike that work, quantitative evidence was provided through OEE and scrap indicators. These comparisons reinforce the distinct contribution of the present study, which provides industrial validation of a mechanically integrated preventive mechanism in CNC machining environments. In contrast to previous studies predominantly focused on assembly systems, digital integration, or qualitative assessments, the proposed approach combines physical error prevention with quantitative operational evaluation through OEE and scrap indicators.
From a managerial perspective, the proposed solution demonstrates that low-cost mechanically integrated interventions can provide measurable operational benefits without requiring major modifications to existing production systems. This characteristic may facilitate industrial adoption, particularly in SMEs and production environments with limited investment capacity. The obtained findings also suggest that physically constrained error-prevention mechanisms may complement digital quality systems rather than replace them, contributing to hybrid quality management strategies combining preventive engineering interventions with monitoring and data-driven approaches.
The present findings extend previous evidence by providing quantitative industrial validation of a mechanically integrated Poka-Yoke mechanism in CNC machining environments through scrap and OEE indicators. Consistent with previous industrial applications of Poka-Yoke, the present case study achieved a scrap reduction of 52.5%, decreasing from 1.18% to 0.56%. This case study additionally provided quantitative operational evidence through OEE monitoring, reporting an improvement of 5 percentage points, increasing from 85% to 90%.
Although developed for center drill positioning in CNC turning operations, the design principle of physically constraining setup execution may be transferable to other machining activities involving repetitive manual fixation procedures and sensitivity to positioning errors. Although statistical generalization is limited due to the single-case study design, analytical transferability may exist for machining environments characterized by repetitive setup activities, manual fixation procedures, and sensitivity to positioning variability.
The transferability of the proposed design principle suggests that physically constrained error-prevention mechanisms may also support setup standardization in other machining environments beyond CNC turning operations. From a quality engineering perspective, the study suggests that physically constrained error-prevention mechanisms may represent an intermediate layer between conventional mechanical controls and emerging cyber-physical quality systems. This perspective extends the current discussion on Poka-Yoke by positioning mechanically integrated interventions not only as isolated operational devices but also as components supporting the transition from conventional preventive controls toward hybrid quality architectures in smart manufacturing environments. Therefore, the findings suggest that mechanically integrated Poka-Yoke mechanisms can contribute to reducing setup-related defects and improving operational performance in CNC turning environments through physical standardization of setup activities.
This complementary perspective is also aligned with recent discussions on integrating traditional continuous improvement approaches with Industry 4.0 technologies (Luiz et al., 2025). Similarly, industrial case studies published have demonstrated that Lean-based interventions remain highly effective in improving operational performance in different manufacturing environments (Silvestre et al., 2022). The present study expands on this knowledge by demonstrating that mechanically integrated Poka-Yoke devices continue to represent a robust and cost-effective alternative, particularly in machining operations where setup quality remains heavily dependent on operator actions.
The present study presents some limitations that should be acknowledged. The results were obtained from a single industrial environment and evaluated during the first two production campaigns after implementation. Therefore, although positive operational impacts were observed, long-term stability and broader industrial applicability require further validation under different machining conditions and production contexts. Future studies may evaluate the applicability of the proposed mechanism in different machining operations and industrial contexts to further assess its transferability and long-term effects.
7. Conclusions
This study investigated the implementation of a mechanically integrated error-proofing mechanism designed to prevent incorrect center drill positioning during setup operations in CNC turning environments. The industrial validation indicated positive operational impacts, including a scrap reduction from 1.18% to 0.56% and an OEE improvement from 85% to 90%, suggesting improvements in process reliability, setup stability, and operational performance.
These findings indicate that physically constrained error-prevention mechanisms can provide measurable operational benefits in machining environments through low-complexity interventions, reinforcing the potential of preventive engineering approaches to improve process robustness at the source of error generation. Therefore, the findings suggest that mechanically integrated Poka-Yoke mechanisms can effectively reduce setup-related variability and support operational performance improvement in CNC turning environments.
From a theoretical perspective, this study contributes to the literature on Poka-Yoke, process improvement, and preventive quality engineering by providing empirical industrial evidence on the design, implementation, and quantitative validation of a mechanically integrated error-prevention mechanism in CNC machining operations. The findings reinforce the relevance of source-level error prevention strategies and extend current knowledge by complementing inspection-based, conceptual, and digitally oriented approaches, demonstrating how physically constrained interventions can translate preventive principles into measurable industrial outcomes.
From a methodological perspective, the study also contributes by demonstrating an industrial validation pathway combining problem diagnosis, root-cause identification, solution development, industrial implementation, and operational assessment of a Poka-Yoke mechanism through quantitative performance indicators. This approach may support future industrial studies seeking to evaluate preventive quality interventions in machining environments. The proposed validation pathway may also support the replication and evaluation of preventive engineering interventions in other machining environments.
From a practical perspective, the proposed mechanism indicates that low-cost mechanically integrated interventions can provide measurable operational benefits without requiring substantial modifications to existing production systems. The obtained results suggest that physically constrained preventive mechanisms may complement digital quality systems, supporting hybrid quality management strategies in machining environments, particularly in SMEs and production systems with limited investment capacity.
This contribution is particularly relevant for manufacturing environments seeking operational improvements through scalable and low-investment solutions. More broadly, the findings suggest that mechanically integrated error-prevention mechanisms should not be interpreted as alternatives to digital quality systems, but rather as complementary interventions within hybrid quality management strategies. Beyond the specific industrial application, such mechanisms may represent a broader approach for reducing operator-dependent variability and supporting process stabilization in manufacturing environments.
The study presents some limitations. The evaluation was conducted in a single industrial environment and considered only the first two production campaigns after implementation. Therefore, the findings should be interpreted considering the specific operational characteristics of the analyzed production system and the limited observation horizon after implementation.
Future studies may evaluate the applicability of the proposed mechanism in other machining operations and industrial contexts, investigate long-term operational effects, and explore the integration of mechanically constrained error-prevention mechanisms with Industry 4.0 technologies, digital monitoring systems, and AI-assisted quality approaches, further assessing their contribution to resilient and hybrid quality management systems in smart manufacturing environments.
Data availability
The survey data is unavailable due to confidentiality issues.
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How to cite this article:
Alcácer, V., Ferreira, R., Mendes, D., & Costa, J. (2026). Mechanical error-proofing in CNC turning industrial validation of a Poka-Yoke device for scrap reduction. Production, 36, e20260035. https://doi.org/10.14488/1980-5411.20260035
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Financial Support
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
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Ethical Statement
The case study was conducted with the authorization of the participating company. All information obtained during the study was treated confidentially and used exclusively for research purposes. No personally identifiable or sensitive data were collected, and the identity of the company has been anonymized where appropriate to protect confidentiality. The research was conducted in accordance with the applicable ethical principles and complied with the General Data Protection Regulation (GDPR) (EU Regulation 2016/679).
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