Tool Overview:

The purpose of this tool is to help manufacturers prioritize their quality management initiatives. For each area of concern, you will receive specific CIMdata recommendations on what capabilities your organization should implement -- or areas you should focus on -- to best achieve your identified quality improvement goals. These recommendations will be made available for download at the end of the tool.


Instructions for Users:

For each quality issue listed, use the slider to identify the percentage of improvement your company is hoping to realize using PLM for closed-loop quality (CLQ).


Scrap and Rework

Reduce costs due to poor designs reaching manufacturing, faulty components, and/or process-related errors.

Closed-loop quality (CLQ) helps organizations keep errors and problems from migrating through the product design process into manufacturing and out into the field. Should an error or problem occur, CLQ facilitates a fast response time by providing early visibility to all key stakeholders. In addition, with CLQ, root cause analysis and corrective and preventative actions (CAPAs) are based on traceable product information. Approval and change processes are controlled, managed, and tracked throughout product development. It is also imperative to close the process loops so that changes, whether they originate in design or manufacturing, are resolved before products go to customers, manufacturing, sourcing, and service. Organizations can produce and deliver a common digital thread that links all product data, reports, and processes for the highest quality products.


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You indicated that you have low expectations for reducing scrap and rework. At this level, you may not believe that CLQ can help. However, our experience with other companies shows this is not the case. While you may not immediately see much room for improvement in these areas, there is always room for improvement so look at:

  • Further refine change processes by adopting a fast-track as well as normal-track process that will relieve burden on resources responsible for executing changes, but also assure that changes occur more quickly, at less cost, without causing additional changes to propagate due to delayed approvals. Look at a CM2 change process.
  • Improve design efficiency and introduce high-quality products by implementing a standard ISO 9001 design control and product realization process. By creating a pre-defined set of data states, you ensure all product development will follow a common process.
  • Efficiently control and distribute all documented policies and procedures, and track training. Extend document management best practices to standard operating procedures (SOPs) and provide management with real-time access to training records with a single source of truth for corporate IP on policy and process.
  • Efficiently control and manage internal and external audits by implementing an ISO 9001 quality governance process that ensures key corporate processes, requirements and directives are being followed.
  • Improve quality by correcting existing issues and preventing recurrence with a standard closed-loop CAPA process with predefined, but configurable, workflows and integrated BOM, parts, and documents. Link all quality inputs (design history, complaints, etc.).
  • Provide R&D with real time visibility into product performance and design issues by centralizing and managing all types of internal and external non-conformances, customer complaints and feedback.

You indicated that you have medium expectations for reducing scrap and rework. At this level, you most likely have change control procedures in place and, perhaps, even a way of managing "standard" parts and reusing previously designed parts. You should look at CLQ to:

  • Automate quality processes that follow the ISO 9001 Quality Management System (QMS) standard with a pre-configured set of PLM and quality processes.
  • Rely on PLM to provide a single source of product data across all product lines and all design activities.
  • Take a model-based engineering approach with predictive quality, risk and reliability (QR&R), integral to the digital product definition. Then use FMEA outputs directly against the CAD models to identify and predict the way the product could fail and its potential harm and hazard impact.
  • Combine early-stage requirements and FMEA to generate CTQs, product validation, and control plans.
  • This codification (CTQs, product validation, and control plans) can be used later with surveillance and monitoring processes (audit, non-conformance, customer experience management, and CAPAs), which should be built into the design tool and PLM suite. Real-world failures observed in manufacturing and field use will be immediately visible to the entire enterprise, including design engineers.

You indicated that you have high expectations for reducing scrap and rework. At this stage, the best way to accomplish CLQ is to have all the core product development and quality management processes embedded into one system. To do so, you should:

  • Define and Predict: We recommend you define your product using requirements linked directly to the design CAD definition, taking a model-based approach. Manage all artifacts in the PLM system. Be sure to perform risk and reliability analysis to identify areas of concern in design or manufacturing. Generate CTQs (Critical to Quality Characteristics).
  • Validate, Control, and Monitor: Use your product requirements to create validation tests and CTQs to generate control plans for manufacturing. Monitor product performance in manufacturing and service using failure mode effects analysis (FMEA) information in surveillance and corrective action systems.
  • Compare and Improve: Use monitoring data to compare predicted versus actual performance and create a product performance grade. Improvements and/or corrective actions should be sent to management for analysis, prioritization, and planning. Manage all changes accordingly via standardized change and configuration processes.
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Field Rejections, Returns, Allowances

Reduce costs that occur due to delivery of poor-quality products to customers, resulting in product returns and/or in-field fixes.

Getting proper and correct data into the manufacturing environment is critical to delivering better products to the field. Yet, field failures and returns can be caused by failures in manufacturing processes, such as poor inbound material quality or tool breakage. However, a primary cause of failed products is inaccurate data. Often, engineering teams and manufacturing teams are working in silos, creating barriers to building a complete digital thread from design concept to manufactured items. Especially important is providing closed-loop feedback by supporting the capture and use of the as-built and as-maintained digital twins during the post-manufacturing stage of a product's lifecycle. Many CLQ capabilities can help.


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You indicated that you have low expectations for reducing product returns and/or in-field fixes, your product development-to-manufacturing data flow is likely streamlined. Consider concentrating on the reverse--assuring that post-manufacturing data is brought back to the product design organization. Look at:

  • Closing quality loops by automatically recording any changes or issues in your PLM system that arise in manufacturing or in the field so they can be used to improve current and future products.
  • Make sure the PLM environment receives the as-built and as-maintained BOMs of critical products. Key groups throughout your organization can use these critical components of digital twins to improve service throughout the product's lifecycle.
  • Implement FMEA and Failure Reporting, Analysis and Corrective Action System (FRACAS) capabilities integrated with product data to facilitate problem resolution.

You indicated that you have medium expectations for reducing product returns and/or in-field fixes. At this level, you probably transfer basic product design information to manufacturing in a reasonable way, but activities such as change control and BOM transfer may not be as complete as you’d like. Look at the following areas to improve the quality of information flow to and from manufacturing:

  • Integrate change management processes across product development and manufacturing groups (and others) to assure that changes are resolved and approved before production begins. Make sure this process accommodates both fast-track (i.e., simple) changes and normal-track (i.e., complex-impact) changes.
  • Assure that the bills of material (BOMs) are complete and that, where the product has variants, the BOM variation is transmitted without requiring the entire manufacturing bill of material (MBOM) be recreated (a source of potential errors).
  • Provide manufacturing engineers with a view into the product design early so they can influence critical BOM structures and provide insight that helps improve designs and enable higher-quality manufacturing (reducing field failures).
  • Provide supplier access to product definitions and quality processes to decrease incoming material errors.

You indicated that you have high expectations for reducing product returns and/or in-field fixes. When you experience major problems related to product failures in the field, you are likely struggling with delivering high-quality data into the manufacturing environment. Consider making the following improvements to your product lifecycle management environment:

  • Make sure that product design data is controlled to assure that it is validated before it is released to manufacturing.
  • Use the PLM environment to grant manufacturing personnel direct, online access to all design data so they can properly and fully plan how to best build the product.
  • Provide the backbone for assuring that manufacturing and field errors are reported to all responsible organizations--all the way back to product design--so they can be fixed, and the fixes are implemented completely. Integrate FMEA tracking and resolution, virtual testing, and approvals processes. Use integrated simulation and analysis to help anticipate and mitigate failures before production based on expected field use cases.
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Regulatory Approval Time, Time to Audit Response

Decrease the cost and amount of time it takes to gain regulatory approval and/or submit a response to an audit of your products and industry.

A fundamental need when working in a regulated environment is finding all information required by regulators, when they require it, and with assurance that it is complete and correct. When done manually, this is a resource-intensive undertaking. Maintaining a complete digital thread of design data allows you to quickly find and recover the latest information. Plus, information that is part of a complete digital product data record (a PLM vault) and controlled by documented processes is almost always of higher quality and validity than data dispersed on paper or personal computer systems. Your organization can harness this data backbone across quality processes to produce highly accurate and trusted regulatory reports.


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You indicated that you have low expectations for decreasing the cost and amount of time it takes to gain regulatory approval and/or submit a response to an audit. At this level, you may not believe that CLQ can help -- or you may not see much room for improvement in these areas. However, our experience with other companies shows this is not the case. There is always room for improvement, so look to:

  • Use PLM to manage regulatory conformance documents, such as DMRs, and link them directly to data sources that help maintain the data in reports even as the data changes. Doing so helps avoid critical data errors and out-of-date data.
  • Create an automated link to, and updates of, governing regulations so that you do not miss critical updates and changes. Make the regulations available online so people may not work from "personal" copies that are likely to become out-of-date.
  • Link regulations to product data and processes.
  • Link product requirements to regulations, product elements, and product testing for cross-tracing when changes to any element occur.

You indicated that you have medium expectations for decreasing the cost and amount of time it takes to gain regulatory approval and/or submit a response to an audit. At this level, you most likely call upon some CLQ and PLM capabilities to help support regulatory compliance. Some additional support elements to consider are:

  • Linking product requirements to conformance reports to ensure that requirement changes do not adversely impact compliance.
  • Automatically downloading the newest governing regulations so that you do not miss critical updates and changes.
  • Making regulation documents available online so people don’t work from "personal" copies that are likely to become out-of-date.

You indicated that you have high expectations for decreasing the cost and amount of time it takes to gain regulatory approval and/or submit a response to an audit. If you expect a high benefit in this area, you operate in a regulated environment but likely find it difficult to, or fail to, report accurate, up-to-date information. You may be responding to regulators using extremely manual discovery and reporting processes. CLQ supported by PLM can streamline the process and assure accurate, defensible reporting and audit responses. Use CLQ to:

  • Manage conformance documents such as Device Master Records (DMRs) and link them directly to data sources that help maintain data in the reports even as the data changes. This helps avoid critical data errors and out-of-date data.
  • Create and track all kinds of nonconformance reports and link these directly to product design data and items, or manufacturing processes, to improve traceability.
  • Manage product data in a common repository (e.g., PLM) so you can control design changes to critical items using automated processes.
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Errors in Manufacturing

Reduce errors due to the delivery of poorly designed or low-quality products to manufacturing, or due to changes that are not completely resolved before product definitions are delivered to manufacturing.

Ideally, design errors should never reach manufacturing. However, when they do, mitigating them as early as possible is of paramount importance to closing quality loops. Errors found late, during manufacturing planning, and especially during production, can be enormously costly. That’s why it’s critical to use a system that assures errors and problems are caught early. PLM with CLQ helps more people look at designs earlier in the design process so they can influence them early enough to have a positive impact.


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You indicated that you have low expectations for reducing manufacturing errors. At this level, you most likely have improved communications between manufacturing and engineering. Concentrate now on enabling a true CLQ process to assure that errors are mitigated and documented on both the engineering and manufacturing sides of the organization, and with the supply chain. You should also use IIoT to monitor what is happening in your manufacturing environment so you can more quickly and proactively resolve issues as they arise. Some concrete steps your organization can take:

  • Implement a CLQ process that starts with the initial design process and engages product design, manufacturing engineering, simulation and analysis, validation testing, field testing, service, and others as appropriate. In other words, enable cross-functional collaboration to create a product development team enabled by a common data repository (e.g., PLM) and common processes (including configuration, change, and quality control).
  • Use an IoT platform to connect manufacturing information with the PLM environment so you can track what is going on and proactively resolve issues before they become critical problems.
  • Provide links to PLM tools so non-conformances can be created directly from the factory floor.
  • Improve supplier communication by allowing suppliers to directly see appropriate data in your PLM and quality systems that applies to what they supply. This eliminates delays communicating issues and closing the quality loop for supplied components and materials.

You indicated that you have medium expectations for reducing manufacturing errors. Companies with medium improvement expectations usually have some level of access and automated process communication between manufacturing and engineering. This can be improved by assuring that communication happens earlier and is bi-directional. To that end:

  • Automate connections between manufacturing solutions such as ERP and PLM to streamline data transfer – and enable it without human interaction – reducing data re-entry errors.
  • Allow appropriate information to flow from engineering to manufacturing and back to engineering. Data sharing in a controlled setting is critical to enabling a highly interactive environment in which problems and issues can be completely, accurately, and rapidly resolved.
  • Allow supplier information to be brought into internal systems so that it can be managed and acted upon in concert with all other product information.
  • Use PLM workflows to facilitate the creation of accurate and complete problem/issue reports that can be tracked as they are acted upon.
  • Measure/reduce the amount of rework due to poor master data quality and/or poor process quality, which is necessary for a successful release of first physical samples.
  • Measure/reduce the amount of engineering change and follow-up documentation.
  • Measure/reduce the amount of re-engineering work after design freeze and the total amount of resources needed to finish development projects.

You indicated that you have high expectations for reducing manufacturing errors. Companies with high expectations for benefits often separate manufacturing information from design information. This is one of the root causes of errors in manufacturing and late identification of problems in the factory. Integration, connectivity, and visibility of all product information early in the design process helps manufacturing engineers influence the design in ways that can dramatically reduce errors and improve manufacturing quality. Recommendations at this stage are to:

  • Improve communication between the factory and product design. To do this, implement a PLM environment that provides cross-functional teams with easy access to real-time product data.
  • Implement a proper change management process by linking PLM workflows to both design and manufacturing information.
  • Promote closed-loop change approval and design approval processes that ensure all relevant stakeholders in that particular area of the product provide approval before production.
  • Measure/reduce the amount of rework due to poor master data quality and/or poor process quality, which is necessary for a successful release of first physical samples.
  • Measure/reduce the amount of engineering change follow-up documentation.
  • Measure/reduce the amount of re-engineering work after design freeze and the total amount of resources needed to finish development projects.
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First Sample Approval in Manufacturing

Reduce the cost of not achieving first sample approval for production.

Organizations can expect a high first-time yield when products are developed in a quality-controlled environment and are tracked and changed using well-defined and controlled processes under a consistent validation regimen. This directly enables faster time to manufacture and thus drives sales (i.e., faster time to cash).


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You indicated you have low expectations for reducing the cost of not achieving first sample approval for production. Even if you don’t anticipate many quality issues, continue to refine the critical interactions between product engineering and manufacturing. This will help ensure that the product is designed to be built and that issues are resolved as quickly and completely as possible. You can achieve this by tightly linking manufacturing and engineering systems and processes to improve communication and remove the chance for human error. Specifically:

  • Integrate data transfers and processes to operate bi-directionally between manufacturing and product engineering.
  • Enable a strong change management and approval process after production release. This ensures changes in manufacturing are directly and immediately known to product engineering where they can be comprehensively resolved so they won't reoccur.
  • Ensure that manufacturing engineering is involved from the start of the product design process. They should have access and review product requirements early enough to assure that products can be manufactured as specified.

You indicated you have medium expectations for reducing the cost of not achieving first sample approval for production. When you expect a medium level of benefit, your first-time sample approval rate is likely "not good enough" to satisfy quality standards. Focus on enabling improved integration, connectivity, and visibility of product information, both to and from manufacturing. To that end:

  • Automate data movement from product engineering to manufacturing systems as much as is practical. This usually requires a tighter level of integration between engineering systems, such as PLM, and manufacturing systems. Take advantage of the integrations provided by the systems’ vendors.
  • Use IIoT capabilities to track early manufacturing issues and allow exploration of root causes through shared issue reporting and resolution processes.

You indicated you have high expectations for reducing the cost of not achieving first sample approval for production. This implies that your first-time yield is not nearly as robust as you would like. You can improve this yield through properly developed and maintained work processes and the data those processes use, change, and control. Specifically:

  • Use a PLM workflow to support the processes used to move information bi-directionally between design and manufacturing so that quality issues can be resolved rapidly and completely.
  • Modify workflows to automate as much data sharing as possible, reducing human errors when data is moved manually or re-entered into manufacturing systems.
  • Assure that change management is automated across the enterprise, and that required reviews and approvals occur before production for all outstanding changes.
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Reduce Design Errors

Reduce the cost and number of design errors that impact new products.

Unresolved design errors are a major source of problems in manufacturing, field delivery, and service. Ideally, your organization will find, resolve, and document all errors during design – before the product moves to production. Unfortunately, this is not always the case and the result is poor-quality products that interrupt manufacturing or cause expensive re-manufacturing and repairs.


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You indicated you have low expectations for reducing the cost and number of design errors. At this level you probably control your data and processes in PLM. Look at advanced areas for further improvement:

  • Create both fast- and normal-track change workflows so that simple changes are resolved quickly and do not propagate additional changes while awaiting resolution.
  • Link (i.e., allocate) requirements to design elements so they can be traced when changes occur. This assures that a change to product design does not violate a requirement, and that changes to requirements can be evaluated so all parts of the product are changed to support the requirement.
  • Place requirements under change control and approval.
  • Link requirements to testing to close the loop on validation of designs and changes.
  • Assure that issues found in products in the field are resolved in product designs so that the same issues do not reoccur.

You indicated you have medium expectations for reducing the cost and number of design errors. At this level, you may already be using PLM to manage product design data, but how are you managing requirements and changes that naturally arise during the design cycle? Be sure to:

  • Manage and validate requirements in the PLM environment as part of the product record instead of using tools such as Microsoft Excel.
  • Close the loop on change management so that whenever a change is discovered, it is placed in a process to be resolved, tracked, and regularly reported on until it is completely resolved and approved for design use or manufacturing.
  • Implement configuration management so that issues and changes are made across all potential configurations of a product.

You indicated you have high expectations for reducing the cost and number of design errors. This points to problems with data management, control, and linking. In environments where uncontrolled data may be changed without prior approval and validation testing, the change can result in an undesirable outcome. To greatly reduce design errors:

  • Manage design data and processes in PLM—this invariably improves product design quality and allows errors to be discovered and managed throughout their resolution.
  • Create and manage change processes in PLM workflows where the workflow can be linked directly to the data being changed. Change processes should be formal, with documented validations and approvals.
  • Link product data to simulation, analysis, and physical testing.

As you make plans to mature your approach to closed-loop quality (CLQ), we hope you find value in these recommendations across 6 key metrics. Based on our experience with manufacturers across industries, you can quickly move up the maturity scale by implementing these proven best practices.

If you would like to discuss your CLQ initiative or see a demo of PTC’s Windchill quality management software, get in touch with one of PTC’s PLM specialists today.