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data-component="mdc-c-module-wrapper" data-module-theme="dark" data-module-background="deep-blue" data-module-category="AnchoredHero" class="ArticleDefault_mck-c-article-default__SfYQE"><style></style><div data-component="mdc-c-background-image" class="mdc-c-bg-image___GJdv1_2734c4f background-image-article-default ArticleDefault_mck-c-article-default__parallax-container__fZ7Iq"></div><div class="ArticleDefault_mck-c-article-default__gradient__Uu21n"></div><div class="ArticleDefault_mck-c-article-default__wrapper-content__XOe9C"><div class="mck-o-container"><div class="mck-o-container--wrapped mck-o-container--mobile-spacing mdc-u-grid mdc-u-grid-col-lg-12"><div data-component="mdc-c-content-block" class="mdc-c-content-block___7p6Lu_2734c4f mdc-u-grid-col-lg-start-1 mdc-u-grid-col-lg-span-10"><div><h1 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_2734c4f mdc-u-ts-2 mck-u-animation-slide-down ArticleDefault_mck-c-article-default__heading__bv6rL"><div>Smart quality in advanced industries</div></h1></div><div data-component="mdc-c-description" class="mdc-c-description___SrnQP_2734c4f mdc-u-ts-10 mck-u-animation-blur-in-800 ArticleDefault_mck-c-article-default__description__sjoe9"><div><time datetime="2021-03-15T00:00:00Z">March 15, 2021</time> | Article</div></div></div></div></div></div></div><div class="mck-o-container"><div class="mck-o-container--wrapped mck-o-container--mobile-spacing mdc-u-grid mdc-u-grid-gutter-xxl"><section data-layer-region="article-body-header" class="mdc-u-grid mdc-u-grid-col-md-12 mck-u-animation-blur-in-400 byline-share-container"><div class="mdc-u-grid-col-md-start-2 mdc-u-grid-col-md-end-8 mdc-u-grid-col-lg-start-3 mdc-u-grid-col-lg-end-8 mdc-u-ts-9"><span> </span><span class="AuthorsByLine_mck-c-authors-byline__author__XdrlD"><span class="AuthorsByLine_mck-c-authors-byline__no-wrap__Ced0j"><a data-component="mdc-c-link" href="/our-people/alvaro-carpintero" class="mdc-c-link-inline___7DRrt_2734c4f mdc-c-link-inline--secondary___YKoOK_2734c4f"><span class="mdc-c-link__label___Pfqtd_2734c4f">脕lvaro Carpintero</span></a>聽</span></span><span class="AuthorsByLine_mck-c-authors-byline__author__XdrlD"><span class="AuthorsByLine_mck-c-authors-byline__no-wrap__Ced0j">Ulrich Huber聽</span></span><span class="AuthorsByLine_mck-c-authors-byline__author__XdrlD"><span class="AuthorsByLine_mck-c-authors-byline__no-wrap__Ced0j"><a data-component="mdc-c-link" href="/our-people/evgeniya-makarova" class="mdc-c-link-inline___7DRrt_2734c4f mdc-c-link-inline--secondary___YKoOK_2734c4f"><span class="mdc-c-link__label___Pfqtd_2734c4f">Evgeniya Makarova</span></a><span> 聽</span></span></span><span class="AuthorsByLine_mck-c-authors-byline__author__XdrlD"><span class="AuthorsByLine_mck-c-authors-byline__no-wrap__Ced0j">Heiko Nick</span></span></div><div class="mdc-u-grid-col-md-start-8 mdc-u-grid-col-md-end-12 mdc-u-grid-col-lg-start-8 mdc-u-grid-col-lg-end-11 ArticleContent_mck-c-article-content__share-tools__kWRRw"></div></section><section class="mdc-u-grid mdc-u-grid-col-md-12 mck-u-animation-blur-in-400"><div class="mdc-u-grid-col-md-start-2 mdc-u-grid-col-md-end-12 mdc-u-grid-col-lg-start-3 mdc-u-grid-col-lg-end-11"><div data-component="mdc-c-description" class="mdc-c-description___SrnQP_2734c4f mdc-u-ts-5"><div class="mck-u-links-inline">Advanced-industries companies have the unique opportunity to implement an approach to quality that goes well beyond execution and compliance.</div></div></div></section><main data-layer-region="article-body" class="mdc-u-grid mdc-u-grid-gutter-xxl"><div class="mdc-u-grid mdc-u-grid-col-1 mdc-u-grid-col-md-12"><div class="mdc-u-grid-col-md-start-2 mdc-u-grid-col-md-end-12 mdc-u-grid-col-lg-start-3 mdc-u-grid-col-lg-end-11"><div class="mdc-o-content-body mck-u-dropcap"><div data-component="mdc-c-module-wrapper" data-module-theme="default" data-module-background="transparent" data-module-category="" class="DownloadsSidebar_mck-c-downloads-sidebar__iFmyt mck-o-xs-right-span"><div data-layer-region="downloads-right-rail"><h3 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_2734c4f mdc-c-heading--title___5qyOB_2734c4f mdc-c-heading--border___K8dj3_2734c4f"></h3><div><div data-component="mdc-c-link-container" class="mdc-c-link-container___xefGu_2734c4f"><a data-component="mdc-c-link" href="#/download/%2F~%2Fmedia%2Fmckinsey%2Findustries%2Fadvanced%20electronics%2Four%20insights%2Fsmart%20quality%20in%20advanced%20industries%2Fsmart-quality-in-advanced-industries.pdf%3FshouldIndex%3Dfalse" class="DownloadsSidebar_mck-c-downloads-sidebar__download-link__fPqFQ mdc-c-link___lBbY1_2734c4f" target="_self" data-layer-event-prefix="Download Link" data-layer-action="click" data-layer-report-type="" data-layer-file-name="smart-quality-in-advanced-industries" data-layer-report-name="smart-quality-in-advanced-industries>"><span data-component="mdc-c-icon" class="mdc-c-icon___oi7ef_2734c4f mdc-c-icon--size-md___yi5fA_2734c4f mck-download-icon"></span><span class="mdc-c-link__label___Pfqtd_2734c4f"> (5 pages)</span></a></div></div></div></div> <p><strong>Technological advancements</strong> have enabled a fundamentally new way of delivering quality. Under this approach, companies view the quality function as a partner and coach that delivers value, not just a business cost. This perspective helps them integrate quality and compliance into regular operations while enabling speed and effectiveness.</p>  <p>The smart-quality approach can be applied across multiple sectors, and a <a href="/industries/life-sciences/our-insights/smart-quality-reimagining-the-way-quality-works">recent article</a>聽described how some pharmaceutical and medical technology companies have implemented this framework. The best smart-quality use cases and technologies will vary by sector. When companies in advanced industries follow the smart-quality approach, leaders must adapt it to suit more complex and deep supply chains. They should also place greater emphasis on improving discrete manufacturing, building global service networks that address quality in the field, and complying with the regulators and standardization bodies that govern advanced industries. New technologies could reshape day-to-day quality operations across the value chain of advanced-industries players (see Exhibit 1 for select examples).</p> <div data-component="mdc-c-module-wrapper" data-module-theme="default" data-module-background="transparent" data-module-category="" class="mck-c-inline-module-container mck-o-md-center"><div class="mdc-u-grid mdc-u-grid-gutter-lg mdc-u-grid-col-sm-1 mdc-u-grid--align-start mdc-u-mb-3 GenericItem_mck-c-generic-item__sGwKL"><div data-component="mdc-c-content-block" class="mdc-c-content-block___7p6Lu_2734c4f mdc-u-grid-gutter-xs GenericItem_mck-c-generic-item__content__gq1m0"><div class="mck-c-eyebrow mdc-u-ts-10"><span> 1</span></div></div></div><div class="mck-u-inline-module-border-top mck-u-inline-module-border-bottom"><picture data-component="mdc-c-picture" class="Exhibit_mck-c-exhibit__image__pyIDm"><source media="(min-width: 768px)" srcSet="/~/media/mckinsey/industries/advanced%20electronics/our%20insights/smart%20quality%20in%20advanced%20industries/svgz-smartqualityadvancedindustries_ex1.svgz?cq=50&cpy=Center"/><img alt="Smart quality integrates new technologies across the entire value chain." src="/~/media/mckinsey/industries/advanced%20electronics/our%20insights/smart%20quality%20in%20advanced%20industries/svgz-smartqualityadvancedindustries_ex1.svgz?cq=50&cpy=Center" loading="lazy"/></picture></div><div class="mck-u-sr-only"></div></div> <h2>Generating value through smart quality</h2> <div data-module-category="" class="PullQuote_mck-c-pullquote__DbaQ5 mck-o-sm-left-span"><blockquote data-component="mdc-c-blockquote" class="PullQuote_mck-c-blockquote__6n21p PullQuote_mck-c-blockquote--hide-quotes__CpGpg mdc-c-blockquote mdc-c-blockquote--is-quotes___TozKo_2734c4f"><p>Smart quality has enabled organizations to reduce their total cost of quality by up to 50 percent.</p></blockquote></div> <p>Companies that have adopted the smart-quality approach have demonstrated that it improves quality far more than traditional measures. For example, smart quality has enabled organizations to reduce their total cost of quality by up to 50 percent. The smart-quality approach has five building blocks, all of which can generate value (Exhibit 2). As more of the building blocks get implemented, the impact compounds.</p> <div data-component="mdc-c-module-wrapper" data-module-theme="default" data-module-background="transparent" data-module-category="" class="mck-c-inline-module-container mck-o-md-center"><div class="mdc-u-grid mdc-u-grid-gutter-lg mdc-u-grid-col-sm-1 mdc-u-grid--align-start mdc-u-mb-3 GenericItem_mck-c-generic-item__sGwKL"><div data-component="mdc-c-content-block" class="mdc-c-content-block___7p6Lu_2734c4f mdc-u-grid-gutter-xs GenericItem_mck-c-generic-item__content__gq1m0"><div class="mck-c-eyebrow mdc-u-ts-10"><span> 2</span></div></div></div><div class="mck-u-inline-module-border-top mck-u-inline-module-border-bottom"><picture data-component="mdc-c-picture" class="Exhibit_mck-c-exhibit__image__pyIDm"><source media="(min-width: 768px)" srcSet="/~/media/mckinsey/industries/advanced%20electronics/our%20insights/smart%20quality%20in%20advanced%20industries/svgz-smartqualityadvancedindustries_ex2.svgz?cq=50&cpy=Center"/><img alt="Smart quality drives measurable impact across all building blocks." src="/~/media/mckinsey/industries/advanced%20electronics/our%20insights/smart%20quality%20in%20advanced%20industries/svgz-smartqualityadvancedindustries_ex2.svgz?cq=50&cpy=Center" loading="lazy"/></picture></div><div class="mck-u-sr-only"></div></div> <p>Here are representative case examples for <a href="/industries/industrials-and-electronics/our-insights/the-ceo-agenda-for-companies-in-advanced-industries">advanced industries</a>聽for the first four building blocks. The fifth building block primarily applies to pharma and medtech companies.</p>  <h3>Smart-quality controls</h3> <p>An aluminum-coil producer achieved 95 percent accuracy in visual-anomaly detection after just five days of training with approximately 1,000 anomaly-free images. The algorithm delivered human-interpretable pictures of the anomaly without requiring an exhaustive training data set with all possible defects. For each image analyzed, the model calculated a confidence score, allowing employees to focus their efforts on images with potential anomalies.</p> <p>Companies may also improve anomaly detection by using cobots鈥攔obots intended for direct interaction with humans鈥攖hat automatically handle parts and pass them to the next operator after successful inspection. McKinsey uses such hybrid systems in its Digital Capability Centers and experienced payback periods of less than six months.</p> <h3>Smart-quality assurance</h3> <p>One automotive OEM used advanced analytics to refine its entire product-development pathway and improve the right-first-time indicator for newly developed parts to more than 80 percent. The analytics model included data from 24 different sources on all factors鈥攂oth human behavior and mechanical processes鈥攖hat could have caused delays. Information from three years of product-development projects and analysis of 1,500 individual car components allowed the company to train the underlying algorithms. It then implemented several improvement initiatives, including those that involved scrutinizing open issues after virtual builds, enforcing stricter rules for virtual-build-process adherence, improving bill-of-materials management, identifying suppliers with low risk scores, and providing more comprehensive onboarding for new team members.</p> <div data-component="mdc-c-module-wrapper" data-module-theme="default" data-module-background="transparent" data-module-category="" class="mck-c-disruptor1up mck-o-md-center mck-u-inline-module-border-top mck-u-inline-module-border-bottom mck-u-screen-only" data-layer-region="disruptor-1up"><div class="mdc-u-grid mdc-u-grid-gutter-md mdc-u-grid-col-lg-12 mdc-u-grid-col-md-12 "><div class="mdc-u-grid-col-md-span-12"><header data-component="mdc-c-header" class="mdc-c-header"><div class="mdc-c-header__block___i1Lg-_2734c4f"><h3 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_2734c4f"><div>Would you like to learn more about our <a href="/industries/industrials-and-electronics/how-we-help-clients">Industrials & Electronics Practice</a>?</div></h3></div></header></div></div></div> <h3>Product and process mastery</h3> <p>By applying advanced analytics to field-quality information, an agriculture-equipment manufacturer reduced the time needed to identify systemic field issues by 45 percent. The analytics engine calculated daily updates of several algorithms involving approximately 450,000 warranty claims over the past five years, as well as other data, such as service tickets drawn up in dealer support. By conducting Weibull analyses, the company could extrapolate expected failure rates for nearly 7,000 critical-part numbers daily, allowing it to identify potential field-quality issues.<span class="FootNote_footnote-holder__tjRqy"><a aria-label="footnote" href="javascript:void(0);" class="FootNote_footnote-wrapper__AIRwL undefined FootNote_inactive__VZfCp" aria-describedby="d571d4f0-2b19-482e-882f-ca5bee39a6eb"><sup class="FootNote_footnotesup__e73z_">1</sup><span class="FootNote_notch-wrapper__b_5NS"><span class="FootNote_notch__omKtY"></span></span><span class="FootNote_tooltip__QtrbA mdc-u-mt-2"><span class="FootNote_footnote-content__r2OVl"><span id="d571d4f0-2b19-482e-882f-ca5bee39a6eb" aria-hidden="true" data-module-theme="light" class="FootNote_footnote-text__VjKgO mck-u-links-inline"> Weibull distributions are often used to assess product reliability and model-failure rates.</span></span></span></a></span> The company also considered other leading indicators, such as those based on data from heavy-duty cycle machines, and expert knowledge from specific dealers to anticipate issues earlier and acknowledge critical issues immediately after their first occurrence. The accelerated issue identification translated into a warranty-cost reduction of about 5 percent. The company will achieve additional value by using insights from the algorithm to prevent issues during product development.</p> <p>One steel supplier reduced its technical reject rate for a high-value product from 20 percent to nearly zero within four weeks. The company gathered and structured coil-process data, meter by meter, for more than 300 variables and 13 months of production in an end-to-end approach. It then applied several analytics methods, including decision trees, random forests, and gradient-boost models, to identify the three process parameters in the rolling mill and annealing furnaces that caused the issues. Heat maps, area charts, and correlation analyses allowed the company to visualize the insights gained and prevent issues from reoccurring.</p> <div data-component="mdc-c-module-wrapper" data-module-theme="default" data-module-background="transparent" data-module-category="" class="mck-c-disruptor1up mck-o-md-center mck-u-inline-module-border-top mck-u-inline-module-border-bottom mck-u-screen-only" data-layer-region="disruptor-1up"><div class="mdc-u-grid mdc-u-grid-gutter-md mdc-u-grid-col-lg-12 mdc-u-grid-col-md-12 "><div class="mdc-u-grid-col-lg-span-4 mdc-u-grid-col-md-span-5 Disruptor1Up_mck-c-disruptor1up__image___2Gc4"><picture data-component="mdc-c-picture"><img alt="The CEO agenda for advanced-industries companies" src="/~/media/mckinsey/industries/advanced%20electronics/our%20insights/the%20ceo%20agenda%20for%20advanced%20industries%20companies/the-ceo-agenda-standard-1536x1536.jpg?cq=50&mh=145&car=16:9&cpy=Center" loading="lazy"/></picture></div><div class="mdc-u-grid-col-lg-span-8 mdc-u-grid-col-md-span-7"><header data-component="mdc-c-header" class="mdc-c-header"><div class="mdc-c-header__block___i1Lg-_2734c4f"><h3 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_2734c4f"><div>The CEO agenda for companies in advanced industries</div></h3></div></header><div data-component="mdc-c-link-container" class="mdc-c-link-container___xefGu_2734c4f mdc-c-link-container--display-column___X0HDD_2734c4f mck-c-disruptor1up__content Disruptor1Up_mck-c-disruptor1up__content--links__VV4lE mdc-u-grid-gutter-md"><a data-component="mdc-c-link" href="/industries/industrials-and-electronics/our-insights/the-ceo-agenda-for-companies-in-advanced-industries" class="mdc-c-link-cta___NBQVi_2734c4f"><span class="mdc-c-link__label___Pfqtd_2734c4f">Read the article</span><span data-component="mdc-c-icon" class="mdc-c-icon___oi7ef_2734c4f mck-link-arrow-right-icon"></span></a></div></div></div></div> <h3>Smart-quality ways of working</h3> <p>An agriculture-equipment manufacturer digitized and automated cross-functional risk and maturity management in product development to reduce new product warranty costs by more than 10 percent. The digital dashboards automatically compiled data from different sources and visualized all maturity KPIs, including the warranty costs forecast at the component and vehicle level, across functions. By using a parameterized resource model for each function, the company was able to assign required project-team capacities at the outset of each project and plan capacity requirements for the portfolio of product-development projects. The company applied industry best practices and took a systematic approach to software testing and validation to help prevent code-based quality issues.</p> <hr/> <p>As the case examples show, a smart-quality approach can deliver multiple benefits to companies in advanced industries. In product development, advanced analytics and digitization allow companies to improve the right-first-time rate, accelerate time to market, and increase product maturity. These changes lead to higher customer satisfaction and lower warranty costs from the start of production. In manufacturing, visual-anomaly detection enables automated quality control with significantly increased effectiveness. In addition, advanced analytics allow fast and effective resolution of production problems and prevent their recurrence. Finally, artificial intelligence accelerates identification of field-quality issues and automated detection of their root causes along the value chain. For best results, companies that embark on the path to smart quality by pursuing use cases in these areas must make a sustained commitment to change. As they implement the five building blocks, both the quality function and the <a href="/industries/industrials-and-electronics/our-insights/organizing-for-speed-in-advanced-industries">organization as a whole</a>聽will achieve higher levels of agility and speed.</p></div><div class="container-placeholder"></div></div></div><div class="mdc-u-grid mdc-u-grid-gutter-xl"><section role="contentinfo" data-layer-region="article-about-authors" class="mdc-u-grid mdc-u-grid-col-md-12 AboutAuthor_mck-c-about-author__nRJzu"><div class="mdc-u-grid-col-md-start-2 mdc-u-grid-col-md-end-12 mdc-u-grid-col-lg-start-3 mdc-u-grid-col-lg-end-11"><h5 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_2734c4f mdc-c-heading--title___5qyOB_2734c4f mdc-c-heading--border___K8dj3_2734c4f mdc-u-align-center"></h5><div data-component="mdc-c-description" class="mdc-c-description___SrnQP_2734c4f mdc-u-ts-8 mck-u-links-inline mck-u-links-inline--secondary mdc-u-mt-5"><div><p><strong><a href="/our-people/alvaro-carpintero">Álvaro Carpintero</a></strong> is a partner in McKinsey’s Madrid office; <strong>Ulrich Huber </strong>is a senior expert in the Cologne office, where <strong>Heiko Nick</strong> is a partner; <strong><a href="/our-people/evgeniya-makarova">Evgeniya Makarova</a></strong> is a partner in the Chicago office.</p> <p>The authors wish to thank Miguel Angel Morán and Marion Duriez for their contributions to this article.</p></div></div></div></section><section class="mdc-u-grid mdc-u-grid-col-md-12 mck-u-screen-only"><div class="mdc-u-grid-col-md-start-2 mdc-u-grid-col-md-end-12 mdc-u-grid-col-lg-start-5 mdc-u-grid-col-lg-end-9"><h5 data-component="mdc-c-heading" class="mdc-c-heading___0fM1W_2734c4f mdc-c-heading--title___5qyOB_2734c4f mdc-c-heading--border___K8dj3_2734c4f mdc-u-align-center">Explore a career with us</h5><div data-component="mdc-c-link-container" class="mdc-c-link-container___xefGu_2734c4f 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href="/industries/industrials-and-electronics/our-insights/the-ceo-agenda-for-companies-in-advanced-industries" class="mdc-c-link-heading___Zggl8_2734c4f mdc-c-link___lBbY1_2734c4f"><div>The CEO agenda for companies in advanced industries</div></a></h6></div></div></div></div></div></div></div></main></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"locale":"en","dictionary":{},"sitecoreContext":{"route":{"name":"Smart quality in advanced industries","displayName":"Smart quality in advanced 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industries."},"displayDate":{"jsonValue":{"value":"2021-03-15T00:00:00Z"}},"body":{"value":"[[DownloadsSidebar]]\n\u003cp\u003e\u003cstrong\u003eTechnological advancements\u003c/strong\u003e have enabled a fundamentally new way of delivering quality. Under this approach, companies view the quality function as a partner and coach that delivers value, not just a business cost. This perspective helps them integrate quality and compliance into regular operations while enabling speed and effectiveness.\u003c/p\u003e\n[[Audio 1]]\n\u003cp\u003eThe smart-quality approach can be applied across multiple sectors, and a \u003ca href=\"/industries/life-sciences/our-insights/smart-quality-reimagining-the-way-quality-works\"\u003erecent article\u003c/a\u003e\u0026nbsp;described how some pharmaceutical and medical technology companies have implemented this framework. The best smart-quality use cases and technologies will vary by sector. When companies in advanced industries follow the smart-quality approach, leaders must adapt it to suit more complex and deep supply chains. They should also place greater emphasis on improving discrete manufacturing, building global service networks that address quality in the field, and complying with the regulators and standardization bodies that govern advanced industries. New technologies could reshape day-to-day quality operations across the value chain of advanced-industries players (see Exhibit 1 for select examples).\u003c/p\u003e\n[[Exhibit 1]]\n\u003ch2\u003eGenerating value through smart quality\u003c/h2\u003e\n[[Pullquote 1]]\n\u003cp\u003eCompanies that have adopted the smart-quality approach have demonstrated that it improves quality far more than traditional measures. For example, smart quality has enabled organizations to reduce their total cost of quality by up to 50 percent. The smart-quality approach has five building blocks, all of which can generate value (Exhibit 2). As more of the building blocks get implemented, the impact compounds.\u003c/p\u003e\n[[Exhibit 2]]\n\u003cp\u003eHere are representative case examples for \u003ca href=\"/industries/industrials-and-electronics/our-insights/the-ceo-agenda-for-companies-in-advanced-industries\"\u003eadvanced industries\u003c/a\u003e\u0026nbsp;for the first four building blocks. The fifth building block primarily applies to pharma and medtech companies.\u003c/p\u003e\n[[MostPopularArticles 5]]\n\u003ch3\u003eSmart-quality controls\u003c/h3\u003e\n\u003cp\u003eAn aluminum-coil producer achieved 95 percent accuracy in visual-anomaly detection after just five days of training with approximately 1,000 anomaly-free images. The algorithm delivered human-interpretable pictures of the anomaly without requiring an exhaustive training data set with all possible defects. For each image analyzed, the model calculated a confidence score, allowing employees to focus their efforts on images with potential anomalies.\u003c/p\u003e\n\u003cp\u003eCompanies may also improve anomaly detection by using cobots\u0026mdash;robots intended for direct interaction with humans\u0026mdash;that automatically handle parts and pass them to the next operator after successful inspection. McKinsey uses such hybrid systems in its Digital Capability Centers and experienced payback periods of less than six months.\u003c/p\u003e\n\u003ch3\u003eSmart-quality assurance\u003c/h3\u003e\n\u003cp\u003eOne automotive OEM used advanced analytics to refine its entire product-development pathway and improve the right-first-time indicator for newly developed parts to more than 80 percent. The analytics model included data from 24 different sources on all factors\u0026mdash;both human behavior and mechanical processes\u0026mdash;that could have caused delays. Information from three years of product-development projects and analysis of 1,500 individual car components allowed the company to train the underlying algorithms. It then implemented several improvement initiatives, including those that involved scrutinizing open issues after virtual builds, enforcing stricter rules for virtual-build-process adherence, improving bill-of-materials management, identifying suppliers with low risk scores, and providing more comprehensive onboarding for new team members.\u003c/p\u003e\n[[Disruptor1UP dis1]]\n\u003ch3\u003eProduct and process mastery\u003c/h3\u003e\n\u003cp\u003eBy applying advanced analytics to field-quality information, an agriculture-equipment manufacturer reduced the time needed to identify systemic field issues by 45 percent. The analytics engine calculated daily updates of several algorithms involving approximately 450,000 warranty claims over the past five years, as well as other data, such as service tickets drawn up in dealer support. By conducting Weibull analyses, the company could extrapolate expected failure rates for nearly 7,000 critical-part numbers daily, allowing it to identify potential field-quality issues.[[Footnote 1]] The company also considered other leading indicators, such as those based on data from heavy-duty cycle machines, and expert knowledge from specific dealers to anticipate issues earlier and acknowledge critical issues immediately after their first occurrence. The accelerated issue identification translated into a warranty-cost reduction of about 5 percent. The company will achieve additional value by using insights from the algorithm to prevent issues during product development.\u003c/p\u003e\n\u003cp\u003eOne steel supplier reduced its technical reject rate for a high-value product from 20 percent to nearly zero within four weeks. The company gathered and structured coil-process data, meter by meter, for more than 300 variables and 13 months of production in an end-to-end approach. It then applied several analytics methods, including decision trees, random forests, and gradient-boost models, to identify the three process parameters in the rolling mill and annealing furnaces that caused the issues. Heat maps, area charts, and correlation analyses allowed the company to visualize the insights gained and prevent issues from reoccurring.\u003c/p\u003e\n[[Disruptor1UP dis2]]\n\u003ch3\u003eSmart-quality ways of working\u003c/h3\u003e\n\u003cp\u003eAn agriculture-equipment manufacturer digitized and automated cross-functional risk and maturity management in product development to reduce new product warranty costs by more than 10 percent. The digital dashboards automatically compiled data from different sources and visualized all maturity KPIs, including the warranty costs forecast at the component and vehicle level, across functions. By using a parameterized resource model for each function, the company was able to assign required project-team capacities at the outset of each project and plan capacity requirements for the portfolio of product-development projects. The company applied industry best practices and took a systematic approach to software testing and validation to help prevent code-based quality issues.\u003c/p\u003e\n\u003chr /\u003e\n\u003cp\u003eAs the case examples show, a smart-quality approach can deliver multiple benefits to companies in advanced industries. In product development, advanced analytics and digitization allow companies to improve the right-first-time rate, accelerate time to market, and increase product maturity. These changes lead to higher customer satisfaction and lower warranty costs from the start of production. In manufacturing, visual-anomaly detection enables automated quality control with significantly increased effectiveness. In addition, advanced analytics allow fast and effective resolution of production problems and prevent their recurrence. Finally, artificial intelligence accelerates identification of field-quality issues and automated detection of their root causes along the value chain. For best results, companies that embark on the path to smart quality by pursuing use cases in these areas must make a sustained commitment to change. As they implement the five building blocks, both the quality function and the \u003ca href=\"/industries/industrials-and-electronics/our-insights/organizing-for-speed-in-advanced-industries\"\u003eorganization as a whole\u003c/a\u003e\u0026nbsp;will achieve higher levels of agility and speed.\u003c/p\u003e"},"isFullScreenInteractive":{"boolValue":false},"hideStickySocialShareBar":{"boolValue":false},"desktopID":{"value":""},"mobileID":{"value":""},"desktopURL":{"value":""},"mobileURL":{"value":""},"desktopPaddingPercentage":{"value":""},"mobilePaddingPercentage":{"value":""},"desktopOverrideHeight":{"value":""},"mobileOverrideHeight":{"value":""},"cerosOembedURL":{"value":""},"cerosRenderMode":{"targetItem":null},"cerosBackgroundColor":{"targetItem":null},"hideByLine":{"boolValue":false},"tableOfContentsTitle":{"value":"TABLE OF CONTENTS"},"accessStatus":{"targetItem":{"key":{"value":"RegisteredUsers"},"value":{"value":"Registered Users"}}},"articleType":{"targetItem":{"displayName":"Article"}},"hasSpecialReport":{"boolValue":false},"contentType":{"targetItem":{"displayName":"Article"}},"sourcePublication":{"targetItem":null},"externalPublication":{"value":""},"mobileReady":{"boolValue":true},"forClientsOnly":{"boolValue":false},"excludeFromClientLink":{"boolValue":false},"originalPublishDate":{"jsonValue":{"value":"2021-03-15T00:00:00Z"}},"footnotes":{"value":"\u003col\u003e\n \u003cli\u003e Weibull distributions are often used to assess product reliability and model-failure rates.\u003c/li\u003e\n\u003c/ol\u003e"},"contributoryPractice":{"targetItems":[{"displayName":"Industrials \u0026 Electronics"}]},"aboutTheAuthors":{"value":"\u003cp\u003e\u003cstrong\u003e\u003ca href=\"/our-people/alvaro-carpintero\"\u003e\u0026Aacute;lvaro Carpintero\u003c/a\u003e\u003c/strong\u003e is a partner in McKinsey\u0026rsquo;s Madrid office; \u003cstrong\u003eUlrich Huber \u003c/strong\u003eis a senior expert in the Cologne office, where \u003cstrong\u003eHeiko Nick\u003c/strong\u003e is a partner; \u003cstrong\u003e\u003ca href=\"/our-people/evgeniya-makarova\"\u003eEvgeniya Makarova\u003c/a\u003e\u003c/strong\u003e is a partner in the Chicago office.\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank Miguel Angel Mor\u0026aacute;n and Marion Duriez for their contributions to this article.\u003c/p\u003e"},"authors":{"targetItems":[{"template":{"id":"3FBABDB63A094F68B1F8E1BBE1BA124E","name":"Author"},"id":"704BAA09415640E895DF132A56BAC8D5","name":"Alvaro Carpintero","authorTitle":{"value":"脕lvaro Carpintero"},"description":{"value":""},"thumbnailImage":{"alt":"","src":null},"profile":{"targetItems":[{"standardImage":{"src":"/~/media/mckinsey/our people/alvaro carpintero/alvaro_carpintero_standard_profile_v2_1536x1152.jpg","alt":"脕lvaro Carpintero"},"emailLinks":{"value":"脕lvaro_Carpintero@mckinsey.com"},"linkedInUrl":{"value":""},"description":{"value":"Coleads the firm鈥檚 pharmaceutical and medical-products work, and helps companies in a variety of sectors with their operations transformations"},"locations":{"targetItems":[{"name":"Madrid","displayName":"Madrid"}]},"firmTitle":{"value":"Partner"},"thumbnailImage":{"alt":"脕lvaro Carpintero","src":"/~/media/mckinsey/our people/alvaro carpintero/alvaro_carpintero_headshot_v2_988x741.jpg"},"url":{"path":"/our-people/alvaro-carpintero"}}]}},{"template":{"id":"3FBABDB63A094F68B1F8E1BBE1BA124E","name":"Author"},"id":"459324C1F36E4325805991A908F561B6","name":"Ulrich Huber","authorTitle":{"value":"Ulrich Huber"},"description":{"value":""},"thumbnailImage":{"alt":"","src":null},"profile":{"targetItems":[]}},{"template":{"id":"3FBABDB63A094F68B1F8E1BBE1BA124E","name":"Author"},"id":"7CEA02EB6CC7472F8C430F31E78C5648","name":"Evgeniya Makarova","authorTitle":{"value":"Evgeniya Makarova"},"description":{"value":""},"thumbnailImage":{"alt":"","src":null},"profile":{"targetItems":[{"standardImage":{"src":"/~/media/mckinsey/our people/evgeniya makarova/evgeniya_makarova_profile_1536x1152.jpg","alt":"Evgeniya Makarova"},"emailLinks":{"value":" Evgeniya_Makarova@mckinsey.com"},"linkedInUrl":{"value":"https://www.linkedin.com/in/evgeniya-makarova/"},"description":{"value":"Serves pharmaceutical, medical-device, and consumer companies as well as regulators on operational improvement, transformation, quality, and compliance issues"},"locations":{"targetItems":[{"name":"Chicago","displayName":"Chicago"}]},"firmTitle":{"value":"Partner"},"thumbnailImage":{"alt":"Evgeniya Makarova","src":"/~/media/mckinsey/our people/evgeniya makarova/evgeniya_makarova_headshot_988x741.jpg"},"url":{"path":"/our-people/evgeniya-makarova"}}]}},{"template":{"id":"3FBABDB63A094F68B1F8E1BBE1BA124E","name":"Author"},"id":"D018527213C04F8A8839B6056DC4DC9F","name":"Heiko Nick","authorTitle":{"value":"Heiko Nick"},"description":{"value":""},"thumbnailImage":{"alt":"","src":null},"profile":{"targetItems":[]}}]},"nonPartnerAuthors":{"targetItems":[]},"interactiveToUse":{"targetItem":null},"enableArticleComponents":{"boolValue":false},"relatedArticles":{"targetItems":[{"sourcePublication":{"targetItem":null},"publicationSource":null,"externalPublication":{"value":""},"title":{"value":"Smart quality: Reimagining the way quality works"},"url":{"path":"/industries/life-sciences/our-insights/smart-quality-reimagining-the-way-quality-works"},"eyebrow":{"targetItem":{"name":"Article"}},"articleType":{"targetItem":{"name":"Article"}},"contentType":{"targetItem":{"name":"Article"}},"description":{"value":"Pharma and medtech companies that adopt a proactive approach to managing the quality function can generate value far beyond improved regulatory compliance."},"standardImage":{"src":"/~/media/mckinsey/industries/life sciences/our insights/smart quality reimagining the way quality works/smart-quality-reimagining-the-way-quality-works-1536x1536.jpg","alt":"Smart quality: Reimagining the way quality works"},"heroImage":null,"thumbnailImage":null},{"sourcePublication":{"targetItem":null},"publicationSource":null,"externalPublication":{"value":""},"title":{"value":"Organizing for speed in advanced industries"},"url":{"path":"/industries/industrials-and-electronics/our-insights/organizing-for-speed-in-advanced-industries"},"eyebrow":{"targetItem":{"name":"Article"}},"articleType":{"targetItem":{"name":"Article"}},"contentType":{"targetItem":{"name":"Article"}},"description":{"value":"Faced with the COVID-19 crisis, companies in advanced industries have become more nimble and accelerated decision making. 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