Manufacturers of all types know that they need to innovate and bring products to market more quickly. But the stakes are significantly higher when it comes to making complex products.
Take U.S.-based chip giant NVIDIA. It began as a major supplier of chips to the PC gaming industry, and has since expanded into healthcare and transportation, including self-driving cars. Those moves significantly raised the stakes when it comes to the cost of product failure. A defective chip in a self-driving car can result in a far more serious outcome than a chip failing in a video game controller.
The key to avoiding product failure and ensuring quality management lies in the data. Today, businesses are generating more of it from more sources than ever — from testing equipment, product sampling, quality audits and customer relationship management applications to customer feedback, call centers and root cause analyses. The challenge lies in harnessing this flood of data, identifying what’s most relevant, and turning it into insights that drive knowledgeable decision-making and processes that are more efficient, accurate and safer.
But it’s not enough just to collect data. Organizations hoping to be successful in today’s competitive environment also need tools and insights for analyzing operations before problems occur.
A significant number of companies have already integrated data analytics into their quality initiatives, and the trend is only projected to grow as more executives see the value of a data-driven strategy. How, then, do you best make use of the data you have, to make informed decisions and address quality problems as early in the product lifecycle as possible?
The key is to use the data to uncover essential information, identify patterns and trends, and adjust processes to accelerate resolutions and improve outcomes. Innovations include predictive analytics, which draws on historical data to identify patterns and predict future outcomes, and prescriptive analytics, which provides a solution to the problem identified. Each type of analytics is making it easier to derive valuable insights that enable companies to prevent problems from occurring, or take corrective actions sooner when they do.
The first question that companies need to answer is: What are the problems that can be solved with data? Legacy IT infrastructures and a reliance on paper-based practices, for example, are not only unable to keep pace with the digital requirements of customers and the supply chain, but they also lack the transparency required to communicate problems effectively. Consider the following ways in which data analytics can be applied throughout the production process:
Designing for quality from the outset. The quality process begins in the product-design phase. Information gleaned from data analytics ensures that you’re choosing the right suppliers to procure the highest-quality raw materials, based on stakeholder and end-user feedback, and identifying potential product defects in each design iteration. Data analysis from previous products and designs can be extremely useful for guiding new design decisions, bringing a higher level of clarity to many aspects of the design process, from the complexity of the system requirements to the impact of design changes.
Creating a single source of truth. By capturing the many sources of quality data from testing, auditing, investigations and the like, you can create a single source of truth that’s centralized. This requires removing the silos between disparate departments, such as Finance, HR and Engineering, and democratizing the data to make it available to all stakeholders for analysis and immediate decision-making.
Analyzing deviations to identify root causes. By capturing and analyzing real-time data from many sources, you can get a 360-view of your processes. That allows you to isolate deviations and create corrective action plans to address the root cause of a problem, whether it’s a one-time nonconformance or systemic issue. All stakeholders can stay informed and collaborate in real time, to resolve issues quickly and ensure continuous improvement.
Using statistical process controls. Data analytics plays an essential role in monitoring, measuring, controlling and improving processes, to ensure specification-conforming products with greater efficiency and less waste. While there will always be variation in a process, data analytics enables you track trends in real time, allowing you to control the quality and be proactive before the variation drifts beyond pre-defined limits.
Conducting document control. Once a decision is made based on analytics, you can update documentation to reflect new processes and technical specifications. You can prevent inadvertent use of obsolete processes or procedures by using the most current version of approved documentation throughout the organization. Further, you can train employees based on revised processes and procedures, while tracking adoption of new documentation, processes and procedures.
Gartner defines analytics as the “autonomous or semi-autonomous examination of data and content using sophisticated techniques and tools to discover deeper insights, make predictions, or generate recommendations.” With the proper analytical tools, businesses can tap their wealth of data to obtain insights that help them answer the million-dollar questions: What’s going on in the business? What should we do about it? How do we do it? And what can we do next?
David Isaacson is vice president of ETQ.
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