The limitless forms of advanced technology are rapidly revolutionizing the ways companies do business and manufacture products.
Fremont, CA: Digital transformation and the fourth industrial revolution is not just a buzzword. The manufacturers, including the life sciences sector, embrace the new normal of using tech to achieve better data-driven operational efficiencies and speed to market. Quality 4.0 that aligns quality management with Industry 4.0 should be the priority for quality leaders. Connected systems and advanced technology solutions offer life sciences companies the digital tools they require to conduct in-line quality affirmation for closed-loop, proactive quality management.
Here are a few components of Quality 4.0 that will help to take the first step in adopting the Quality 4.0 approach.
Data – Data has been driving quality, and various updated regulations and standards highlight the importance of data-driven decision-making. But most of the market falls back in its adoption of real-time metrics, with only 22 percent of typical companies using real-time visibility of quality metrics in customer service, supplier performance, engineering, and manufacturing. Companies should seek ways to combine data from numerous systems to ensure accuracy and transparency.
Analytics – Above one-third of the market identifies the poor metrics as the primary barrier to accomplishing quality objectives. Typical quality metrics describe what happened, why it happened, and what can happen next, but they cannot prescribe what actions are to be taken, and insight only provided with the help of big data, AI, and machine learning (ML). Companies are well-served to create an analytics strategy after or concurrently with a data strategy; otherwise, the insights will be of little value.
Scalability – Several companies cite fragmented data sources and systems as a key challenge in achieving quality objectives. Without a global scale, the traditional quality and Quality 4.0 alike fail to reconcile processes, competencies, best practices, and the lessons learned throughout the company. Cloud computing is of particular value to scalability, along with data lake technologies, beginning by assessing the current scalability – or the ability to support the data volume, devices, users, and analytics on a global scale – of an in-house system.
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