Driving Data-Driven Decision Velocity for Better Manufacturing Quality in 2024
It’s no longer a secret that data is fast becoming one of the most critical business assets of enterprises, so they’re generating and collecting more data than ever before. Yet, in the quest to store up data riches, many manufacturers now are grappling with how to invest that wealth wisely – how best to derive actionable insights from data, which translates into business success.
The problem is that data is often stored in a variety of different systems and repositories, making it difficult to access, manage and integrate it so that it can provide deeper, richer and more contextual insights. Siloed data is not easily and quickly accessible to the people who need it most. Yet, in 2024 the promise of a unified view of data from key sources across the enterprise may just become a reality, making data more visible and actionable enterprise wide.
According to McKinsey Global Institute, data-driven organizations are 23 times more likely to acquire customers, and six times as likely to retain customers than those that are not.
To be considered a data-driven organization in 2024, manufacturers will need to provide this unified view of data, making it more easily accessible regardless of the system in which it is housed. This will be made possible thanks to open systems that enable seamless integration via application programming interfaces (APIs) and cloud-native systems accessible from anywhere.
The New Manufacturing Mandate: Decision Velocity at Scale
Integration of data into a connected ecosystem, is a key enabler of data velocity, a term coined by Doug Laney, a Gartner analyst, in 2001. It means the ability to swiftly generate, process, and transfer data within an environment. While swift and integrated data is essential, however, it’s no longer enough. Manufacturers need to boost decision quality and velocity at scale. They need a means to receive deeper insights and faster and more accurate decisions from the data. It must enable real-time decision-making, proactive response to changing market conditions and awareness and analysis of issues that can impact quality. Decision velocity, driven by data analytics, is now at the epicenter of the quality management universe.
Decision velocity is enabled by data analytics and it is transforming the way organizations ensure product, corporate and operational excellence. It plays a pivotal role in optimizing processes, minimizing defects and enhancing overall quality. Consider some of the following scenarios where data analytics plays a starring role:
Real Time Monitoring: One of the primary contributions of data analytics to quality management in manufacturing is the ability to gather, analyze and interpret vast amounts of data generated throughout the production process. By harnessing data from sensors, machinery and other sources, manufacturers can gain valuable insights into the performance of equipment and the quality of outputs. This real-time monitoring enables quick identification of deviations from quality standards, facilitating prompt corrective actions to prevent defective products from reaching the market.
Defect Forecasting: Predictive analytics, a type of data analytics driven by AI, leverages historical data and uses advanced algorithms to predict the likelihood of defects occurring in products or the production process. By understanding the likelihood, preventive measures can be implemented, reducing the likelihood of defects and ensuring a higher level of quality in the final product. As a result, the cost of poor quality can be minimized, while the pace of continuous improvement accelerates. Predictive analytics also can help to optimize maintenance schedules, leading to improved equipment effectiveness and reduced downtime for unexpected maintenance issues.
Root Cause Analysis: As a central component to leading quality management systems (QMS), root cause analysis employs data analytics to identify patterns and correlations within documentation and other types of data. When defects occur, manufacturers can turn to the data to understand the underlying causes, whether they are related to raw materials, equipment malfunctions or human error, to determine the best solution to address the root cause.
Supplier Management: Data analytics can provide visibility into the entire production ecosystem, including suppliers, logistics, and inventory management to ensure that only high-quality raw materials are used in the production process. Additionally, data analytics can optimize inventory levels, reducing the risk of overstocking or shortages to reduce costs and ensure on-time production processes.
Empowering the Connected Worker
While data and the digital tools that bring it to life are essential, humans will remain in the driver’s seat when it comes to decision velocity and quality outcomes. This encompasses humans across the manufacturing enterprise. From workers on the shop floor to executives in the C-suite, access to relevant, integrated data, as well as the training and digital tools to analyze it for greater decision velocity, is the real secret to quality management and improved worker safety.
Effective quality management – fueled by integrated enterprise-wide data from across the manufacturing ecosystem and advanced data analytics – is a tremendous lever to enabling greater decision-velocity, corporate agility and better brands.
Author: Vick Vaishnavi is CEO of ETQ, a leading provider of enterprise quality management systems for some of the world’s largest manufacturers. As CEO, Vick is responsible for leading the overall business strategy and operations of ETQ. As a seasoned business and technology leader, Vick has extensive experience guiding software and other technology firms to industry leadership and business growth.
For more information: www.etq.com
