Data-driven manufacturing is an approach that relies on factual information and a set of Key Performance Indicators (KPIs) to guide decision-making in the production process. Instead of relying on guesses, anecdotal evidence, or intuition, manufacturers use data from various sources, including shop floor equipment, operators, and the supply chain, to inform their decisions.
Accurate data collection is essential for the success of data-driven manufacturing. Direct data collection from machines is, not only more efficient than manual collection, but it also minimises the risk of human error, leading to a more accurate and unbiased stream of data. The emergence of new technologies plays a crucial role in enabling manufacturers to collect and process data from their operations, including advanced analytics, Industrial Internet of Things (IIoT) devices, and automation systems. These have contributed to a surge in solutions capable of extracting real-time insights from manufacturing data.
Once data is collected, visualisation tools and user-friendly interfaces, for example, can make the data accessible and actionable for decision-makers, processing and displaying manufacturing data in a way that is easy for users to understand and consume. By basing decisions on accurate and real-time data, manufacturers can identify areas for improvement, optimise processes, and enhance their overall performance.
Being data-driven in manufacturing offers a range of benefits, including increased visibility, the application of AI and machine learning for advanced analytics, automation of data collection and decision-making, and significant cost savings through streamlined processes and waste reduction. These advantages collectively contribute to a more agile, efficient, and competitive manufacturing operation for the following reasons:
Data-driven manufacturing provides leaders with a holistic view of performance across the entire organisation, including individual asset performance and overall operations. This depth of insight enables decision-makers to identify opportunities for improvement. Decision-makers can use data to pinpoint specific areas for improvement, whether that’s addressing poor-performing shifts, reducing machine downtime, or resolving production bottlenecks. This targeted approach enhances the precision and effectiveness of decision-making.
AI and machine learning
Large datasets empower manufacturers to deploy machine learning algorithms for solving complex problems, such as for example, using unsupervised machine learning to detect anomalies in CNC machines. These analytical efforts open the door to advanced practices such as predictive maintenance, where machines are maintained proactively based on data-driven predictions rather than reactive responses.
The ability to run machine learning algorithms facilitates data-driven decision-making, allowing manufacturers to move beyond traditional approaches and embrace more sophisticated and forward-looking strategies.
Data-driven strategies support the automated collection of data through devices and software. This reduces manual efforts in the data collection process, ensuring accuracy and efficiency. As manufacturers progress in their analytical journey, they move towards predictive analytics. This shift enables the use of data for automated decision-making. The system can autonomously analyse data, predict potential issues or opportunities, and take actions to optimise processes without direct human intervention.
Data complements lean manufacturing principles by providing the necessary information to streamline production processes and minimise waste. Accurate and detailed data is essential for measuring production improvements and verifying that changes lead to actual cost savings. Data-driven insights contribute to identifying areas where processes can be optimised, leading to increased efficiency and reduced waste. This directly translates into cost savings and improved overall financial performance.
The value of data-driven decisions
Data-driven manufacturing relies on accurate data collection, advanced analytics, and technology to drive informed decision-making, reduce costs, and improve operational efficiency. The continuous advancement of technology in this field offers manufacturers new opportunities to optimise their processes and stay competitive in the ever-evolving manufacturing landscape.