Over the years, manufacturers have relied on data to gain a competitive edge. However, one of the significant transformations happening today revolves around the way data is gathered. Previously, companies would deploy technicians to physically move about the facility, checking gauges, filling out forms, and painstakingly recording machine operation and maintenance history. These methods were not only laborious but also prone to errors and inaccuracies, making them ineffective for informed decision-making.

The advent of IoT devices and sensors has revolutionized how manufacturers leverage data and analytics. By connecting equipment and operations, companies can now tap into a wealth of information that was previously untapped. As a result, businesses have begun adopting digital software and connected devices to reduce the labor associated with manual data collection and documentation. Apart from easing the burden on workers, these technological advancements also enhance data fidelity, thereby amplifying the power of analytics and enabling the creation of more precise models.

The Evolution of Analytics in Manufacturing

Commonly referred to as the Manufacturing Analytics Journey, manufacturers navigate through multiple stages in their pursuit of predictive and prescriptive strategies.

• Data: The initial step involves the collection of data for descriptive analytics, serving as a foundation for uncovering what transpired.
• Information: Converting this data into diagnostic analytics transforms it into meaningful information, enabling manufacturers to comprehend the reasons behind specific events. A manufacturing analytics solution can facilitate this process.
• Understanding: With a sufficient amount of information gathered, a deeper understanding of processes can be attained. Statistical models, utilizing predictive analytics, can then forecast potential future occurrences.
• Knowledge: As more accurate models are developed, data evolves into knowledge, and prescriptive analytics can address the crucial question of what actions should be taken."

While the urge to connect everything and rush through the process may arise, it is crucial to first define clear objectives and establish benchmarks to monitor enhancements in performance. It is essential to determine the required data and quantity of data necessary for the transition from descriptive to prescriptive analytics. At the initiation of this journey, it is advantageous to establish a unified platform for all data collected. Furthermore, ensure that all stakeholders, including devices, individuals, and vendors, are provided appropriate access to this platform.
Real-time data and monitoring can provide precise information, aiding in the establishment of benchmarks, attainment of valuable statistical measures, and prompt notification of any changes compared to manual or unconnected devices. Although these steps require time, each offers its own set of advantages. One of the key benefits of utilizing analytics is the ability to predict future outcomes with a noteworthy degree of accuracy.

Benefits and Applications of Predictive Analytics in Manufacturing

With the advent of connected real-time devices, manufacturers now have access to a vast amount of data points. This wealth of information enables them to predict the remaining production time or the number of pieces that can be manufactured before encountering a failure. Unlike traditional maintenance plans that rely on average estimates, predictive analytics utilizes this data to accurately determine when parts need replacement or maintenance. By implementing such a system, manufacturers can significantly reduce unplanned downtime and effectively eliminate the need for costly and unnecessary maintenance services. This approach, known as Predictive Maintenance Analytics, revolutionizes the manufacturing industry by leveraging data and advanced analytics to optimize maintenance processes and enhance operational efficiency.

Predictive Maintenance Analytics

Use Case: Mitigating Downtime, Minimizing Tool Failure, and Streamlining Maintenance Demands

Embracing the concept of predictive maintenance offers manifold advantages. By employing data collection techniques, organizations gain actionable insights to accurately anticipate maintenance requirements instead of relying on assumptions. Consequently, this approach maximizes equipment uptime, empowering managers to proactively plan essential maintenance activities or implement necessary adjustments before any catastrophic failure occurs.
The efficacy of predictive analytics improves exponentially with the accumulation of substantial data and the subsequent identification of correlations. A noteworthy example involves the discovery of a direct link between tool failure and the amplification of equipment amperage. While monitoring amperage posed challenges, the same information could be acquired by enabling a feature on the equipment's software dashboard to capture spindle load data.

Quality Analytics

Use Case: Alerts for Quality Issues, Minimizing Scrap

By monitoring performance, one can receive alerts in the event of processes going beyond acceptable tolerance levels or indicating potential quality concerns. The ability to promptly halt or modify a process can significantly reduce or even eliminate material wastage and rework. For instance, let's consider a scenario where a pneumatic cylinder gradually drifts out of alignment over time. If workers only notice and intervene after approximately 1,000 units have been produced, along with several hours of production wasted, the repercussions are substantial. Anticipating maintenance and quality issues earlier can enhance the value of applications involving materials that are prone to volatile pricing or market fluctuations.

Predicting Demand Analytics

Use Case: Leveraging KPI and ERP for Improved Insights

By closely monitoring individual procedures and overall lead times, valuable insights regarding material and production requirements can be obtained. As interconnected capabilities continue to expand, it becomes crucial to identify key performance indicators (KPI) that can enhance the effectiveness, worth, and precision of software tools such as enterprise resource planning (ERP). Furthermore, in scenarios where external factors such as political events or natural calamities may heavily impact material costs, leveraging data to anticipate consumption rates and shipping can significantly streamline supply chain management. Accurately predicting volume, timelines, and market demand facilitates efficient economic and cost management when considering new equipment, products, or processes. Additionally, the utilization of workforce analytics plays a pivotal role in this context.

Workforce Analytics

Use Case: Predicting education and workforce demands

In the realm of unpredictable markets, the application of predictive analytics for demand can significantly enhance the management of labor and talent acquisition. An area of particular concern is the Skills Gap observed in the manufacturing sector. By leveraging comprehensive data spanning the entire manufacturing process, from the operation level to the broader industry landscape, manufacturers can forecast the future requirements for skills and labor. This enables companies to collaborate more effectively with educators, post job opportunities earlier, and proactively enhance or retrain their existing workforce to meet the evolving labor demands.

The Future of Predictive Analytics in the Manufacturing Industry

Future Use Case: Remote Maintenance of Tools

The utilization of technology and analytics allows for the transformation of data into valuable knowledge. With the continuous expansion of connectivity, there is an observable shift towards enhanced tracking and monitoring of assets through remote and mobile means. This advancement in data delivery capability will lead to an increase in the utilization of remote and mobile diagnostic analytics, consequently reducing the demand for field technicians. Moreover, with the implementation of reliable remote diagnostics, it may even become feasible to provide maintenance suggestions or relevant information to on-site operators, thus further diminishing the reliance on field technicians.

Future Use Case: Risk and Insurance Assessments

Increased tracking and monitoring capabilities have the potential to boost subscriptions, insurance policies, and warranties through the utilization of analytics. Connected devices can facilitate enhanced flexibility in equipment. For instance, original equipment manufacturers (OEMs) can remotely incorporate or remove features, as well as track data and install software through subscription plans. This adaptability extends to the subscription and its associated features, which can be revised according to fluctuating demand. Diagnostic analytics possess the potential to alter the coverage of insurance policies and warranties, dictating the extent of their protection. Manufacturing analytics and connected technology enable the verification or invalidation of operator, equipment, or design errors. The power of mathematics in elucidating, comprehending, and competing is well-recognized. In the realm of manufacturing, future triumphs may be determined by those who possess the most precise and extensive knowledge of digital models and analytics.

Manufacturing is undergoing a digital revolution, fueled by connected devices, and data analytics. By transitioning from traditional manual data collection methods to digital software and IoT devices, manufacturers can harness the power of real-time data to drive decision-making, optimize operations, and gain a competitive edge. Embracing these technological advancements will undoubtedly shape the future of the manufacturing industry, maximizing efficiency, and enabling sustainable growth.