Manufacturers have long relied on data for a competitive edge, but the way data is gathered is evolving. In the past, technicians manually collected data by walking around the plant, checking gauges, filling out forms, and recording machine operation and maintenance history. These manual methods are labor-intensive, error-prone, and inefficient for data-driven decision-making. However, the rise of IoT devices and sensors has transformed how manufacturers collect and utilize data. By connecting equipment and operations, companies can now leverage digital software and connected devices to streamline data collection and documentation processes. These technological advancements not only reduce manual labor but also enhance data fidelity, empowering analytics and enabling more accurate models.

 Advancement of Analytics in Manufacturing

The journey towards predictive and prescriptive strategies in manufacturing, also referred to as the Manufacturing Analytics Journey, involves progressing through several key stages:

Data: The initial stage involves collecting data for descriptive analytics to establish a baseline and answer the question of what happened.

Information: Processing the collected data into diagnostic analytics helps in understanding why certain events occurred, effectively transforming data into actionable information. Utilizing a manufacturing analytics solution can facilitate this process.

Understanding: Accumulating sufficient information allows for a deeper understanding of processes, enabling the development of statistical models that can forecast potential future outcomes through predictive analytics.

Knowledge: With the creation of more accurate models, data evolves into knowledge, and prescriptive analytics come into play by providing insights on what actions should be taken.

When considering connecting various systems and following processes, it is crucial to first define clear objectives and establish benchmarks to track performance enhancements. Understanding the type and quantity of data required to shift from descriptive to prescriptive analytics is essential. To kickstart this journey, consolidating all collected data onto a unified platform can be advantageous. Moreover, ensuring that all relevant stakeholders - be it devices, individuals, or suppliers - have appropriate access to this platform is imperative. The use of real-time data and monitoring can provide precise insights, aiding in the establishment of benchmarks, attainment of N-values, and swift identification of changes compared to non-connected or manual approaches. Although these steps demand time and effort, each progression offers unique advantages. One significant benefit of leveraging analytics is the capability to predict outcomes with a high level of accuracy.

Predictive Analytics for Manufacturers

Having real-time connectivity in devices allows for the collection of a greater amount of data points, enabling more accurate predictions of production output and maintenance needs. Unlike traditional maintenance plans which are based on general estimates, using data and manufacturing analytics helps in forecasting potential failures, ultimately reducing unplanned downtime and avoiding unnecessary maintenance costs.

Maintenance Analytics

One term that offers numerous advantages is predictive maintenance. One key benefit is the ability to utilize data to make accurate predictions about when maintenance is required, rather than relying on assumptions. This proactive approach can enhance equipment uptime, enabling managers to schedule maintenance or make necessary adjustments preemptively before any breakdown occurs. As more data is gathered and correlations are made, predictive analytics becomes increasingly precise. For instance, in a specific case, it was discovered that tool failure correlated with an increase in equipment amperage. Although tracking amperage was challenging, spindle load data could be accessed by activating a feature in the equipment's software dashboard. Through monitoring the spindle load, a more efficient and cost-effective method was employed to predict the number of parts that could be produced until tool failure. By analyzing the increased load, it became feasible to potentially shorten this span. By linking data and identifying trends, the capabilities of analytics in enhancing quality control and decision-making processes are broadened.

Quality Analytics

Monitoring performance allows for early detection of deviations in processes that may lead to quality issues. This proactive approach can help prevent material waste and rework by enabling timely adjustments or halting of processes. For instance, in a case where a pneumatic cylinder gradually shifted out of alignment, corrective action was only taken after 1,500 units had already been produced and multiple hours of production time wasted. Anticipating maintenance and quality concerns in advance can be especially beneficial for applications involving materials with volatile prices or market fluctuations. This strategic foresight adds value by mitigating potential financial risks associated with unstable market conditions.

Demand Analytics

Tracking both individual processes and overall lead times provides valuable insights into material and production requirements. As interconnected capabilities continue to grow, key performance indicators (KPIs) will be identified to enhance the effectiveness, value, and precision of software tools like Enterprise Resource Planning (ERP) systems. In scenarios where material costs can fluctuate significantly due to factors like politics or natural disasters, leveraging data to forecast consumption rates and shipping needs can yield significant advantages in optimizing supply chain management. By accurately predicting volume, timelines, and market demand, organizations can effectively manage economics and costs associated with implementing new equipment, developing new products, or refining existing processes.

Workforce Analytics

Predictive demand analytics can be utilized in fluctuating markets to enhance the management of labor and talent acquisition. A major challenge facing the manufacturing industry is the Skills Gap. By leveraging data from various sources, manufacturers can anticipate the skills and labor required in the future. This enables companies to collaborate more efficiently with educational institutions, post job listings in advance, and provide training to current employees to address labor shortages.

Future of Predictive Analytics in the Manufacturing Industry

Remote Maintenance

Leveraging technology and analytics transforms raw data into valuable insights. With the proliferation of connectivity, there is a noticeable shift towards enhanced remote and mobile tracking and monitoring of assets. The capacity to deliver precise data will enhance remote and mobile diagnostic capabilities. This movement is expected to decrease reliance on on-site technicians. The implementation of highly accurate remote diagnostics could potentially allow for maintenance recommendations or instructions to be given to on-site operators, further minimizing the necessity for field technicians.

Risk Assessments

The enhanced tracking and monitoring capabilities of equipment through analytics have the potential to boost subscriptions, insurance policies, and warranties. Connected devices can lead to more adaptable equipment, such as allowing original equipment manufacturers (OEMs) to remotely add or remove features, data tracking, and software through subscriptions. This flexibility enables adjustments based on changing demand. Moreover, diagnostic analytics may impact the coverage of insurance policies and warranties. Manufacturing analytics and connected technology can detect and rectify operator, equipment, or design errors. Mathematics plays a crucial role in explaining, comprehending, and competing in this field. The future success of manufacturing could depend on who possesses the most accurate and extensive knowledge of digital models and analytics.

How Nirmalya Business Intelligence Can Help Manufacturers

Maximize your manufacturing output with Nirmalya BI, a powerful platform that offers a deep dive into predictive analytics for workforce trends, maintenance planning, quality assurance, demand projections, and risk evaluation. Gain valuable insights that enable you to optimize operations, reduce downtime, deliver superior products, meet market demands, and safeguard against potential threats.

For further information, a comprehensive solution that allows businesses to efficiently manage various functions using artificial intelligence, machine learning, and business intelligence capabilities, reach out to us.