How Predictive Maintenance for Manufacturing Leads to Effective Data Management

Manufacturing companies today are standing at the edge of a digital revolution.

As data gathering and analytics continue to grow more advanced, manufacturers can use these technologies to increase productivity, improve customer satisfaction, and create better societal and environmental benefits.

With economic uncertainty, lingering impacts from COVID-19, and an urgent need to address global environmental concerns, harnessing the power of data has become vital for manufacturers who want to thrive in the competitive landscape.

So why isn’t everyone taking advantage of these solutions?

3 key challenges

Data gathering and analytics are the key to unlocking more value across all parts of your manufacturing operations. They lead more efficient and productive assets with longer lifespans and less waste in the production process.

However, there are three main challenges that often prevent manufacturers from capturing the full value of their data:

Insufficient technology: Outdated technology, data security risks, and technological challenges can often hinder a manufacturer’s ability to gain actionable insights from their data.
Organizational hurdles: Organizations often lack clear roles and responsibilities among their team or have overly complex internal governance systems. This creates confusion and limits communication across departments.
A lack of data applications: Many manufacturers lack the necessary infrastructure or applications to harness the full potential of their data. For instance, a computerized maintenance management system (CMMS) or Asset Investment Planning (AIP) software.

While achieving data excellence can be difficult for organizations facing these challenges, once the right systems and procedures are in place, there is no limit to the value that manufacturers can gain from analyzing their data and implementing findings to improve outcomes.

The guide to data excellence

There are several stages on the journey to data excellence, each building on the last to improve operational efficiency and drive continuous improvement.

Step 1: Achieve actionable insights
The first stage of data excellence involves manufacturers harnessing operational data from various sources within the production environment to analyze and transform their findings into actionable insights. By gaining deeper visibility into their operations in real-time, manufacturers can identify inefficiencies, optimize processes, and make informed decisions promptly.

Step 2: Predictable outcomes
By identifying and understanding past trends and patterns, manufacturers can anticipate potential issues of their assets and facilities and take proactive measures to optimize performance and mitigate risks. For example, predictive maintenance models can predict the likelihood of equipment failures, preventing expensive unplanned outages.

Step 3: Self-optimizing systems
Self-optimizing systems can analyze historical and real-time data to adapt to changing conditions, allowing them to automatically adjust to maintain efficiency and productivity levels to meet organizational goals.

Empowering manufacturers with Smart Assets

One solution that empowers manufacturers on their journey towards data excellence is Brightly’s Smart Assets.

Brightly’s Smart Assets is simple, easy-to-implement IoT monitoring software that can be integrated seamlessly with Brightly’s Asset Essentials CMMS to monitor all your assets by collecting their data through sensors. These sensors collect and transmit critical information about the asset – such as temperature, pressure or vibrations and triggers work order – to provide actionable insights that drive proactive maintenance and optimize asset performance.

If you’d like to learn more about succeeding in today's fast-paced business landscape, be sure to check out our webinar: Agile Empowerment: Data-Driven Decisions, Continuous Improvement, and Cultivating Workplace Happiness