Analyzing Production Data to Eliminate Downtime

Analyzing Production Data to Eliminate Downtime
Analyzing Production Data to Eliminate Downtime

More than two-thirds of industrial enterprises experience unexpected equipment failures causing downtime at least once a month. For the average manufacturer, this results in losses of around $125,000 per hour. Depending on the industry, the cost can be even higher—for automakers, for example, a single hour of downtime can reach up to $2.3 million.

To reduce downtime and the high costs of production halts, the industrial sector is turning to data analytics. Manufacturers are adopting IIoT technologies that collect real-time data on equipment condition, as well as applying ML and AI algorithms to analyze Big Data and detect anomalies.

These technologies form the foundation of Predictive Maintenance—an approach where companies use historical and real-time data to identify issues before they escalate into major failures. In this article, I’ll explain how to start leveraging equipment condition data analytics and what results it can deliver for manufacturers.


How to start using data for manufacturing analytics

To turn your data from a mere collection of information into actionable insights, you need a well-thought-out strategy, a reliable IT infrastructure and skilled professionals. It’s essential to determine what data to collect, how to process it and how to analyze it. Here’s what I recommend for gradually implementing manufacturing analytics:

  • Define goals and expectations. Determine the purpose of your analytics—whether it’s to reduce maintenance costs, increase equipment productivity, improve quality control, or optimize resource usage.
  • Prepare your team of IT specialists and analysts. Assess the capabilities of your current IT staff and data analysts and organize training on using new tools for data collection, cloud platforms and visualization systems. This step can be overseen by the heads of IT and analytics departments.
  • Conduct an equipment audit. An automation engineer or maintenance manager should assess which machines are already capable of collecting data—for instance, through built-in sensors—and where IIoT sensors will need to be installed.
  • Identify what data to collect and how frequently. There’s no need to gather everything at once. Start with critical equipment—machines whose downtime directly affects overall productivity and KPI performance. The data collection frequency will depend on the metrics being tracked. For example, temperature data is typically collected every minute, while engine rotation speed may be recorded 1-10 times per second.
  • Install sensors and configure network infrastructure. At this stage, the Network Engineer's task is to ensure stable data transmission from the sensors to a centralized storage point.
  • Select a data storage solution. The Data Architect designs the data storage architecture, choosing between on-premise servers or cloud storage. It’s important that these solutions align with the type of data being collected—different databases are needed for relational data and time-series data. The right choice impacts the speed of data processing. Additionally, you’ll need tools for data archiving, as the volume of data will grow, and you'll need flexibility to scale.
  • Set up data processing and cleaning. Raw data may contain errors, so the Data Engineer must implement validation and filtering processes.
  • Gradually implement analytical models. Once enough data is accumulated, you can begin detecting faults using machine learning algorithms. Over time, this will improve the work of analysts by automatically identifying anomalies in large datasets. At this stage, an ML Engineer will need to be involved.
  • Ensure cybersecurity at all levels. Even basic data collection and processing systems must be secure—access restrictions and data encryption are essential. Without this, data could become a vulnerability for the entire production process.


What hinders effective data usage

Most companies collect vast amounts of data about their operations, but much of it remains unprocessed, offering no value. There are many challenges on the path to effective analytics, with the most common being:

  • Unstructured and low-quality raw data. 95% of companies acknowledge the difficulty of using unstructured data. Without proper cleaning and normalization, data can be incomplete, duplicated, or delayed. An analyst or AI model receiving poor-quality data will draw incorrect conclusions.
  • Integration issues. Challenges can arise due to varying data quality in existing systems—SCADA may use one data format, while ERP uses another.
  • Employee resistance. Implementing production analytics tools requires changes in how staff work and the acquisition of new skills. If employees are not ready for this, it can slow down the adoption of analytics.

Each of these challenges has solutions. If you're unsure about implementing data analytics in manufacturing, start with a pilot project on one production line or in one workshop, and gradually scale it up.


What results can be achieved

Data-driven Predictive Maintenance can reduce downtime by 35-45% and reduce associated financial losses by 5–20%. Additionally, it enables:

  • Automatic anomaly detection. AI systems automatically identify deviations in temperature, vibration, pressure, rotation speed and a variety of other parameters. This speeds up analytics, allowing technical teams to quickly receive alerts about potential failures and address them at earlier stages when repairs are still cost-effective.
  • Optimize production processes. Real-time equipment data helps select the most optimal operating mode.
  • Transition to Predictive Analytics. Advanced ML algorithms can forecast the potential failure time, and AI can automatically generate recommendations for preventive maintenance. Moreover, such systems also consider historical wear patterns and operating conditions, not just current data.
  • Reduce spare parts and repair costs. When equipment is repaired based on need rather than a fixed schedule, there’s no need to keep a large inventory of spare parts.

Today, data management is transforming almost every industry, from education to heavy manufacturing. Interestingly, according to the KPMG Global Tech Report, the industrial sector is one of the leaders in adopting AI—surpassing the financial services, energy and FMCG sectors in terms of data management maturity.

More than half of senior executives at European companies acknowledge that businesses using data to make decisions are outpacing their competitors, and if they don’t start doing the same, they will fall behind. Therefore, data management is quickly becoming an essential element of competition for every modern business.

About The Author


Illia Smoliienko is the chief software officer at Waites, a leading provider of condition monitoring and predictive maintenance solutions for industrial enterprises. He has more than a decade of experience in industrial IoT and the implementation of PdM strategies. Under Illia’s leadership, large-scale IIoT-based monitoring projects have been deployed for global companies such as DHL, Nike and Tesla.


Did you enjoy this great article?

Check out our free e-newsletters to read more great articles..

Subscribe