4 ways utilising historical machine data drives operational excellence

In today’s market, leading manufacturers are increasing their competitiveness by ensuring their production systems achieve three key criteria:

  1. Minimised downtime
  2. Maximised output
  3. Reduced operating costs

There are of course numerous technologies and initiatives organisations can adopt to achieve these goals. But the smart decisions that reduce downtime and operating costs while ramping up output are reliant on interpreting good, reliable data.

Real-time data, as we’ve discussed in another blog, is crucial, but historical machine data can sometimes go overlooked.

So how does engagement with historical data drive operational excellence?

1. Gaining insights by comparing historical performance

Historical data provides a valuable context for understanding your current machine data.

By comparing data, you can track what has improved and where there is room for improvement in plant/machine performance. These comparisons can be made across a range of Overall Equipment Effectiveness (OEE) KPIs, including:

  • Order execution
  • Production analysis
  • Resource scheduling
  • Downtime

Direct comparison with historical data reduces the likelihood that operational and production issues go overlooked. For example, viewing resourcing scheduling data in isolation wouldn’t necessarily indicate whether there has been a drop in performance. It is the direct comparison that allows teams to identify issues and make changes to ensure machines/processes are optimised. This kind of comparative analysis and quick identification of issues can be streamlined with business intelligence dashboards.

Users can expand these insights by entering certain data manually for future comparisons, such as:

  • Detailed maintenance inspection information (traceability)
  • Causes of downtime (downtime tagging)
  • Statistical analysis data for statistical process control (SPC)

2. Understanding the effects of historical decisions

There are numerous reasons why teams will make configuration changes to system operations, and these changes can take place over months, even years. Whether changes were made to ramp up production in response to new customer demands, or enacted to improve machine usage after a statistical analysis, it can be valuable to take a historical view of these configurations.

By tracking and analysing what operational parameters have been in place and how they impacted KPIs, you are building a reservoir of knowledge to draw from when future changes are needed. This resource can be leveraged across the team to identify opportunities to improve operational efficiency.

3. Enhancing decision-making

Well-utilised historical machine data doesn’t just stay within the department. By integrating historical data into tools such as the organisation’s human resource management (HRM) and enterprise resource planning (ERP) platforms, you can enhance the sophistication of your analysis by connecting machine data with the wider organisation’s operational data insights.

Enhanced decision-making based on this integration can cover a range of important factors across the organisation, for example:

  • Scheduling additional shifts
  • Ordering raw materials based on projected demand
  • Requesting additional workforce from outsourced contractors
  • Scheduling calibrations

4. Streamlining maintenance schedules

Teams can use historical machine data to set up simple machine learning data models that establish maintenance intervals. Rather than doing laborious, manual work to analyse a machine’s maintenance needs, an operator can be quickly notified of upcoming maintenance tasks for different machines. These notifications can be easily set up according to a variety of factors, including:

  • Certain measurement parameters have been exceeded (such as when SPC reports a higher than normal amount of machine tool updates for tool offset)
  • An established period of machine use has elapsed
  • Environmental conditions that affect the measurement process

Leveraging historical machine data is simpler than ever with HxGN SFx | Asset Management. As well as enhancing machine insights, SFx Asset Management offers a range of monitoring capabilities to help you drive greater productivity in your manufacturing operations.

Watch this video for a quick overview of how SFx Asset Management helps you maximise efficiency and minimise downtime.


  • Nicholas Baldwin

    Nicholas Baldwin is Market Director for Hexagon XALT Solutions, EMEA. Nicholas is responsible for business development in the area of digital plant operations, Industry 4.0 data integration solutions, and digital transformation for smart manufacturing, smart infrastructure, smart construction and smart city services. He holds a Bachelor’s Degree in International Business from Pace University, New York.

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