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Understanding Diagnostic Analytics in the Context of Smart Manufacturing

The manufacturing sector is often considered one of the pivots of growth for any developing economy. From job creation to producing more efficient machines, the sector offers a diverse range of use cases that have a significant economic impact. Additionally, the manufacturing sector is also witnessing a fair share of innovations in its core operational principles, thanks to the deep penetration of digital technology across various facets of the industry.

Studies estimate that the global market size for digital transformation in the manufacturing sector is expected to grow toUSD 767.82 billion by 2026.

As the digital wave continues to take a commanding position in the industry, we are witnessing the evolution of smart manufacturing. Autonomous operations of manufacturing facilities, intelligent machines powered by AI and IoT, robotics, etc. are innovations that have made it into mainstream adoption in the manufacturing sector and are no longer restricted to experimental prototypes at manufacturers with deep pockets. One of the most widely adopted technology innovations in the context of smart manufacturing is the leveraging of diagnostic analytics.

Introducing Diagnostic Analytics

Diagnostic analytics refers to the analytics-driven approach that seeks to determine the cause of events that triggered a behavioral change in an entity. From a manufacturing perspective, an example of diagnostic analytics is the use of analytical processing to determine the reasons behind the failure of equipment in the manufacturing facility or evaluate the performance of operational machines when there is a marked decrease in their productivity levels.

Diagnostic analytics forms a key component of the larger industrial analytics market which according to studies will be alone worth USD 47.46 billion by 2026.

The Role of Diagnostic Analytics in Smart Manufacturing

Diagnostic analytics allows manufacturers to identify root causes of problems or problematic events that happen in their operations. Data analysts use deep-dive analytical processes to spot patterns or trends that relate to abnormal behavior or problems that have been identified. It involves a plethora of data analysis tasks such as data mining, drill-down discovery, statistical analysis, principal component analysis, probability evaluations, and much more.

Let us explore four ways in which diagnostic analytics makes life easier for manufacturers especially in the era of smart manufacturing:

  • Improved Productivity: As discussed earlier, there may be instances where a manufacturing process may be disrupted owing to equipment failure or lack of 100% optimization of machinery. With diagnostic analytics, data scientists can help engineers uncover hidden problems within machinery and pinpoint the exact operational condition in which the machine displayed an abnormal behavior or failed to meet its expected operational efficiency. Smart machinery offers real-time data that can be drilled through analytics to uncover insights that help in rectifying productivity drops and machine failures in a manufacturing establishment.
  • Proactive Maintenance: Adding to the previous point, diagnostic analytics helps to uncover patterns of operational behavior that led to the failure of machinery or lowered efficiency figures. This can help in creating proactive maintenance cycles which ensure that machinery and equipment have longer life cycles with better productivity assurance.
  • Faster Defect Management: Machinery and components can fail for numerous reasons and even though proactive maintenance checks can reduce the risk, there will be failure events. With diagnostic analytics, the resulting commotion and widespread disruption of business activities surrounding the manufacturing ecosystem can be reduced considerably. By quickly running massive loads of data from machinery and operational environments in powerful diagnostic analytical platforms, engineers can uncover and attend to defects faster thereby saving valuable time that would have otherwise been wasted as idle time. This can directly impact the production commitments of manufacturers and can become a key competitive advantage in the market.
  • Aid Prescriptive Analytics: Prescriptive analytics deals with using data analytics to identify possible solutions to challenging problems faced in a business. With diagnostic analytics uncovering the root cause of several problems, these insights can be used to model prescriptive solutions that can address or prevent such defects or unusual behavior from occurring in the future. They work in tandem to ensure that a manufacturing establishment runs at the most optimal conditions without disruptions over a long period of time.

 Diagnostic analytics has the potential to improve operations across a wide gamut of manufacturing scenarios. It can help data scientists to direct engineers to the exact pain points they need to focus on solving while running periodic maintenance checks or while inspecting the manufacturing processes for any signs of downward productivity trends. It plays an important role in assuring all stakeholders such as clients, vendors, and end consumers about the resilience and reliability of a manufacturing facility.

In the era of smart manufacturing, the amount of data available across the length and breadth of manufacturing operations is immense. Get in touch with us to see how this treasure trove of data can be used to run advanced analytical programs like diagnostic analytics to help your manufacturing business stay in top shape.




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