Industrial Analytics Study: How Real-Time Integration Leverages Industrial Internet of Things

The study ‘Industrial Analytics Report 2016/17‘ indicates how basic integration can be used for Industrial Analytics and IoT. The range of Industrial Analytics (IA) covers collection, analysis and usage of data generated in operations of company of the industrial sector which is manufacturing and selling physical products with a complete lifecycle. For the study, interviews with more than 150 professionals in industrial analytics and decision-makers in companies were made.

Enabling legacy, third-party and Salesforce, SAP integration is one of the most foundational technologies that Industrial Analytics relies on today and will in the future. Real-time integration is essential for enabling connectivity between Internet of Things (IoT) devices, in addition to enabling improved methods for analyzing and interpreting data. The recently published study was lead by the Industrial Analytics team of the Digital Analytics Association Germany (DAAG) research and analysis of the study by research firm IoT Analytics, sponsored by Hewlett-Packard Enterprise and Comma Soft and Kiana Systems, both data science service companies.

> ‘Industrial Analytics Report 2016/17‘ PDF download

Key Takeaways of the Industrial Analytics Study

1. Increased revenue and customer satisfaction are the main benefits of IA

– Real-time integration using Industrial Analytics will enable companies to increase their revenue (33 percent), increase general customer satisfaction (22 percent) and increase product quality (11 percent).

In common, the major share of industrial professionals who completed the interviews for the study, betray Industrial Analytics as a catalyst for revenue growth in the near future, but less by cost reduction. Typical approaches for Internet of Things companies to enlarge their revenue are upgrading existing products, changing the business model of existing products, and creating new business models.

Exhibit Biggest Benefits of Industrial Analytics IoT

2. Predictive maintenance and customer-related analytics are the most important applications

– For many companies, the more pervasive their real-time integration is, the more effective their IoT and Industrial Analytics strategies will be.

Manufacturers will be able to attain predictive maintenance and prescriptive maintenance of machines at shop floor (79 percent). Most important application of Industrial Analytics in the next one to three years is preventative maintenance, followed by customer- and marketing analytics (77 percent) and product usage analysis in the field (76 percent).

Exhibit Most Important IoT Applications

3. The majority of companies has a strategy on data analytics – few have projects already completed

68 percent of decision-makers have a company-wide data analytics strategy, 46% have a dedicated organizational unit and only 30 percent have completed actual projects, further underscoring the enabling role of integration in their analytics and IoT strategies.

The study found that out of the remaining 70 percent of industrial organizations, the majority of firms have ongoing projects in the prototyping phase.

Exhibit Many Companies with Data Strategy vs few Completed Projects

4. The role of Advanced Analytics and Business Intelligence (BI) is increasing

– BI and predictive analytics tools and advanced analytics platforms importance to enabling industrial data analysis will double in the next five years. Due to the interviewed professionals, Business Intelligence tools will rise in importance to 77 percent (from 39 percent). Predictive analytics will increase to 69 percent (from 32 percent). The role of traditional industrial data analytics spreadsheets is expected to lower from 54 percent to 27 percent.

Exhibit Increasing Role of Advanced Analytics and Business Intelligence for IoT

5. Industrial Analytics technology modules are industrial apps and systems
integrated plus sensor data

– The Industrial Analytics technology stack is designed to scale

based on the integration of legacy systems, industrial automation apps and systems, MES and Supervisory Control and Data Acquisition (SCADA) systems integration combined with sensor-based data.

6. Communication and learning: It’s about smart integration of processes and products 

The final consequence for a successful Industrial Internet of Things (IIoT) i.e. Industry 4.0, will be smart modules and processes up to smart products.

Smart sensors at a shop-floor will be able to execute autonomous decisions as well as trade-offs regarding manufacturing execution based on real-time integration. When everything communicates within production environments and learns from its decisions, the performance will be improved significantly.

Exhibit Why IoT is a Game Changer for Industrial Analytics

– Source / All exhibits: IoT Analytics

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