Defining Industrial Analytics
Industrial Analytics (IA) describes the collection, analysis and usage of data generated in industrial operations and throughout the entire product lifecycle, applicable to any company that is manufacturing and selling physical products. It involves traditional methods of data capture and statistical modeling. However, most of its future value will be enabled by advancements in connectivity (IoT) and improved methods for analyzing and interpreting data (Machine Learning).
A fascinating report on how Industrial Analytics is maturing based on advances in the areas of IoT, machine learning, and big data analytics was published this week. You can download the Industrial Analytics Report 2016/17 report here (58 pp., PDF, free, opt-in). This study was initiated and governed by the Digital Analytics Association e.V. Germany (DAAG), which runs a professional working group on the topic of Industrial Analytics. Research firm IoT Analytics GmbH was selected to conduct the study. Interviews with 151 analytics professionals and decision-makers in industrial companies were completed as part of the study. Hewlett-Packard Enterprise, data science service companies Comma Soft and Kiana Systems sponsored the research. All research and analysis related steps required for the study including interviewing respondents, data gathering, data analysis and interpretation, were conducted by IoT Analytics GmbH. Please see page 52 of the study for the methodology.
Key Insights Gained:
- Increased revenue (33.1%), increased customer satisfaction (22.1%) and increased product quality (11%) are the top three benefits of Industrial Analytics. The majority of industrial organizations see Industrial Analytics as a catalyst for future revenue growth, not primarily as a means of cost reduction. Upgrading existing products, changing the business model of existing products, and creating new business models are three typical approaches companies are taking to generate revenue from Industrial Analytics.
- Predictive and prescriptive maintenance of machines (79%) is the most important application of Industrial Analytics in the next 1 – 3 years. Customer/marketing-related analytics (77%) and analysis of product usage in the field (76%) are the second- and third-most important. The following graphic provides an overview of the 13 most important applications of Industrial Analytics.
- 68% of decision-makers have a company-wide data analytics strategy, 46% have a dedicated organizational unit and only 30% have completed actual projects. The study found that out of the remaining 70% of industrial organizations, the majority of firms have ongoing projects in the prototyping phase.
- Business Intelligence (BI) tools, Predictive Analytics tools and Advanced Analytics Platforms will be pivotal to enabling industrial data analysis in the next five years. Business Intelligence Tools such as SAP Business Objects will increase in importance to industrial manufacturing leaders from 39% to 77% in the next five years. Predictive Analytics tools such as HPE Haven Predictive Analytics will increase from 32% to 69%. The role of spreadsheets used for industrial data analytics is expected to decline (i.e., 27% think it is important in 5 years vs. 54% today).
- The Industrial Analytics technology stack is designed to scale based on legacy system, industrial automation, MES and SCADA systems integration combined with sensor-based data. IoT Analytics GmbH defines the technology stack based on four components inclouding data sources, necessary infrastructure, analytics tools, and applications. The following graphic illustrates the technology stack.
- Industrial Internet of Things (IIoT) and Industry 4.0 will enable an era of shop-floor smart sensors that can make autonomous decisions and trade-offs regarding manufacturing execution. IoT Analytics GmbH predicts this will lead to smart processes and smart products that communicate within production environments and learn from their decisions, improving performance over time. The study suggests that Manufacturing Execution System (MES) agents will be vertically integrated into higher level enterprise planning and product change management processes so that these organizations can synchronously orchestrate the flow of data, rather than go through each layer individually.