Industrial Analytics The Catalyst Industries Need Now More Than Ever
Published on : Friday 05-04-2024
The prominence of industrial analytics is looming and leveraging industrial analytics is no longer a choice but a necessity, says Malavikha A.
Data is undeniably an exponentially growing asset and the significance of harnessing data to gain analytical insights for enterprises is no longer a novel concept. What is new, however, is the transformational realisation happening in industries where data is no longer perceived as an operational by-product but as an integral part of critical business decisions.
Analysts predict that the Industrial Analytics market is expected to touch over USD 55 billion by 2029, growing at a CAGR of 16.6% from 2022 to 2029. Digital transformation fueled by Industry 4.0 and the increase in the number of IoT and IIoT installations across production lines in industries is expected to drive this market.
What is Industrial Analytics?
Industries generate vast amounts of data during day-to-day operations rising from IoT (Internet of Things) and IIoT (Industrial Internet of Things) devices. The manufacturing industry is one the most data-prolific industries out there, generating an average of 1.9 petabytes per year according to the McKinsey’s Global Institute. Some examples of such devices include temperature, pressure and proximity sensors; air, water and noise quality monitors; PLCs (programmable logic controllers); RFID tags and barcode scanners; smart energy monitors; video surveillance systems; security and access control systems and the list goes on. Industrial Analytics refers to collecting, cleansing and processing such data generated by industrial operations to gather meaningful and actionable analytical insights.
Let us try to understand how industrial analytics is different from traditional BI (business intelligence). Firstly, business intelligence is primarily designed for structured data where queries are known beforehand and enable faster data retrieval from cubes. Such BI capabilities are not optimised to handle huge volumes and data formats generated by industries that originate from thousands of sensors, devices, servers and cloud systems.
In contrast, industrial analytics solutions are designed to ingest massive streams of data and provide actionable insights in real-time. For example, this includes identifying patterns and trends in data for descriptive analytics (describe anomalies in what has happened and is happening in the plant), predictive analytics (predict failures that could happen in future based on the past and present performance) and prescriptive analytics (prescribe corrective actions that are needed to avoid potential implications and failures in future). This is possible because, in addition to the traditional Extract, Transform and Load (ETL) processes, Industrial Analytics also helps to process vast numbers of variables and suggest potential variables of interest using edge computing, AI (Artificial Intelligence) and ML (Machine Learning) capabilities.
Secondly, business context is key for industrial analytics, especially for 'enterprise of enterprises'. For example, temperatures recorded from different sensors on different machines installed in different production lines could mean different things as thresholds may vary for every machinery/process and it’s very important to contextualise the data that is collected to gather meaningful insights.
What are the key benefits and use cases for Industrial Analytics?
Reduced downtime with predictive maintenance
Industrialists always try to maximise the value of every asset to boost productivity and profitability. In today’s ‘Always On’ world, even a single machinery breakdown can prove to be very expensive. Therefore, it becomes significant to carefully monitor machine performance to facilitate timely intervention before a breakdown.
On one hand, industrial analytics can analyse historical data performance data collected from various sensors installed on machines to predict which one is more likely to fail, where the operating limits are above/below thresholds, and what circumstances typically cause machines to fail. This allows industries to deploy personnel and repair equipment to reduce downtime in the unlikely event that a machine does fail. On the other hand, industrial analytics also helps with root cause analysis by studying correlation between contributing events, abnormalities, factors and past breakdowns to determine the actual cause of the failure and suggest predictive maintenance routines by constantly monitoring input parameters and thresholds to reduce downtime.
Boost automation and productivity
With the fourth industrial revolution, more and more industries are moving to industrial automation using technologies like IIoT sensors, robots and PLCs to boost productivity while maintaining quality. Automated processes are also extremely repeatable with unwavering precision – ensuring consistent high-quality output. PLCs and robots also enable factories to adapt design changes and changes to assembly lines quickly and shift between products for greater agility and accelerated time-to-market. Since, all these automations aim to minimise errors and maximise efficiency, this is where industrial analytics plays a vital role to monitor large volumes and variety of incoming data in real-time to detect any anomalies instantly to allow proactive human intervention.
Boost sales and customer-satisfaction
Now let’s shift gears from data generated on factory floors and production lines to consumer and competitive data. In addition to optimising processed and production data, knowing how to mine consumer and market data and extract value from it is key to ensure overall success of industries. Consider for example the retail industry, where taking the right products to the right customers is a big challenge – customers today look for more personalised and prioritised customer experience. Here, industrial analytics can help to study customer buying patterns, marketing campaign success rates in different regions, customer feedback and competitive pricing to suggest the right strategy and product positioning to boost sales. Let’s consider another example of the banking industry, much like retail. Here industrial analytics can help predict which customers are likely to disengage with the bank based on consolidated customer profile analysis and prescribe targeted marketing programs to reduce churn. Similarly, even the agriculture industry can benefit greatly with industrial analytics capabilities to predict the success of deploying advanced farming techniques such as novel irrigation methods and fertilizers to maximise yield and return on investment.
How can industries be ready for transformation with industrial analytics?
Let’s now look at how industries can prepare themselves to leverage the full transformational potential that industrial analytics has to offer.
Democratise data and make data available to everyone
The traditional approach to industrial analytics involves data scientists defining data semantics and training data models to cleanse, transform and process the data. But this process is restrictive and creates a dependency on data scientists – therefore it becomes important to make data accessible to everyone to facilitate self-service analytics, resulting in benefits in everyday plant operations.
It also becomes important to leverage AI and ML capabilities to leverage automated pattern recognition in the Industrial Analytics platform to reduce dependency on data scientists and really democratise analytical insights. This will promote accurate performance and failure prediction, improved root cause analysis and automated quality monitoring.
In addition to investing in smart analytics platforms, it’s also vital to educate and train industry employees to understand insights as long gone is the notion that analytics is only confined to IT users – it is now for all business users.
Harmonise data and create single source of truth
One of the main complexities of industrial analytics stems from the varied forms of data that are generated from hundreds of IoT and IIoT sensors installed in the factory floor – for example time series data and sensor data that can come in different factors like percentages, degrees, chemical levels, inches, speed, minutes and so on. Now couple this with the fact that such data is collected from different devices and stored in physical servers, in the cloud, or disparate databases. Such isolated data makes it difficult to recognise patterns, identify correlations and anticipate trends.
Unified analytics is the solution to accurately define relationships and draw inference from the data, it is extremely important to remove data silos and harmonise this data to form a single source of truth of the data and invest in a good master data management system. It’s also equally important to foster a strong data-driven culture with data accessibility, security and shareability – all capabilities of strong data governance.
Leverage edge computing and edge analytics
Despite the vast volumes of data collected, industrial analytics can only work when there is direct query architecture, i.e., when analytics is brought to where the data lies and not the other way around. Collecting and analysing data in real-time is crucial to be able to uncover potential anomalies and take corrective measures in time. Edge computing can reduce latency and bandwidth costs and ensure faster processing and troubleshooting. This also avoids multiple data replications. On the flip side, edge computing also comes with some challenges in terms of network connectivity and security due to the distributed computing paradigm. To overcome this, it’s of paramount importance to invest in a robust and scalable network infrastructure, establish security protocols and invest in the right data and analytics platform that can simplify the scale and complexity of edge computing.
Conclusion
Now more than ever, the prominence of industrial analytics is looming and leveraging industrial analytics is no longer a choice but a necessity for industries. Investing now in the right industrial analytics solution will boost performance, improve operational efficiency and reduce costs for industries, and be a pivotal partner in helping industries stay relevant and stay ahead of the curve.
The views expressed in this article are those of the author and may not reflect those of SAP.
Malavikha works as a Product Manager for Analytics at SAP Labs India. She is the lead product manager for SAP Analytics Cloud OEM (embedded analytics) in SAP LoB applications with a focus on Customer Experience, Usage and Adoption tracking and Product Enablement. She has previously led cross-product analytics accelerators for various LOB and Industry solutions globally at SAP. She is actively involved in Product Positioning and Marketing of SAP Analytics Cloud in Analyst Research Forums and Analytics Communities including Gartner. She has been associated with SAP Analytics for 10 years now. Malavikha is a University Gold Medalist in B.E from Visvesvaraya Technological University (VTU) and is passionate about driving innovation and customer centricity in data and analytics products. LinkedinIn: https://www.linkedin.com/in/malavikhaa/
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