Predictive Maintenance Solution for Industries - Cyient
- 1. Your assets are talking. Are you listening? Improve your business performance through actionable intelligence with our Predictive Maintenance Solution
- 2. Contents Introduction 3 Problem Statement 3 Solution – How It All Works 4 Successful Implementation Results 6 Predictive Asset Maintenance Solution Benefits 6 Conclusion 7 About Cyient 9 2
- 3. Introduction The data analytics revolution has descended upon us with the potential to transform how companies organize, operate, develop talent, create value, and service their customers. The groundswell has happened and momentum is building in many companies, but only a few are reaping major rewards from their data. Companies are doing a great job of connecting assets, equipment, and devices, and gathering all that information en masse into distributed databases. Unfortunately, it is all too often a one-sided conversation of recording what is being said by the equipment. The challenge is finding ways to listen to what the equipment is telling us and responding in useful and actionably intelligent ways that improve business operations and reduce costs. Like the conversations we have in our daily lives, it is vital to listen to what is said and respond to what we hear. It is understandable why people feel it is better to leave the interpretation of data insights to the experts, given the complexity of methodologies, the increasing importance of machine learning, and the sheer scale of data sets. However, the strength of analytics lies in making informed decisions based on data and embedded into work processes. Effective listening enables companies to move away from reactive maintenance activities and move toward predictive maintenance activities. This can be a daunting task that not only requires solid tools to enable it, but also tools that connect with and enable employees to listen with ease to what these devices are saying. True success will come from breaking down the wall of silence between employees and the assets they are managing. Unfortunately, many asset management solutions follow a pattern of gathering and reporting without considering how to support the change from reactive to predictive maintenance. This white paper covers the typical challenges of developing solutions that asset-intensive industries experience while implementing predictive maintenance programs. It also focuses on ways to overcome these challenges by driving profit and growth from new technologies and leveraging the opportunities offered by big data and new digital operating models. Problem Statement Today’s competitive business environment requires companies to bring their products to market quickly and more cost-effectively than ever before. Reduced downtime, improved operational efficiency, optimized maintenance costs, effective forecasting, and improved production efficiency are the continuous demands the industry is facing. To achieve these, companies need to consider moving away from a responsive maintenance strategy and moving toward an informed reactive maintenance strategy. The end goal of a proactive maintenance strategy can be even more difficult to attain without the right solution. The digitization of equipment and the Internet of Things (IoT) is dramatically changing business models for equipment manufacturers and operators. Businesses must keep up with the rapid pace of technology change, business innovation, and security concerns, while delivering on expectations. 3 It is important for us to not only hear what our data is saying, but to respond to what we are hearing.
- 4. Advanced analytics and machine learning are becoming embedded into organizations today. Big data helps store what is being said and advanced analytics process it into meaningful bites. However, once processed, data still needs to be turned into something useful. The real goal is improved business performance through actionable intelligence—not pristine data sets, interesting patterns, or killer algorithms. This is what every company is after. Advanced data analytics is a means to an end. It’s a discriminating tool that identifies and then implements a value-driving answer. That answer will vary depending on the company, industry, or geography. No matter the starting point, the insights unleashed by analytics should be at the core of an organization’s approach to continuously define and improve performance as competitive dynamics evolve. The other aspect is enabling technologies that help employees make decisions efficiently. Without this, businesses cannot make advanced analytics work for them. The focus should be on leveraging big data and analytics to provide employees with tools to work efficiently rather than make them experts in statistics. Within asset management, these employees are technicians, repair and maintenance workers, and factory line operators and foremen. Access to the right tools and advanced analytics translates into better- informed and timely decision-making for those who regularly interact with the equipment asset. Solution – How It All Works Many companies are realizing that the pace of business development is not in sync with the capacity or capability of their underlying IT infrastructure. Additionally, many IoT connections are supported outside of the information technology (IT) infrastructure, such as operations technology (OT), and aligning the two disparate objectives of IT and OT can be challenging. Cyient’s Predictive Maintenance Solution integrates with and sits right on top of existing IT and OT technologies, providing the insight and intelligence sought sooner. It is a highly scalable, cloud-based solution that is designed to manage and maintain simple to complex equipment intelligently. It helps companies build analytic algorithms that enable predictive maintenance programs with the connected assets it manufactures or maintains. This solution includes collecting data from the connected equipment, analyzing it, and delivering insights into quality operations in real time. Actionable intelligence from the analysis is delivered via an elegant, intuitive web interface that can be securely accessed from anywhere. Cyient’s Predictive Maintenance Solution empowers users to perform a deep dive analysis, scenario planning, and search for anomalous events and non-intuitive correlations. Using machine intelligence to identify patterns and move to a predictive analytics model over time, the solution enables preventative maintenance at the right time to prevent problems before they occur. A three-fold approach for designing and implementing the Predictive Maintenance Solution is as follows: 1. Leveraging existing technology on the back- end. 2. Applying the framework’s proprietary models and algorithms to data in ways that provide 4 DOES YOUR DATA HAVE A PURPOSE? IF NOT, YOU’RE JUST SPINNING YOUR WHEELS!
- 5. meaningful insights. 3. Placing the flexible front-end on top as a standalone portal that can seamlessly integrate with the current tools providing an environment unique to each business. The core of the Predictive Maintenance Solution is designing predictive analytics for equipment and integrating planned maintenance activities in a visual dashboard that generates insights and suggests actions for equipment maintenance. Given below is an example of graphics seen in a typical dashboard. Predictive Maintenance Solution Technical Architecture High-Level Representation 5 Data Sources • Equipment Sensor • Maintenance Records • Quality Data • Work Order Management System • Reactive • Predictive • Legacy Models • Health Monitoring • Failure Prediction • Maintenance Planning Analytics Models Alert Engine & Dashboard Predictive Maintenance Solution Remote Monitoring Engineer Business User Database / ETL
- 6. The solution aims to support original equipment manufacturers (OEMs) by leveraging a powerful front-end experience with quality analytics, models, and approaches. These support new digital services, such as predictive maintenance or remote condition monitoring. The heart of Cyient’s Predictive Maintenance Solution is analytics—the ability to perform virtually limitless transformations, calculations, and functions on asset data. Using this analytics solution, manufacturers of machines and industrial equipment will be able to benefit from increased revenue and customer satisfaction while reducing claims and warranty costs. Their customers can reduce downtime, maintenance related costs, and energy consumption, while increasing the lifetime of their machinery. OEMs will have timely and continuous visibility into assets and equipment as a result of the convergence of OT with IT, and machines becoming increasingly connected with production management, manufacturing execution, logistics, and enterprise planning systems. By applying advanced analytics to the data their systems generate, they can identify and predict performance bottlenecks and make informed decisions about how to improve asset operations, manage their workforce, optimize supply chain risks, and enhance the product design process. Successful Implementation Results A heavy equipment OEM, which effectively implemented the Predictive Maintenance Solution achieved an average savings of 11% in scheduled repair costs. In addition, overall maintenance costs were reduced by 30%, with up to 70% fewer breakdowns. These results directly affected production capabilities and throughput expectations. Predictive Maintenance Solution Benefits To leverage the power of data combined with analytics, companies can be successful when there is a solution in place to easily access and find the appropriate information. The focus should be on designing metrics that are powerfully predictive and informative, but also transformative for the organization. The Predictive Maintenance Solution is designed to enable a continuous improvement process from 6
- 7. an analytics perspective and from the insight generated. Additional benefits include: Maximizes quality: An enterprise view of quality performance data and early identification of potential issues and root causes helps maximize production yield, manage the cost of quality, and increase customer satisfaction. Reduces unplanned downtime: Predictive models monitor the system in near-real time to identify patterns that indicate a performance issue or likely failure before it occurs. Data visualization integrated with advanced analytics provides detailed information regarding the nature and severity of the problem, allowing companies to address issues before they cause downtime or performance decline. Optimizes planned maintenance cycles: Automatic alerts enable businesses to plan and prioritize asset issues during regularly scheduled maintenance windows. By using maintenance resources more efficiently, companies can lower operational costs, maintain production, and increase equipment availability. Identifies and resolves root causes: Helps determine the real drivers of performance issues out of multiple measures and conditions and enables corrective and preventive action (CAPA). Case management workflows support speedy and repeatable problem resolution, highlight the best corrective action, and improve reliability, equipment efficiency, and quality. Provides prompt answers: The tools run independently from the existing data and enable you to view the desired insights faster. Lowers the cost of ownership: The Predictive Maintenance Solution supports repair-or- replace decision analysis—downtimes, production losses, part costs, labor costs, event probabilities, etc., are all considered to find the most profitable solution. Conclusion The digitization of business is here and it is putting more pressure on industrial companies to deliver intelligent equipment for their customers. While others talk about capturing or storing data, Cyient’s Predictive Maintenance Solution captures and analyzes asset data and provides real-time insights via a series of easy- to-use web applications focused on monitoring and improving asset performance that leads to operational efficiency. Companies that listen to their equipment and focus on analytics tied with improved business processes can expect a faster return on their investment than those that use analytics without an effective solution to deliver insights. Companies can now differentiate themselves by implementing the Predictive Maintenance Solution, which enables: Highly scalable data management: Combine sensor data with other critical information for monitoring, model development, root-cause analysis, and reporting. Predictive modeling: Accurately predict failure of assets and equipment before it occurs. Integrated advanced analytics: Obtain powerful, integrated, causal analysis of asset failures, and performance issues. Model management: Automatically track the accuracy of predictive models over time by tracking and documenting all actions, from model development through model retirement. Enterprise business intelligence: Access the latest maintenance and operations performance indicators through a web-based, point-and-click interface. Data-driven culture: Organizations are finding that embedding data-driven decision-making into the culture is critical for success. The Predictive Maintenance Solution fosters and aligns with developing a data-driven culture focused on actionable insights. 7
- 8. Cyient’s Predictive Maintenance Solution was designed with a single purpose—to empower equipment manufacturers and owners in mission critical industries with intelligent analysis of the data flood coming from connected equipment. The solution analyzes existing equipment data for trends and important statistics, presenting that information to manufacturers in real time in an easily digestible format sitting on top of the data streams that already exist. To learn more about how your organization can improve business performance through actionable intelligence, visit go.cyient.com/ienr- analytics-d. 8
- 9. © 2017 Cyient. Cyient believes the information in this publication is accurate as of its publication date; such information is subject to change without notice. Cyient acknowledges the proprietary rights of the trademarks and product names of other companies mentioned in this document. September 2017 About Cyient Cyient (Estd: 1991, NSE: CYIENT) provides engineering, manufacturing, geospatial, network, and operations management services to global industry leaders. We leverage the power of digital technology and advanced analytics capabilities, along with our domain knowledge and technical expertise, to help our clients solve complex business problems. As a Design-Build-Maintain partner that takes solution ownership across the value chain, we empower our clients to focus on their core, innovate, and stay ahead of the curve. Relationships lie at the heart of how we work. We partner with organizations in ways that best suit their culture and requirements. With nearly 14,000 employees in 21 countries, we combine global delivery with proximity to our clients, functioning as their extended team. Our industry focus spans aerospace and defense, medical, telecommunications, rail transportation, semiconductor, utilities, industrial, energy, and natural resources. For more information, please visit www.cyient. com. Contact Us NAM Headquarters Cyient, Inc. 99 East River Drive, 5th Floor East Hartford, CT 06108 USA T: +1 860 528 5430 F: +1 860 528 5873 EMEA Headquarters Cyient Europe Ltd. High Holborn House 52-54 High Holborn London WC1V 6RL UK T: +44 20 7404 0640 F: +44 20 7404 0664 APAC Headquarters Cyient Limited Level 1, 350 Collins Street Melbourne, Victoria, 3000 Australia T: +61 3 8605 4815 F: +61 3 8601 1180 Global Headquarters Cyient Limited Plot No. 11 Software Units Layout Infocity, Madhapur Hyderabad - 500081 India T: +91 40 6764 1000 F: +91 40 2311 0352 cyient.com connect@cyient.com 9