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How manufacturers unlock business value through IoT analytics

As the Internet of Things (IoT) becomes more prevalent in the manufacturing industry, companies are capitalizing on the tremendous business opportunities it can provide. Embedded sensors and other "edge technologies" are enabling manufacturers to gain deeper insights into customer needs and behavior patterns. These insights provide significant opportunities for cost savings and incremental revenue by enabling the entire manufacturing ecosystem to respond quickly and appropriately to changes in customer usage.

Microsoft estimates the opportunity for IoT-generated data at $371 billion, which will be realized both by cost-containment and revenue-generation. Capitalizing on this opportunity requires solutions to contextually analyze multiple data sets to generate real-time insights and often create close-loop action on the instrumented products or assets.

Monetization strategies typically require a scale approach after successful proofs of concept or pilot programs. A scale approach to IoT monetization must address:

  • Deep customer understanding of the intended design objectives and outcomes
  • Understanding of IoT consumption – a human or a machine as the end point
  • Identifying information needs for data as an asset class
  • Converting raw "bits and bytes" into meaningful insights and foresights
  • Defining "actions for change" from an infrastructure and organizational skills perspective

These steps naturally require a rethink on how to "absorb" IoT-generated data and how an organization creates capabilities to leverage "next-generation analytics" to deliver the promise of IoT monetization.

IoT analytics at the network’s edge

Enabled by a new generation of sensors and related data collection technology, IoT analytics can collect, organize, analyze, and communicate results in real time at the point of origination – the device level (as well as centrally, if needed). IoT-enabled analytics powers Distributed Decision Making (DDM), where informed actions can be made quickly and locally.

All this is possible because of the sensing and computational capability of distributed "things" at the edge of the IoT network, their ability to collectively communicate and collaborate, and their capacity to apply analytics insights at a local level.

Edge analytics is being driven by the business needs of individual manufacturers to better understand and respond to their customers, as well as to achieve operational savings. This is particularly useful in applications that require a lot of bandwidth, such as smart lighting, parking spaces, offshore oil rigs, or alerting a manufacturer to switch off a valve with forewarning of a potential leak.

As with any transformation capability-building scenario, manufacturers must make sure that edge analytics is part of an overall IoT strategy. Otherwise, distributed data collection and processing can create information silos and, potentially, unintended security vulnerabilities. These vulnerabilities can become apparent if technology is deployed in isolation from other elements of the enterprise information and security architecture.

Monetization potential – how to profit

Companies today are realizing the monetization potential of IoT analytics both for making money and saving money. The opportunity to generate incremental revenue extends beyond the usual end-user customer scenario to opportunities that span a manufacturer's entire ecosystem of partners and stakeholders. For example, if a car contains sensors that detect when tires are wearing out, those insights can be packaged, productized and sold as advisory services to enable:

  • Vehicle owners to schedule replacement
  • Tire manufacturers to improve design
  • Auto insurers to proactively encourage tire replacement
  • Stores and restaurants to advertise support and "while-you-wait" services

IoT-enabled operational savings also provide significant opportunities to improve profitability and increase competitiveness. IoT on the shop floor can help predict and reduce outages; IoT in the warehouse can help reduce spoilage and optimize inventory storage and accessibility; IoT-enabled fleet services can be used to track goods and reduce delivery delays by anticipating bottlenecks and providing alternative routing; IoT in dealerships and service centers can help to reduce parts inventory and improve labor scheduling.

According to a 2015 report by market research consultancy Frost and Sullivan, a 50 percent penetration of IoT in manufacturing will create cost savings of between 2 and 4 percent, or $500 billion worldwide. Harley-Davidson reconfigured and equipped its facility in York, PA, with sensors and location awareness programs to reduce the time it takes to produce customized motorbikes from a 21-day cycle to six hours. Flextronics, a global leader in design, manufacturing, distribution, and after-market services, uses real-time data correlation, analytics, and enhanced data visibility to quickly identify and address irregularities in components supply. Rio Tinto, one of the world's largest metals and mining corporations, estimates current savings of over $300 million by deploying autonomous mining – driverless trucks to move iron ore controlled by staff located over 1,200 miles away.

In total, business consultant McKinsey estimates the value created by IoT applications will be $1.2 trillion to $3.7 trillion by 2025.

Analytics is at the core of these benefits as IoT data is contextualized, creating enriched insights. The actions and automated responses that create this value all depend on the effectiveness of the IoT analytics strategy and how it is deployed.

Crucial IoT analytics considerations

There are three fundamental areas where manufacturers can improve their use of IoT analytics:

  • Connected products: These are products that are embedded with three core elements: physical components, "smart" components and connectivity components.

    The embedded sensors utilize connectivity to communicate and exchange data about the state of the product with other products and systems in its environment. Connected products improve product functionality, reliability and utilization, as well as customer satisfaction and loyalty.
  • Connected supply chain: A production line, when connected to partners and suppliers, enables all stakeholders to understand interdependencies, material flows, information and process cycle times. IoT systems can anticipate logjams and shortages of material. Manufacturers and partners can then use this real-time data to work collaboratively on practical alternatives to offset these issues.
  • Informed manufacturing: Informed Manufacturing, sometimes called "Industry 4.0", refers to networked, intelligent machines that enable people, processes, products and infrastructure to coordinate seamlessly, creating finished goods that are more time- and cost-efficient to produce – and meet or exceed customer expectations.

Getting the data right

A key component of IoT analytics is getting the right kind of data. This will differ from business to business, at different levels of the business, and again among the larger community of partners, suppliers and stakeholders. It needs to be of high value, easy to access, available in real time, applicable to a significant portion of their business processes and customers, and provide inputs which, when properly analyzed, can help drive meaningful change.

Data analysis can include descriptive analytics ("What has happened?"), predictive analytics ("What could happen?"), or prescriptive analytics ("What should we do?"). While manufacturers will typically use some combination of all three based on a particular issue or circumstance, prescriptive analytics provides the greatest potential value by going beyond observable and actionable insights to recommended and forward-thinking action.

Increasing the odds of success

IoT transformation needs to be thought of as an integrated process – versus siloed or sequential initiatives. A clear mapping of all data elements and potential stakeholder insights is essential. Contextual data such as historical trends and issues relating to equipment condition and plant utilization can generate deeper operational insights, predictions and recommendations than traditional business intelligence approaches. A common data model needs to be developed that can combine unstructured and structured data, as well as standardized and interoperable global interfaces.

"""Manufacturers need to find data that, when properly analyzed, can help drive meaningful change."

Manufacturers can increase their odds of success with IoT analytics by:

  • Identifying business-critical insights and applications at multiple points in their ecosystem
  • Identifying and prioritizing key stakeholders and their needs
  • Pinpointing the processing and decision-making required at every level and stage of the IoT network, primarily focused on reporting and advanced analytics
  • Defining a strategy and approach for distributing IoT analysis and insights, including the use of edge analytics
  • Making data/information/insights an integral part of systems and processes within the organization and across the extended ecosystem
  • Establishing decision-making logic based on insights from advanced analytics

Moving forward

To get maximum value, manufacturers – collectively and individually – should embed appropriate IoT analytics-driven process changes across each stage of the value chain to ensure data produced by devices and sensors is analyzed in synchronization with specific use-case requirements. Moving from descriptive and predictive analytics to prescriptive analytics will enable manufacturers to anticipate and address issues before they happen. They must also focus on developing a portfolio product and delivery services that provide value across multiple nodes of their enterprise ecosystem, generating additional opportunities for both cost savings and incremental revenue.

About the authors

Aala Santhosh Reddy, Senior Researcher, Cognizant Research Center

Dr. Gautam Sardar; Senior Director, Innovation, Cognizant Manufacturing & Logistics

About Cognizant

Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process services, dedicated to helping the world's leading companies build stronger businesses. Headquartered in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry and business process expertise, and a global, collaborative workforce that embodies the future of work. With over 100 development and delivery centers worldwide and approximately 255,800 employees as of September 30, 2016, Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world.

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