IoT Analytics: Reaching New Levels of Maturity and Deployment

IoT World Today and SoftwareAG recently surveyed technology decision-makers and influencers from manufacturers that have implemented IoT projects to provide real-world insights in undertaking IoT deployments and data analytics projects.

IoT Analytics: Reaching New Levels of Maturity and Deployment
IoT Analytics: Reaching New Levels of Maturity and Deployment. Photo by Franck V. on Unsplash

This article provides an analysis of this survey and highlights:

  • Primary Drivers for Adopting Analytics
  • The Benefits of Data Analytics for Various Use Cases
  • Key Challenges Utilizing Data Analytics Platforms
  • Steps to Achieving Analytics Independence
  • Roles/Functions Involved in Data Analytics Platform Decisions

Table of contents

Survey Objective
Methodology
Highlights of the Results of this Research Survey
The Expanding Role of IoT Data Analytics in Manufacturing
Data Analytics Usage: Growing Maturity
Achieving Analytics Independence
Conclusion

Survey Objective

The purpose of this survey is to collect insights from technology decision-makers and influencers from manufacturers that have implemented IoT projects and to advise on some of the key data analytics and operations challenges these organizations face and how to move forward.

Therefore, in this survey, we focused on companies that had extensive experience with IoT deployments and those leaders that were involved in the decision to purchase and deploy analytics platforms.

This report presents the key findings from the survey and our analysis. Our hope is that for those organizations undertaking IoT deployments and data analytics projects that these insights from real-world experiences will further increase the odds of IoT success.

Methodology

On April 10, 2020, Informa Engage emailed invitations to participate in an online survey to subscribers of IoT World Today. By April 20, 2020, we had received 287 completed surveys. Of those 287 respondents, 202 were qualified for inclusion in the analysis by meeting both of the following criteria:

  • Reported company involvement with data analytics;
  • Did not represent an IT company

The following analyses are based on those 202 respondents.

Highlights of the Results of this Research Survey

  • Primary Drivers for Adopting Analytics: The primary driver for adopting analytics is improved operational efficiency (63%), followed by reduced costs (53%), and improved customer service (50%).
  • The Benefits of Data Analytics for Various Use Cases: Data analytics has proven especially beneficial for risk management/cybersecurity/monitoring competitive landscapes (81% “extremely” or “very” beneficial) and supply chain optimization (80%). These were followed closely by operational efficiency (75%), regulatory compliance (75%), and predictive maintenance (74%).
  • Key Challenges Utilizing Data Analytics Platforms: Those manufacturers using data analytics extensively across their operations identify their top three challenges as data overload (38%), making meaningful use of data in real-time (33%), and cost (30%).
  • Steps to Achieving Analytics Independence: The steps to becoming fully independent on analytics are training existing employees (55%), and working to improve analytics-driven decision-making capacity (55%), followed by a focus on data quality and then evolving the maturity of data analytics usage (43%).
  • Utilizing a Systems Integrator: One in four respondents overall report using a systems integrator to manage their data analytics platforms. An additional 41% are considering doing so.
  • Roles/Functions Involved in Data Analytics Platform Decisions: The roles and functions most commonly involved in the decision to purchase/deploy analytics platforms include the IT Team (48%), C-Suite (44%), Operations Team (41%) and the Business Unit (39%).

The Expanding Role of IoT Data Analytics in Manufacturing

Manufacturers have a common goal and that’s to innovate digitally. They want to create smart products in the market and ensure efficient manufacturing processes at every stage, from design and engineering to supply chains and delivery. In the recent past, these organizations have sought to deploy IoT production at scale and establish connected ecosystems around their industrial and commercial offerings. However, a lack of overall IoT maturity has limited their ability to reach scale and integrate all assets, both inside and outside their factories.

Also, sophisticated custom requirements demand fast prototype designs and accurate performance metrics. Along with operational efficiencies, these companies desire increased visibility into their shop floor environments using data analytics. They want to use IoT-enabled tools and platforms to not only boost production quality at every stage, improve operating margins, and increase yields, but also enhance future designs and improve quality using IoT data. So we asked respondents about the primary drivers for analytics adoption.

Respondents report that they key goals for their analytics adoptions are improved operational efficiency (63%).

Adopting Data Analytics: Efficient Operations, Reduced Costs, Improved Products and Services

Respondents report that the key goals for their analytics adoptions are improved operational efficiency (63%), followed by reduced costs (53%), and better customer service (50%). In the digital era, operational excellence in manufacturing is based on an ability to integrate and connect different assets internally inside a factory, or externally with remote assets and equipment. Manufacturers can then obtain accurate performance metrics into all aspects of production, from individual machine performance to supply chain capabilities.

Manufacturers have a common goal and that’s to innovate digitally.

Chart 1: What are the primary drivers for adopting analytics at your company? Base: All respondents; multiple answers permitted (n=202).
Chart 1: What are the primary drivers for adopting analytics at your company? Base: All respondents; multiple answers permitted (n=202).

By embedding IoT technology in machines, equipment, and products, manufacturers can capture and collect information, analyze data for patterns that signal problems then take swift corrective actions. For example, they’re deploying comprehensive IoT solutions to gather input from different assets, including shop floor machinery and remote equipment, to detect quality issues, such as temperature variations, production line anomalies, and performance degradation. Moreover, they’re deploying predictive analytics to reduce unplanned downtime through sensors and actuators embedded in the equipment.

Our survey found that while reduced costs offer a strong impetus for IoT analytics adoption, manufacturers also recognize the importance of more efficient customer services. In turn, connected products and the increased number of services tied to those products through servitization offer opportunities to create new revenue streams and foster increased collaborations with customers. Manufacturers can then use IoT-enabled analytics to correlate information from multiple customer sources to improve product designs, drive usage-based business models, and power new consumer and commercial products.

Historically, the information technology (IT) and operational technology (OT) departments in asset-intensive industries such as manufacturing have functioned independently, running separate processes, based on different priorities and requirements. However, digital services are gradually changing that model. Today, IT/OT convergence enables administrators to capture a single view of their manufacturing environments and gain new benefits, such as improved visibility, higher operational efficiencies, and considerable cost savings.

Adopting Data Analytics: Efficient Operations, Reduced Costs, Improved Products and Services

Respondents report that the key goals for their analytics adoptions are improved operational efficiency (63%), followed by reduced costs (53%), and better customer service (50%). In the digital era, operational excellence in manufacturing is based on an ability to integrate and connect different assets internally inside a factory, or externally with remote assets and equipment. Manufacturers can then obtain accurate performance metrics into all aspects of production, from individual machine performance to supply chain capabilities.

By embedding IoT technology in machines, equipment, and products, manufacturers can capture and collect information, analyze data for patterns that signal problems then take swift corrective actions. For example, they’re deploying comprehensive IoT solutions to gather input from different assets, including shop floor machinery and remote equipment, to detect quality issues, such as temperature variations, production line anomalies, and performance degradation. Moreover, they’re deploying predictive analytics to reduce unplanned downtime through sensors and actuators embedded in the equipment.

Our survey found that while reduced costs offer a strong impetus for IoT analytics adoption, manufacturers also recognize the importance of more efficient customer services. In turn, connected products and the increased number of services tied to those products through servitization offer opportunities to create new revenue streams and foster increased collaborations with customers. Manufacturers can then use IoT-enabled analytics to correlate information from multiple customer sources to improve product designs, drive usage-based business models, and power new consumer and commercial products.

Historically, the information technology (IT) and operational technology (OT) departments in asset-intensive industries such as manufacturing have functioned independently, running separate processes, based on different priorities and requirements. However, digital services are gradually changing that model. Today, IT/OT convergence enables administrators to capture a single view of their manufacturing environments and gain new benefits, such as improved visibility, higher operational efficiencies, and considerable cost savings.

Accelerating IoT Deployments to Achieve Quantifiable Gains

When it came to deploying IoT data analytics platforms, survey results underscored this convergence. For instance, the primary roles and functions most commonly associated with the purchase and deployment of analytics platforms included IT (48%), C-suite leaders (44%), and Operations teams (41%). These results have implications for how and where analytics solutions are employed across a manufacturing environment and suggest significant levels of interdependency between stakeholders. It’s also clear that both IT and OT teams frequently contend with data overload and how best to onboard new technologies into existing production environments.

Chart 2: Which roles or functions are involved in the decision to purchase/ deploy analytics platforms? Base: All respondents; multiple answers permitted (n=200).
Chart 2: Which roles or functions are involved in the decision to purchase/ deploy analytics platforms? Base: All respondents; multiple answers permitted (n=200).

As an example of IoT data analytics in action, industrial equipment company Gardner Denver employs the IoT to enable its network of equipment distributors and suppliers to capture real-time operational data, increase machine quality, and reduce downtimes. By deploying the Cumulocity IoT platform by Software AG and embedding sensors into its machinery, the company can offer comprehensive data analytics to ensure continuous operation of its industrial equipment.

Through operational and technical dashboards, Gardner Denver’s customers can perform immediate fault detection, configure remote industrial assets, and reduce overall repair costs. The Cumulocity platform represents a unified, comprehensive IoT solution that delivers value quickly and allows organizations to develop sophisticated custom products as their needs and capabilities evolve. The platform enables manufacturers to accelerate their IoT vision through seamless device connectivity, cloud-accessible edge architectures, and real-time streaming and predictive analytics.

Manufacturers are adopting unified IoT platforms and combining information from connected products and processes.

Data Analytics Usage: Growing Maturity

The creation of ecosystems that capitalize on new technologies, such as artificial intelligence (AI), virtual and augmented reality (V/AR) and machine learning (ML), combined with advanced manufacturing and the shift toward a data-driven economy all point to a new level of IoT deployment maturity.

Manufacturers are adopting unified IoT platforms and combining information from connected products and processes with data analytics, automation, and ML to create new business models. Survey results indicate that conceptualization and pilot IoT initiatives of years past have moved beyond previous limitations to enabling full IoT deployment at production scale.

Manufacturers Rely on IoT Analytics, Yet Still Face Challenges

Respondents report that in their manufacturing environments, they’re most likely to use descriptive analytics (data on what happened), followed by diagnostic analysis (data on why an event happened) and finally, predictive analytics (data on events likely to happen in the future). By deploying a range of analytics, these companies are using IoT data to perform connected manufacturing tasks, from machine calibrations and remote maintenance to supply chain improvements. For example, monitoring equipment, identifying faults, and sharing real-time data with supply chain partners not only improves the manufacturing process overall, but it also increases the value that service partners can then offer to customers.

Chart 3: How would you characterize your company’s current use of data analytics? Base: All respondents (n=202); Respondents using data analytics extensively (n=66).
Chart 3: How would you characterize your company’s current use of data analytics? Base: All respondents (n=202); Respondents using data analytics extensively (n=66).

Indeed, for those respondents who find data analytics to be extremely beneficial, more than half cite operational efficiency (75%) and predictive maintenance (74%) as critical. Moreover, companies reported that IoT data analysis was also extremely beneficial for supply chain optimization (80%) and risk management (81%). Yet as IoT deployments continue to expand, manufacturers also face obstacles.

Monitoring equipment increases the value that service partners can then offer to customers.

Chart 4: How would you characterize your company’s current use of data analytics? Base: All respondents (n=202); Respondents using data analytics extensively (n=66).
Chart 4: How would you characterize your company’s current use of data analytics? Base: All respondents (n=202); Respondents using data analytics extensively (n=66).

The challenges extend across several areas, from IoT integration with existing processes, scalability, and resiliency requirements to overall security. While short-term solutions and individual IoT tools may produce results, they often lead to siloed projects and disconnected processes. In our report, the top three challenges respondents encounter using IoT data analytics platforms are cost (38%), making meaningful use of data in real-time (37%), and budget challenges (35%).

Chart 5: What are your key challenges with using data analytics platforms? Base: All respondents (n=202); Respondents using data analytics extensively (n=66); multiple answers permitted.
Chart 5: What are your key challenges with using data analytics platforms? Base: All respondents (n=202); Respondents using data analytics extensively (n=66); multiple answers permitted.

Overcoming IoT obstacles: The path to connected manufacturing

In general, C-level leaders often assume they must choose between deploying one-off, specialized IoT tools or developing sophisticated custom solutions. Although they want to accelerate their IoT programs, they face obstacles in terms of technical complexity and unnecessary risks. And while our survey found that a majority of respondents (64%) are building their analytics platforms using internal resources, these approaches can pose uncertainties in terms of security, complexity, and long-term resiliency.

By contrast, production-scale, unified platforms enable manufacturers to transition to the next level of IoT maturity in terms of flexible, distributed architectures that support cloud-based applications and dashboards, on-premise legacy solutions, edge processing, and hybrid approaches. For example, a global leader in imaging technology and managed printer services was facing increased competition, ineffective field service maintenance, and a lack of supply chain visibility.

The organization wanted to harness the power of a unified IoT platform and deploy advanced analytics to gain key insights across their production processes and remote field services. The company deployed Software AG’s Cumulocity IoT platform to track equipment conditions, perform predictive maintenance, and monitor supply chain processes. Working with Software AG enables them to reduce equipment servicing costs by 40% while significantly increasing fleet reliability. The company depends on Cumulocity IoT to anticipate potential breakdowns, minimize downtimes through scheduled maintenance, and automate service requests.

Working with Software AG enables them to reduce equipment servicing costs by 40% while significantly increasing fleet reliability.

Due to its flexible and distributed architecture, the Cumulocity IoT platform fulfils 70% to 85% of IoT requirements as soon as it’s deployed. Immediate gains like these alleviate the need and expense of having complex, in-house engineering engagements around IoT deployments. Moreover, a range of IoT capabilities are available through Cumulocity IoT, including device connectivity, messaging, integration, streaming analytics, machine learning, and predictive analytics as well as process modelling and IT portfolio management.

Chart 6: How are you building your analytics platform? Base: All respondents (n=202); Respondents using data analytics extensively (n=66).
Chart 6: How are you building your analytics platform? Base: All respondents (n=202); Respondents using data analytics extensively (n=66).
Chart 7: What were the key steps you took to become fully independent on analytics? Base: Respondents exclusively using internal resources to build their analytics platforms; top three answers permitted (n=46).
Chart 7: What were the key steps you took to become fully independent of analytics? Base: Respondents exclusively using internal resources to build their analytics platforms; top three answers permitted (n=46).

Achieving Analytics Independence

Manufacturers are increasingly looking for ways to deliver innovation quickly. However, many often deploy inefficient IoT tools and applications that lack integrations, or else they rely on IoT initiatives that face impending obsolescence. In many ways, these IoT technology dead-ends offer organizations an opportunity to reassess the IoT landscape and identify the benefits and productivity gains of a comprehensive IoT platform. Moreover, a software partner like Software AG can help businesses, regardless of where they are in their IoT journey, evaluate technical complexity, program risk, and the path forward.

Based on our report, the key elements of analytics independence are training current employees (55%) and working to improve analytics-driven decision making (55%), followed by a focus on data quality and evolving the maturity of data analytics usage (43%). While these results represent a range of businesses with diverse interests and specialized products, they all have one goal— increased digital innovation.

A software partner like Software AG can help a business evaluate technical complexity, program risk, and the path forward.

Taking a Holistic, Unified Approach to IoT

For manufacturers, two of the many ways to solve operations challenges are using systems integrators and/or automation. In general, small enterprises and businesses use external vendors, such as cloud providers, to offload certain operations tasks and/or change how tasks are performed to increase efficiency. As a single, integrated platform, Cumulocity can provide a stepping stone approach to acquire more IoT capabilities and achieve analytics independence. In addition to offering a complete IoT solution, the platform provides a high degree of modularity allowing organizations to integrate preexisting analytics tools or dashboards.

Across manufacturing, the availability of inexpensive technology and more tech-savvy engineering teams have enabled companies to build their IoT solutions. However, such an approach can pose operations and data-related risks. The ability to choose SaaS, PaaS and on-premise solutions from a unified platform offer a critical advantage. Whether it’s real-time data streaming for predictive analytics or greater workflow automation, a flexible and distributed infrastructure makes it easier for companies to gain IoT maturity, starting small and providing value and scalability as their business grows.

The Critical Role of Systems Integrators and Solutions Providers

To be sure, when asked about extending IoT implementations, a majority of respondents perform analytics processing in the cloud (62%), while a smaller percentage do so in local data centres (45%). Moreover, for those organizations that use data analytics extensively, one in four (25%) report doing so with edge computing. Of course, it’s important to have an IoT software solution that can connect to everything within the four walls of a factory as well as beyond to remote locations.

For example, U.S. manufacturer of industrial tools and hardware, Stanley, Black, & Decker wanted to innovate digitally, create smart products in the market, and ensure greater efficiency in their manufacturing processes. After evaluating several IoT solutions, they selected the Software AG’s Cumulocity IoT platform due to its ease of use and the ability to deploy use cases into production quickly. Moreover, a multitude of business units can simultaneously develop and build different products in one space. These analytics development tools are designed for both business users and developers alike and they feature smart rule wizards, filtering, correlation, aggregation and pattern detection.

Increasingly, solution providers are playing a critical role in helping organizations navigate through IoT interoperability issues, from connecting OT systems with new platforms to accessing relevant data through mobile applications and dashboards. For many, achieving a high degree of integration has been an obstacle to gaining full value and dynamic IoT scalability. These new manufacturing scenarios require collaborations between IT together with OT and this represents a significant departure from the past.

Integration issues with the IoT are also the result of a diversity of IoT products, protocols, and environments as well as connectivity and networking standards. These requirements have important implications for a host of disruptive technologies, from the cloud and AI to machine learning and edge analytics. And while on-boarding new technologies into their existing IT environments present challenges, a unified IoT platform approach offers substantial cost and operational benefits.

Conclusion

Manufacturers around the globe know the impact of unplanned downtime on machinery and equipment. For small enterprises and businesses, the cost and delay to production are simply untenable. Fortunately, connecting processes using IoT and analyzing real-time data offers new possibilities for competing in today’s digital marketplace. Indeed, having versatile and scalable IoT deployments to optimize asset reliability, streamline operations, and launch connected products have become imperative.

For many organizations, the pressure to deliver product innovation can push them to design and build their IoT platforms. However, their IT and OT teams then become saddled not only with managing these connections but also performing efficient data analysis. In addition to security risks, these in-house deployments can also have issues around scalability and long-term resiliency.

Source: SoftwareAG

Thomas Apel Published by Thomas Apel

, a dynamic and self-motivated information technology architect, with a thorough knowledge of all facets pertaining to system and network infrastructure design, implementation and administration. I enjoy the technical writing process and answering readers' comments included.