Reinventing IIoT Platforms for Discrete Manufacturers

Illustration: © IoT For All The Industrial Internet of Things (IIoT) is hot right now, as manufacturing represents perhaps the largest greenfield opportunity left for digitization. Yet, IoT platform implementations have historically had a high rate of failure within this vertical.

Source: Reinventing IIoT Platforms for Discrete Manufacturers

What’s contributing to these failure rates, and what needs to change?

This post examines common approaches for enabling Industrial IoT initiatives, the pros and cons, and their culpability for the high failure rate. We then introduce a new approach to IIoT Platforms that are already driving rapid, continuous value creation for discrete manufacturers and the companies that provide and service their manufacturing assets.

The IIoT Platform “Revolution”

The term Industry 4.0 encompasses a promise of a new industrial revolution—for the industrial sector, one that marries advanced manufacturing techniques with the Internet of Things to create systems that are not only interconnected but communicate, analyze, and use the information to drive further intelligent action back in the physical world.

Today is the internet moment for manufacturing, and with it comes the gold rush of providers ready to enable the industry’s digital transformation.

At this point, it would be impossible to work within the manufacturing space and not have spent the past few years bombarded by pitches for IIoT platforms claiming to best support the Industry 4.0 revolution.

These magical platforms market their unique machine learning (ML), artificial intelligence (AI), and edge/cloud/fog technologies to enable any industry’s fabled digital transformation through predictive models, digital twins, and fully-automated workflows.

There are more than 450 IoT platforms to choose from, according to IoT Analytics. It can be easy to think Industry 4.0 has indeed arrived, and manufacturing’s digital transformation is finally at hand. The data, however, tells another story.

IoT implementations have had a historically high rate of failure. Cisco produced a report of survey results indicating that companies considered 76 percent of their Industrial IoT initiatives failures. This has led to more significant hesitation on the part of manufacturers to embark on digital transformation journeys.

So what’s driving companies to fail at such a high rate when a majority said that IoT initiatives looked good on paper?

This article focuses on the story’s technology component and IoT platforms specifically, with the goal of both identifying and proposing a solution to manufacturing’s platform problem.

The Platform Problem

There are many different IoT platforms, including application enablement platforms, device management platforms, analytics platforms, and others.

In July 2019, Gartner published its first-ever 2018 Magic Quadrant for Industrial IoT, which included companies that provide IIoT platforms that work in multiple verticals. However, no company crossed Gartner’s bar for execution, and not one made it above the midway horizontal line (indicating a strong ability to execute). This demonstrates that successful execution and value attainment is elusive.

The challenge with a platform is that it can be time-consuming and expensive to implement and deploy. The investment required to be trained on the platform and model and build the initial applications and solutions that generate value for the customer can be prohibitive.

When evaluating return on investment (ROI), use cases, rather than the underlying platform, ultimately drive value. Since most generic IoT platforms can’t deliver packaged manufacturing use cases in the form of services, applications, or solutions by themselves, the onus of enabling a use case using an IoT platform falls on the customer or systems integrator.

Many manufacturing transformation leaders have struggled with determining a tangible and acceptable ROI from their IoT platform investments. More often than not, their projects go over budget, deployment times run long, interoperability issues occur across legacy systems, or planning and resources aren’t allocated appropriately. This all leads to a disappointing ROI or even cancellation of the initiative.

The Discrete Manufacturing Challenge

Manufacturing has several unique challenges that are difficult to address with generic IIoT platforms.

Data Variety

Not only are there many distinct types of equipment—Lathes, Mills, Plastic Injection Molding, Stamping, Laser Cutters, Robotics—but depending on the mechanisms available for acquiring data from those systems, the data points can be diverse. To provide effective tools for analyzing that data across distinct systems, the data must be transformed into a standard data model.

Data Volume

Manufacturing equipment, and discrete manufacturing equipment, in particular, are very complex. A machine is a large system of components that work in coordination, resulting in hundreds of distinct data points that change constantly.

Depending on the application, there may be situations where it’s required to capture data at rates of 100Hz or 100KHz. Platforms consuming this must analyze data at multiple levels within the system to avoid sending and storing unnecessary data when only the aggregate or computed result is sufficient.

These systems must be capable of performing complex processing where it’s most appropriate—at both the edge and in the cloud.

Data Speed

While some systems can provide value with low fidelity and high latency, certain IoT use cases require much more real-time data to be effective. Edge technology is required to process high volumes of data, make decisions in milliseconds or less, and act to potentially prevent damage to the machine or the workpiece.

Number of Disparate Systems

Integrating legacy systems is a complicated task. Having robust data models for each application serving the vertical are required to capture events adequately. Furthermore, having a deep understanding of how the data from each of those systems interact within manufacturing is also necessary to make correlations and provide coherent analysis for process improvement.

IT Infrastructure

Highly elastic and scalable systems are a new entrant to manufacturers and their IT organizations. Due to the intense processing and storage requirements of IoT, it can be prohibitively costly to overprovision a system for peak load at all times.

Utilizing secure cloud systems with virtual hardware architectures programmed to be highly available, scalable, and fault-tolerant with multiple data centers separated geographically for disaster recovery is even more important when considering the value that a successful IoT initiative can bring to an organization.

Investing in IIoT Platforms

Discrete manufacturers invest in IIoT platforms to answer four basic questions:

• What’s happening?
• Why is it happening?
• What’s going to happen next?
• What can I do about it?

To correctly answer these questions and deliver business value, one needs to understand their data within the context of their own operations. This requires a singular focus on the vertical to realize the immediate and continuous value promised when undertaking an Industry 4.0 digital transformation initiative.

With the complexity introduced by these challenges, it should be no surprise that generic IoT platforms often fall short when it comes to manufacturing.

Limitations of On-Premises Packaged Applications

Currently, manufacturers have options to embrace data-driven manufacturing other than IoT platforms. Instead of building out applications and solutions that meet specific needs using a platform, manufacturers often opt to buy a packaged application or solution or hire a third-party to build a solution to leverage various platforms, tools, and technologies.

These packaged applications and solutions have proliferated the market for decades, and the primary benefit is time to value. They can be set up quickly at a relatively lower cost and rapidly drive incremental value.

One disadvantage of packaged applications and solutions is the customer must adapt their processes to conform to the software. Packaged applications and solutions can also be difficult to customize, extend, and integrate with the many disparate systems in a manufacturing facility.

The customization of packaged applications and solutions and customized integrations to other disparate applications and systems across the manufacturing enterprise can create a maintenance nightmare and has resulted in paralysis at many global manufacturers.

These issues are a few of many that have led to the opportunity we see today in the market for something better.

A New Approach: Jump Starting the Platform

Until recently, the options for enabling IoT initiatives and driving digital transformation for discrete manufacturers were limited to the horizontal platform approach, integrating a packaged application or solution, or some combination of each through a myriad of disparate product and service vendors.

But what if you could have your cake and eat it, too? What if you could have the continuous improvement and innovation opportunity provided by a platform with the immediate benefit and ROI of a packaged service, application, or solution?

Adding Vertically Packaged Applications

To offer quick time to value, vertically focused applications that provide actionable information for specific user personas are necessary. The foundation itself provides the data, but the value is in making the data actionable.

There are many opportunities to deliver packaged applications that make manufacturing data actionable for various consumers within the manufacturing lifecycle. Here are a few examples we’ve developed to ensure our users can create rapid value.

Built-in capacity utilization reporting allows plant managers to make capital equipment purchase decisions. Knowing how your factory’s machine utilization compares with the industry average can either drive the decision to purchase more equipment or new equipment and whether to invest in internal operations to optimize the utilization of existing equipment.

A tablet application mounted at the machine empowers operators to add human context to machine data and meet production goals.

Through text and email notifications delivered to their mobile phones, the app enables operators to respond to problems faster and more efficiently with instructions that display when a machine needs attention, such as an inspection, a tool change, or maintenance. The app also tracks changeovers and setups so that when the process exceeds the expected time, the supervisor is called over to manage the issue proactively.

For OEMs and distributors of manufacturing equipment, a packaged application enables remote monitoring and diagnosis of machine health problems assets in the field and at customer sites in real-time.

With the application, service technicians can remotely visualize machine diagnostics and conditions to quickly identify problems, allowing them to either help troubleshoot and resolve the issue without the on-site visit or, if they need to go to the plant, bring the right tools and order the right parts in advance of a plant trip. The result is better service and improved machine uptime for the customer, and reduced costly on-site service visits.

Platform Extensibility

The challenge with vertically packaged applications is that more use cases drive rapid value creation with manufacturing data than any company can build and support alone. Thus, robust APIs are built which allow third parties to extend the platform and build their own vertically packaged applications that allow even more value creation opportunities to be driven with the data. This requires more investment of time and risk, but the payoff can be large.

For example, machine builders have unique domain expertise with regards to the design and operation of their own machines. This gives them a natural advantage when it comes to the optimization of these complex and highly specialized assets. The challenge, however, is to deliver a solution to enable machine builders to leverage this expertise.

It’s tremendously difficult to seamlessly deploy an application across a fleet of machines, not to mention to maintain this solution over time. The challenge is to leverage as few apps as possible to monitor, analyze, and manage the proper notifications for their various equipment types for the customer who has many different assets.

Leveraging a verticalized IoT solution, machine builders can now provide advanced analytics and custom micro-services to their customers through a scalable, vertically integrated cloud platform. For example, machine builders can offer algorithms customized for their own equipment that deliver predictive health notifications of spindle life. Benefits such as this create competitive differentiation for machine builders and an opportunity to drive incremental growth through new services and business models.

The ultimate end customer, the discrete manufacturer, and their consulting partners also possess unique insight and expertise regarding their manufacturing operations, processes, and products.

Integrations with Existing Third-Party Applications

There is an ecosystem being developed of modern, cloud SaaS manufacturing applications. These are designed to integrate with other SaaS applications through APIs easily. For manufacturers who use these applications, integrations and time to value is shortened.

Industrial IoT data can drive value across this ecosystem with integrations into ERP/MES, BI, Quality, HR, and CMMS/Maintenance. As this ecosystem evolves with more vertically focused applications in the market, the time to value and need for customization is greatly reduced.

This provides the manufacturer a complete solution across the entire manufacturing stack that is vertically integrated for your business, flexible for business process changes, and easily updated and maintained through cloud deployments.

Legacy ERP/MRP systems that try to tackle every problem do not support integrations, require costly customization and configuration, and prevent the business process changes.

Conclusion

In the age of Industry 4.0, discrete manufacturing’s digital transformation requires a new approach that creates confidence and inspires future innovation. The initial success of an implementation is so important, not just to demonstrate the value of the initial technology but to realize the belief in the benefits enabled through Industrial IoT technology.

This is, of course, not the end of the digital transformation story; it’s just the beginning. As users gain confidence in the value of the technology, it’s essential to provide customers a roadmap to continuous innovation enablement. We call this roadmap the manufacturing analytics journey.

Only by incorporating this approach can IIoT platforms deliver real business impact for discrete manufacturing. Without it, most of them will continue struggling to deliver on the promise of Industry 4.0.