Unlock the business value of IoT using performance management

The Internet of Things (IoT) had enormous potential to change all our lives. But to maximize the value of connected devices and speed up service delivery, it is essential to have control at scale with real-time usage and performance metrics.

It goes without saying that equipment manufacturers will sell more machines if they can provide integrated solutions to enable their customers to improve overall equipment effectiveness and energy efficiency, better control costs, resolve issues causing downtime, and effectively manage devices wherever they are located.

According to IDC, an increase in commercial and consumer adoption is expected to drive global IoT spend to $1.1 trillion in 2023. However, all these “things” can only fulfill expectations if they are appropriately managed. “While organizations are investing in hardware, software, and services to support their IoT initiatives, their next challenge is finding solutions that help manage, process, and analyze data being generated from all these connected devices,” explains Carrie MacGillvray, group vice president, internet of things, 5G and mobility at IDC.¹

At the same time, enterprises are increasingly deploying their IoT software, including applications, analytics, and platforms, to the cloud. By the end of 2023, nearly one-third of IoT software spending will go toward public cloud deployment.

While some organizations may decide to stick with on-premises platforms, the scalability and benefits of a public cloud infrastructure provide a real opportunity to speed up product development and reduce start-up costs. IoT performance management is crucial.

Optimum performance is essential for maximum business value

IoT is transforming industries across the board, from manufacturing to health care. Allowing enterprises to operate more efficiently, make smarter decisions, and service their customers better. Despite these benefits, IoT brings with it some management headaches. If devices are not operating at their optimum level, enterprises will be disappointed in the value extracted.

These challenges include ensuring firmware updates and security patches on devices don’t impact performance levels, maintaining visibility into the IoT ecosystem, and monitoring how IoT applications interact with cloud services to avoid shortfalls.

Data availability and collection also often cause obstacles. Even with automation and programmable logic controllers (PLCs), many enterprises still collect using ad-hoc manual processes, extracting data using Excel spreadsheets and other simplistic methods. Data related to run time could be more efficiently stored in an enterprise resource planning (ERP) system, and data related to output in a manufacturing execution system (MES). Data linked to maintenance and downtime sitting in computerized maintenance management systems.

Diagnosing the root cause of performance is another concern. This can require the input of large improvement teams working over weeks, sometimes months, to make any difference. This is where an integrated performance management solution can provide all the operational functionality and capabilities necessary to enhance actual equipment performance.

Meeting performance management head on

Defining performance, quality, and availability targets that are coherent for a production environment also proves a puzzle for many enterprises. Tracking the six significant losses, which prove illusive to many company processes, including planned, unplanned, and micro stops, together with start-up and production rejects, is critical.

There are often frustrating trade-offs, making it more difficult to analyze the most effective ways to improve OEE (Overall Equipment Effectiveness). This common metric is designed to measure an asset’s level of productivity, and the rich stream of data provided by IoT devices can improve OEE in several ways:

• analyzing historical data to optimize performance planning
• getting red flag warnings on the degradation of systems
• tapping into predictive maintenance to avoid expensive downtime

All these continually improve the customer experience and create new outcome-based services built around uptime, consistent quality output, and reduced energy consumption, for example. In addition, OEE calculations can help increase machine life by predicting maintenance needs and enabling customers to get maximum value from their machine investment. Manufacturers are increasingly offering OEE-as-a-service.

IoT analytics can be added to improve the customer experience continually. This is simplified using robust IoT tools that allow artificial intelligence (AI) and machine learning (ML) to provide a smooth analytics path.

Digital twins can also bring additional benefits, taking real-time IoT data and applying AI and data analytics to optimize performance. Manufacturers can glean valuable new insight by being constantly fed with data from both production and the product. Digital twins can be deployed during any or all the lifecycle stages.

Taking performance analytics to the next level

Huntsman, a multinational chemicals company that prides itself on its innovation could see that it had a data disconnect between process experts and scientists. The inability to merge daily data, new sensor data, and lab-analysis into one created bottlenecks and batch quality issues.

The multinational opted to adopt TrendMiner, a Software AG self-service industrial software solution designed to continuously improve production processes in smart factories and industry 4.0 operations. Self-service industrial analytics accelerates root-cause analysis and helps find new areas for optimization.

TrendMiner democratizes the analytics allowing process experts to analyze, monitor, and predict production processes themselves, without the help of data scientists…it is a powerful tool using a combination of daily data, new sensor data, and lab-analysis data, enabling us to scale our operations through global collaboration. The use cases are compelling when it comes to showing the value we’re getting out.”

Jasper Rutten, Advanced Analytics Manager, Huntsman

The transformation has enabled Huntsman to move from being an experience-driven to data-driven operation, reduce costs, and enhance production. It can now build soft sensors on operating conditions, for example, to predict the product quality of certain chemicals. In addition, it is now possible to have 24/7 quality control instead of relying on lab analyses only available during working hours.

IoT performance must align with business objectives

The IoT landscape is changing. “Organizations implementing IoT are increasingly focusing on the business outcomes of the technology. IoT initiatives are no longer driven by the sole purpose of internal operational improvement,” according to Gartner.² Equipment manufacturers need a clear understanding of customers’ business goals.

Adopting IoT and achieving business outcomes is a complex one. Manufacturers need a well-defined IoT strategy and a solid metric to measure success. Performance management addresses issues in real-time. This improves reliability, increases customer satisfaction and retention, and lowers overall product support and ongoing maintenance costs.

Equipment manufacturers who do not invest in performance management for their assets are putting their heads in the sand. Customers will not be able to achieve their targets in a cost-effective way, which will end up seeing equipment manufacturers lost customers to the competition in an increasingly disruptive marketplace.