27, Sep, 2021
Predictions 2019: Better ROI For The Industrial Internet Of Things

Predictions 2019: Better ROI For The Industrial Internet Of Things

Yet this journey that manufacturers and technologists have been on hasn’t come easy. Years of investment have gone into doing IoT right, not just connecting devices.  2019 is the year IoT gets refined, making “manufacturing intelligence” a bold reality for improving ROI.

Here’s what we can expect to see as three of the biggest IIOT trends this year:

The Algorithm Economy takes root

As business decision makers continue to approve larger Industrial IoT projects beyond pilot stages, they will drive manufacturing intelligence. Data from the Industrial IoT is becoming a valuable enterprise asset and the real advantage comes with the development of specialized algorithms.

In working with a large packaging films manufacturer, we have seen how they have been able to use their data as a leverage to gain a competitive edge. They used big data analytics and their proprietary know-how to understand parameters that if controlled in the production environment, can yield ever greater product quality. It began with them creating a manufacturing data lake by collecting sub-second data for over 300 parameters, over six months across the entire production process, and correlating this data to quality failure data from machines and IT systems. They then overlaid their specialized knowledge of the packaging process to enhance quality benchmarks by over 20 percent. And this is just the beginning of their endeavors. As they mine their digital footprint they are continuously innovating and creating even bigger breakthroughs.

Companies across the industrial world are now looking to incorporate their own algorithms to benefit their manufacturing process. By processing vast amounts of data, data scientists are now able to solve multi-layered problems. Up until now, the data being gleaned from the shop floor provided data scientists with only partial and siloed data. Now, having access to the complete picture that includes all of the data, a new set of reporting applications can be built, in addition to process controls, for true centers of excellence (CoE). Today, just one expert in a single location can analyze 50 or more plants across the world by working with the core software platform layer and be in a position to push these algorithmic enhancements back down to the plants via the edge IOT infrastructure at the click of a button.

Data Commons create a mark in the industrial world

There is increased interest in Original Design Manufacturers (ODMs) getting production/quality visibility from their supply chains.  Industrial conglomerates around the world are now tapping into analytics from their supply chains. The goal is to get a closer control on the inputs that affect bottom lines. This extends beyond the concepts of Just-In-Time production that looked at collapsing response times and inventories across the supply chain.

This year, ODMs are looking to proactively enhance compliance with quality norms and production benchmarks among their suppliers. Traceability across the supply chain is just one key benefit that is driving this advancement. Creating a data commons that enables automated sharing of production, quality, energy, maintenance data between ODMs and their suppliers is the way for them to achieve these objectives. While such initiatives have existed in the past, what makes these data commons unique is that they are pulling IIoT data directly from machines and sharing them with the ODMs via Application Programming Interfaces (APIs) exposed by IIoT core software platforms.

This trend in 2019 will force parts suppliers to implement the IIoT. In fact, the early adopters in the automotive supply chain are already using their IIoT enabled data sharing of productivity and quality as a differentiator to garner more business from ODMs. 

The Digital Native factory arrives: Making the factory edge more intelligent with more compute, networking, storage and software interfaces

In 2019, manufacturers will only open a greenfield manufacturing facility if the digital footprint of that factory is already in place. Existing facilities have been rapidly enhancing the quality of their digital footprint to benefit from IIoT. Every factory has to be in a position to tap into machine data and make it available for IT applications. This requires every equipment, automation process and IT system implemented to be built with the capability to share data via open protocols and standards. All equipment manufacturers of note have already committed to adding IP interfaces and support for open data standards and protocols like MTConnect and OPC-UA. Machine data traversing a dedicated networking infrastructure can service stringent SLAs. The rapid rise of the intelligent edge layer for IIoT requires that more computing happen within the factory.

The factory’s appetite for more computers and storage within its facilities has increased dramatically. New software in the digital factory has enabled many new applications that were previously impossible. Across the world, workforce productivity and skillsets are being re-evaluated. Today, a leading tire company scans a worker’s credentials and skillset in operating a machine in real-time. If there are any flags regarding that worker, the machine will not start. This automation avoids quality and safety issues right at the source, ones that may result from an unauthorized or unskilled worker operating that machine. It also allows for the dynamic allocation of resources to machines and processes that maximize outcomes from a particular work shift.

It’s 2019 and manufacturers are going into overdrive implementing the Industrial IoT (IIoT). Picture millions of machines on shop floors across thousands of factories that direct their human capital and traditional business intelligence software with the goal of attaining massively improved productivity through real time intelligence. Machines are now pushing data into the cloud to increase uptime, production quality, and energy efficiency.