By Keith Higgins, vice president of digital transformation for Rockwell Automation
In 2019, we saw a 400 percent growth in digital transformation projects moving through the post-implementation stage, according to a recent study by Rockwell Automation.
The maturation of digitization projects will continue throughout 2020, and with this trend, the industry will evolve from exploring the primary benefits of data-driven solutions to understanding how these projects can be used as a resource to help scale smart factory initiatives.
Digital transformation has reached its inflection point. As organizations move their initiatives from project roll-out to continuous process improvement in 2020, scaling becomes a key topic of conversation.
Specifically, the industry will tackle the following challenges as digital business strategies scale and mature:
- High volumes of industrial infrastructure becoming integrated/connected
- Orchestrating multisite roll-outs
- Tighter operations technology / information technology (OT/IT) integration as more OT infrastructure (devices, production lines, plants) are tied into digital transformation initiatives
One stop shop
Customers today seek single-vendor, full-stack solutions to power industrial digital transformation initiatives.
Industrial organizations are struggling to effectively deploy and maintain comprehensive, unified digital transformation initiatives – and given the complexity of IIoT systems, customers crave end-to-end partners that can support wide-scale deployments.
In fact, 57 percent of respondents to a recent Rockwell Automation global survey said that having an end-to-end solution provider was extremely important for their digital transformation initiatives.
To experience return on investment from digital transformation initiatives, organizations must be able to quickly and efficiently gather enormous quantities of industrial data, include context with the data, and turn it into actionable insights in real-time.
Businesses that are digitally transforming their operations need a complete ecosystem that can help them simplify technology deployments and quickly achieve results.
Customers will pursue vendors that provide a single source for services, solutions, and updates to the entire IIoT ecosystem – or vendors that leverage partnerships with other vendors to create unified solutions to serve customer needs.
Seamlessly connecting all levels of a business and turning raw data into powerful insights happens when devices and systems are integrated, and data is standardized. However, most vendors can’t do this alone.
In 2020, driven by customer demand, more vendors will offer full-stack solutions with the right mix of expertise and technologies to increase digital transformation success and drive project ROI.
The augmented reality workforce arrives: Enabling the ‘bionic’ worker
One of the main concerns for industrial organizations in 2019 was the growing skills shortage and the need for employee cross-training.
As a potential solution, employers looked to modernized technology initiatives, such as augmented reality (AR), enabled by digital transformation, to gain an advantage in recruiting, training, and employee development.
For example, novice workers can use augmented and mixed reality headsets for training in a digital environment to learn how to handle problems to develop specific skill sets in the industrial environment with high precision and little training before they’re ever exposed to them, such as increased line speed, quality issues, machinery downtime, and hazardous conditions.
In previous years, these solutions were still considered mostly hypothetical, yet in 2020, we will see industrial organizations implement fully deployed AR training initiatives.
This shift to “bionic” workers will combine machine and human capabilities to not only increase productivity, tighten production schedules, maximize revenue and protect workers from the injuries associated with repetitive physical tasks, but also develop and enhance human capital from generation to generation via more efficient training.
OT discovery becomes automated
Smart manufacturing requires convergence between IT and OT data to drive visibility, collaboration, and efficiency within plants and facilities and across operations.
However, two decades after automation networks on the plant floor became ubiquitous, it’s still generally true that information accessibility between plant floor devices (OT) – and the people and systems that can create new value from them (IT) – proves to be a significant challenge.
To remove the complexity and domain expertise required to access plant floor devices and systems, manufacturers are turning to auto-discovery tools that identify assets, collect and integrate data with full OT context, and produce models fully shareable with IT systems.
By connecting existing OT infrastructure to smart factory networks and IT initiatives, and continuously generating relevant data insights and measurements, auto-discovery capabilities reduce the technical knowledge and time needed by OT teams to map industrial infrastructure and improve operational efficiency.
Gartner predicts that 50 percent of OT providers will partner with IT-centric providers for IoT offerings over the next year.
Indeed, in 2020, effective OT/IT integration will become key to accelerating innovation and achieving productivity gains at digital transformation scale.
Context is king: The value of OT context becomes clear
Copious amounts of data is produced on the factory floor every second. However, all this data needs an extra layer to be useful for factory floor operations technology staff: context.
By applying context to data pulled from the factory floor, OT teams better understand the insights the data holds and how it impacts the machines and processes they’re responsible for.
On a factory floor, a machine temperature reading without context provides no information to the OT team about the machine approaching a point of overheating, meaning events on that machine need to be redirected.
Without context, the value of OT data erodes. Beyond providing continuous insights, the contextual OT data enables IT teams’ digital transformation initiatives across the organization.
In 2020, the value of OT context will drive an increasing demand for interfaces that showcase an analytical combination of OT and IT into a single set of insights.
Sharing data models between OT and IT allows users to make actionable, data-driven decisions in real-time.
Also, unified visibility into contextualized data will boost workforce productivity, improve the performance of the enterprise, optimizes assets, and execute production with predictability.
Four key use cases drive digital transformation
To realize the most compelling business outcomes for potential digital transformation projects, enterprise organizations must identify and prioritize which use cases are most critical to overall digital transformation success and deliver the highest ROI.
While a large number of use cases have been identified and put into production, in 2020, the following four use cases will emerge as key drivers of successful digitization at-scale:
- Asset optimization: Real-time asset health monitoring and predictive maintenance insights substantially reduce production downtime while maximizing asset performance, utilization, and useful life. Maintenance, equipment and repair costs are reduced, while mean time to repair and first-time fix rates are improved.
- Scalable production management: Comprehensive orchestration of enterprise systems and factory operations ensures efficient transformation of raw materials into finished goods. This includes maximizing production forecasts, uptime and throughput, optimizing production for changing recipes and operating conditions, ensuring high product quality, improving overall equipment effectiveness (OEE) key performance indicators (KPIs), and more.
- Real-time operational intelligence: Introducing real-time visibility, actionable insights, and powerful analytics into factory operations reduces costs and increases performance and efficiency. It also enables standardized KPIs to measure operational consistency and gains across multiple lines and entire operations. Further gains include reducing scrap and improving inventory turnover.
- Digital workforce productivity: Skills shortages, labor costs, and rising production complexity are critical challenges all industrial organizations face today. Digital workforce productivity solutions including augmented reality ensures workers are properly trained/re-trained, improve productivity and safety, and deliver substantial impact on labor costs and overall operational productivity.