How IT/OT Convergence Is Critical to Industry 4.0 Success
Industry 4.0 is about taking advantage of new technology capabilities to improve business outcomes by moving from an efficiency-based approach of lean manufacturing and principles of scale to resilient and data-driven decisions and flexible operational capabilities.
By embracing Industry 4.0, companies can modernize the remote workplace and collaborative capabilities. Organizations can conduct predictive monitoring and diagnostics of operations and assets. Process automation and advanced analytics can improve processes and decision-making. The supply chain becomes resilient and connected.
Industry 4.0 technology initiatives include collaboration platforms, connected assets, artificial intelligence, robotic process automation (RPA), service and warehouse robotics, ecosystems, and networks. Each of these technologies requires some level of information technology and operational technology (IT/OT) integration to be successful.
Top 5 Goals of Industry 4.0
According to IDC, operational performance improvements are the top priority for investing in IT/OT integration, including improved throughput and service reliability achieved at the same or lower cost.
Improved product and/or service quality rank second and encompass responding more quickly to product and service issues and predicting issues. Enhanced personnel and public safety, more comprehensive security coverage, and reduced cost through the ability to share resources across IT/OT round out the top 5 goals of IT/OT convergence.
The Industry 4.0 Continuum
Experts at IDC identified the Industry 4.0 Continuum as a framework for building resilient, data-driven operations. This continuum is a series of activities to deliver on Industry 4.0 initiatives and can take place across:
- Assets and products to improve their ROI and enhance their capabilities
- Workers with augmentation of decision making and skill development
- Process re-orchestration or a more dynamic approach to detecting and reacting to change, whether voluntary or involuntary
The stages of the continuum are digitize, monitor, diagnose, control, and autonomous:
- Digitize: Convert analog signals and insight to digital and establish secure network connectivity.
- Monitor: Monitor assets or processes remotely through a digital interface.
- Diagnose: Merge asset or process data with contextual data to identify root causes.
- Control: Utilize a digital interface to remotely control or configure an asset or process.
- Autonomous: Orchestrate an asset or process using rules or event-based triggers.
Shifting Investment Priorities
Emphasis on edge and security will unlock the value of cloud-based analytics and on-site decision-making while meeting the requirements of both.
What IDC sees in these changes is a more grounded and realistic approach where companies that have been piloting a lot of technologies for some years are now going back to fix the foundation and use all these new capabilities at scale.
Security and IT/OT integration are key learnings from the past few years that companies are investing in now.
When asked to identify their top 3 investment priorities, respondents put the integration of OT systems with IT systems and sensors at the top of the list. Adopting the internet of things (IoT) by deploying sensors that can wirelessly connect to a network comes in as the second priority, while security investments come in third.
These new priorities replaced cloud-based applications and analytics, which ranked highly in 2018.
Roadblocks to IT/OT Integration
IDC research discovered that the top 3 barriers to IT/OT integration at organizations involve legacy skills and technologies. Incompatibility of legacy applications and lack of expertise on how to achieve integration rank at the top just below concerns about security.
Top IT/OT challenges relate to the complexity of the tech landscape, skills and staffing gaps, and strategy. Companies are looking to partners and providers for the expertise to help overcome these barriers.
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The Future of IT/OT Convergence
IDC experts made some predictions about how IT/OT convergence will influence Industry 4.0. Here is an overview of a few of their predictions:
Prediction 9: Edge to Cloud
By 2024, 60% of industrial organizations will integrate data from edge OT systems with cloud-based reporting and analytics, moving from single-asset views to site-wide operational awareness.
Prediction 5: Services Embedded in Organization
By 2025, 60% of G2000 customers with OT infrastructure will partner with services vendors to leverage their global infrastructure and experience related to engineering and digital deployments.
Prediction 9 and 5 are based on the observation that operating models are shifting and companies are becoming more integrated with their ecosystems to implement these technologies and to manage both the technologies and the assets that are connected through them.
Legacy systems are difficult to untangle and integrate, so this is a key area where services are being deployed. As an extension of that, preparing legacy data and OT non-architectures for a scalable analytics strategy is an area where extra hands are needed.
Ongoing management of assets is becoming more heterogeneous because OEMs are offering managed services, third-party service providers are becoming more connected and integrated, and managed services around ongoing management of the digital component is also a key area of interest.
Prediction 2: Digital Twins for Operations
By 2023, 20% of new industrial assets deployed will leverage preconfigured digital twins to commission assets 50% faster, enabling digital twins for entire operations settings and processes.
For companies looking to advance their asset management strategies, the number one pain point is the time and effort required to stand up and manage the asset models going forward. IDC has talked to some companies that take up to 2 years to model a single operation. As the number of connected assets increases, this process is becoming unmanageable. Providers are quickly building out marketplaces and libraries of digital twins to accelerate that commissioning cycle.
The full set of data required to model and analyze an asset often comes from 10 or more applications. Each of those carries some valuable context, but you don’t need to model all that data to get started. The digital twin model is all about a scalable, repeatable methodology to a data model that captures that context and improves analytical outcomes.
Data models need to be managed within operations. That means they need to be intuitive and unified. The distribution of analytics approaches across 5+ methods is an area of complexity that needs to be addressed.
In a 2019 survey, over 70% cited digital twins as “Important” or “Very Important.” This finding aligns with the need for frameworks to improve decisions with data. That’s how IDC sees companies evolving their perspective on digital twins.