Cloud Computing – A Game Changer
Published by : Industrial Automation
Peter Reynolds elaborates upon how cloud computing transforms engineering into agile Industry 4.0 value centres.
Most industrial organisations today still do not fully understand cloud computing, cloudc security, or the applicability of the cloud to the industrial environment. ARC Advisory Group often hears talk about “migrating to the cloud.” However, cloud computing is not a destination to which to migrate or store data, but an enabler for business transformation. A cloud business approach takes advantage of the global and parallel nature of cloud computing. Companies that understand this can innovate and transform.
Cloud computing enables agility and innovation by providing organisations with global access to data and the flexibility to employ software applications of their choice. Cloud computing-enabled software-as-a-service (SaaS) can also support concurrent engineering to optimise design and compress project schedules, plus reduce upfront software expenditures and ongoing support costs to a significant degree.
With the emergence of Industry 4.0 and Industrial IoT, cloud computing services companies and engineering and industrial software companies have a tremendous opportunity to change the game for engineering. Companies have collaborated to shift the deployment model for engineering simulation tools for design and operations. The objective here is to increase security and reduce reliance on IT service organisations, ultimately putting more control into the hands of engineering organisations. This transforms simulation into a higher-value agile service, provides a transition for legacy software, and allows users to retain invaluable intellectual property (IP) for engineering. The ubiquitous nature of cloud computing shifts procurement of software and solutions from central IT to the engineering operations groups to better support Industry 4.0 and Industrial IoT approaches and strategies.
Engineering Operations Organisations Can Now Procure Software-as-a-Service (SaaS) Within many industrial organisations, the IT group has traditionally been responsible for and has control over procuring, deploying, and supporting engineering software. While IT still has an important role in planning the technology investment stack to secure and sustain IT solutions for a multitude of internal customers across the enterprise, the move from the private cloud to public SaaS model in the technology stack diminishes this role to a significant degree (see figure 1. below).
NIST defines software as a service (SaaS) as a capability provided to the consumer to use the provider’s applications running on a cloud infrastructure. The applications are accessible from various client devices through either a thin client interface (such as a web browser) or a program interface. The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. SaaS typically involves the sale of a service provided over a period of time. Generally, in a software licensing arrangement, the customer obtains rights to use the software on its own computers.
In the past, over-burdened IT groups often could not accommodate the specific priorities and agility requirements of the business units. IT priorities are often strongly influenced by demand in areas such as finance, network infrastructure, and other ancillary IT services. The rigorous IT Demand Management process defined by the ISACA (Information Systems Audit and Control) standards organisation aims to understand, anticipate, and influence customer demand for services. This process works with the Capacity Management process to ensure that the service has sufficient capacity to meet the required demand for particular services. Cloud computing changes this. Business units can now externalise IT services. What, in the past, was a service under “care and control” of internal IT, is now performed by vendor- managed services as a SaaS application.
The IT function can be reduced to resolving simpler problems and, if required, managing interfaces. Business units can now procure software as-a-service on cloud-based infrastructure from companies to reduce demand on internal IT services, simplify problem resolution, and (if required) manage interfaces. For this deployment model, these applications are delivered as a SaaS model. The SaaS model commoditises the IT datacentre, making many IT services superfluous. SaaS applications change the business and IT relationship completely, reducing the load on IT groups and enabling engineering departments to focus on mission-critical engineering design and optimisation activities.
In the past, IT roles would encompass all services, often leaving business units out of critical decisions and key processes. Increasingly, more of what was previously done by internal IT or IT shared service organisations will be done by business units (see figure 2). This is the essence of IT/OT (operational technology) convergence. This means that engineering organisations will procure their own technology using SaaS models.
The IT role can then focus on overseeing and optimising critical shared services across the enterprise and overseeing business services strategy and architecture. Managing technology procurement and integration still requires leadership, but this leadership is likely to be provided by someone reporting to the head of business services. This IT/OT convergence brings a stronger focus on the application of technology and operational efficiency or cost saving throughout the business.
Engineering Organisations Recognise the Importance of SaaS
Leading process industry users recognise the importance of SaaS models and how much of the IT services (networks, storage, provisioning, etc.), has been commoditised. These users adopt a project culture focused on “try fast, build fast, fail fast,” helping users deliver the value of Industry 4.0 with greater agility. Industry 4.0 may enable the value propositions associated with predictive maintenance, on-demand training and simulation, or process optimisation and “what-if” analyses. Some also realise that adopting standard best practices for engineering applications is an important part of digital strategy, since it can help avoid costly and unnecessary customisation. Many also recognise that the SaaS delivery model is both typically more secure and faster to deploy than the legacy on-premise model.
Industry 4.0 and digitalisation initiatives can drive strategy away from central IT organisations. For engineering organisations, SaaS models help decouple the sometimes- over-complicated IT demand process to help increase agility. Process industry leaders can choose an external third-party to help with their Industry 4.0 agenda but may also give the internal digitalisation organisation (or IT) the first chance, especially if there is an opportunity to use an IT-provided platform. SaaS models from SaaS providers are particularly attractive for engineering Industry 4.0 programs. Typical metrics for an Industry 4.0 program include:
• Accelerator projects have success criteria defined
• Financial results (NPV) are key measures
• Businesses sign off on value to be delivered
• Responsiveness to opportunities and how quickly project teams move between stage gates.
SaaS Changes Engineering Simulation Forever
A few decades ago, IT professionals began to use the term “service-oriented architecture” to describe the assembly of many web-based user interfaces for desktop applications. Usually, the deployment model was to set this up in an internal data centre or in servers rented from a third-party-hosted solution. Today, the situation is very different, with SaaS deployments becoming increasingly important. A SaaS software application is built specifically for cloud computing and virtualisation environments. SaaS applications are designed, developed, and deployed to reap maximum functionality and services from a cloud computing and virtualisation infrastructure. Although SaaS applications might be like conventional software applications, the back-end computation, scalability, and parallel processing are compatible with and support a cloud infrastructure. SaaS applications have the following characteristics:
• Massively parallel: The application incorporates parallelisation techniques within task execution and data storage.
• Complete utilisation of cloud resources: The application should use infrastructure APIs and other procedures to simplify tasks and use most or all available resources.
• Cross-cloud paradigm: The application should be easily migrated and deployed within multiple cloud providers.
• Supports business models; multi tenancy, automatic updates; self-service applications; scalability and volume of users.
The cloud deployment approach can also accelerate the engineering process, from the initial design feasibility performed by the engineering and procurement contractors (EPCs), through front end engineering design, detailed design, installation and commissioning, and – ultimately – through operations and decommissioning. The transformational opportunities presented by Industry 4.0 (often referred to as the fourth industrial revolution), motivate engineering organisations to rethink work processes and the use of engineering tools and how they each support objectives to design with greater agility.
This is increasingly important in today’s process manufacturing environment in which variability in both feedstocks and energy costs demands unprecedented agility. In the past, depending on the project phase (conceptual design, detailed design, startup and commissioning, process optimisation, etc.), and responsible party (process licensor, EPC, owner-operator, etc.), a variety of different engineering tools were typically employed to support either steady-state or dynamic simulation. In most cases, each of these tools had different models for process simulation, different data entry requirements, and different data and human interfaces. This increased engineering effort, complexity, and cost and inhibited agility. It also prohibited effective use of concurrent engineering, which can reduce costs and help compress project schedules.
The “platforming” or IT/OT convergence of process engineering simulation tools helps engineers create reusable workflows and automate data authoring in a broader platform for simulation. The new work processes created enable people to focus on more value-adding activities and shortens the time needed to design and optimise by moving from sequential to simultaneous engineering. “Re-platforming” will happen much faster in the cloud. Peter Reynolds, Contributing Analyst, ARC Advisory Group. Peter performs research on technology areas such as process optimisation and asset performance management for industrial manufacturers.