Digital Transformation of Manufacturing Industries Trends
It is real, but it’s difficult to say how long it will take. It will surely take many years to fully realize, but is already happening faster than many expected. This blog presents some of key digital transformation trends in manufacturing ARC tracks in 2018.
Advanced Analytics and Machine Learning
The most noticeable and probably most important trend today is the proliferation of advanced analytics and machine learning (artificial intelligence, cognitive computing, etc.) The technology has reached a tipping point and can now deliver value in setting after setting. In turn, this fuels demand for smart connected sensors, digital networks, and other ways to collect and move data to the analytics systems – and vice versa. Is mass displacement of workers on the horizon? Perhaps. But it is clear that these technologies will result in substantial change.
Platform vs. Platform
Cloud application platforms provide a modern approach for developing and deploying software applications. The approach is gradually displacing the older client/server model, in which large, complex, monolithic applications were created and run. In industrial companies, the client/server model came to dominate both the IT and the OT software spaces in recent decades. The pace of this changeover is accelerating, however, as more and more companies embrace the modern platform approach. This has also sparked a “platform vs. platform” competition in the marketplace, with large suppliers seeking to establish the dominant platform ecosystem and the broadest library of third-party applications and smaller suppliers trying to figure out just how they should compete in the emerging environment.
We can see the drive toward open systems in two levels: the platforms/apps level and the automation level. At the platform/apps level, many of the competing cloud platforms are based on the open source Cloud Foundry platform. At the automation level, ExxonMobil and The Open Group are the primary movers behind an open process automation initiative, although Namur has a complementary effort in progress.
Supply Chain Digitization
As the digitization of the supply chain progresses, new approaches are disrupting established business models. Omnichannel retailing uses a variety of channels in a customer’s shopping experience, including research before a purchase. These channels include retail stores, online stores, mobile stores, mobile app stores, and telephone sales. The customer dictates how a transaction occurs. Digitized systems and processes facilitate the customer journey to transact and be served. Digitization also enables personalization of products and services, and customers increasingly seek out personalized products or prefer to shop where they can select from larger assortments of SKUs. Autonomous vehicles and enhanced product, pallet, and container tracking and real-time status are also making inroads in warehouses and on the highways.
Edge and Fog
Cloud-only approaches can no longer keep up with the volume, latency, mobility, reliability, security, privacy and network bandwidth challenges of the industrial plant. Fog computing distributes compute, communication, control, and storage closer to where the data originates, enabling faster processing time and lowering network costs. Fog pools the resources and data sources between devices that reside at the edge and other nodes in the network. Any device with computing, storage, and network connectivity – such as industrial controllers, switches, routers, embedded servers, and video surveillance cameras – can be a fog node.
Smart Products and Services
By offering smart, connected products, manufacturers can position themselves to improve their customers’ experience with their products. Digitization enables manufacturers to improve the performance of their service operations through remote connectivity and enables predictive maintenance; continuous uptime; rapid service response; and the opportunity to offer incremental, revenue-producing products and services.
Another trend is the migration from selling products to selling the value of the product, or product-as-a-service. Examples include an aircraft engine builder billing airlines on the amount of thrust provided, instead of just an aircraft engine and a maintenance contract. A compressor company sells compressed air as a service, instead of compressors.
By selling outcomes or product-as-a-service, a manufacturer or OEM retains ownership of the asset itself, and provides all required maintenance, service, and repair to meet agreed-upon service level agreement (SLA) levels.
Smart Factories, Plants, Operations
In plant operations, digitization means we must recognize that digitization involves connectivity to much more than the production machines and systems. Wearables, augmented reality helmets or glasses, mobile devices, smart carriers, smart containers, smart components, smart products, autonomous machines, video, third-party services, social applications, additive manufacturing, voice control, remote sensing, and more are now an active part of the real-time, data-rich environment in the plant. Autonomous inventory movers can navigate independently throughout the manufacturing plant, delivering components where needed and on time. Still, connectivity only improves performance when advanced analytics and execution software are also applied.
Additive manufacturing continues to make unbelievable strides towards the manufacturing mainstream. It has progressed farther and faster than almost anyone foresaw. Driven by materials science and design software advances, this technology can already build optimum parts – in significant volume – that cannot be made any other way. The hype around 3D printing may have died down, but it may be the most potentially disruptive technology in manufacturing. Companies that neither prepared for or anticipating its emergence on an industrial scale could soon find themselves at a significant disadvantage.
Asset Performance Management
Improving asset performance can improve efficiencies in two main areas: enhancing production performance and offering new business models and services based on smart, connected products. That being the case, it’s no surprise to find many solutions focused on predictive maintenance, asset analytics, and asset management. There are opportunities to better utilize assets, coordinate with operating and business needs, improve the availability of replacement parts, and improve the efficiency of field service groups. Equipment manufacturers are rapidly adopting IIoT to offer asset health monitoring and predictive maintenance subscriptions to create new sources of aftermarket revenue.
A smart city is connected, intelligent and optimized by a municipality to reduce costs, increase safety, attract investment, be sustainable, and enhance livability. Smart cities depend upon the digital enhancement of assets and the deployment of sensor networks with ubiquitous multimodal connectivity and smart governance. Smart cities present tremendous opportunities for industrial IoT, but challenges abound. Technology innovation often outpaces policy, including standards for data privacy. The bureaucratic red tape involved with government contracts also remains a major obstacle. Startups with novel solutions are shut out of the contract process and the transition to service-based models does not align with the often-archaic government thinking and processes. Nevertheless, the trend towards smart cities is clear.
This is an excerpt of an article published by Greg Gorbach on ARC Advisory Group website. Read also about Digital Transformation of Manufacturing Industries Top Hurdles to Overcome and digital transformation opportunities here.