Previously in Part 5 of our Artificial Intelligence (AI) blog series, we examined the key challenges to implementing AI and how to overcome those barriers. Now, let’s explore the immense value AI promises to bring to industrial sectors—both today and into the future.
Digital Continuity & Digital Twins
Digital continuity is about creating an environment where all information from every phase of a product or asset’s lifecycle – from concept to disposal or reuse – is captured in real time and transformed into actionable insights.
Manufacturers have long wanted this kind of full lifecycle visibility, but they have been frustrated by either siloed data, or data deserts (nonexistent data for certain objects and events). Now, enhanced data access technologies and sensor-based IoT connectivity can deliver the comprehensive data and data views needed.
What’s more, a “digital twin” of a physical process, product, asset or environment can provide a unique, authoritative and consistent referential for this lifecycle data. For instance, a 3D CAD model of a product can provide a referential to which data can be linked from conception through to design, engineering, manufacturing and post-sales service.
Going a step further, a simulation-capable twin like Dassault Systèmes’ 3DEXPERIENCE twin provides more than just a consistent, “single-version-of-the-truth” referential. It is an extremely powerful tool for iterating through scenarios and options for everything from design to fabrication to maintenance and repair, for continuous improvement and innovation. And the more real-world data the digital model is fed, the more accurate and valuable are the simulations it supports.
The Right Digital Collaboration Infrastructure
Digital transformation requires a general foundation of social, mobile, cloud and Internet technologies. Connecting people, places, and objects, and enabling anywhere, anytime collaboration, is at the heart of the digital age.
For digital continuity and the proper functioning of digital twins, a business platform with integrated search capabilities is a requirement. This type of platform can connect to or crawl all relevant internal and external resources, and provide unified views of both structured and unstructured information across these silos. With the right platform, these views can include dashboard metrics and recommendations based on advanced analytics.
And if that platform is 3D-based like the 3DEXPERIENCE platform, decision makers can view and drill down on KPIs that are integrated into 3D renderings of products, assets, or environments. This provides an intuitive visual means of reviewing performance for all stakeholders, as well as providing designers with immediate, in-context feedback on the performance impacts of their design choices. As with recommendations, these performance simulations are powered by advanced analytics.
Advanced Analytics for Informed Decisions in Context
Traditional business analytics are descriptive in nature. They provide summary or detailed views of a current state of affairs (e.g., project progress or spending reports) or an analysis of historical events (e.g., lifecycle cost or revenue for a product line). They are “what is” and “what has been” analytics.
Advanced analytics go further. They enrich such descriptions with critical context, helping decision makers to not only better understand “what is,” but to make more accurate “what will be” predictions, and wiser “what should be” decisions.
Advanced analytics can be performed using traditional mathematical or statistical techniques, or newer big data-enabled machine learning (ML) strategies. It is these newer, ML strategies which are key to digital transformation in industrial markets.
Industrial companies already have masses of under-utilized structured and unstructured data scattered across active and legacy systems, and the Industrial IoT (IIoT) is adding enormous new streams of valuable real-time data.
To sift through and make sense of this big data, transforming it into business insights and new products and services, discipline is required. First, organizations need to enrich it with context and enable digital continuity. This will facilitate more efficient collaboration and, as a result, it will be much easier to develop AI strategies in general, and ML techniques in particular.
Part 6: Realizing the Value of Artificial Intelligence in Industrial Sectors
Read our White Paper on Artificial Intelligence In Industrial Markets
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