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Over the past few years, there have been increasing efforts to improve industrial sustainable operations. But the accelerating pace of climate change means sustainability projects can no longer afford to tinker only at the edges. We need scalable green solutions, and we need them fast.
“Three years ago, sustainable operations were nice to have,” says Daniel Coudriet, Manufacturing Intelligence Offering Lead at the global technology consulting firm Capgemini. “But with recent studies that the world is in danger of never meeting its climate change goals, today they’re a must.”
The Growing Business Case for Industrial Sustainability
In addition to addressing the climate change challenge, optimized use of resources like energy and raw materials leads to cost savings. “Achieving sustainability goals is also becoming increasingly important for talent attraction, for good citizenship, and to comply with regulations,” Coudriet says.
Sustainability also “opens new doors to a lot of new opportunities and helps companies expand their markets into new ones,” says Guillaume Pichard, Senior Architect at Capgemini.
And as imperative as sustainable operations might be, execution challenges are aplenty—especially in manufacturing. Crucially, obtaining high-quality and real-time data can be problematic. But it is key in being able to leverage AI and machine learning algorithms at the edge and in the cloud, Coudriet says. The questions that enterprises need to ask, he explains, are: “Can I get some data? Can I rely on that data to make decisions? Can we reliably get this data? Do I have a reliable infrastructure, a data acquisition, and aggregation gateway that will work all the time to support my critical manufacturing processes?”
Macro-level insights are not enough. Companies need the end-to-end process to be digitalized to extract reliable data throughout the production process. When it comes to energy consumption data, for example, “you need to take a magnifying glass and put it on your electricity bill to pinpoint exactly where you can save costs,” Pichard says.
But many companies working with brownfield equipment might not be able to extract the right data from such legacy systems, Coudriet points out. Therefore, additional sensors may need to be added in parallel to existing systems to provide the missing data.
Another challenge is change management, being able to adapt or modify business operations to set and meet new KPIs, according to Francois Calvignac, Digital Manufacturing Enterprise Architect at Capgemini. In addition, companies’ second- and third-tier suppliers’ operations must come under the sustainability microscope. “This creates an organizational and technical challenge. It expands and tightens the requirements you’re placing on your ecosystem,” Calvignac explains.
#Macro-level insights are not enough. Companies need the end-to-end process to be #digitalized to extract reliable #data throughout the #production process. @Capgemini via @insightdottech
An Accelerator Framework
To help manufacturing enterprises overcome these various challenges, Capgemini has developed a reproducible framework that can be customized and deployed for each organization. The framework is designed to help companies meet their industrial sustainability goals quickly, and at scale.
“We bring a streamlined, highly effective, tools-based methodology, which is a good framework to understand the current state of the customer’s operations, where they want to be, and where the gap is between the two,” Pichard says. “Second, we come with prebuilt technological components, whether it’s hardware thanks to Intel or software based on Capgemini’s own development.”
For every customer, Capgemini draws a roadmap with specific ROIs. Implementations mix and match cloud-based architecture configurations depending on needs. Most solutions use Intel IIoT edge infrastructure with near real-time capabilities and artificial intelligence in the cloud using the Intel® Distribution of OpenVINO™ Toolkit.
“Thanks to the framework and prebuilt technological components, we’re able to fast-forward projects and deliver desired results quickly,” Pichard says.
Actions for Sustainable Industrial Operations
Quick results are exactly what a global consumer product company was looking for when it approached Capgemini with a proof of concept for sustainable business operations. It needed help rolling out the concept at scale.
Capgemini deployed its tried-and-tested framework again. It worked with the organization “to build a roadmap from the current situation” and to drill down into the details of what kinds of data and data sources would be needed to meet the use cases and train the algorithms, according to Calvignac.
Energy consumption data from edge devices (existing HVAC unit controllers) are routed to a cloud-based algorithm that precisely regulates the operation of 500+ HVAC units in about 20 industrial sites around the world. “We combined project management skills with data science, IoT connectivity, HVAC systems knowledge, and thermodynamics know-how to drive a fast-paced rollout at scale,” Coudriet says. “Industrialization and rapid rollout at scale was possible thanks to the use of Capgemini’s digital manufacturing architecture framework.”
After a year of operations, the company has seen a 20% reduction in HVAC energy use and has saved millions of euros in utility bills by adopting Capgemini’s HVAC control optimization solution.
The Future of Sustainability
The smart HVAC optimization solution was born out of one reproducible framework that scaled around the world, and expect more automation and variations for different kinds of operations in the future. “The predefined frameworks are at version 1.0,” Coudriet says. “The next step is to prebuild cases for certain types of production and for certain types of assets, whether that’s in manufacturing or agrichemical or other sectors. The goal is to deliver easily deployable and self-optimizing use cases via our accelerator platform as libraries of prepackaged solution modules that accelerate the implementation of sustainability solutions for our customers.”
“It’s a huge market for us and for Intel, and we have to be able to meet that demand,” Coudriet says. A reliable and reproducible framework gets it done.
This article was edited by Christina Cardoza, Associate Editorial Director for insight.tech.