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Isaac Rousso, Charles Cote, Jean-Jacques Caraco and Randy Potter
4 Oct 2022

How connecting IoT-enabled applications within manufacturing, energy, and industrial operations environments can heighten performance visibility, improve machine uptime, and substantially increase production

Have you ever stopped and counted the number of devices connected to your network in your home? Recently, I was amazed to realize that I have over 50 devices connected to the network in my four-member family home. While we’ve all grown accustomed to the convenience of living in a connected environment, far greater value from the Internet of Things (IoT) is being seen in connecting manufacturing and industrial operations environments. Here, IoT can increase production through heightened performance visibility and improved machine uptime.

IoT: Connecting people – and machines and devices

Large manufacturing and industrial operations house thousands of workers, and manufacturing machines and devices. These elements are all generating data, messages, and video – within buildings that often cover more than one million square feet. It’s mind boggling to compare where we were just a few years ago to today with the current explosion of IoT and devices that generate and aggregate data to keep us connected to our work and our world. Furthermore, all these devices need to connect to a network and share all this valuable data for use either at the edge or downstream.

The following reference from Gartner Research’s Forecast Analysis: Enterprise and Automotive IoT, Worldwide report shows the growth rate of IoT within various sectors.

Achieving clearly defined outcomes, deeper business integrations, and enterprise-level adoption

Companies investing in IoT solutions continue to progress from early initiation and exploration towards achieving clearly defined outcomes, deeper business integrations, and enterprise-level adoption. Early IoT investments and implementations have concentrated on connecting assets and equipment and consolidating the management of data from diverse types of devices and for different use cases. More recently, based on evolving requirements, the focus on diverse endpoints, edge, and cloud has enabled these earlier IoT solutions to be greatly expanded upon. As IoT continues to grow as the bridge spanning digital business and technology, Capgemini is offering services to connect and implement these solutions. Capgemini does this by providing complete IoT solutions and removing the technical complexities of integrating IT with operational technology (OT). 

Capgemini has moved customers from pilots, proofs of concept, and early implementation projects to focusing on achieving pragmatic and measurable business outcomes from IoT investments. Some of the most significant outcomes are workforce productivity, process optimization, heightened customer experience and loyalty, and remote monitoring and control. These targeted outcomes and value arise from insights garnered through connected machines and applications.

IoT platforms orchestrate and manage data; however, achieving measurable business results from these investments requires domain- and process-specific applications that tightly integrate into the broader IT/OT enterprise application ecosystem. Capgemini facilitates this shift to IoT-enabled applications and helps clients with technical expertise, solutions functionality, breadth of services, cost control, and partner relationship management. We accomplish this utilizing our ADMnext offering that effectively merges IT and OT (with IoT in the middle). 

IoT solutions – Connecting IT and OT

This shift from distinct IoT platforms into an integrated IoT-enabled application landscape appears below in a hierarchical representation from Gartner.

IoT-enabled applications with ADMnext

The shift from stand-alone IoT platforms to IoT-enabled applications is an opportunity that adds solution benefits through orchestrating the management of devices, data, analytics and applications, integration, and security, and achieving concrete and measurable business outcomes using these applications. We accomplish this by applying lessons learned from IT application management and engineering systems management (IT/OT) with ADMnext. This shift continues to resonate well with Capgemini clients that are focusing on end-to-end solutions from asset to edge to a scaled enterprise application. This ensures that insights do not remain siloed in discrete applications and become integrated and actionable pieces of information that are used throughout the broader enterprise environment. 

Organizations successfully realizing digital business transformations are scaling and expanding their IoT platform investments to integrate with their enterprise systems. Stand-alone IoT platforms must integrate with domain- and process-specific applications to enable the composability and modularity that are core to modern application ecosystems. Capgemini has taken the best IoT platforms and capabilities and combined them with enterprise applications using ADMnext, in order to create robust solutions. In crafting these solutions, Capgemini has committed to the following key principles:

  • Repositioning stand-alone IoT platforms to enterprises undertaking digital business transformations by communicating the value (business outcomes) as delivered by platform-enabled applications – rather than the platform itself
  • Developing applications that address critical and underserved business outcomes
  • Crafting forward-looking and sustainable IoT platform capabilities and applications that ensure data lifecycle and interoperability – now and into the future; platform extensibility for multiple applications rests on having useful data that’s integrated if needed.

Where has Capgemini deployed integrated IoT platform and enterprise systems?

Delivering a multi-stream predictive maintenance solution for a leading automotive technology company

Predictive maintenance applications illustrate the shift from connecting assets and gathering data for condition-based monitoring purposes (“what is”) to leveraging platforms that integrate multiple device data streams and the use of AI and ML for improved “what will be” insights. These applications also enable businesses to move away from condition-based and scheduled maintenance to predictive solutions that optimize costs and extend lifetimes for industrial assets. Predictive maintenance solutions are also adding to the rise in the use of AI and ML – highlighting a shift in the center of value.

Capgemini IoT in automotive

Capgemini delivered a comprehensive IoT predictive maintenance solution to help a major automotive technology player transform from a static maintenance process to a dynamic one. Based on IBM PMQ, the Capgemini team integrated applications with the client’s IoT platform with the following elements:

  • Connectivity and data acquisition – A solution based on most valuable data access
  • A monitoring dashboard – On-demand machine status
  • Predictive maintenance technologies, analytics, and forecasting to anticipate machine failure or machine throughput
  • Data capitalization – Knowledge recorded and available for easy diagnostics and training.
Overall, this Capgemini IoT predictive maintenance solution delivered the following key benefits:
  • Substantial savings through reduced maintenance costs, scraping, rework, and raw material consumption
  • Improved efficiency by increasing TRS by up to 5% and heightening machine availability

A comprehensive and accurate energy production monitoring solution for a major European energy provider

A large division of this major European energy company serves to provide residential photovoltaic panel systems. This division gathers production power, home consumption, and battery charge level metrics from solar panels. These metrics are pushed to a business application that aims to supervise installations and provide different dashboards to the customer. However, this process was suffering from a lack of reliability and scalability – and services for final customers were being disrupted and causing huge hotline and support costs.

To fix these issues, the Capgemini team deployed our open-source device management solution, X-IoT, which has been developed in collaboration with our partner Intel. We’ve also integrated this offering with Azure IoT bricks to provide an end-to-end IoT solution for energy consumption monitoring within an industrial context for manufacturing clients. Overall, this solution includes:

  • An IoT ingestion platform that enables secure, reliable, industrialized, agile, and easy data collection from IoT sensors deployed on industrial assets or other group assets
  • An entry point and gateway between IoT devices and information systems
  • All the necessary features to handle the complexity of connecting and managing IoT devices at scale
  • Proper management and versioning of the schema describing both the device properties and messaging content
  • Reliability, scalability, and services for customers previously facing significant hotline and support costs.

The Capgemini team’s IoT solution also added:

  • Full architecture based on our XIoT platform, developed in collaboration with our partner Intel: hardware, protocols and communication, and cloud to identify bottlenecks
  • Mitigation actions and a roadmap following several scenarios, and short- and long-term solutions
  • A pilot project for technical validation where, the solution is hosted on Microsoft Azure cloud infrastructure, data is collected using a secured MQTT protocol, device management is provided by the Capgemini XIoT platform, and XIoT integrates a Lora Server and Sigfox connector to manage the LPWAN network.
Overall, with Microsoft Azure, Capgemini’s energy production monitoring solution delivered the following key benefits:
  • Heightened security by gathering data from industrial devices deployed in refineries
  • Better management of data flows and devices with the provision of firmware updates and accurate device management
  • Increased performance through the building of a scalable architecture that is better able to manage ramp ups

The comprehensive architecture of Capgemini’s IoT Accelerator

Capgemini continues to extend IoT-enabled applications through our Intelligent Industry Solutions offerings such as XIoT, IOP, I4SSM, Plant Control Tower, Reflect IoD, Andy3D, Smart Office, and Digital Twin. The Capgemini portfolio is called the IoT Accelerator. Below is the IoT Accelerator high-level architecture. 

To learn more about how Capgemini can support the growth of your business through the next generation of IoT-enabled applications with ADMnext, please contact:

Isaac Rousso
NA Innovation Office
Chief Architect

Charles Cote
NA Technical Solution Lead – IoT Smart Services Portfolio

Jean-Jacques Caraco
ACS Director

Randy Potter
VP Innovation Office & ADMnext


Isaac Rousso

Enterprise Architect Director
Isaac is a technology leader and trusted business partner with 20+ years of experience leading IT and business transformation initiatives within the automotive, manufacturing, engineering, and life sciences industries.

Charles E. Côté

Director and Chief Architect, Application Development and Management Expert in Enterprise Architecture and IoT Solutions
Charles is a leader in Capgemini’s Enterprise Architecture (EA) practice, working with organizations to evaluate enterprise capabilities, and setting up and delivering programs for transformation and innovation.

Jean Jacques Caraco

Connected Solutions Director
I lead a center of expertise dedicated to connected solutions. We build assets that are either accelerators or end-to-end solutions for our customers.

Randy Potter

Expert in Enterprise Architecture
“I lead a team of enterprise architects advising Fortune 500 clients on their digital transformation, innovation, and the impact of new technology on their businesses. I advise the Fortune 2000 on their cloud strategy and implementation to support digital, IoT, Industry 4.0, and other initiatives. I also help organizations identify and achieve savings in excess of 10x their investment, while accelerating innovation cycles.”