C3 IoT

Organizations frequently implement a maintenance strategy for their fleets of vehicles using a combination of time and usage based maintenance schedules. While effective as a whole, time and usage based schedules do not take into account driving patterns, environmental factors, and sensors currently deployed within the vehicle measuring crank voltage, ignition voltage, and acceleration, all of which have a significant influence on the overall health of the vehicle.In a typical fleet, a large percentage of road calls are related to electrical failure, with battery failure being a common cause. Battery failures result in unmet service agreement levels and costly re-adjustment of scheduled to provide replacement vehicles. To reduce the impact of unplanned maintenance, the transportation logistics company was interested in a trial of C3 Vehicle Fleet Analytics.

Approximately 17,000 wells in the customer's portfolio have beam pump artificial lift technology. While beam pump technology is relatively inexpensive compared to other artificial lift technology, beam pumps fail frequently, at rates ranging from 66% to 95% per year. Unexpected failures result in weeks of lost production, emergency maintenance expenses, and costly equipment replacements.

Baltimore Gas and Electric Company (BGE) wanted to optimize the deployment and ongoing health of its advanced metering infrastructure (AMI) network and identify and reduce unbilled energy usage. BGE wanted a solution to deliver an annual economic benefit of $20 million.

Unexpected failures of building equipment can result in significant problems for facility operators. For example, refrigeration system downtimes result in expensive loss of perishables for retailers, or drugs for pharmacies. HVAC system downtimes drive need for emergency maintenance activities, and result in reduced customer satisfaction.

Pella’s commitment to continually improve its processes for its team members and the environment has led the company to seek innovative technologies that advance overall productivity and the quality of its manufacturing operations.

One of Europe’s largest integrated electric power companies was looking for analytics solutions to reliably forecast equipment failure and improve condition-based maintenance for its coal-fired power plant. With a diverse array of coal, oil, and gas/CCGT power plants, the utility’s more than 50GW worldwide generating portfolio has been under pressure to streamline global operations and reduce generating costs (both CapEx and operations /maintenance O&M expenses) by 7-10%.

To understand their complex global energy consumption and emissions footprint, Cisco needed a software solution to help them manage disparate energy- and emissions-related data inputs across their enterprise, integrate and analyze their sustainability and energy metrics, embark upon energy and emissions mitigation projects, and report results publicly. Cisco also needed to perform detailed interval analysis of their high energy-usage facilities.

Global energy leader ENGIE is implementing an ambitious digital transformation strategy that is vital to the Fortune Global 500 company’s plan to confront the major challenges posed by climate change and promote people’s access to reliable, innovative, socially responsible, low carbon, and decentralized energy. To do this, ENGIE plans to invest €1.5 billion in new businesses and digital over the next three years.

To increase efficiency, develop new services, and spread a digital culture across the organization, Enel is executing an enterprise-wide digitalization strategy. Central to achieving the Fortune 100 company’s goals is the large-scale deployment of the C3 AI Suite and applications. Enel operates the world’s largest enterprise IoT system with 20 million smart meters across Italy and Spain.

In tandem with its 6 year-long smart meter rollout plan, Con Edison sought to implement Advanced Metering Infrastructure (AMI) operations on top of a comprehensive enterprise data analytics platform for improved operational insight and customer service for its base of more than four million customers. In order to improve customer service and operations across its region, one of the largest integrated utilities in the United States has rolled out the C3 AI Suite and C3 AMI Operations application on AWS. Con Edison’s project objectives were to deliver on the utility’s commitments for presenting customer data, establish AMI operations across 5 million smart meters to ensure operational health, and build a federated data image platform for analytic capabilities. The utility’s smart meter deployment will generate between 100 terabytes and 1 petabyte of data per year, so choosing a platform that could scale and continue to perform analytics on an ever-larger data set was vital.

The company recognized that asset digital twins, which enable monitoring, diagnosis, and predictive maintenance, would help improve service-level agreements (SLAs) for its installed base. Because downstream changes to asset configuration are typically not reflected in engineering drawings, maintaining accurate BOMs in systems-of-record is a challenging task.Historically, the manufacturer has employed several hundred technical specialists to maintain BOMs at an annual cost of more than $100 million.The manufacturer sought a scalable, productized solution to perform this parsing and analysis automatically across all its product lines.

The building systems division of a Fortune 500 manufacturer provides equipment and services that provide comfort to customers while optimizing building energy expenditures. Historically, the building systems conducted chiller maintenance reactively which led to business disruptions and downtimes and costly emergency repairs that ultimately impacted customer satisfaction.The manufacturer needed a reliable solution that rapidly integrated all relevant equipment and facility data sources and allow it to reduce downtime and costly, unscheduled maintenance for its commercial Heating, Venting & Cooling (HVAC) chiller systems.

The U.S.-based manufacturer designs and delivers a broad set of cutting-edge products, including radio frequency filters, amplifiers, modulators, attenuators, and more. However, the manufacturer had been experiencing lower than expected overall yield in some of its most complex products.

Load and cluster sensor data for use in a predictive modelTrain a machine learning model to predict chiller failureDemonstrate speed of development and deployment by completing project in < 1 week

The bank’s commercial lending process is complex. A team of hundreds of relationship managers, analysts, and credit officers must efficiently provide many credit products and services to thousands of businesses spread across dozens of countries and sectors.To do this, the bank must quickly assess the credit worthiness of its clients, which requires collecting, reviewing, and analyzing thousands of fields of both qualitative and quantitative information including financial statement line items, performance metrics, credit history, and natural language assessments of the borrower’s business prospects.Credit officers need to navigate disparate systems, iterate with relationship managers and credit analysts on credit structure and conditions, and request additional information before making a final decision.

To improve the management of a diverse portfolio of more than 500 facilities worldwide, from typical office buildings to very energy-intensive labs and data centers.With such a disparate portfolio, the company needed a software solution to manage energy and greenhouse gas (GHG) emissions, integrate and analyze sustainability and energy metrics, and report results from energy and emissions mitigation projects for its annual sustainability report and the Carbon Disclosure Project.

The company had attempted to address forecasting challenges using traditional demand forecasting solutions that relied on statistical algorithms that typically generated a demand forecast every week. However, given the short shelf life of food products, sales orders were sent to the plant with little lead time, sometimes on a daily basis.The company procured additional rule-based solutions to improve production scheduling, but ran into challenges when trying to optimize schedules and failed to significantly improve manufacturing operations. 

Throughout its long history, the company has grown organically as well as through many strategic acquisitions.The high degree of IT complexity hampers the company’s ability to obtain critical business insights and answer questions such asWhat product lines / SKUs should be prioritized to help condense lead times?Where in the supply chain do parts or products get stuck (i.e., “lazy inventory”)?What is the optimal allocation of inventory across distribution centers to maximize OTIF for each product line / SKU?While these challenges were significant in the normal course of business, they became critical issues during the peak of the COVID-19 pandemic.

ENGIE, the largest power producer in the world, chose the C3 AI Platform on Amazon Web Services (AWS) to create and implement a platform for enterprise-wide digital transformation and a center for IoT excellence, spanning customer service, metering, energy management, and maintenance.The first applications developed and deployed into production on the ENGIE digital platform include performance optimization for gas power plants, efficiency optimization of heating and cooling plants, energy analysis and management for enterprise and consumer customers, and anomaly detection of wind turbines.

A Fortune 500 biotechnology company, with a wide portfolio of medicines and diagnostics for chronic and life-threatening conditions, set a strategic priority to leverage AI/machine learning solutions to improve manufacturing asset reliability and optimize maintenance costs.However, site operators were inundated with alarms that captured only a small percentage of failures and provided only minutes of lead time prior to a failure event. As a result of unanticipated centrifuge failures, millions of dollars were lost due to interrupted operations and discarded products.The company searched for an AI-enabled solution that can predict impending centrifuge failures with more accuracy and more lead time to reduce unplanned downtime and maintenance costs.

A global manufacturer of aircraft engines, avionics, and other aviation products maintains 90 major product lines with variations that require tens of thousands of parts from hundreds of manufacturers spread across the globe.The complexity of the supply chain, and the costs of maintaining inventory, are significant. For just two of the components of its aircraft engines, the company maintains $600 million in parts inventory, including $400 million in fast-moving inventory, from hundreds of suppliers. Optimizing inventory levels can mitigate supplier delays and improve gross margins and revenue.