Download PDF
iTesso Optimizes Utilization of Azure Platform with Help from New Relic
Technology Category
- Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Functions
- Business Operation
Use Cases
- Predictive Maintenance
- Real-Time Location System (RTLS)
Services
- Cloud Planning, Design & Implementation Services
The Challenge
iTesso, a software company for the global hospitality industry, was facing challenges with its cloud environment. The company's flagship SaaS product, iTesso, is a cloud-native enterprise lodging system built on the Windows Azure platform. However, the company was often unaware of performance issues until customers started calling the contact center to complain. The feedback from customers was often vague and difficult to validate, making it hard to identify and address the root cause of the issues. The company needed a more comprehensive approach to monitoring its application. The iTesso system is complex and full of dependencies, and the company wanted to monitor everything as a whole, not just separate components.
About The Customer
iTesso, formerly known as Hotel Concepts - Brilliant, is a software company that creates solutions for the global hospitality industry. For over two decades, the company has developed comprehensive solutions for hotels and hotel chains including InterContinental Hotel Group, Best Western International, Van der Valk and select properties in the Groupe du Louvre. The company is headquartered in the Netherlands and has regional offices in Paris, London, Sydney and Atlanta, enabling it to provide global hotel companies with regional and local support in rolling out chain-wide solutions. iTesso's flagship SaaS product, also called iTesso, is a cloud-native enterprise lodging system built on the Windows Azure platform.
The Solution
The iTesso team initially considered developing a homegrown monitoring solution, but decided against it as they wanted to focus on their core competencies. They found their solution in New Relic, a performance monitoring software, which was recommended by Azure gurus at a conference. The team installed New Relic in less than an hour and started running the software in production. New Relic provided them with a clear overview of the problem, allowing them to identify problems in their system, pinpoint where the problems originate, and hand them off to a developer for further investigation and remediation. New Relic became a fundamental part of their daily operations, with the team reviewing transactions from the previous day every morning, identifying the slowest processes, performing an initial analysis and determining next steps.
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
Remote Monitoring & Predictive Maintenance App for a Solar Energy System
The maintenance & tracking of various modules was an overhead for the customer due to the huge labor costs involved. Being an advanced solar solutions provider, they wanted to ensure early detection of issues and provide the best-in-class customer experience. Hence they wanted to automate the whole process.
Case Study
Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
Case Study
Aircraft Predictive Maintenance and Workflow Optimization
First, aircraft manufacturer have trouble monitoring the health of aircraft systems with health prognostics and deliver predictive maintenance insights. Second, aircraft manufacturer wants a solution that can provide an in-context advisory and align job assignments to match technician experience and expertise.
Case Study
Integral Plant Maintenance
Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).
Case Study
Leading Tools Manufacturer Transforms Operations with IoT
Stanley Black & Decker required transparency of real-time overall equipment effectiveness and line productivity to reduce production line change over time.The goal was to to improve production to schedule, reduce actual labor costs and understanding the effects of shift changes and resource shifts from line to line.
Case Study
Asset Management and Predictive Maintenance
The customer prides itself on excellent engineering and customer centric philosophy, allowing its customer’s minds to be at ease and not worry about machine failure. They can easily deliver the excellent maintenance services to their customers, but there are some processes that can be automated to deliver less downtime for the customer and more efficient maintenance schedules.
