Improving productivity and quality in Textiles
Technology Category
- Application Infrastructure & Middleware - Data Exchange & Integration
- Application Infrastructure & Middleware - Data Visualization
- Functional Applications - Enterprise Asset Management Systems (EAM)
- Functional Applications - Remote Monitoring & Control Systems
- Platform as a Service (PaaS) - Data Management Platforms
- Sensors - Utility Meters
Applicable Functions
- Discrete Manufacturing
Use Cases
- Factory Operations Visibility & Intelligence
About The Customer
OVERVIEW: - Owner-operated company founded in 1997 - Mid-sized manufacturer with 5 plants in Europe - Each plant runs approx 150 production lines, running 800 spindles per production line - Produces high quality textiles across entire value chain (aerospa
The Challenge
VISIBILITY - Currently unable to see amount of power consumed per machine or plant; need to account for energy used per batch. WASTE - 10% of product fails quality standards, unable to identify point of failure (initial spinning, weaving, final processing, etc). INACCURACY - Acquiring OEE measurements and compiling them is a manual process, which lacks visibility into all parts of the production process and is error-prone. LACK OF INTEROPERABILITY - Outdated machinery still has long, effective lifespans but needs to connect to newer, digital assets
The Solution
Information transparency is key to get to the root cause of profit losses in manufacturing. The Industrial IoT unleashes the data silos in productions lines, and turns data into real insights. relayr’s Smart Manufacturing Solution allows you to collect, manage, and analyze data straight from production line machines, regardless of the level of automation. With relayr Smart Manufacturing you can easily change your current IT production line environment into a predictive model.
Data Collected
Energy Consumption Rate, Energy Cost Per Unit, Power Consumption, Production Efficiency
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Accelerate Production for Spirit AeroSystems
The manufacture and assembly of massive fuselage assemblies and other large structures generates a river of data. In fact, the bill of materials for a single fuselage alone can be millions of rows of data. In-house production processes and testing, as well as other manufacturers and customers created data flows that overwhelmed previous processes and information systems. Spirit’s customer base had grown substantially since their 2005 divestiture from Boeing, resulting in a $41 billion backlog of orders to fill. To address this backlog, meet increased customer demands and minimize additional capital investment, the company needed a way to improve throughput in the existing operational footprint. Spirit had a requirement from customers to increase fuselage production by 30%. To accomplish this goal, Spirit needed real-time information on its value chain and workflow. However, the two terabytes of data being pulled from their SAP ECC was unmanageable and overloaded their business warehouse. It had become time-consuming and difficult to pull aggregate data, disaggregate it for the needed information and then reassemble to create a report. During the 6-8 hours it took to build a report, another work shift (they run three per day) would have already taken place, thus the report content was out-of-date before it was ever delivered. As a result, supervisors often had to rely on manual efforts to provide charts, reports and analysis.
Case Study
AUDI AG 20% PRODUCTIVITY IMPROVEMENT
In the automotive industry, niche products are becoming more and more important. The automobile industry has changed considerably over the past few years. No doubt: to remain competitive, instant reaction to specific customer requests is of utmost importance. In a parallel to the growing individualization of our society, the range of models available from Audi has increased as well.
Case Study
Automotive Component Manufacturer Improves with Datonis IoT Solution
As a part of their Industry 4.0 initiative, the customer primarily wished to leverage IoT for maximizing operational efficiencies, productivity, reducing the energy footprints and maximizing capacity utilization. But there were several challenges at the outset:Firstly, the customer had multiple assembly lines with a diverse set of machines, systems and sensors, all communicating on different protocols. As such, primarily they needed a partner who could connect diverse set of assets on to a single platform and make use of underutilized ‘dark’ data.Secondly, the amount of data, data types and their applications was so vast, that the platform handling it, needed to be scalable and flexible.Lastly, Varroc faced the typical challenge of innovating in ‘Brownfield’ markets – wherein the real bottleneck is in integrating IoT in tandem with both the new and legacy equipment without any further CAPEX for asset substitution.
Case Study
IoE Increases Operational Efficiencies and Improves Energy Management
For many manufacturing companies today, the opportunity to connect people, process, data, and things created by the Internet of Everything (IoE) presents a new way to look at factory automation. The opportunity for digital innovation often arises when a company is expanding capacity or building a new production facility. Mahindra and Mahindra, one of India’s leading automakers, seized the opportunity to deploy a connected factory of the future at their new Chakan facility.
Case Study
IIC Factory Operations Visibility & Intelligence Testbed
Contextual and background data is often buried and in various formats; Storage and distribution of large amounts of raw data requires considerable compute resources and infrastructure; The cost of installation and maintenance of new computers, servers, and network equipment to collect and distribute data needs to be minimized. GOAL Establish new open environments for future factory operation visualization scenarios, with data upload and simulation capability to augment physical factory equipment; Provide a cloud-based platform that can combine factory sensor and operational data to enhance the visibility and analysis of key manufacturing processes, thereby enabling improvement of operational efficiency.
