In today’s competitive world, every manufacturing organization is striving to deliver high quality, reliable products with an optimized cost-performance balance and increasingly rapid development cycles. During the full product lifecycle – from requirement gathering to product retirement – a huge amount of data is generated; some of this is a well organized, but much more is distributed across texts and spreadsheet files, on local computers and on websites, blogs, periodicals.
With advanced big data analytical techniques, the insights locked in these files can be unleashed and used to not only improve the development, production and supply processes, but also transform the product design process so as to become truly customer-centric.
Capgemini’s Engineering Analytics solution will enable manufacturers and asset-intensive industries to leverage data from all aspects of the manufacturing value chain to derive the insights required for process reengineering, value creation and value delivery.
Our analytical services in this space include:
- Production Monitoring analytics solution deployed in a fraction of the time and cost of traditional IoT solutions, using the Cloud Data Lake, with Cloudera on Microsoft® Azure, generate real business insights in just 90 days.
- Product design analytics:Maximize product engineers effectiveness using content mining
- Supplier risk analytics: Supplier quality analysis & risk driver identification, quality & risk scoring, rationalization & optimal selection
- Factory analytics:Machine performance & control, energy consumption analysis, predictive machine maintenance, stochastic demand & supply planning, process benchmarking
- Asset performance and operations control:Performance analysis, segmentation, adaptive control limits, real-time monitoring
- On-board diagnosis and predictive maintenance:Correlation of events/usage to failures, root cause analysis & driver identification, failure prediction & recommendation explore failure anomalies
- Connected assets and service recommendation:Analyze usage pattern, recommend optimal services offer, real-time asset control
- Advanced Planning & Scheduling: Analyze disruptions & operational impact, stochastic demand & supply planning of resources (e.g. assets, manpower, services, etc.)
- Warranty analytics: Financial budgeting & reserving, claims & supply side optimization, recall & product improvement modeling, coverage & pricing strategy formulation
- Service 0ptimization: Dealer/service provider performance analysis, predict the profitable customers who may sign/renew services contracts, service price optimization
- Capgemini Digital Control Room analytics: a solution framework combining IOT information with financial, plant, asset, equipment and supply chain data, leveraging SAP Leonardo.
- There are three critical enablers to ensure successful Engineering Analytics delivery: Engineering Domain Expertise, Core Analytics & Data Science expertise and Technology Platform expertise. Capgemini brings a unique combination of engineering experts through its Global Engineering Services team, data science experts and technologists to deliver this to our clients. Our partnership with the leading technology vendors and our in-house global technology environment set up reduces the implementation time and improves the quality of deliverables.
The insights gained from analyzing the engineering data can be used in multiple ways to improve the efficiency of product development by minimizing the design iterations and also to improve the user experience. Typically, our solution delivers benefits to our customers such as:
- Up to 15% reduction in product development cost
- Significant improvement in customer approval through improved product quality
- Up to 10% improvement in production throughput
- Machine down-time reduction in the range of 5-7%
- Operations cost reduction through asset performance optimization
- Reduction in maintenance cost through predictive maintenance
- Improved reliability, and reduction in warranty costs
For more information contact Anne-Laure Thieullent or Shanthi Srinivasan.