Manufacturing IoT Data Platform

Building The Bridge Between Legacy And New Applications

A global manufacturer operates factory sites across the globe with a heterogeneous set of machines and processes around them. Proof of concept initiatives around digital twin and new applications have shown promising results but the challenge of combining this with their existing systems landscape was stalling a further production rollout. Xapix helped bridge the challenges between newer solution development and the existing application landscape with its integration platform as a service solution focused on IoT.

Challenge

The manufacturer faced a number of challenges around bringing new solutions around it’s IoT platform into production. While a new cloud platform was being implemented which helped to manage load and provide modern interfaces, the connections into the existing IT world were lacking, and the development team quickly had a long backlog of tasks as their expertise was now even more in demand by a number of internal stakeholders. 

“The future with the proof of concepts around digital twins and a set of new applications looked so bright. But two years in the reality of our existing systems world hit us. We couldn’t connect it at scale.” - VP Digital Factory

The main challenges they faced were:

• Combining the real time data streams from the various sensor with data from on-premises applications (such as SAP R3)
• Ensuring that certain data remains on-premises on a factory level 
• Bringing in existing web services and APIs from both their internal systems and select partners
• Ensuring data governance and data compliance to certain schemas
• Support for hybrid cloud environments given the company’s need to run in various global regions on multiple cloud platforms
• Ensuring a link of authentication and monitoring back into the key enterprise systems 
• Automate manual process to avoid further manual upload or mass file transfers

Highlights

• Low code enablement to empower domain experts 
• Secure and scalable integration of modern streaming and APIs with existing legacy interfaces in a multi-cloud environment
• Detailed insights and logging 
• Comprehensive data governance across the factories

Machine Data Types

• Machine performance data such as throughput, runtime, operational status
• Tools and components information 
• Inventory and supply chain status

Why Xapix

Our customer had started the implementation of the solution of a major cloud provider which serves an integral part of the infrastructure by gathering and storing real-time data from the machines and devices for instance. Also digital twin solutions and other applications are starting to be available from the cloud providers - yet the missing piece was the link across systems and also the ability to bring in the business expertise from domain experts.

The scope of solutions evaluated included the development of the integrations from scratch which proved to be not scalable and the evaluation of several integration platforms as a service provider. 

The reason why they chose Xapix were as follows:

• Focus on the specific IoT-related challenges 
• Proven ability to support multiple cloud vendors in various core regions
• No-code component to enable domain experts besides core developers 
• Fast development and integration ability to bridge legacy and modern environments

Xapix Platform Features

• Deployment of a visual low-code solution that empowered domain experts to create and maintain complex data transformations and orchestration
• Combined the request-based (API), streaming-based processing and metadata from legacy systems
• Type system based approach for ensuring data quality
• Complete logging and traceability of all operations
• Detailed performance and usage insights 
• Prebuilt templates for dedicated use cases (e.g. contextual data) as well as authentication and privacy mechanisms across teams 
• Services deployment automation

Results

In comparison to the proposed timeline of the development services suppliers, the integration timeline was cut from 12 months to 3 months and overall annual operations and maintenance cost were reduced by more than 50%. This allowed the team for faster rollout across further applications which further improved the unit economics.

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