Revolutionizing Product Lifecycle Management: AI-Based Cloud Platform for SMEs

Technology

Revolutionizing Product Lifecycle Management: AI-Based Cloud Platform for SMEs

06 Engineers

26 Months

70%

Decrease in operational cost

Technology

Angular.js, Node.js, Neo4j, PostgreSQL

Services

Product Conceptualization

Location

Chicago, USA

Overview

TechVariable undertook a project for a Chicago-based client offering end-to-end product development services. They required a Product Lifecycle Management (PLM) platform to optimize their processes. After significant communication and through the implementation of regression testing, new features were successfully added to maintain system functionality. The result was a cloud-based PLM software successfully deployed across ten client sites, reducing costs and enhancing speed-to-market.

Challenges

Our client, a Chicago-based service provider, required a robust PLM software solution to streamline their end-to-end product and service processes. Several technical and logistical issues arose, including:

Changing Requirements: Feedback from end-users constantly shifted requirements, necessitating longer working hours to ensure quality and meet deadlines.
Advanced Technology: The client's existing platform used advanced, cloud-agnostic technology but lacked sufficient documentation, making navigating difficult.
Time Zone Differences: The team needed to adjust working hours to align with the users' time zone for seamless communication.
Project Objectives: The primary objectives included developing a highly functional, fully customizable solution, ensuring high quality for each release, managing the application lifecycle within short-release cycles, and maintaining thorough process documentation.

High Level Design Architecture

Solutions

TechVariable adopted a knowledge management approach to overcome these challenges, maintaining close communication with the client and delivering numerous test cases. The team was organized as follows:

The key solutions included:

Automated Regression Testing: Enabled the team to add features without compromising overall functionality.
GUI-Based Configuration Module: Allowed for easy extension of existing data configurations or adding new definitions.
Machine-Learning Algorithms: The system utilized ML algorithms for working with new datasets, improving data analytics significantly for end-users.

Result

Increased Speed-to-Market: Implementing the PLM software enabled faster delivery times for products.
Cost Reduction: The enhanced software efficiency resulted in substantial cost savings.
Successful Implementation: The cloud-based PLM software was successfully deployed at over ten enterprises.
Innovation: The project resulted in creation of the first fully cloud-based PLM.