SoftServe’s client is a worldwide manufacturing company that wanted to improve product lifecycle management, reduce manufacturing time, and save costs associated with utilizing high-risk parts during production. The shortage of just a single part had wide-ranging impacts on the client’s ability to manufacture their entire product, endangering the future of their market share.
Rapidly evolving data science technologies have the potential to address the complex issues associated with modern supply chain and risk management. These technologies offer solutions that integrate machine learning (ML) and lead to efficient production, less risk, and increased savings.
SoftServe’s AI team built, trained, and deployed a set of ML models that assess the risk associated with the client’s thousands of products and millions of components. SoftServe built an ML solution that delivered:
- Time savings by reducing risk assessment in early supply chain stages from hours to minutes
- Predictability by optimizing the client’s supply chain using decade-long forecasts
- Stability in “unexpected event” scenarios by running “what-if” simulation engine results
Leveraging AWS infrastructure and other tools and technologies, SoftServe built a solution that delivered near real-time prediction workflow for scoring the risks on incoming bills of materials and complex training data collection from multiple data sources.
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