Operational Analytics: Employees, Equipment and Cost

The goal of this project was to optimize operational cost by predicting equipment downtime, towards conducting preventive maintenance & hence optimizing resource and cost.

JASStek applied Advanced Analytics approach using multiple datasets including parts warranty, machine sensor, supervisor notes, historic maintenance sheets, etc. towards developing insights. The predictive forecast model used multiple advanced analytics algorithms and machine learning to predict equipment downtime.

Actionable insights resulted in:

• Improved operational efficiency
• Reduced maintenance cost
• Optimum number or resources at job
• Meeting production goals
• Enhancing safety
• Automating production lines
• IoT - Data driven decisions.

This project started off as a PoC, and lead to the adoption of an enterprise wide strategic approach to data driven decisions.