Prism Johnson
From reactive breakdowns to predictive maintenance across 25+ manufacturing plants
Paper-Based Chaos Across 25 Plants
As one of India's leading building materials manufacturers, Prism Johnson's maintenance operations were drowning in paperwork and reactive firefighting.
Manual Logbooks
Maintenance records tracked in physical registers at each plant. No centralized visibility into equipment health.
Missed Preventive Maintenance
Scheduled maintenance often forgotten or delayed. Resulted in frequent unexpected breakdowns during production.
Technician Coordination Nightmare
Coordinating specialized technicians across plants was manual chaos. Long delays when expertise wasn't locally available.
No Breakdown Patterns
Recurring issues went unidentified. Same problems kept happening without root cause analysis or prevention.
Manufacturing Complexity Broke Simple Software
Generic maintenance software couldn't handle the complexity of cement, tiles, and RMC production across diverse facilities.
Equipment Diversity Ignored
Ball mills, kilns, crushers, batching plants—each needs different maintenance protocols. Generic tools treated everything the same.
Months of Manual Configuration
Required IT teams to spend 4-6 months configuring workflows, forms, and rules for each plant. By the time setup finished, processes had already changed.
Rigid Workflows
Fixed processes didn't match how different plants actually operated. Teams reverted to manual workarounds defeating the purpose.
Missing Safety Compliance
No built-in safety checklists for high-risk equipment. Compliance tracking required separate manual systems.
Setup in Days, Not Months—AI Learns Your Workflows
Instead of spending months configuring software, Prism Johnson was live in 5 days. The AI observed their operations and automatically built a system matching their exact workflows.
AI Learned Equipment Types
Simply uploaded equipment lists and past maintenance logs. AI automatically created maintenance schedules, checklists, and protocols for kilns, crushers, conveyors—no manual configuration needed.
Workflow Auto-Configuration
AI watched how teams handled work orders in the first week. Automatically configured approval chains, technician routing, and escalation rules matching their actual processes.
Self-Optimizing Schedules
AI analyzed production patterns and maintenance history to schedule work during optimal windows. Continuously adjusts as patterns change—no manual reconfiguration.
Plant-Specific Adaptation
Each of 25 plants operates differently. AI configured unique workflows per location automatically—cement plants got different rules than tile facilities, all without IT involvement.
The Transformation
Before: Months to Configure, Immediately Outdated
IT teams spent 4-6 months manually configuring workflows and forms
Required expensive consultants to map processes and set up rules
By the time configuration finished, processes had already evolved
Every process change needed IT tickets and weeks of reconfiguration
Different plants forced into identical workflows that didn't fit anyone
After: AI Configured in 5 Days, Continuously Adapts
Uploaded equipment data and maintenance logs—AI configured everything in 5 days
Zero consultants or IT resources needed for setup or maintenance
System continuously learns and updates configurations as operations evolve
Process changes handled automatically by AI without manual reconfiguration
Each plant got custom-fit workflows automatically tailored to their unique operations
Results That Matter
Manufacturing excellence through intelligent maintenance
Configuration That Thinks for Itself
What would have taken 6 months and expensive consultants took 5 days with zero IT involvement. The AI didn't just configure Fieldproxy to match Prism Johnson's workflows—it continues learning and adapting daily. When processes change, the system evolves automatically. When new equipment arrives, the AI configures protocols instantly. Prism Johnson's teams focus on manufacturing excellence, not software configuration.