Predictive Solutions: Moving Maintenance from Unplanned to Planned
PREDIKTO ENTERPRISE SUITEPredikto Enterprise Suite is our SaaS pipeline turning your raw data into actionable outputs.
The platform is designed for complex, distributed systems. Our cloud-based service mean you avoid unnecessary IT overhead, and we can quickly scale to your needs.
Journey from Data Silos to Proactive Operations
Now that you have a plan, let’s execute.
Our software and services provide the entire data analytics hierarchy. Delivering results and value at each step along the way.
Let’s follow a fictional company on a journey to operationalization.
- Professional grade to gyms, with SLAs
- Personal grade to consumers, with warranty service
Due to bulk of their equipment, all service is on-site by a technician.
They would like to reduce maintenance costs and increase customer satisfaction.
Problem & Data Identification
- Unsure of maintenance cost-drivers
- Siloed data sets
- Identifies overall strategy
- Identifies quick-win pilot: “repeat service visits and broken drive trains”
Predikto kicks off pilot and transfers Fast Fit data to Predikto’s Enterprise Suite, a cloud-based SaaS.
- Predikto receives the data in it’s online intake environment
- Predikto automates the ETL process
Predikto’s back-end software enables it’s employees to set up and monitor these processes in a fast and repeatable manner.
Business Intelligence Insights
- Business Intelligence via Predikto RECON’s Data Lake Interface
- Coverage gap in their service tracking Apps in March and April of 2017
- Devices with serial numbers in the 200s break twice as often
- Anomalous data from their installed temperature sensors
AXIOM: Rules Engine
- Creates rules based on known faults
- Useful for identifying emergent faults that need to be assessed quickly
Approximately 3 weeks from the receipt of data, Predikto can present a set of viable rules
The data from Predikto Axiom is delivered via Predikto Recon. Rule results, watch lists, and underlying data are available, and updated nightly.
- Drive train should be replaced after 3000 hours of use
- Use of the wireless feature causes premature failure of the console
They update their suggested maintenance plans, and their engineering team investigates a new wireless integration.
- Creating and scoring thousands of potential features
- One data scientist can do the work of ten
Example features: number of days since a technician has visited, average usage per day, temperature variation during a single day.
- Correlations between incidents and indicators
- Available in RECON and also to download
- The temperature variation is extreme
- The unit is used too infrequently (i.e. home use as a laundry hanger)
- Score and select models to predict failure events
- Models are made with lead-time in mind
- Delivered via Predikto RECON: interpretations, asset status, knowledgebase
- Contextualization enables trust and action on predictions
Predikto has the capability to interpret all predictions made by our platform.
We provide the top features that influenced that specific prediction
- Users gain trust
- Associate signals with failure
- Support decisions to act or not act on a prediction
Operationalize & Live Data
- Turn reactive to proactive through updated procedures
- Set up live data feeds
- Set up a maintenance pipeline via Predikto’s APIs
- Stream updates and scheduling Fast Fit’s maintenance app
- Onsite technicians preemptively fix co-located equipment
- Update owner portal with tips & tricks
- Update temperature sensor and wireless connection point installed with new units
- Ingests live data feed
- Issues daily predictions
- Automatically searches for new features and models to keep models running at peak performance