Design of predictive systems
A predictive system helps a company anticipate an event, a deviation or a future need based on data. It relies on historical data, field signals and calculation rules. Teams use predictive systems to reduce failures, limit disruptions and better plan resources. The system integrates into an internal application, a business software or an industrial environment.
Pepps designs predictive systems that follow your processes and constraints. The team structures your data, defines relevant variables and implements a model linked to a specific objective. You obtain actionable forecasts for your teams, with a clear monitoring and control framework.
Pepps, your partner for predictive systems
A predictive system delivers value when it drives action and integrates with your tools. Pepps analyses your context, data sources and objectives. The team defines a clear project scope and roadmap.
Predictive maintenance triggers actions based on risk. Pepps builds systems that predict failures or defects using measurements and historical data.
Risk-based actions include:
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Planning targeted inspections
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Prioritising interventions
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Ordering spare parts before failure
- Commander une pièce avant la panne
A company adjusts its purchasing, production and logistics based on demand. Pepps implements a predictive system that estimates volumes over a defined time horizon.
Supported decisions include:
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Adjusting procurement
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Planning resources
Quality drift sometimes appears before a visible defect. Pepps implements systems that identify abnormal patterns and alert teams.
Targeted outcomes include:
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Reduced scrap rates
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Tracking of affected batches
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Targeted control on sensitive points
Pepps adds indicators and alerts to your business management software. Your teams access risk scores, trends and historical data.
Possible features include:
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Priority lists by risk level
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Threshold- and role-based alerts
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Enriched equipment or product records
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Action and decision tracking
Pepps implements dashboards that consolidate key indicators. Management monitors trends, while field teams track priorities.
The system can trigger actions in third-party tools. Pepps implements the rules and ensures traceability.
Examples of actions include:
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Creating a maintenance ticket
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Sending an alert to a manager
A predictive system evolves over time. Data changes and usage patterns shift. Pepps implements model monitoring and access management.
Monitored aspects include:
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Gap between forecasts and actual results
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Volume of false positives
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Accuracy trends over a defined period
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Version and parameter logs
Pepps also defines roles and permissions. The team secures data access and protects exchanges between systems.
Values embedded in every digital project
Whatever the type of digital solution—whether a mobile application, a logistics tracking tool, a maintenance application or a dynamic dashboard—our approach remains the same: do things right, with you, for you.
What is a predictive system used for in a company
A predictive system supports decision-making and planning. It reduces uncertainty around issues that generate costs or delays. It also helps prioritise actions when teams need to make trade-offs.
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Equipment failure forecasting
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Demand estimation by period
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Anticipation of stock shortages
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Quality drift detection
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Workload and lead time estimation
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Prioritisation of field interventions
Success conditions for a predictive system
A predictive system depends on a clear framework. A company must define a measurable objective, usable data and field-based validation. Pepps establishes this framework from the very start of the project.
A measurable objective and a clear scope
Pepps defines the event to be predicted and the time window. The team specifies the expected level of accuracy and the business use case. This step prevents results that are difficult to apply in practice.
Structured and consistent data
Pepps identifies data sources and checks field consistency. The team handles missing values, duplicates and format discrepancies. It also defines update rules to keep the data usable over time.
Validation aligned with your usage
A predictive model must be tested against real-world conditions. Pepps organises tests on actual cases. The team compares forecasts with observed outcomes and adjusts variables and thresholds based on the gaps.
