Machinery App Development

Equipment-intensive industries (manufacturing, construction, mining, and agriculture) building machinery management platforms need engineering teams who understand the specific data architecture challenges of asset lifecycle tracking, sensor telemetry ingestion, predictive maintenance scheduling, and the spare parts and procurement workflows that keep equipment running. Scrums.com provides dedicated software engineering teams for machinery app development, deploying production-ready systems with append-only asset event ledgers, configurable maintenance rule engines, IoT sensor integration pipelines, and the analytics infrastructure that drives equipment uptime and OEE performance.

Equipment Asset Registry, Operational Status, and Utilisation Tracking

The asset registry is the master record for every piece of equipment: make, model, serial number, year of manufacture, purchase date, asset_category (MANUFACTURING_EQUIPMENT | CONSTRUCTION_MACHINERY | AGRICULTURAL_EQUIPMENT | MATERIAL_HANDLING | VEHICLES), and current location. Asset status follows a state machine (OPERATIONAL, DEGRADED, UNDER_MAINTENANCE, OUT_OF_SERVICE, DECOMMISSIONED) with each transition appended to an asset_status_events table. The current status is always derived from the latest asset_status_events row, never from a mutable field on the asset record.

Asset location tracking for mobile equipment uses a current_asset_positions materialised view, updated from GPS telemetry. Location changes write to an asset_location_events table (arriving at a site, departing a site, entering a designated zone) rather than overwriting the location field on the asset record. Site assignment history is always traceable from asset_location_events.

Utilisation tracking derives machine hours and operational cycles from telemetry data. An asset_runtime_log table accumulates ENGINE_ON and ENGINE_OFF events from OBD integrations or operator check-in/check-out workflows. Total runtime hours for any asset over any period is computed from asset_runtime_log as a materialised view, never stored as a mutable counter. If a telemetry device submits an erroneous event (for example, a false ENGINE_ON during transport on a flatbed), correcting the source row and refreshing the view corrects all downstream maintenance schedules that depend on runtime hours.

Predictive Maintenance, Fault Detection, and Work Order Integration

The maintenance system's effectiveness depends on the quality of its detection rules and its work order workflow: predictive capability without a reliable execution workflow generates alerts that go unresolved.

Maintenance rules are stored in a maintenance_rules config table: asset_id (or asset_category for fleet-wide rules), trigger_type (RUNTIME_HOURS | CALENDAR_INTERVAL | CONDITION_BASED | CYCLE_COUNT | MILEAGE), threshold_value, severity (CRITICAL | MAJOR | MINOR | ADVISORY), and a suppression flag for planned downtime windows. Rules are configuration, not code: adding a new maintenance interval requires inserting a row, not a deployment.

Fault detection reads sensor telemetry against a fault_detection_rules config table: sensor_type, detection_method (THRESHOLD | RATE_OF_CHANGE | Z_SCORE | PATTERN_MATCH), threshold_config (JSONB), and severity. When a fault is detected, a fault_events row is created (asset_id, rule_id, detected_severity, detected_at) with a status machine: OPEN / ACKNOWLEDGED / UNDER_INVESTIGATION / RESOLVED | FALSE_POSITIVE. Status transitions write to fault_status_events.

Work orders are generated automatically from maintenance_due_events and fault_events above a configured severity threshold. The work_orders table follows a state machine (CREATED / ASSIGNED / IN_PROGRESS / PARTS_REQUIRED / ON_HOLD / COMPLETED | CANCELLED) with each transition written to work_order_events. Work orders are never deleted; CANCELLED is a terminal state. Completion writes a work_order_completions row: technician_id, completed_at, duration_minutes, resolution_code, and next_service_due_at computed from the triggering maintenance rule's interval.

Machinery apps like these are built and delivered by dedicated engineering teams through our mobile app development service.

Spare Parts Management, Procurement, and BOM Architecture

The parts catalogue holds every component with its manufacturer part number, equipment compatibility (as a MultiReference to asset_registry), unit of measure, and lead time. Bill-of-materials structures are stored with immutable versioning: each BOM revision writes new rows linking parent_part_id to child_part_id with quantity and position; the previous version is never modified, so historical work orders always reference the BOM that was active when they were created. Parts inventory tracks on-hand quantity by location (warehouse and bin) through append-only parts_transactions; quantity_on_hand is always a SUM, never a mutable counter. Reservations follow a state machine: REQUESTED / RESERVED / ALLOCATED / CONSUMED | RELEASED, with SELECT...FOR UPDATE at reservation time preventing double-allocation across concurrent work orders. Reorder rules in reorder_rules_config (min_qty, reorder_point, preferred_supplier_id) trigger purchase order creation automatically. Purchase orders move through DRAFT / SUBMITTED / ACKNOWLEDGED / PARTIALLY_RECEIVED / RECEIVED | CANCELLED; goods received are recorded in immutable goods_received_notes rows, and supplier prices are versioned in supplier_catalogue_prices with effective_from/effective_to: new rows for updates, never mutations.

Remote Monitoring, Telemetry Integration, and Machinery Analytics

Sensor data arrives via MQTT ingest queue: each message carries a device_id, reading_timestamp, and metric_key, with ON CONFLICT DO NOTHING enforcing idempotency so duplicate transmissions never corrupt the dataset. Readings land in TimescaleDB hypertables partitioned by time, keeping range queries fast as data volumes grow. An OPC-UA adapter bridges SCADA systems for manufacturers with existing automation infrastructure. Alert rules are configuration: alert_rules stores metric_key, operator, threshold, severity, and cooldown_seconds; rule changes take effect immediately without a deployment. Triggered alerts move through TRIGGERED / ACKNOWLEDGED / RESOLVED | AUTO_RESOLVED, with every transition written to alert_events. OEE analytics (availability, performance, and quality) are derived from production_runs and fault_events via oee_snapshots, a materialised view that is always recomputed from event logs, never from a mutable OEE counter. Analytics materialised views include mtbf_by_asset_by_model, mttr_by_asset_by_technician, parts_consumption_rate_by_asset, and energy_consumption_by_shift: all recomputable and auditable back to source events.