OpenText Internet of Things Platform - DeSL Integration and Automation

Common Integration Use Cases Between OpenText Internet of Things Platform and DeSL

• Connected product development for smart apparel and wearables

  Data flow: OpenText Internet of Things Platform to DeSL

  Sensor data from connected garments, RFID tags, or embedded devices can be sent into DeSL to enrich product development records with real-world usage and performance insights. Product teams can review temperature, motion, location, or durability data during design iterations to improve fit, material selection, and product features. This helps fashion and retail brands shorten development cycles and design products based on actual field performance rather than assumptions.

• Quality monitoring for manufacturing and production environments

  Data flow: OpenText Internet of Things Platform to DeSL

  IoT data from factory equipment, environmental sensors, and production lines can be linked to DeSL product and process records to track conditions that may affect product quality. For example, humidity, machine vibration, or temperature deviations can be associated with specific production batches or styles. This gives product development and quality teams a shared view of issues that may impact consistency, reducing rework and improving first-pass yield.

• Material and component traceability across the supply chain

  Data flow: Bi-directional

  DeSL can send approved material, style, and supplier master data to OpenText Internet of Things Platform, while IoT events from warehouses, mills, and logistics nodes can flow back into DeSL. This supports end-to-end traceability for fabrics, trims, and finished goods, including location, handling conditions, and chain-of-custody events. Operations teams gain better visibility into where materials are and whether they were exposed to conditions that could affect compliance or product integrity.

• Cold chain and condition monitoring for sensitive fashion and retail inventory

  Data flow: OpenText Internet of Things Platform to DeSL

  For products with special storage requirements, such as luxury goods, cosmetics, or temperature-sensitive materials, IoT sensors can monitor warehouse and transport conditions. Exceptions such as excessive heat, humidity, or shock can be pushed into DeSL against the relevant product or shipment record. This helps supply chain and product teams identify risk early, quarantine affected inventory, and prevent quality issues before goods reach stores or customers.

• Automated exception management for delayed or disrupted production

  Data flow: OpenText Internet of Things Platform to DeSL

  When IoT-enabled equipment or logistics assets report downtime, delays, or route deviations, those events can be synchronized into DeSL workflow queues. Product development, sourcing, and planning teams can then assess whether launch dates, sample approvals, or production milestones need to be adjusted. This improves responsiveness across teams and reduces the impact of disruptions on seasonal product calendars.

• Digital product record enrichment with operational performance data

  Data flow: OpenText Internet of Things Platform to DeSL

  DeSL product records can be enriched with ongoing operational data from IoT devices after a product is launched. For example, return rates, usage patterns, or environmental exposure data can be linked to specific styles, sizes, or material combinations. Merchandising and product teams can use this information to refine future collections, reduce defects, and improve product-market fit.

• Supplier performance and compliance monitoring

  Data flow: Bi-directional

  DeSL can provide supplier, material, and order context to OpenText Internet of Things Platform, while IoT data from supplier facilities can feed back into DeSL for monitoring. This enables tracking of production conditions, asset utilization, and compliance indicators at supplier sites. Procurement and sourcing teams can use the combined view to evaluate supplier reliability, identify recurring process issues, and support more informed sourcing decisions.