DISCRETE MANUFACTURING

Discrete Manufacturing Customer

A Global Discrete Manufacturing Company manages spare parts across large, multi-site client portfolios where uptime, SLA performance, and cost control depend on parts availability. That work is constrained by fragmented data, inconsistent processes, and supply uncertainty. Working with Rive, the company set out to fix the root cause first: the absence of a complete and reliable asset and spare-parts data foundation. This case study covers the initial implementation at a manufacturing site managed by the company, completed within days.

INDUSTRY

Discrete Manufacturing

Operational Context

Multi-Site Operations

Source Systems

IBM Maximo

Scope

Maintainable Asset Information

The Challenge

Spare-parts management at the company was limited less by tooling than by data. Five constraints recurred across sites:

Data and identification

Asset and parts records were incomplete. Technicians often lacked accurate BOMs, model and serial numbers, and specifications, and the same part existed under multiple SKUs and supplier references.

Data and identification

Asset and parts records were incomplete. Technicians often lacked accurate BOMs, model and serial numbers, and specifications, and the same part existed under multiple SKUs and supplier references.

Visibility

There was no unified view of inventory across client sites, technician vans, storerooms, and systems. Parts were frequently repurchased while identical stock sat unused elsewhere.

Visibility

There was no unified view of inventory across client sites, technician vans, storerooms, and systems. Parts were frequently repurchased while identical stock sat unused elsewhere.

Inventory execution

Receiving, issuing, transfers, and returns were inconsistently recorded, and weak labeling and cycle counting left low trust in inventory data.

Inventory execution

Receiving, issuing, transfers, and returns were inconsistently recorded, and weak labeling and cycle counting left low trust in inventory data.

Planning

Ordering was largely reactive. Min/max levels were outdated, leaving critical parts unavailable while capital sat in excess or obsolete stock.

Planning

Ordering was largely reactive. Min/max levels were outdated, leaving critical parts unavailable while capital sat in excess or obsolete stock.

Supply and lifecycle

OEM lead times for HVAC, controls, and electrical components were long and unpredictable, and many assets relied on obsolete or discontinued parts.

Supply and lifecycle

OEM lead times for HVAC, controls, and electrical components were long and unpredictable, and many assets relied on obsolete or discontinued parts.

The Approach

Rive began with the asset-data foundation rather than inventory optimization, since a reliable asset master did not yet exist. The work moved through a sequence of purpose-built agents, each with a human review step

Asset data capture

Field users photograph an asset and its nameplate in Rive’s mobile app. The Asset Data Capture Agent reduces a lengthy manual capture process to seconds, extracting manufacturer, model, serial number, and technical specifications from a single asset image, applying DIN 276 classification, and creating a structured record.

Asset data capture

Standardized asset types

The Asset Type Creator Agent groups assets sharing a manufacturer and model into reusable blueprints, preventing repeated documentation of identical equipment.

Standardized asset types

The Asset Type Creator Agent groups assets sharing a manufacturer and model into reusable blueprints, preventing repeated documentation of identical equipment.

Spare-parts master cleanup

Existing records are imported from IBM Maximo, and the Spare Parts Deduplication Agent flags likely duplicates using semantic and attribute matching across names, descriptions, part numbers, manufacturers, and locations. Users confirm or reject each match.

Spare-parts master cleanup

Asset-to-parts matching

The Asset-to-Parts Matching Agent links each spare part to the relevant asset types using Maximo records, manufacturer data, and technical attributes.

Asset-to-parts matching

The Asset-to-Parts Matching Agent links each spare part to the relevant asset types using Maximo records, manufacturer data, and technical attributes.

Filling gaps

The Spare Parts Creation Agent completes missing information from OEM documents, official manufacturer sources, and broader research, attaching a confidence score, rationale, and source to every recommendation for expert review.

Filling gaps

Replacements and alternatives

The Parts Replacement Agent identifies compatible alternatives, regional suppliers, and indicative pricing for critical, obsolete, or long-lead-time components.

Replacements and alternatives

The Parts Replacement Agent identifies compatible alternatives, regional suppliers, and indicative pricing for critical, obsolete, or long-lead-time components.

Results

Within days, Rive established the asset and spare-parts data foundation for the site. The implementation delivered:

A structured digital register of the site’s maintainable assets, with AI-driven extraction of manufacturer, model, serial number, and asset-specific attributes.

Standardized DIN 276 classification and reusable asset-type blueprints for equipment sharing the same manufacturer and model.

A consolidated spare-parts master imported from IBM Maximo, with potential duplicates identified for human review.

Automated matching of existing parts to asset types, plus identification of missing relationships from OEM documents, manufacturer sources, and technical research.

Confidence scores, rationale, and source evidence on every AI-generated recommendation, with human review and approval for duplicate resolution, matching, and newly suggested parts.

Alternative parts and supplier options for emergency sourcing and maintenance planning.

The result is a connected asset and spare-parts data model. It gives maintenance execution, inventory availability and supply planning a reliable operational backbone, instead of optimization layered on unreliable records.

What’s Next

With the data foundation in place, Rive and the company are extending the work into inventory execution (receiving, issuing, transfers, returns, kitting, and cycle counting), planning and optimization (stocking policies and reorder levels based on criticality, failure history, and lead times), and supply and lifecycle management (identifying supplier risk, obsolete parts, and alternative sourcing before they become emergencies). Together these move the company from reactive spare-parts management toward a proactive, data-driven operating model.