Relex adaptors

Governed target patterns for Relex planning and forecasting

Early visibility of feed definitions and transformation logic catches issues before data loads into Relex. Every record is checked against interface constraints before reaching the platform. Code-printed feeds replace manual builds.

Supported modes

Target

Earlier planning data visibility

Better scenario testing

Lower late-cycle integration rework

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Explore planning structures and data sources by dependency

How to keep your Relex programme clear, controlled, and on track

Relex readiness is not cutover readiness. It is driven by history depth, feed specification quality, source consolidation logic, and signal relevance. Unlike ERP implementations that optimize for transaction cutover, forecasting platforms depend on deep historical signal and controlled iteration to deliver value.

Anything that remains unresolved about history scope, feed cadence, source consolidation, and data volume at design stage represents programme risk.

Feed specifications establish the intended interface, but they do not expose the full range of source complexity, history availability, consolidation conflicts, and initialization patterns that will emerge during platform load. The range of possible history records, deduplication gaps, and source-system variations remain implicit until they collide with real data at initialization.

When those details remain hidden, they surface during feed testing or platform load, forcing rework after feed design and source mapping decisions have been finalized.

In most programmes, these are treated as unavoidable unknowns. In practice, they are known unknowns.

They can be revealed early in a structured way by examining real data volumes, deduplication requirements, history depth, and feed compliance from legacy and POS systems, well before feed specifications are locked.

Programmes stay controlled when those known unknowns are translated into clear feed validation rules, multi-source consolidation maps, history build strategies, and forecasting readiness criteria that can be planned for early rather than reacted to late.

Making the full scope, feed complexity, and initialization logic visible early keeps decisions aligned, reduces rework, and allows the programme to progress with confidence.

The challenge is not feed complexity. It is how early known unknowns are revealed.

These are known risks with known mitigations. The patterns that surface are rooted in organisational specific operating behaviour, but are predictable from legacy data. Making them visible early turns risk into controlled delivery.

Use the data stream to validate earlier

Feed specifications and transformation designs capture the intended interface, but real data from legacy systems, POS, and master data reveals the consolidation patterns, history availability, and signal complexity that may not be fully visible in planning alone.

It shows what the platform will actually have to ingest and initialize against—not just what the feed design expects it to receive.

Working with real data early allows programmes to validate assumptions about history depth, deduplication logic, multi-source consolidation, and forecasting model signal quality when change is cheap, before those issues surface later in delivery.

This enables the data stream to act as an early validation mechanism for forecasting readiness and planning outcomes, reducing the risk associated with known unknowns and bringing greater control to programme cost and timelines.

Identify Relex objects that will drive your cutover risk

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What it accelerates

Interface templates: pre-built feed structures aligned to Relex specifications for common planning domains
ERP-to-Relex mapping: templates transform source data into Relex-compatible formats with exception handling
Feed validation: every record checked against interface constraints before loading, catching issues early
Reconciliation: evidence packs confirm feed completeness and accuracy against source system totals
History support: progressive history builds for sales data, demand patterns, and seasonal profiles

How this adaptor works in your programme

The controlled non-determinism model applied to Relex:

1Human decisions: consultants define interface scope, feed frequencies, domain mappings, and exception handling for the Relex implementation
2AI-assisted optioning: surfaces common source-to-Relex mapping patterns and highlights data gaps
3Governed specs: locked decisions cover feed structures, transformation rules, and validation criteria
4Deterministic generation: code-printing produces feed generation scripts, transformation logic, and orchestration from the governed spec
5Deterministic validators: every feed record checked against Relex interface constraints and referential integrity rules
6Rehearsal and cutover: proven feed generation and loading chain validated at every rehearsal

AI boundary: AI never processes customer data; it supports mapping and delivery configuration only. When AI assists with code generation, the output is reviewed, QA'd, and verified in test runs before deployment to any system.

Learn about deterministic validators and risk controls →How AI-assisted optioning works →

Where elfware fits in your programme

Elfware runs the data stream mechanics in a way that makes scope, dependencies, and data behaviour visible early and repeatably as solution design evolves. We provide the bridge between your in-house legacy experts and your Oracle Retail implementation partners, helping surface hidden scenarios and establish governed data assets early enough to strengthen functional design without undermining operational imperatives.

This reduces data unknowns early, shortens rehearsal cycles, and removes avoidable manual scripting from the migration stream.

Source vs target usage

As a target (planning and forecasting platform)

Loading data into Relex for replenishment, demand forecasting, and promotions. Feeds typically sourced from ERP, POS, and master data systems.

Product master feeds: items, hierarchies, attributes, dimensions, Demand Forecasting, Replenishment, Promotions
Location feeds: stores, warehouses, distribution centres, location attributes
Supplier feeds: suppliers, lead times, minimum order quantities, delivery schedules
Sales / demand data: POS transaction history, sales aggregates, demand patterns
Inventory feeds: stock on hand, in-transit, allocations, replenishment parameters
Promotion data: planned promotions, promotional pricing, campaign schedules. Promotion event and markdown data is partially available via price calendars; campaign-level event capture may require manual input for initial phases

Typical artefacts delivered

Interface feed templates

Pre-built feed structures aligned to Relex interface specifications for common planning data domains.

Mapping templates

Source-to-Relex mapping documents covering transformation rules, default handling, and code translations.

Orchestration / feed scheduling

Feed generation and loading sequences ensuring correct data domain ordering and dependency management.

Deterministic validators

Feed-level validators checking record structure, referential integrity, and domain constraints before loading.

Reconciliation / evidence pack

Feed counts, value totals, and cross-reference checks confirming accuracy against source systems.

Interfaces and data domains

Domain	Typical entities	Cadence	Notes
Products(view objects)	Items, hierarchies, attributes, dimensions, bar codes	Full + delta	Product dimension data (volume, weight) may be incomplete; introduction and termination dates not consistently populated
Locations(view objects)	Stores, warehouses, DCs, location attributes, clusters	Full + delta	Store closure dates not currently available; warehouse closure data partially available for key periods
Suppliers(view objects)	Suppliers, lead times, MOQs, delivery schedules	Full + delta	Safety lead times not available for pilot scope; supplier currency standardised to euros for phase one
Sales / demand(view objects)	POS transactions, sales aggregates, demand history	Daily / weekly	Minimum 2 years history required for forecasting model initialisation; 3+ years recommended for seasonal profiling
Inventory(view objects)	Stock on hand, in-transit, allocations, parameters	Daily snapshot	Historical stock snapshots required for model calibration; minimum 12 months recommended plus history window
Promotions(view objects)	Promotion headers, offers, forecasts, uplift factors	As planned	Relies on price calendar feeds; reason codes for price changes inconsistently recorded and may require enrichment

Data migration objects

Browse all data migration target objects supported by this adaptor. These are the destination objects within Relex that adaptor templates load data into — not APIs, integrations, or database tables.

View all DM objects

Common risks and how we mitigate them

Interface specification changes between Relex versions

Adaptor templates are maintained against current Relex interface specs and updated when new versions are released.

Source data quality issues affecting feed accuracy

Pre-feed validators check source data quality and report issues before transformation, preventing bad data from reaching Relex.

Feed timing and dependency ordering

Orchestration patterns ensure feeds are generated and loaded in the correct sequence (e.g., products and locations before inventory).

History data volume for forecasting model initialisation

Progressive history feeds load data in managed tranches with reconciliation at each stage.

Multiple source systems feeding Relex

Adaptor handles consolidation from multiple ERP and POS sources with deduplication and golden-record logic.

Reconciliation across source ERP and Relex

Evidence packs provide end-to-end reconciliation from source system through transformation to Relex loaded data.

These Relex-specific risks are instances of broader patterns that affect all complex migration programmes. Learn about programme-wide risk controls

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Frequently asked questions

What do you need to start?

The Relex interface specification for your implementation, clarity on which planning domains are in scope (replenishment, demand, promotions), and access to the source system data that will feed Relex.

How long to first prototype?

Typically 1 to 2 weeks from kick-off to a working prototype demonstrating interface feed generation, validation, and loading for a representative data domain.

How do validators reduce risk?

Validators check every feed record against Relex interface constraints, referential integrity rules, and domain-specific business logic before loading. Objective pass/fail evidence is produced at every rehearsal.

Do you support history builds?

Yes. The Relex adaptor supports progressive history feeds for sales transaction data, demand patterns, and seasonal profiles required for forecasting model initialisation.

How do adaptors evolve over time?

Each Relex engagement adds new interface patterns, domain templates, and validators. These are folded back into the reusable library for future programmes.

Which Relex modules do you cover?

The adaptor provides templates for replenishment, demand forecasting, and promotions. Coverage is extended during the Decide phase based on the specific Relex modules in scope for your implementation.

Need an adaptor for a different application?

We can stand up new adaptors quickly using the same code-printed delivery model, validator stack, and evidence patterns used across the library.

Get in touch to discuss a new adaptor

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