Inferensys

Glossary

Sourcing Rule

A predefined policy that dictates the sequence of supply locations or production sources the Available-to-Promise (ATP) engine should evaluate to fulfill a customer order in a specific region.
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ORDER FULFILLMENT LOGIC

What is a Sourcing Rule?

A foundational policy within order promising engines that defines the prioritized sequence of supply locations evaluated to fulfill customer demand.

A sourcing rule is a predefined policy that dictates the sequence of supply locations or production sources the Available-to-Promise (ATP) engine should evaluate to fulfill a customer order in a specific region. It acts as a prioritized routing table, specifying which plant, distribution center, or external supplier to check first, second, and third when inventory is requested. By codifying this logic, organizations ensure that order promising consistently aligns with cost, speed, and customer-tier strategies without manual intervention.

Sourcing rules are typically configured by a combination of customer region, product, and order type, allowing for granular control over fulfillment logic. For example, a rule might prioritize a local distribution center for standard orders but switch to a regional plant for high-priority customers. When integrated with Global ATP and multi-sourcing optimization, these rules enable the engine to automatically split orders across multiple locations, minimizing cost-to-serve while respecting constraints like shelf-life requirements or trade compliance.

FULFILLMENT POLICY

Core Characteristics of a Sourcing Rule

A sourcing rule is a predefined policy that dictates the sequence of supply locations or production sources the ATP engine evaluates to fulfill a customer order. It is the primary mechanism for encoding regional fulfillment strategy into automated order promising.

01

Ranked Source Sequence

The core of a sourcing rule is an ordered list of supply locations. The ATP engine evaluates sources in strict sequence:

  • Primary Source: The preferred warehouse or plant, typically the closest or lowest-cost option.
  • Secondary Source: Activated only when the primary source cannot fully satisfy the order.
  • Tertiary Sources: Additional fallback locations evaluated sequentially until demand is met. This ranking ensures fulfillment aligns with business priorities like cost minimization, service level agreements, or inventory balancing.
02

Geographic Applicability

Sourcing rules are assigned to specific ship-to regions or customer zones, creating a geographic fulfillment map:

  • A rule for West Coast customers might prioritize a California distribution center.
  • A rule for European Union orders might restrict sourcing to EU-based warehouses for customs compliance.
  • Postal code ranges or country codes define the rule's scope. This regional binding ensures that the ATP check respects both logistical efficiency and regulatory constraints.
03

Product Assignment

Sourcing rules are linked to specific products or product categories, enabling differentiated strategies:

  • High-velocity SKUs may source from multiple regional hubs for redundancy.
  • Slow-moving or specialized items may source from a single central warehouse.
  • Hazardous materials may be restricted to certified facilities only. This granularity allows supply chain architects to optimize fulfillment based on product characteristics, demand patterns, and handling requirements.
04

Effective Date Ranges

Sourcing rules can be configured with valid-from and valid-to dates to support seasonal or transitional strategies:

  • A pre-launch rule sources from a single plant during product ramp-up.
  • A peak season rule activates overflow warehouses during holiday demand spikes.
  • A phase-out rule redirects sourcing as a distribution center is decommissioned. Date-bound rules enable hands-free transitions without manual intervention in the ATP engine.
05

Split Sourcing Logic

When a single source cannot fulfill the full quantity, the sourcing rule governs order splitting behavior:

  • No Split: The entire order must be fulfilled from one source; otherwise, it backorders.
  • Partial Split Allowed: The ATP engine splits the order across multiple sources in ranked sequence.
  • Split Threshold: A minimum quantity per split shipment to avoid uneconomical partial deliveries. This logic balances fulfillment speed against shipping cost and customer experience.
06

Integration with Allocation

Sourcing rules interact with allocation management to reserve inventory for specific channels or customers:

  • A rule may skip a source if its allocated inventory for the customer's channel is exhausted.
  • Committed ATP checks respect allocation boundaries defined at the source-product level.
  • This prevents a high-priority channel from consuming inventory reserved for another. The combination of sourcing rules and allocations creates a multi-layered fulfillment governance framework.
SOURCING RULE CLARIFICATIONS

Frequently Asked Questions

Clear, technically precise answers to the most common questions about sourcing rule logic, configuration, and its role within autonomous order promising systems.

A sourcing rule is a predefined, configurable policy that dictates the sequence of supply locations or production sources an Available-to-Promise (ATP) engine must evaluate to fulfill a customer order in a specific region. It acts as a prioritized routing table, telling the system to first check on-hand inventory at the primary distribution center, then at a secondary warehouse, and finally to evaluate a Capable-to-Promise (CTP) scenario at a manufacturing plant. This deterministic logic replaces manual decision-making, ensuring that order promising consistently aligns with corporate strategies like minimizing cost-to-serve, reducing freight spend, or prioritizing high-margin channels. In an autonomous supply chain, the sourcing rule is the fundamental instruction set that governs the initial allocation search before any dynamic optimization or multi-agent negotiation occurs.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.