Capacity service

In an omnichannel retail ecosystem, a fulfillment promise is only as reliable as the operational constraints behind it. Sterling Intelligent Promising evaluates real time signals across the supply chain network to determine when a product can realistically be picked, packed, and delivered. However, physical inventory availability is only part of the equation. A fulfillment center may have ten thousand units in stock, but if operational capacity only supports picking one hundred units, any estimated delivery date that ignores that constraint quickly becomes unreliable. The result is missed delivery commitments, reduced customer trust, and long term impact on brand reputation and retention.

The Capacity service bridges the gap between digital promises and physical reality. It provides a generic, highly scalable architectural framework to mathematically model real-world operational constraints. While immediately applicable to fulfillment throughput and warehouse staffing, the underlying data model is designed to represent any finite resource. This forward thinking architecture paves the way for tracking carrier pickup bandwidth, specialized docking space limitations, and eventually item level processing constraints. Sterling Intelligent Promising does not just count packages; it measures the operational effort required to move them.

Overview

To determine the most optimal sourcing location for a fulfillment request, the Sterling Intelligent Promising architecture separates concerns across its specialized modules. The Capacity service computes and provides the capacity availability metrics, which determines whether a location has the physical resources, labor, and logistics bandwidth to execute the fulfillment tasks.

The Capacity service acts exclusively as the unified source of truth for these operational limits. It tracks throughput consumption and supplies this critical constraint data to the Promising and Optimization modules. The Promising module then utilizes this data to enforce business shipping constraints and compute accurate delivery dates, while the Optimization module leverages it to evaluate node capacity load balancing and minimize overall fulfillment costs.
Note: Implementing capacity constraints is entirely optional. If capacity is not configured for a specific node, the system treats that location as having infinite bandwidth, and downstream modules relies entirely on inventory availability and transit times for their routing decisions.
The underlying architecture is driven by several core entities that work in tandem to compute capacity availability and process reservations, which are detailed in subsequent sections:
  • Service category
    Defines the type of work or operational domain being measured. For more information, see Service category.
  • Operation calendar
    Establishes the baseline schedule and time slots for standard operations. For more information, see Operation calendar.
  • Resource pool
    The centralized execution profile that binds nodes, calendars, and capacity units of measure together. For more information, see Resource pool.
  • Capacity availability
    The precomputed cache of available operational bandwidth designed for sub second lookups. For more information, see Capacity availability.
  • Overrides
    Tactical adjustments at the calendar or resource pool level to handle real time disruptions or temporary closures. For more information, see Overriding availability.
  • Bookings
    The transactional mechanisms, both single and atomic, that securely lock in capacity during the checkout lifecycle. It also handles the concept of backlog when scheduled fulfillments are delayed. For more information, see Booking.
The Capacity service also provides tenant level settings to alter default behavior, which include:
  • Capacity time zone
    Determines when the capacity location resets its daily capacity level.
  • Open capacity days
    Establishes the forward capacity availability horizon by determining the number of days to account for operational constraints. Dates beyond this horizon are assumed to have no capacity availability.
  • Booking expiration minutes
    Dictates how soon a capacity booking expires. It is highly recommended to pair this with your inventory reservation expiration.
  • Time zone determination
    Decides whether the capacity should reset based on the localized node time zone or the centralized tenant time zone.

Business use case and value

The Capacity service supports diverse supply chain scenarios, allowing fulfillment managers to map specific operational constraints directly into the digital fulfillment workflow.
Scenario 1: Limiting daily package volume at a major distribution center
A high volume distribution center ships 5,000 packages daily but must cap outbound volume to prevent overwhelming the loading dock and carrier pickup operations. By configuring a Resource Pool mapped to a shipping category for the distribution center and a daily operation calendar, the Capacity service automatically calculates the daily fulfillment capacity availability. As orders are accepted or reserved, the capacity level drops. The Promising module recognizes the depletion of capacity and dynamically reroutes new requests to alternative nodes until the next available capacity slot.
Scenario 2: Managing throughput for a small retail BOPIS kiosk
A retail store acting as a fulfillment node features a restricted customer pickup area that can only accommodate ten orders per day. By defining a resource pool for the pickup service category at the small retail node, the Capacity Service can throttle the order assignment for pickup while allowing ship to home fulfillment to continue uninterrupted. The Promising and Optimization engines evaluate this constraint data during the per-purchase and post purchase phases, ensuring store pickup orders remain under the localized footprint.

Value realization

Deploying the Capacity service transforms a reactive fulfillment network into a proactive, protected operation.
  • Guaranteed delivery accuracy
    By supplying real time operational capacity metrics, the service ensures that promising evaluations push delivery dates to the next viable slot when bandwidth is exhausted, rigidly protecting your customer service level agreements.
  • Warehouse protection and backlog handling
    Explicit constraints protect your store operation strategies. By tracking systemic backlogs, the Capacity service prevents warehouse operations from drowning in unachievable order commitments and allows backlog capacity to be carried forward to future capacity days.
  • Unified source of truth
    Both the Promising and Optimization engines consume the exact same centralized capacity availability from the Capacity service that ensures deterministic and consistent routing decisions across the entire enterprise architecture.