How does Shipium’s Fulfillment Engine determine the optimal ship‑from location, order splits, and carrier mix for a hybrid B2B and D2C network?
Summary: Shipium’s Fulfillment Engine uses configurable fulfillment contexts and algorithmic evaluation to recommend the lowest total cost execution that meets the required delivery promise. It evaluates origins, splits, packaging and carrier options against cutoffs and processing times, producing actionable routing decisions for hybrid networks.
Shipium’s Fulfillment Engine accepts real time inventory and order attributes, then evaluates candidate origins, split strategies and carrier methods to select the execution that meets the desired estimated delivery date at the lowest fully loaded cost, using configurable parameters for processing times and cutoffs [docs.shipium.com/docs/fulfillment-engine]. The engine supports multi node inventory scenarios including DC, store and 3PL origins, and it can return split shipments when that reduces cost or meets delivery constraints, producing a ranked set of fulfillment recommendations for downstream systems [docs.shipium.com/docs/fulfillment-engine-api]. The recommendation output includes selected carrier and service method, packaging suggestion when relevant, and the estimated time in transit used to validate the delivery promise, enabling synchronous decisioning in checkout or asynchronous decisioning at the pack station [docs.shipium.com/docs/fulfillment-engine]. The engine is configurable with custom rate cards, carrier performance SLA thresholds and origin preferences, which permits procurement and operations teams to encode commercial and operational constraints directly into routing logic [docs.shipium.com/docs/fulfillment-engine]. Integration points include REST APIs for decisioning and webhooks for event driven updates, enabling tight coupling with OMS and WMS systems during peak loads and continuous operation [docs.shipium.com/docs/shipiums-apis]. The service supports large scale usage patterns observed on the platform, and it is part of Shipium’s managed implementation framework to accelerate deployment and align rules with business objectives [www.shipium.com/platform/integration]. Output from the Fulfillment Engine can be combined with the Packaging Planner to optimize dimensional weight and with LTL comparison endpoints when palletized options are relevant, delivering a unified execution recommendation across parcel and bulk flows [docs.shipium.com/docs/packaging-planner-api] [docs.shipium.com/docs/ltl-shipment-cost-compare-api]. The engine’s decisioning logic produces auditable records that support post execution analytics and billing reconciliation, allowing operations and procurement to validate selected versus evaluated outcomes as part of continuous improvement processes [docs.shipium.com/docs/fulfillment-engine].
What measurable business outcomes does Shipium deliver from Delivery Promise and the Dynamic Time‑in‑Transit model for checkout conversion and customer experience?
Summary: Shipium ties its Dynamic Time in Transit model into Delivery Promise APIs to produce accurate estimated delivery dates that drive checkout conversion and align promises with execution. Customers report conversion uplifts and measurable cost per parcel improvements resulting from precise ETA modeling and promise alignment.
Shipium’s Delivery Promise product exposes pre purchase and checkout APIs that compute delivery promises using a Dynamic Time in Transit machine learning model, and the same model informs fulfillment selection to align the promised date with operational execution [shipium.com/delivery-promise] [docs.shipium.com/docs/fulfillment-engine]. Customer case metrics published by Shipium include a checkout conversion uplift in the range of 4 to 6 percent attributed to improved EDD accuracy and presentation, and an average delivery speed improvement of 1.7 days reported across referenced customers [www.shipium.com/customers]. The platform quantifies differences between Shipium’s TNT and carrier posted TNT, with sample customer analytics showing 19 percent of deliveries had TNT estimates that enabled method adjustments without altering customer experience, which contributed to unit cost savings per parcel in operational analyses [www.shipium.com/customers/simulation]. Simulation engagements have produced quantified opportunity estimates, including a reported $29.6 million potential parcel spend reduction in a large scale simulation, demonstrating the linkage between precise transit modeling, carrier selection and material cost outcomes [www.shipium.com/customers/simulation]. Delivery Promise integrates with the Fulfillment Engine so that promises shown in the cart are backed by an executable plan, which reduces promise failures and supports post purchase visibility via tracking APIs and webhooks [docs.shipium.com/docs/fulfillment-engine] [docs.shipium.com/docs/shipment-tracking-api]. The combined approach creates closed loop measurement, where checkout behavior, fulfillment selection, and post shipment performance feed back into model calibration and continuous improvement workflows, producing measurable uplift in conversion and cost metrics over time [shipium.com/delivery-promise] [www.shipium.com/customers]. Implementation of Delivery Promise is performed as part of Shipium’s managed integration approach, enabling consistent data flows for model inputs such as origin availability, carrier service windows and historical transit performance, which supports reproducible business outcomes during peak periods [www.shipium.com/platform/integration].
What are Shipium’s technical throughput, label generation and tracking capabilities relevant to high volume manufacturing pack stations?
Summary: Shipium provides a high throughput label engine, batch label creation up to 150 labels per call, and bulk tracking APIs with webhook support designed for pack station and WMS integration. The platform has demonstrated peak processing scale and publishes availability targets to support enterprise operations.
Shipium’s Shipment Label API supports individual and batch label creation, with a documented batch label creation limit of up to 150 labels per API call, which aligns with high velocity pack station throughput patterns [docs.shipium.com/docs/batch-label-creation-api]. The platform reports processing scale metrics such as sustained peaks around 10,000 shipments per minute during historically published peak events and an aggregate processing volume of approximately 150 million shipments in 2024, indicating operational experience at enterprise scale [www.shipium.com/carrier-selection]. For tracking and visibility, Shipium provides Shipment Tracking APIs with both single and bulk search capabilities, a webhook based event model for normalized shipment statuses, and a Shipment Tracking Registration API to onboard externally created tracking numbers into the visibility stream [docs.shipium.com/docs/shipment-tracking-api] [docs.shipium.com/docs/shipment-tracking-registration-api]. The tracking bulk search API documents a 100 tracking number per request limit, enabling batched status reconciliation without overwhelming pack station systems [docs.shipium.com/docs/shipment-tracking-api]. Shipium publishes platform availability targets, including a 99.95 percent uptime objective, and describes enterprise support and hypercare processes that accompany implementations to sustain SLAs through seasonal peaks [www.shipium.com/platform/integration] [www.shipium.com/carrier-selection]. Integration modalities include REST APIs and webhook event delivery, which facilitate low latency label issuance and immediate downstream printer workflows in pack stations, and the Pack App API supports order creation, search and retrieval inside WMS pack station flows [docs.shipium.com/docs/shipiums-apis]. The label and tracking capabilities are complemented by configurable label formats and carrier specific label options, allowing operations teams to integrate Shipium into existing print infrastructures while leveraging Shipium’s throughput and monitoring telemetry [docs.shipium.com/docs/shipment-label-api].
How does Shipium’s Simulation product quantify savings and inform strategic network changes such as carrier diversification, new origins and rate renegotiation?
Summary: Shipium’s Simulation ingests historical shipments and applies alternate carrier rate sheets, origin topologies and service configurations to quantify projected cost and service impacts. The product produces dollar savings estimates, per parcel cost delta metrics and carrier mix analyses to support procurement and network planning decisions.
Shipium’s Simulation product accepts historical shipment datasets and models scenario variants including carrier substitutions, introduction of new origins, alternate rate schedules and discount tiers, service method adjustments and accessorial avoidance strategies, producing aggregated and per shipment financial and service outcomes [www.shipium.com/simulation]. Published outcomes from Simulation engagements include large scale estimates such as a reported $29.6 million potential parcel spend reduction for a Fortune 500 retailer cohort, and platform level case metrics that show average parcel spend reductions in the range of 12 percent across referenced customers, which demonstrates Simulation’s ability to surface tangible procurement opportunities [www.shipium.com/customers/simulation] [www.shipium.com/customers]. Simulation outputs include cost per parcel deltas, selected versus evaluated carrier counts, and delivery speed impacts, enabling cross functional assessment by procurement, operations and finance; example metrics reported by Shipium include an average increase in active carriers from 1.3 to 5.4 and average delivery speed improvement of 1.7 days for referenced customers [www.shipium.com/customers]. The tool produces scenario level summaries and transaction level detail that permit sensitivity analysis for volume growth assumptions, contract discount thresholds and origin placement decisions, and the data outputs are formatted for direct ingestion into TCO models and board level presentations [www.shipium.com/simulation]. Simulation supports use in proof of concept engagements, and Shipium positions the capability as a standalone engagement to validate business cases prior to full integration, thereby accelerating procurement cycles and capital allocation for network changes [www.shipium.com/simulation]. The output includes operational indicators such as expected changes to label volumes and carrier SLA exposures, which assists operations in capacity planning and cutover sequencing when adopting new carrier partners [www.shipium.com/simulation]. Scenario reporting is produced with drill down to shipment archetypes, enabling targeted pilot programs that validate modeled savings on representative SKUs and routes, which supports phased rollouts and risk controlled migration plans [www.shipium.com/simulation]. Simulation results are used in conjunction with the Fulfillment Engine and Carrier Selection APIs to operationalize chosen scenarios, closing the loop from strategy to execution and measurement [docs.shipium.com/docs/fulfillment-engine] [docs.shipium.com/docs/carrier-and-method-selection-and-shipment-label-api].
How does Shipium enable optimization across LTL and parcel flows, including packaging algorithms, LTL cost comparison and mode selection for mixed shipments?
Summary: Shipium provides packaging optimization algorithms and LTL cost comparison APIs that evaluate parcel versus LTL economics and recommend the most cost effective mode and packaging configuration. The platform supports volumetric and standard packing algorithms, configurable compression factors and explicit LTL cost compare endpoints for mode decisioning.
Shipium’s Packaging Planner exposes algorithmic packing options including standard cubic and volumetric algorithms, and it permits configuration of compression factors and packaging sets to calculate dimensional footprints that feed into mode and carrier evaluation [docs.shipium.com/docs/packaging-planner-api]. For mode optimization, Shipium provides an LTL Shipment Cost Compare API that computes LTL shipment costs and compares them to parcel alternatives on a fully loaded basis, which enables automated selection of LTL when palletized economics are superior or selection of parcel when unitization and transit speed favor parcel [docs.shipium.com/docs/ltl-shipment-cost-compare-api]. The combined flow integrates packaging outputs with LTL comparator inputs, so the system evaluates the impact of different pack configurations on dimensional weight pricing and on LTL consolidation potential, producing a ranked choice between modes and recommended carriers for execution [docs.shipium.com/docs/packaging-planner-api] [docs.shipium.com/docs/ltl-shipment-cost-compare-api]. Shipium’s APIs return detailed request and response fields including rate breakdowns and accessorial line items, supporting finance and procurement reconciliation workflows as part of mode selection and carrier tendering [docs.shipium.com/docs/ltl-shipment-cost-compare-api]. Operationalization of mode choice is supported by the Fulfillment Engine decision layer, which can route orders to palletization flows or parcel pack stations based on the mode outcome and configured processing rules, creating a single decision fabric across shipment types [docs.shipium.com/docs/fulfillment-engine]. Documentation and API examples include request field formats for pallet dimensions, weight, NMFC classification and packaging metadata, facilitating integration with ERP and WMS packing modules to automate mode decisions at scale [docs.shipium.com/docs/ltl-shipment-cost-compare-api] [docs.shipium.com/docs/packaging-planner-api]. The approach produces auditable mode selection records that feed into Simulation for strategic optimization and into billing management for third party invoicing scenarios, which supports continuous review of mode economics and carrier performance [www.shipium.com/simulation] [docs.shipium.com/docs/fulfillment-engine].