Connected ERP, 3PL, and BI data into governed pipelines with AI agents for inventory and demand planning decisions data engineering.
Industry
Logistics
Services
Technologies
Regional 3PL & distribution operator partnered with Spectrum to address operational and technology gaps in logistics. Inventory and demand data lived in disconnected ERP, warehouse, and spreadsheet models — planners could not run scenarios quickly. Spectrum applied a phased delivery model — 5 months — aligning stakeholders, compliance needs, and production cadence. Since 2016, Spectrum has delivered similar programs with managed teams and fixed-cost options.
Inventory and demand data lived in disconnected ERP, warehouse, and spreadsheet models — planners could not run scenarios quickly.
ERP, WMS, and 3PL feeds disagreed on inventory and lead times.
Planners exported spreadsheets for what-if analysis — days per cycle.
Data science models never connected to operational approval flows.
Business would not act on recommendations without explainability.
Planners at the 3PL were spending more time reconciling ERP, warehouse, and carrier exports than adjusting to demand shifts. Spectrum treated a single planning truth as the deliverable — not a slide about AI. Forecasting and an assistant came after the numbers agreed, and every ERP write stayed behind a human sign-off.
The same SKU in Rotterdam could show three different on-hand values depending on which system you opened. We agreed contracts for item, location, and lead-time semantics with operations and finance, then built governed pipelines into Snowflake with dbt tests that block loads when keys drift or quantities fall outside tolerance. High-velocity lanes stream through Kafka; slower 3PL feeds land in nightly batches with quarantine tables planners can inspect. Monday planning meetings now start from one dataset — not a debate about which export is “closest.”
What-if work used to mean desktop spreadsheets emailed between sites — a multi-day loop before anyone could answer a dock-delay question. We put planners inside Power BI views backed by versioned models they can rerun with explicit assumptions: promo lift, safety stock, carrier cutoff changes. Each run stores drivers and outputs so finance can compare scenarios without reverse-engineering cells. Cycles that stretched across a week now close in a working session, with named versions instead of “final_v7_REAL.xlsx.”
The assistant reads governed inventory, in-transit, and forecast context — planners ask in plain language, but nothing posts to ERP without approval. Proposed changes arrive as a short summary and field-level diff; reviewers accept, edit, or reject from the same screen they already use for exceptions. Adoption improved when teams realized the tool would not auto-book stock during a live call with a customer. Data science experiments finally sat inside the same approval path operations trusted.
Every figure in the planning views traces to source system, load timestamp, and transformation version. When operations or finance challenges a number, the team exports that path in minutes — without rebuilding spreadsheets from memory.
Faster scenarios
Planning source of truth
AI recommendations
Planning runs on a governed data platform with near-real-time inventory signals, versioned forecasts, and an approval-gated assistant — not a disconnected spreadsheet and model sandbox.
dbt-tested pipelines land SKU, site, and lead-time data in Snowflake with quarantine tables for rejected loads. Kafka streams high-velocity movement events; batch jobs cover slower 3PL feeds so planners see one timeline instead of three conflicting exports.
Versioned ML models expose drivers and outputs to Power BI for scenario reruns — promo lift, safety stock, carrier delays. Each scenario run is named and comparable so finance can audit assumptions without opening desktop macros.
A copilot reads governed datasets and drafts answers in natural language, but ERP write-backs require human approval with field-level diffs. Tool access is read-only until a reviewer confirms — adoption improved when planners trusted the gate.
Lineage from dashboard metrics back to source system, load time, and transformation version is exportable for operations challenges. Alerting fires when pipeline SLAs slip so planning meetings are not the first place anyone learns data is stale.
Role-based views separate planner, finance, and admin capabilities; PII in movement notes is masked at ingest. Model and prompt versions are pinned per environment so production recommendations are reproducible during audits.
Spectrum addressed bottlenecks and compliance needs while keeping delivery incremental and measurable.
Delivered and measured in production with stakeholder sign-off.
Delivered and measured in production with stakeholder sign-off.
Delivered and measured in production with stakeholder sign-off.
Planners work from one governed dataset with faster scenarios and approved ERP updates — manual reconciliation before Monday meetings is no longer the norm.
Faster scenario planning
Planning source of truth
Data quality SLA
Tell us about your goals. We respond within one business day.
Typical engagement · 5 months
Deployed LLM agents and workflow orchestration across CRM, ERP, and documents — with guardrails, audit trails, and human review where decisions matter ai automation.
Enterprise
AI-Powered Enterprise Automation
AI-assisted automation connecting CRM, documents, and approval flows — reducing manual processing time for operations teams ai automation.
Professional Services
Enterprise Workflow Automation
Assessment-led migration to cloud with phased cutover, security hardening, and DevOps pipeline establishment cloud migrations.
Logistics
Enterprise Cloud Migration
Start your transformation
Custom software, AI automation, and delivery teams — confidential scoping and a same-day response from our architects.
Prefer a discovery call first?
Book AI Readiness Audit