AI in Logistics — PieSoft.ai

The problem

Four places logistics operations bleed cost

Each one is a candidate for AI. The right starting point depends on where your margin is leaking most.

Static

Routing Inefficiency

Static route plans built overnight do not account for real-time traffic, weather, capacity changes, or same-day order additions. The gap between planned and actual costs accrues daily — in fuel, driver overtime, and missed SLAs.

Manual

Document Processing

Bills of lading, proof of delivery, customs declarations, and carrier invoices all require manual data entry to get into your TMS or ERP. At volume, this is a headcount problem — and errors create disputes that cost more than the labor.

Volatile

Demand Variability

Demand forecasting errors create two problems simultaneously: too much capacity in slow periods, not enough when demand spikes. Both cost money — either through underutilized assets or through spot market premiums when you scramble for capacity.

Reactive

Carrier Capacity Management

Carrier selection happens late, often under pressure. Performance data is scattered. RFP cycles are labor-intensive. AI can score carriers on lead time, on-time delivery, and capacity reliability — and surface that at the point of shipment creation.

Use cases

Three systems that move the P&L

Scoped to your specific constraints — TMS, ERP, network, freight modes. We build for your environment, not a generic one.

Intelligent Routing

Dynamic routes that respond to real conditions — not last night's plan

We build routing optimization systems that factor real-time traffic, weather, vehicle capacity, driver hours-of-service, delivery time windows, and same-day order adds — continuously, not just at morning dispatch. Route plans update as conditions change throughout the day.

Multi-stop route optimization: minimize distance, time, or cost based on your objective
Real-time traffic and weather integration for dynamic re-routing
Delivery time window compliance with SLA alerting
Same-day order insertion without manual re-dispatch
Driver hours-of-service compliance checks built into route generation

Works with your TMS — or without one

We integrate with MercuryGate, Oracle TMS, SAP TM, and proprietary dispatch systems. For fleets without a formal TMS, we can serve as the routing layer directly.

Last-mile delivery window prediction

The system predicts delivery windows based on current route progress and traffic conditions — so customers get accurate ETAs and failed deliveries drop because someone is actually home.

Document Processing

Auto-extract BOL fields, validate PODs, automate customs — without manual keying

Logistics documents are messy — scanned at angles, printed on thermal paper, filled out by hand, and received at volume. We build extraction pipelines that handle real-world document quality, validate extracted data against known shipment records, and flag exceptions for human review rather than silently entering wrong data.

BOL extraction: shipper, consignee, commodity, weight, PRO number, and more
POD image validation: signature present, delivery address match, timestamp check
Customs declaration auto-population from commercial invoice and product master
Carrier invoice audit: match against contracted rates, flag discrepancies
Exception queue: low-confidence extractions routed to a reviewer with the source image

Handles poor-quality scans

We preprocess images to correct skew, improve contrast, and remove artifacts before extraction — so the model works on real-world scan quality, not clean PDFs. Confidence scores are returned with every field.

Works for parcel and freight

Different modes produce different documents with different layouts. We train mode-specific extraction models — LTL, FTL, parcel, ocean, air — so each model is tuned to the documents it will actually see.

Demand Forecasting

SKU-level forecasts that account for seasonality, promotions, and supplier lead times

We build demand models trained on your order history, enriched with external signals — promotional calendars, market indices, weather patterns where relevant — and integrated with supplier lead time data so procurement recommendations account for actual order-to-receipt timelines, not assumed ones.

SKU-level demand forecasting with configurable horizon (weekly, monthly, quarterly)
Seasonal decomposition and promotional lift modeling
Supplier lead time integration for procurement timing recommendations
Confidence intervals: surface the range, not just the point estimate
Forecast accuracy tracking and model self-monitoring

What data you need to get started

Minimum: 18–24 months of order history at the SKU level. Better: purchase orders, receipts, returns, and promotional calendars. We assess your data in week one of the engagement and tell you what forecast horizon and accuracy you can realistically achieve.

Surfaces in your planning tool

Forecast outputs integrate with SAP IBP, Oracle ASCP, Blue Yonder, or your ERP planning module. Planners see AI recommendations alongside actuals in the tool they already use.

Beyond the three core use cases

Carrier management and last-mile optimization

Two areas where AI delivers fast ROI with data you already have — without a major integration project.

Carrier Performance Scoring

We build carrier scorecards from your shipment history — on-time delivery rate, damage rate, invoice accuracy, capacity availability. Scores update weekly and surface at the point of carrier selection so the right carrier gets the load, not just the cheapest one at the time.

Multi-dimensional scoring: OTD, damage, invoice accuracy, capacity reliability
RFP automation: pre-rank carriers before the bid goes out
Capacity alert: flag carriers showing availability decline before it affects your lanes

Last-Mile Delivery Optimization

Failed deliveries are expensive — the re-attempt cost is often 2–3x the original delivery. We build systems that predict delivery window success based on recipient history and route conditions, and that trigger proactive outreach when a delivery is at risk of failure.

Delivery window prediction: accurate ETAs pushed to recipients in real time
At-risk delivery detection: identify likely failures before the attempt
Proactive rescheduling: automated outreach and recipient-self-service reschedule

FAQ

Frequently Asked Questions

Can AI routing work with our existing TMS? +

Yes. We integrate with MercuryGate, Oracle TMS, SAP TM, JDA/Blue Yonder, and proprietary dispatch systems. The integration approach — bidirectional API, scheduled sync, or event-driven webhook — depends on your TMS capabilities, which we assess in week one. For systems without modern APIs, we work with data exports and push back via the TMS's import interface.

How does document AI handle poor-quality scanned documents? +

Real logistics documents are often photographed at angles, printed on thermal paper that has faded, or scanned at low resolution through a multi-function printer. We build preprocessing pipelines that correct skew, normalize contrast, and remove noise before extraction. Each extracted field returns a confidence score — fields below the configured threshold go to an exception queue for human review, so wrong data does not silently enter your system.

What data do we need to build a demand forecasting model? +

The minimum is 18–24 months of order history at the SKU level, with date, quantity, and ship-to location. More signal improves accuracy: purchase orders, receipts, returns, promotional activity calendars, and any external data relevant to your demand drivers (weather, market indices, economic indicators). We assess your data in week one and tell you what forecast accuracy and horizon is realistic given what you have — we do not start building until that is agreed.

How quickly can routing improvements be deployed? +

For fleets with clean stop and delivery data available via API, a routing optimization pilot can be running in 6–8 weeks. The pilot typically runs in shadow mode first — generating route recommendations alongside your existing dispatch, so dispatchers can compare and we can tune the model against your real constraints before going live. Full deployment typically follows 2–4 weeks after pilot validation.

Does this work for both parcel and freight? +

Yes, but the implementation differs significantly. Parcel optimization focuses on delivery sequence, time windows, and failed-delivery reduction. Freight — LTL and FTL — involves lane optimization, load consolidation, and carrier selection logic. We scope the engagement to your specific freight modes and network. Document AI is also mode-specific: BOL formats for LTL differ from ocean freight manifests, and we train separate extraction models for each.

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