AI Features (Priority Order)

AI Features (Priority Order)

Overview

Plock is AI-native — not AI-bolted-on. Every data model is designed with AI querying in mind. The AI layer runs continuously; warehouse workers are alerted, not asked to check.

Target user for AI: A warehouse worker with dirty gloves asking var finns SKU-X on a cracked phone screen — not an analyst building pivot tables.

Language: Swedish first. Short, direct responses. Action-oriented. Mobile-friendly.

Priority 1 — Plock AI Chat (Launch)

Type: LLM natural language interface Model: Claude API (claude-sonnet-4-5) Language: Swedish

What it does

• Answer warehouse questions in natural language Swedish
• Execute actions with single confirmation (move stock, reserve, assign pick)
• Access real-time inventory, orders, performance data
• Multi-turn conversation with warehouse context injected

Example queries

• Hur manga enheter av SKU-1234 har vi? — live stock count + trend
• Vilka ordrar ar forsenade idag? — list with root cause
• Flytta 50 enheter fran A-01-01 till B-02-03 — confirmation + action

Architecture

• System prompt includes: warehouse schema snapshot, org context, user role, current time
• Function calling for write operations (move, reserve, assign)
• Organization data never sent raw to Claude — only sanitised context
• Response target: less than 3 seconds

Priority 2 — Smart Picking (Launch)

Type: Route optimisation Engine: OR-Tools (Google) — VRP/TSP solver Reduction target: 30-70% travel time

What it does

• Automatically generates optimal pick routes when orders are released
• Groups orders into picking waves based on zone, carrier cutoff, priority
• Outputs sequential bin list on mobile scanner
• No configuration required — runs on every order release

How it works

1. Order released — ML microservice receives order + bin locations
2. OR-Tools VRP solver computes shortest path across warehouse zones
3. Optimised pick list returned to mobile app in under 2 seconds
4. Worker follows sequence — no backtracking

ROI

• Industry data: 30-70% of warehouse time is walking (not picking)
• Conservative 20% efficiency gain = 8,800 SEK/month per worker saved

Priority 3 — Anomaly Detection (Launch)

Type: Statistical detection on inventory transactions Algorithms: Z-score + isolation forest (scikit-learn) Trigger: Real-time, on every transaction

What it detects

• Sudden stock drops (theft, mispick, data error)
• Unusual pick rates (demand spike or data entry error)
• Inventory discrepancy after goods receipt
• Carrier label printed but no scan out (lost shipment risk)

Alert flow

1. Transaction recorded — Python microservice analyses against rolling baseline
2. Z-score above 3 sigma or isolation forest flags as outlier — alert created
3. Warehouse manager receives push notification + AI chat message
4. Manager confirms or dismisses — feedback improves model

Priority 4 — Demand Forecasting (Month 6)

Type: Time-series forecasting Models: XGBoost + Prophet (Facebook) Input: Order history (minimum 90 days)

What it does

• Predicts demand per SKU for next 7/14/30 days
• Generates reorder recommendations with suggested quantity and timing
• Adjusts for seasonality (Swedish calendar events, e-commerce peaks)
• Accessible in AI Chat: Vad ska vi bestalla hem nasta vecka?

Priority 5 — Smart Slotting (Month 6)

Type: ML-based placement recommendations Input: Pick frequency, order affinity, SKU dimensions

What it does

• Recommends optimal bin locations for each SKU based on pick frequency
• Groups frequently co-picked SKUs near each other (affinity slotting)
• Identifies slow-movers in prime locations and suggests moves
• Presented as actionable task list, not a forced migration

Priority 6 — Labor Planning (Month 12)

Type: Shift optimisation Constraints: Swedish labour law (Arbetstidslagen)

What it does

• Predicts staffing needs based on demand forecasts
• Generates compliant shift schedules (max hours, breaks, rest periods)
• Suggests overtime or temp worker needs 1-2 weeks in advance
• Integrates with order volume forecasts from Demand Forecasting module

AI Cost Model (per customer)