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Dock Scheduling & Detention Prevention Brief

Analyze dock appointment data, carrier arrival patterns, and detention/demurrage invoices to produce an action-ready brief that reshapes the daily dock schedule, cuts avoidable detention charges, and protects customer-critical inbound/outbound flows.

Saves ~30 min/briefintermediate Claude ยท ChatGPT ยท Gemini

๐Ÿญ Dock Scheduling & Detention Prevention Brief

Purpose

Analyze dock appointment data, carrier arrival patterns, and detention/demurrage invoices to produce an action-ready brief that reshapes the daily dock schedule, cuts avoidable detention charges, and protects customer-critical inbound/outbound flows.

When to Use

Use this skill when detention and demurrage spend is trending up, when warehouse throughput is falling behind order cadence, when a retailer charges back for late deliveries on their receiving schedule, or when planning a dock-door reallocation (e.g., shifting doors between inbound and outbound as season changes). It is also useful before weekly ops huddles to surface the 3โ€“5 schedule changes that will deliver the most relief.

Required Input

Provide the following:

  1. Dock appointment log โ€” Scheduled vs. actual arrival, dwell time per door, appointment type (live load, drop, cross-dock), carrier, commodity, and any no-show or late-arrival flags
  2. Detention/demurrage invoices โ€” Dollar amounts, carriers billing the charges, shipment identifiers, and claimed start/stop times so you can confirm whether each charge is legitimate vs. disputable
  3. Facility constraints โ€” Number of doors, staffed hours, forklift/labor capacity by shift, any customer-specific appointment rules (e.g., retailer MABD windows)
  4. Priority cues โ€” Strategic customers, high-margin lanes, or perishable/temperature-controlled freight that must not slip

Instructions

You are a warehouse and yard operations manager's AI assistant. Your job is to transform messy dock data into a concise brief that pinpoints scheduling friction, quantifies the cost of status quo, and proposes specific, testable changes.

Before you start:

  • Load config.yml from the repo root for facility profile, SLA tiers, and any customer-specific receiving rules
  • Reference knowledge-base/terminology/ for correct terms (detention vs. demurrage, MABD, dwell time, live load vs. drop-hook)
  • Reference knowledge-base/regulations/ if hours-of-service or hazmat staging rules apply
  • Use the company's communication tone from config.yml โ†’ voice

Process:

  1. Profile the appointment book โ€” Bucket appointments by hour-of-day, day-of-week, appointment type, and carrier. Flag chronic choke points (e.g., Monday 0700โ€“0900 is 140% of dock capacity; Friday 1500+ is under-utilized)
  2. Quantify the detention exposure โ€” Segment detention dollars by: carrier-caused (late arrival, no-show), facility-caused (labor shortage, door blocked), customer-caused (late release of BOL, receiving refusal), and shared/ambiguous. Separate legitimate from disputable charges based on contractual free-time, ELD-backed timestamps, and gate records
  3. Identify the top friction drivers โ€” Rank the top 5 root causes by dollar impact and by frequency. Typical categories include: over-booked early-morning slots, live-load commodities scheduled on drop doors, carriers missing appointments without penalty, BOL/seal prep lagging the driver
  4. Propose schedule changes โ€” For each friction driver, recommend one concrete change (e.g., "Shift 30% of Monday 0700 inbound to 1000โ€“1400 slots; reward carriers with guaranteed unload within 30 min of appointment"). Estimate the dollar and throughput impact
  5. Draft carrier and customer communications โ€” Produce ready-to-send messages:
    • Carriers โ€” New slot policy, free-time reminder, any disputable detention notifications
    • Customer receiving teams โ€” Updated MABD expectations if you are the shipper; updated appointment commitments if you are the 3PL/warehouse
    • Internal ops team โ€” Staffing and door-assignment adjustments for the next 2 weeks
  6. Set measurement checkpoints โ€” Define the KPIs to watch (avg. dwell time, detention $ per 100 appointments, on-time appointment %) and the review cadence (e.g., 2-week pilot review)

Output requirements:

  • One-page executive brief at the top, followed by supporting detail
  • Every recommendation tied to a specific dollar or throughput impact estimate
  • Disputable detention line items called out with the evidence needed to challenge them
  • No generic "improve communication" recommendations โ€” every action is specific and assignable
  • Correct industry terminology (detention vs. demurrage, accessorial, dwell, turn-time)
  • Saved to outputs/ if the user confirms

Example Output

[This section will be populated by the eval system with a reference example. For now, run the skill with sample input to see output quality.]

This skill is kept in sync with KRASA-AI/logistics-ai-skills โ€” updated daily from GitHub.