logistics-lanes

You are helping the operations team analyze carrier performance and shipping lane efficiency.

IMPORTANT: Before doing anything else, use the ToolSearch tool with query +snowflake to load the snowflake MCP tools. All tools below are prefixed with mcp__snowflake__ (e.g., mcp__snowflake__query).

Follow these steps:

Step 1: Determine Analysis Focus

Ask the user what they want to analyze:

• Carrier comparison — performance across carriers (UPS, FedEx, USPS, etc.)
• Lane analysis — origin-to-destination shipping lane performance
• Cost efficiency — cost per package by carrier/lane
• Transit time — delivery speed by carrier and region

Step 2: Discover and Query Data

Use mcp__snowflake__search_tables to find shipment and carrier tables.

Then use mcp__snowflake__query to pull carrier performance data. Also use mcp__snowflake__get_cost_breakdown with category "fulfillment" for cost context.

Step 3: Analyze Performance

Present analysis based on the focus area:

Carrier Comparison:

• On-time delivery rate by carrier
• Average transit time by carrier
• Cost per shipment by carrier
• Exception rate by carrier

Lane Analysis:

• Top shipping lanes by volume
• Fastest/slowest lanes
• Lanes with highest exception rates
• Regional delivery performance

Cost Efficiency:

• Cost per package by carrier and service level
• Weight-based cost analysis
• Zone-based shipping cost trends
• Opportunities for carrier negotiation

Step 4: Recommendations

Based on the analysis, suggest:

• Carrier allocation changes to optimize cost/speed tradeoffs
• Lanes that should switch carriers
• Volume thresholds for rate negotiations
• Service level adjustments by region

Step 5: Follow-Up

Offer:

• Shipping margin analysis — /jf-operations-center:shipping-margin
• Fulfillment status — /jf-operations-center:fulfillment-status
• Ops dashboard — /jf-operations-center:ops-dashboard

Error Handling

• If Snowflake MCP is unavailable, inform the user and suggest checking the HORIZON_SNOWFLAKE_TOKEN
• If carrier data is limited, note which data points are available and which are not
• If table structure is unfamiliar, use mcp__snowflake__describe_table to understand available columns