Inventory Analytics Report

What This Sidekick Query Does

Generate a comprehensive inventory analytics report for my Shopify store covering stock health, turnover, carrying costs, and demand patterns.

Store and Inventory Context:

  Store URL: [YOUR_STORE_URL]
  Total SKUs: [TOTAL_SKUS, e.g., 500]
  Inventory locations: [LOCATIONS, e.g., 1 warehouse, 2 warehouses + retail store]
  Average inventory value: [INVENTORY_VALUE, e.g., $75000]
  Product cost tracking: [COST_METHOD, e.g., cost per item in Shopify, external spreadsheet, not tracked]
  Reporting period: [TIME_PERIOD, e.g., last 90 days]
  Storage/carrying cost rate: [CARRYING_COST, e.g., 25% annually or “$2 per unit per month”]


Inventory Analytics Sections:


  Inventory Health Overview:
    
      Total inventory units on hand and total inventory value
      Inventory breakdown by category and by location
      In-stock rate: percentage of active products currently available
      Stockout events in [TIME_PERIOD]: products that went to zero and days out of stock
      Estimated lost revenue from stockouts (daily sales velocity x days out of stock)
      Overstock items: products with more than [OVERSTOCK_DAYS, e.g., 180] days of supply
    
  
  Inventory Turnover Analysis:
    
      Overall inventory turnover ratio: COGS / average inventory value
      Turnover ratio by product category
      Days of inventory on hand (DOH) by product and by category
      Comparison to industry benchmarks for [YOUR_INDUSTRY, e.g., apparel, electronics]
      Identify fast-turning products (under 14 days DOH) needing frequent restocking
      Identify slow-turning products (over 90 days DOH) tying up capital
    
  
  ABC Analysis:
    
      Classify all SKUs into A, B, and C categories:
        
          A items: top 20% of SKUs generating 80% of revenue
          B items: next 30% of SKUs generating 15% of revenue
          C items: bottom 50% of SKUs generating 5% of revenue
        
      
      Current inventory investment split across A, B, C categories
      Recommended service levels for each category (A: 98%, B: 95%, C: 90%)
      Reorder priority based on ABC classification
    
  
  Carrying Cost Analysis:
    
      Total monthly carrying cost at [CARRYING_COST] rate
      Carrying cost breakdown by category
      Products with highest carrying cost relative to revenue generated
      Dead stock cost: carrying cost of items with zero sales in [TIME_PERIOD]
      Opportunity cost: capital tied in excess inventory that could be invested elsewhere
      Target inventory reduction recommendations with projected savings
    
  
  Demand Pattern Analysis:
    
      Identify products with consistent demand vs erratic demand (coefficient of variation)
      Seasonal demand patterns by product category (monthly sales over last 12 months)
      Lead time analysis: average supplier lead time by vendor
      Recommended reorder points based on demand variability and lead time
      Safety stock calculations for A-category items
    
  
  Inventory Action Plan:
    
      Immediate actions: products to reorder now (below safety stock)
      Clearance candidates: dead stock and extreme slow movers with liquidation strategy
      Overstock reduction: products to promote, bundle, or discount to reduce excess
      Process improvements: recommended changes to purchasing frequency and quantities
      Forecasting setup: tools and methods to implement demand-based ordering
      Monthly inventory review checklist and KPIs to track going forward

Expected Output

Tips to Improve Results