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Product Quantities Over Time Documentation

Menu Location: Reports > Products > Product Quantities Over Time

Access Level: Manager and above

Last Updated: 2026-03-01

Overview

The Product Quantities Over Time report tracks sales volume for specific products across defined time periods. This helps identify product trends, seasonal demand patterns, and optimal inventory levels for individual products.

Primary Functions:

  • Track product sales volume over time
  • Identify seasonal demand patterns
  • Compare product performance periods
  • Optimize inventory ordering
  • Spot emerging trends
  • Inform product discontinuation decisions

Page Layout

Header Section

  • Report Title: "Product Quantities Over Time"
  • Product Selector: Choose product(s) to analyze
  • Date Range: Select time period
  • Granularity: Daily, weekly, monthly view

Summary Stats

  • Total Quantity Sold: Units in selected period
  • Average per Period: Mean quantity per day/week/month
  • Peak Period: Highest volume date/week/month
  • Trend Direction: Up, down, or stable

Trend Chart

  • Line Graph: Quantity sold over time
  • Trend Line: Regression line showing overall direction
  • X-Axis: Time periods
  • Y-Axis: Quantity sold
  • Hover: Exact quantities on specific dates

Data Table

Period Quantity Sold Change vs Previous % Change Revenue
Jan 1-7 125 lbs +15 lbs +13.6% $625
Jan 8-14 140 lbs +15 lbs +12.0% $700
Jan 15-21 132 lbs -8 lbs -5.7% $660

Selecting Products

Single Product Analysis

Steps:

  1. Click "Select Product" dropdown
  2. Search by product name
  3. Select product
  4. Report loads for that product only

Best For:

  • Deep dive on specific product
  • Seasonal pattern analysis
  • Inventory planning for one product

Multiple Product Comparison

Steps:

  1. Click "Select Product" dropdown
  2. Check boxes for multiple products (up to 5)
  3. Report shows all on same chart
  4. Each product as different color line

Best For:

  • Compare similar products
  • Analyze product substitution
  • Category performance comparison

Product Category Analysis

Steps:

  1. Click "Select by Category"
  2. Choose category (Produce, Meat, Dairy, etc.)
  3. Option to show aggregate or individual products
  4. View category total trend

Best For:

  • Category-level planning
  • Broad inventory decisions
  • Supplier negotiations

Configuring the Report

Date Range Selection

Preset Ranges:

  • Last 30 Days: Recent performance
  • Last 90 Days: Quarterly view
  • Last 6 Months: Seasonal patterns
  • Last Year: Full seasonal cycle
  • Year to Date: Current year
  • Last Year Same Period: YoY comparison
  • Custom: Specific dates

Granularity Options

Daily:

  • Day-by-day quantities
  • Best for: 7-30 day periods
  • Use: Promotional campaign analysis

Weekly:

  • Week totals
  • Best for: 1-6 month periods
  • Use: Standard trend analysis

Monthly:

  • Month totals
  • Best for: 6+ month periods
  • Use: Seasonal pattern identification

Understanding the Data

Metrics Explained

Total Quantity Sold:

  • Sum of all units sold in period
  • Includes all order types
  • Measured in product's unit (lbs, each, etc.)

Average per Period:

  • Mean quantity per day/week/month
  • Helps forecast future demand
  • Smooths out one-time spikes

Peak Period:

  • Highest volume date/week/month
  • Indicates maximum demand
  • Useful for capacity planning

Trend Direction:

  • Overall pattern: up, down, stable
  • Based on regression analysis
  • Helps predict future demand

Common Use Cases

Use Case 1: Identify Seasonal Product Patterns

Goal: Understand when product is in high/low demand

Steps:

  1. Select seasonal product (e.g., strawberries)
  2. Date Range: Last Year
  3. Granularity: Monthly
  4. Review chart for pattern
  5. Note peak months (June-Aug for berries)
  6. Note low months (Dec-Feb)
  7. Document for future planning
  8. Adjust inventory purchasing by season

Example Insights:

  • Strawberries peak June (+200% vs. avg)
  • Butternut squash peak October (+150%)
  • Tomatoes consistent year-round
  • Citrus peak December-January

Result: Seasonal purchasing calendar

Use Case 2: Determine Product to Discontinue

Goal: Identify consistently low-performing products

Steps:

  1. Select suspected underperformer
  2. Date Range: Last 6 Months
  3. Granularity: Monthly
  4. Review average monthly quantity
  5. If < 10 units per month consistently
  6. Calculate carrying costs vs. revenue
  7. Consider discontinuation
  8. Check if seasonal (may be off-season)

Decision Criteria:

  • Low volume (< 10 units/month)
  • Declining trend
  • High carrying cost
  • Low profit margin
  • Not seasonal explanation

Result: Data-backed discontinuation decision

Use Case 3: Optimize Inventory Ordering

Goal: Determine optimal order quantity and frequency

Steps:

  1. Select product
  2. Date Range: Last 90 Days
  3. Granularity: Weekly
  4. Calculate average weekly sales
  5. Note standard deviation (volatility)
  6. Determine reorder point: avg weekly × lead time in weeks
  7. Determine order quantity: 2-4 weeks of avg sales
  8. Set min/max inventory levels
  9. Update inventory management system

Example Calculation:

  • Product: Ground Beef
  • Avg weekly sales: 180 lbs
  • Supplier lead time: 1 week
  • Reorder point: 180 lbs (1 week supply remaining)
  • Order quantity: 540 lbs (3 weeks supply)
  • Max inventory: 720 lbs (4 weeks)

Result: Optimized inventory levels

Use Case 4: Measure Promotional Impact

Goal: Evaluate product promotion success

Steps:

  1. Note promotion start/end dates
  2. Select promoted product
  3. Date Range: 2 weeks before to 4 weeks after promo
  4. Granularity: Daily
  5. Compare pre-promo avg to during-promo quantities
  6. Check post-promo sales (did they sustain?)
  7. Calculate incremental units sold
  8. Compare to promotion cost
  9. Determine ROI

Example:

  • Pre-promo avg: 50 lbs/week
  • During promo: 125 lbs/week
  • Incremental: 75 lbs/week × 2 weeks = 150 lbs
  • Revenue increase: 150 lbs × $5 = $750
  • Promo cost: $200
  • ROI: $550 profit

Result: Promotion effectiveness analysis

Use Case 5: Compare Product Substitutes

Goal: Understand customer preference between similar products

Steps:

  1. Select 2-3 similar products (e.g., regular vs. organic apples)
  2. Date Range: Last 6 Months
  3. Show all on same chart
  4. Compare trends
  5. If one rising, other falling = substitution
  6. Calculate total category demand
  7. Adjust inventory mix accordingly

Example:

  • Regular Apples: Declining 5% per month
  • Organic Apples: Growing 15% per month
  • Insight: Shift toward organic preference
  • Action: Increase organic inventory, reduce regular

Result: Optimized product mix

Export and Analysis

Export Options

CSV Export:

  • Date and quantity columns
  • Import to Excel for deeper analysis
  • Create custom charts
  • Share with suppliers

PDF Report:

  • Formatted report with chart
  • Summary statistics
  • Print or email to stakeholders

Supplier Share:

  • Export format optimized for supplier
  • Helps suppliers forecast your demand
  • Strengthen supplier relationships

Troubleshooting

Chart Shows No Data

Check:

  1. Product has sales in selected date range?
  2. Product categorized correctly?
  3. Cancelled orders excluded correctly?

Solutions:

  1. Expand date range
  2. Verify product is active
  3. Check filter settings

Trend Seems Wrong

Check:

  1. Are refunds/cancellations affecting data?
  2. One-time spike skewing average?
  3. Product recently introduced (not enough history)?

Solutions:

  1. Review data for outliers
  2. Use longer time period to smooth spikes
  3. Wait for more history to accumulate

Can't Compare Products

Check:

  1. Products measured in same units?
  2. Too many products selected (5 max)?

Solutions:

  1. Compare products with same unit type
  2. Reduce selection to 5 or fewer
  3. Use separate reports for different comparisons
  • Bestselling Results - Overall product performance
  • Order Contents Dump - Detailed order-level data
  • Inventory Management - Adjust stock based on trends
  • Future Demand - Project future product needs
  • Products - Edit product details

Best Practices

Analysis Frequency

  1. Weekly: High-volume or promoted products
  2. Monthly: Standard products
  3. Quarterly: Low-volume specialty items
  4. Annually: Seasonal pattern review

Inventory Optimization

  1. Calculate average - use for reorder point
  2. Account for variability - add safety stock
  3. Consider lead time - order early enough
  4. Monitor trends - adjust if demand changing
  5. Review seasonality - prepare for peaks

Strategic Planning

  1. Identify stars - growing products, invest in
  2. Identify dogs - declining products, consider exit
  3. Seasonal prep - plan 6-8 weeks before peak
  4. Supplier negotiation - use data for better terms
  5. Marketing focus - promote growing products

Quick Reference Card

Task Action/Location
View product trend Select product, choose date range
Find seasonal pattern Select product, Last Year, Monthly view
Compare products Select multiple products (up to 5)
Calculate reorder point Avg weekly sales × supplier lead time
Measure promo impact Date range around promo, Daily view
Export for supplier Export > CSV, share data
Identify declining product Review trend line, calculate % decline
Optimize inventory Use average + std deviation

FAQs

How far back does data go?

Typically 2-3 years of historical data. Check with administrator for your system's retention period.

Can I track by customer type?

Some systems allow filtering by customer segment (wholesale, retail, etc.). Check filter options or use Order Contents Dump for segmented analysis.

What if quantities vary wildly week to week?

High variability suggests: promotional cycles, seasonal peaks, or small sample size. Use longer periods (monthly vs weekly) to smooth volatility.

Should I use daily, weekly, or monthly view?

Daily: 7-30 day analysis, Weekly: 1-6 months (most common), Monthly: 6+ months for seasonal patterns.

How do I calculate safety stock?

Safety stock = (Max daily sales × Max lead time) - (Average daily sales × Average lead time). Or use 1-2 standard deviations above average.

Can I see which customers buy this product?

This report is aggregate. For customer-specific data, use Order Contents Dump or Customer Product Preferences report.

What's a good inventory turnover rate?

Perishables: 8-12 turns per year, Non-perishables: 4-6 turns per year. Higher turnover = more efficient but requires frequent ordering.

How do I account for seasonality in ordering?

Run Last Year report, note peak months, increase orders 6-8 weeks before historical peak, reduce after peak.

Can I track product bundles or kits?

If bundle is a separate product, yes. If ad-hoc combination, track individual components.

What if trend shows steady decline?

Investigate: 1) Customer preference shifting?, 2) Competitor offering?, 3) Price too high?, 4) Quality issues?, 5) Seasonal off-period? Address root cause or phase out.

Change Log

2026-03-01

  • Initial documentation created
  • All sections completed following template structure

End of Documentation

For additional help, contact your system administrator or Kiva Logic support.