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Produce History Summary Documentation (ALPHA)

Menu Location: Reports > Products > Produce History Summary

Access Level: Manager and above

Last Updated: 2026-03-01 Status: ALPHA - Feature in development

Overview

The Produce History Summary provides aggregate historical data on produce product sales, availability, and pricing trends over time. This alpha-stage tool helps with long-term inventory planning and seasonal analysis, though features are still being refined based on user feedback.

Primary Functions:

  • View historical produce sales trends
  • Analyze seasonal availability patterns
  • Track price fluctuations over time
  • Compare year-over-year performance
  • Identify long-term demand trends
  • Export historical data for analysis

ALPHA Notice: This feature is under active development. Some functionality may change, and occasional data inconsistencies may occur. Report any issues to support.

Page Layout

Header Section

  • Date Range Selector: Choose historical period (supports multi-year ranges)
  • Product Filter: Filter by specific produce items or categories
  • Aggregation: Group by week, month, quarter, or year
  • Export Button: Download historical data

Main Content Area

Timeline visualization and data table showing produce metrics over selected time period, with trend indicators and comparison tools.

Summary Statistics

  • Total volume sold across period
  • Average weekly/monthly sales
  • Seasonal peak periods
  • Year-over-year growth rates

Report Data & Columns

Column Description Calculation/Source
Product Name Produce item name Product catalog
Time Period Week/Month/Quarter depending on grouping Aggregation setting
Units Sold Total quantity sold in period Sum of order line items
Revenue Total revenue generated Units sold × avg price
Avg Price Average price during period Revenue / units sold
Availability % Percentage of time product was in stock Days available / total days
Peak Week Highest sales week in period Maximum weekly sales
Low Week Lowest sales week in period Minimum weekly sales
YoY Change Year-over-year change percentage Current year vs previous year

Filters & Search Options

Date Filters

  • Historical Range: Select multi-year periods for trend analysis
  • Quick Periods: Last Year, Last 2 Years, Last 5 Years, All Time
  • Comparison Mode: Compare two equal time periods side-by-side

Product Filters

  • Produce Category: Vegetables, Fruits, Herbs, Salad Greens, etc.
  • Organic vs Conventional: Filter by organic certification
  • Local vs Imported: Filter by source (if tracked)
  • Seasonal vs Year-Round: Filter by availability pattern

Display Filters

  • Aggregation Level: Daily, Weekly, Monthly, Quarterly, Yearly
  • Minimum Volume: Show only products meeting minimum sales threshold
  • Active Products Only: Exclude discontinued items

Common Use Cases

Use Case 1: Seasonal Planning

Goal: Prepare inventory for upcoming season based on historical patterns

Steps:

  1. Set date range to same season in previous 2-3 years
  2. Filter to seasonal produce category
  3. Group by week to see detailed patterns
  4. Identify peak weeks and volumes
  5. Compare year-over-year to spot growth trends
  6. Use data to forecast this year's needs
  7. Order inventory 2-3 weeks before historical peak

Example: Reviewing "Summer Berries" from June-August 2023, 2024, 2025. Strawberries peak in mid-June (weeks 24-26), averaging 180 units/week with 15% YoY growth. Plan for 210 units/week June 2026.

Use Case 2: Year-Over-Year Performance

Goal: Measure product category growth

Steps:

  1. Select full calendar year for current and previous year
  2. Choose produce category to analyze
  3. Group by month for monthly comparison
  4. Review YoY change column
  5. Identify products driving growth or declining
  6. Investigate causes of significant changes
  7. Adjust marketing and inventory strategy

Use Case 3: Price Trend Analysis

Goal: Understand pricing fluctuations over time

Steps:

  1. Select specific produce item
  2. Set 2-year date range
  3. Group by month
  4. Review avg price column over time
  5. Identify seasonal price patterns
  6. Coordinate with suppliers on pricing
  7. Plan promotions during low-price periods

Use Case 4: Long-Term Demand Forecasting

Goal: Predict future demand based on multi-year trends

Steps:

  1. Select 3-5 year historical range
  2. Group by quarter
  3. Analyze growth rate trends
  4. Identify cyclical patterns
  5. Calculate trend line (may require Excel export)
  6. Project forward for next year's planning
  7. Build safety margins into forecasts

Use Case 5: Product Lifecycle Analysis

Goal: Determine if products are growing, stable, or declining

Steps:

  1. Export full historical data for key products
  2. Calculate quarterly or annual growth rates
  3. Categorize products:
    • Growth (10%+ annual increase)
    • Stable (-10% to +10% change)
    • Decline (10%+ annual decrease)
  4. Investigate declining products for causes
  5. Double down on growing products
  6. Consider discontinuing persistent decliners

Troubleshooting

Historical Data Missing or Incomplete

Symptoms: Gaps in historical data or "No data available" messages

Common Causes:

  1. Business started tracking after requested date range
  2. Product was added to catalog after period start
  3. Data migration issues from previous systems
  4. Alpha feature limitations

Solutions:

  • Verify date range aligns with business operations
  • Check when specific products were added to catalog
  • Report significant gaps to system administrator

Calculations Seem Incorrect

Note: As alpha feature, some calculations may be refined

Check:

  1. Verify aggregation level matches expectations
  2. Ensure date range is correctly set
  3. Cross-reference with other reports for validation
  4. Export data to Excel for manual verification

If Persistent: Document specific discrepancy and report to support for alpha feedback

Slow Loading or Timeout

Solutions:

  1. Reduce date range (try 1 year instead of 5)
  2. Filter to specific product category
  3. Use higher aggregation (monthly vs weekly)
  4. Run reports during off-peak hours
  5. Export data for offline analysis if needed
  • Addons History - Recent add-on sales (operational timeframe)
  • Product Detail Reports - Current product performance
  • Inventory Management - Current stock and ordering
  • Sales Reports - Overall revenue analysis

Permissions & Access

Required Access Level: Manager or higher

Access Level Capabilities:

  • Manager: View reports, export data, basic analysis
  • Administrator: All Manager capabilities + access to raw data
  • Kiva Admin: All features + provide alpha feedback, access beta features

Best Practices

Working with Alpha Features

  1. Cross-validate data with established reports
  2. Report bugs and inconsistencies to support
  3. Provide feedback on desired features
  4. Don't rely solely on alpha features for critical decisions
  5. Maintain backup data analysis methods

Historical Analysis

  1. Use minimum 2-year comparison for trend validity
  2. Account for business growth when comparing periods
  3. Identify and note anomalies (pandemic, supply disruptions, etc.)
  4. Combine with qualitative knowledge (supplier changes, etc.)
  5. Update forecasts regularly as new data accumulates

Things to Avoid

  • Don't base critical decisions solely on alpha feature data
  • Don't assume all historical periods have complete data
  • Don't ignore seasonal patterns when forecasting
  • Don't forget to factor external events (weather, economy, etc.)

Quick Reference Card

Task Action/Location
View last 2 years Date range: 2 years ago to today
Compare year-over-year Comparison mode, select 2 calendar years
Find seasonal peaks Group by week, review peak week column
Analyze price trends Select product, 2-year range, group by month
Export for Excel Apply filters, click Export
Identify growth products Sort by YoY change (highest first)
Long-term forecasting 3-5 year range, group by quarter

FAQs

Why is this feature marked ALPHA?

The system is still refining data aggregation methods and adding features based on user feedback. Core functionality works but may evolve.

How far back does historical data go?

Depends on when your business started using the system and tracking this data. Typically 2-10+ years available.

Can I trust the data for planning?

Use as one input among several. Cross-validate with other reports and your business knowledge. Report any discrepancies.

Will this replace other reports?

No - this complements existing reports by providing long-term historical view. Current operational reports remain primary tools.

When will this feature be production-ready?

Depends on feedback and testing. Check with system administrator for roadmap updates.

Can I provide feedback on this feature?

Yes! Contact support with suggestions, bugs, or feature requests. Alpha feedback helps shape final product.

Change Log

2026-03-01

  • Initial documentation created
  • Documented alpha status and limitations
  • All sections completed following template structure

End of Documentation

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