Manufacturing Methodology

Scope Boundary

This diagram communicates the scope boundary as aligned with the Food Loss and Waste Accounting and Reporting Standard[17]. Note that ReFED’s analysis also includes food sent to donations, although donations are not considered a destination within the Standard.

NOTES

• “Food Donation” has been added as a Destination

• “Biomaterial Processing is referred to as “Industrial Uses” in our model

• “Co/anaerobic digestion” is referred to as “Anaerobic digestion” in our model

• “Controlled Combustion” is referred to as “Incineration” in our model

• “Refuse/discards” is referred to as “Dumping” in our model

Calculations

Surplus Food Calculations

Master Unsold Food Equation:

Tons Unutilized Ingredients

+ Tons Finished Product not Shipped

+ Tons Buyer Rejections

-——————————————————-

= Tons Unsold Food

In ReFED’s data model, the following calculations are repeated for every state, year, and manufacturing food type before any aggregation is done.

Table 5. Calculations Performed to Quantify U.S. Manufacturing Surplus Food

DATA ITEM	DATA SOURCE OR CALCULATION	EXAMPLE
National US Dollars Wholesale Value Shipped	U.S. Census Bureau Annual Survey of Manufactures[3]	$__manufacturing_example_national_us_dollars_shipped_wholesale__ wholesale value of __manufacturing_example_bls_naics_category_doctored__ products shipped in __manufacturing_example_year__ from U.S. manufacturers
Percent of Shipments Used in Food Products	U.S. Census Bureau Annual Survey of Manufactures - Industry Product Analysis[3]	__manufacturing_example_food_percent__% of __manufacturing_example_bls_naics_category_doctored__ shipments in __manufacturing_example_year__ were used in food products
Retail Markup	U.S. Census Bureau Annual Retail Trade Survey[4]	The average gross margin for U.S. grocery retailers in __manufacturing_example_year__ was __manufacturing_example_retail_gross_margin_percentage_of_sales__% (See Appendix H)
National US Dollars Retail Value Shipped	= National US Dollars Wholesale Value Shipped * ( 100% + Retail Markup )	= $__manufacturing_example_national_us_dollars_shipped_wholesale__ wholesale value shipped * (100% + __manufacturing_example_retail_gross_margin_percentage_of_sales__% margin) = $__manufacturing_example_finished_product_national_us_dollars_shipped__ retail value of manufacturing products shipped
Retail Price per Lb (National)	U.S. Grocery Retail Dollar-to-Weight Conversion Factors Report[39]	Average retail price $__TODO_NOT_IN_DATA_LB_CONVERSION__ per lb for __manufacturing_example_bls_naics_category_doctored__ products
National Tons Shipped	= National US Dollars Retail Value Shipped / Retail Price per Lb / 2,000 lbs per ton	= $__manufacturing_example_finished_product_national_us_dollars_shipped__ retail value shipped / $__TODO_NOT_IN_DATA_LB_CONVERSION__ per lb national average retail price = __manufacturing_example_finished_product_tons_supply__ tons shipped from U.S. manufacturers
National Employees	U.S. Bureau of Labor Statistics Employee Levels[45]	__TODO_NOT_IN_DATA_IS_PRE_CALCULATED__ __manufacturing_example_bls_naics_category_doctored__ employees in the U.S. in __manufacturing_example_year__
State Employees	U.S. Bureau of Labor Statistics Employee Levels[45]	__TODO_NOT_IN_DATA_IS_PRE_CALCULATED__ __manufacturing_example_bls_naics_category_doctored__ employees in __manufacturing_example_state__ in __manufacturing_example_year__
State Share of Employees	= State Employees / National Employees	= __TODO_NOT_IN_DATA_IS_PRE_CALCULATED__ __manufacturing_example_bls_naics_category_doctored__ employees in __manufacturing_example_state__ in __manufacturing_example_year__ / __TODO_NOT_IN_DATA_IS_PRE_CALCULATED__ __manufacturing_example_bls_naics_category_doctored__ employees in the U.S. in __manufacturing_example_year__ = __manufacturing_example_percent_of_employees_in_state__% of __manufacturing_example_bls_naics_category_doctored__ employees in __manufacturing_example_state__ in __manufacturing_example_year__
US Dollars Retail Value Shipped	= US Dollars National Retail Value Shipped * State Share of Employees	= $__manufacturing_example_finished_product_national_us_dollars_shipped__ retail value shipped from all U.S. manufacturers * __manufacturing_example_percent_of_employees_in_state__% of employees located in __manufacturing_example_state__ = $__manufacturing_example_finished_product_us_dollars_shipped__ estimated retail value of __manufacturing_example_bls_naics_category_doctored__ products shipped from __manufacturing_example_state__ in __manufacturing_example_year__
Tons Shipped	= National Tons Shipped * State Share of Employees	= __TODO_NOT_IN_DATA_CALCS_SKIP_STEP__ tons shipped from all U.S. manufacturers * __manufacturing_example_percent_of_employees_in_state__% of employees located in __manufacturing_example_state__ = __manufacturing_example_finished_product_tons_shipped__ estimated tons of __manufacturing_example_bls_naics_category_doctored__ products shipped from __manufacturing_example_state__ in __manufacturing_example_year__
Buyer Rejection Rate	Expert Interviews	According to expert interviews, about __manufacturing_example_percent_buyer_rejections__% of __manufacturing_example_finished_product_refed_food_department__ shipments are rejected by buyers
US Dollars Sold	= ( 100% - Buyer Rejection Rate ) * US Dollars Retail Value Shipped	= ( 100% - __manufacturing_example_percent_buyer_rejections__% ) * $__manufacturing_example_finished_product_us_dollars_shipped__ shipped from __manufacturing_example_state__ = $__TODO_NOT_IN_DATA_CALCS_TAKE_DIFFERENT_ROUTE__ sold from __manufacturing_example_state__
Tons Sold	= ( 100% - Buyer Rejection Rate ) * Tons Shipped	= ( 100% - __manufacturing_example_percent_buyer_rejections__% ) * __manufacturing_example_finished_product_tons_shipped__ tons shipped from __manufacturing_example_state__ = __TODO_NOT_IN_DATA_CALCS_TAKE_DIFFERENT_ROUTE__ sold from __manufacturing_example_state__
US Dollars Buyer Rejections	= US Dollars Retail Value Shipped - US Dollars Sold	= $__manufacturing_example_finished_product_us_dollars_shipped__ shipped from __manufacturing_example_state__ - $__TODO_NOT_IN_DATA_CALCS_TAKE_DIFFERENT_ROUTE__ sold from __manufacturing_example_state__ = $__TODO_NOT_IN_DATA_CALCS_SKIP_STEP__ buyer rejections
Tons Buyer Rejections	= Tons Shipped - Tons Sold	= __manufacturing_example_finished_product_tons_shipped__ tons shipped from __manufacturing_example_state__ - __TODO_NOT_IN_DATA_CALCS_TAKE_DIFFERENT_ROUTE__ tons sold from __manufacturing_example_state__ = __TODO_NOT_IN_DATA_CALCS_SKIP_STEP__ tons buyer rejections
% of Buyer Rejections Sold via Discount Outlets	Expert interviews	Based on expert interviews, ReFED assumed that __manufacturing_example_percent_of_buyer_rejections_sold_via_resale__% of product rejected by buyer quality assurance teams ends up being sold via other channels and does not get wasted.
Tons Unsold Buyer Rejections	= Tons Buyer Rejections * ( 100% - % of Buyer Rejections Sold via Discount Outlets )	= __TODO_NOT_IN_DATA_CALCS_SKIP_STEP__ tons buyer rejections * (100% - __manufacturing_example_percent_of_buyer_rejections_sold_via_resale__% sold via discount outlets) = __manufacturing_example_finished_product_tons_unsold_buyer_rejections__ tons unsold buyer rejections
US Dollars Unsold Buyer Rejections	= Tons Unsold Buyer Rejections * Retail Price per Lb	= __TODO_NOT_IN_DATA_CALCS_SKIP_STEP__ tons unsold buyer rejections * $__TODO_NOT_IN_DATA_LB_CONVERSION__ per lb * 2,000 lbs per ton = $__manufacturing_example_finished_product_us_dollars_unsold_buyer_rejections__ unsold buyer rejections
% of Finished	Tesco Supplier Case Studies[44] (See Appendix I)	In the General Mills Tesco Supplier Case study (used as a proxy as no __manufacturing_example_bls_naics_category_doctored__-specific study was available), __manufacturing_example_percent_finished_product_not_shipped__% of manufactured products are finished into a final product but never shipped.
PRODUCT NOT SHIPPED
Tons Production	= Tons Shipped / ( 100% - % of Finished Product not Shipped )	= __manufacturing_example_finished_product_tons_shipped__ tons shipped from __manufacturing_example_state__ / ( 100% - __manufacturing_example_percent_finished_product_not_shipped__% of Finished Product not Shipped) = __manufacturing_example_finished_product_tons_production__ tons __manufacturing_example_bls_naics_category_doctored__ products production in __manufacturing_example_state__
US Dollars Production	= US Dollars Retail Value Shipped / ( 100% - % of Finished Product not Shipped )	= $__manufacturing_example_finished_product_us_dollars_shipped__ shipped from __manufacturing_example_state__ / ( 100% - __manufacturing_example_percent_finished_product_not_shipped__% of Finished Product not Shipped) = $__manufacturing_example_finished_product_us_dollars_production__ __manufacturing_example_bls_naics_category_doctored__ products production in __manufacturing_example_state__
Tons Finished Product not Shipped	= Tons Production - Tons Shipped	= __manufacturing_example_finished_product_us_dollars_production__ __manufacturing_example_bls_naics_category_doctored__ products production in __manufacturing_example_state__ - __manufacturing_example_finished_product_tons_shipped__ tons __manufacturing_example_bls_naics_category_doctored__ products shipped from __manufacturing_example_state__ = __manufacturing_example_finished_product_tons_unshipped_product__ tons __manufacturing_example_bls_naics_category_doctored__ products not shipped
US Dollars Finished Product not Shipped	= US Dollars Production - US Dollars Shipped	= $__manufacturing_example_finished_product_tons_production__ __manufacturing_example_bls_naics_category_doctored__ products production in __manufacturing_example_state__ - $__manufacturing_example_finished_product_us_dollars_shipped__ __manufacturing_example_bls_naics_category_doctored__ products shipped from __manufacturing_example_state__ = $__manufacturing_example_finished_product_us_dollars_unshipped_product__ __manufacturing_example_bls_naics_category_doctored__ products not shipped
Recipe Tons Ingredient per Ton Finished Product	Multiple Data Sources[2],[7],[9],[10],[16],[19],[20],[21],[18],[23],[24],[27],[28],[31],[33],[36],[37],[22],[57]	__TODO_DATA_IN_RECIPES_BUT_DROPPED_IN_MODEL__ tons Out of scope ingredients (e.g., water, gums) per ton finished __manufacturing_example_bls_naics_category_doctored__ products __manufacturing_example_ingredients_herbs_spices_and_seasonings_tons_ingredient_category_per_ton_product__ tons __manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__ per ton finished __manufacturing_example_bls_naics_category_doctored__ products __manufacturing_example_ingredients_flour_and_meal_tons_ingredient_category_per_ton_product__ tons __manufacturing_example_ingredients_flour_and_meal_refed_food_category__ per ton finished __manufacturing_example_bls_naics_category_doctored__ products __manufacturing_example_ingredients_shortening_and_lard_tons_ingredient_category_per_ton_product__ tons __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__ per ton finished __manufacturing_example_bls_naics_category_doctored__ products __manufacturing_example_ingredients_baking_milks_tons_ingredient_category_per_ton_product__ tons __manufacturing_example_ingredients_baking_milks_refed_food_category__ per ton finished __manufacturing_example_bls_naics_category_doctored__ products __manufacturing_example_ingredients_baking_yeast_tons_ingredient_category_per_ton_product__ tons __manufacturing_example_ingredients_baking_yeast_refed_food_category__ per ton finished __manufacturing_example_bls_naics_category_doctored__ products
Tons of each Ingredient Utilized in Finished Product	= Tons Production * % by Weight of each Ingredient	Water and additives are not considered “food” in this methodology. __manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__ : = __manufacturing_example_finished_product_tons_production__ tons of __manufacturing_example_bls_naics_category_doctored__ products produced * __manufacturing_example_ingredients_herbs_spices_and_seasonings_tons_ingredient_category_per_ton_product__ tons __manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__ per ton finished product = __manufacturing_example_ingredients_herbs_spices_and_seasonings_tons_ingredients_utilized__ tons __manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__ utilized __manufacturing_example_ingredients_flour_and_meal_refed_food_category__ : = __manufacturing_example_finished_product_tons_production__ tons of __manufacturing_example_bls_naics_category_doctored__ products produced * __manufacturing_example_ingredients_flour_and_meal_tons_ingredient_category_per_ton_product__ tons __manufacturing_example_ingredients_flour_and_meal_refed_food_category__ per ton finished product = __manufacturing_example_ingredients_flour_and_meal_tons_ingredients_utilized__ tons __manufacturing_example_ingredients_flour_and_meal_refed_food_category__ utilized __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__: = __manufacturing_example_finished_product_tons_production__ tons of __manufacturing_example_bls_naics_category_doctored__ products produced * __manufacturing_example_ingredients_shortening_and_lard_tons_ingredient_category_per_ton_product__ tons __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__ per ton finished product = __manufacturing_example_ingredients_shortening_and_lard_tons_ingredients_utilized__ tons __manufacturing_example_ingredients_shortening_and_lard_tons_ingredient_category_per_ton_product__ tons __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__ utilized __manufacturing_example_ingredients_baking_milks_refed_food_category__ : = __manufacturing_example_finished_product_tons_production__ tons of __manufacturing_example_bls_naics_category_doctored__ products produced * __manufacturing_example_ingredients_baking_milks_tons_ingredient_category_per_ton_product__ tons __manufacturing_example_ingredients_baking_milks_refed_food_category__ per ton finished product = __manufacturing_example_ingredients_baking_milks_tons_ingredients_utilized__ tons __manufacturing_example_ingredients_baking_milks_refed_food_category__ utilized __manufacturing_example_ingredients_baking_yeast_refed_food_category__ : = __manufacturing_example_finished_product_tons_production__ tons of __manufacturing_example_bls_naics_category_doctored__ products produced * __manufacturing_example_ingredients_baking_yeast_tons_ingredient_category_per_ton_product__ tons __manufacturing_example_ingredients_baking_yeast_refed_food_category__ per ton finished product = __manufacturing_example_ingredients_baking_yeast_tons_ingredients_utilized__ tons __manufacturing_example_ingredients_baking_yeast_refed_food_category__ utilized
Ingredient Utilization Rates	Tesco Supplier Case Studies[44]	Tesco Supplier Case Studies provided ingredient utilization rates for each of the ingredients. For example, __manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__ has a utilization rate of __manufacturing_example_ingredients_herbs_spices_and_seasonings_ingredient_utilization_rate__%.
Tons Ingredients Purchased	= Tons Utilized Ingredients / Ingredient Utilization Rate	__manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__: = __manufacturing_example_ingredients_herbs_spices_and_seasonings_tons_ingredients_utilized__ utilized / __manufacturing_example_ingredients_herbs_spices_and_seasonings_ingredient_utilization_rate__% utilization rate = __manufacturing_example_ingredients_herbs_spices_and_seasonings_tons_supply__ tons __manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__ purchased __manufacturing_example_ingredients_flour_and_meal_refed_food_category__: = __manufacturing_example_ingredients_flour_and_meal_tons_ingredients_utilized__ tons __manufacturing_example_ingredients_flour_and_meal_refed_food_category__ utilized / __manufacturing_example_ingredients_flour_and_meal_ingredient_utilization_rate__% utilization rate = __manufacturing_example_ingredients_flour_and_meal_tons_supply__ tons __manufacturing_example_ingredients_flour_and_meal_refed_food_category__ purchased __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__: = __manufacturing_example_ingredients_shortening_and_lard_tons_ingredients_utilized__ tons __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__ utilized / __manufacturing_example_ingredients_shortening_and_lard_ingredient_utilization_rate__% utilization rate = __manufacturing_example_ingredients_shortening_and_lard_tons_supply__ tons __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__ purchased __manufacturing_example_ingredients_baking_milks_refed_food_category__: = __manufacturing_example_ingredients_baking_milks_tons_ingredients_utilized__ tons __manufacturing_example_ingredients_baking_milks_refed_food_category__ utilized / __manufacturing_example_ingredients_baking_milks_ingredient_utilization_rate__% utilization rate = __manufacturing_example_ingredients_baking_milks_tons_supply__ tons __manufacturing_example_ingredients_baking_milks_refed_food_category__ purchased __manufacturing_example_ingredients_baking_yeast_refed_food_category__: = __manufacturing_example_ingredients_baking_yeast_tons_ingredients_utilized__ tons __manufacturing_example_ingredients_baking_yeast_refed_food_category__ utilized / __manufacturing_example_ingredients_baking_yeast_ingredient_utilization_rate__% utilization rate = __manufacturing_example_ingredients_baking_yeast_tons_supply__ tons __manufacturing_example_ingredients_baking_yeast_refed_food_category__ purchased
US Dollars Ingredients Purchased	= Tons Ingredients Purchased * Wholesale Price per Ton	__manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__: = __manufacturing_example_ingredients_herbs_spices_and_seasonings_tons_supply__ tons __manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__ purchased * $__manufacturing_example_ingredients_herbs_spices_and_seasonings_wholesale_price_per_ton__ per ton = $__manufacturing_example_ingredients_herbs_spices_and_seasonings_us_dollars_supply__ __manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__ purchased __manufacturing_example_ingredients_flour_and_meal_refed_food_category__: = __manufacturing_example_ingredients_flour_and_meal_tons_supply__ tons __manufacturing_example_ingredients_flour_and_meal_refed_food_category__ purchased * $__manufacturing_example_ingredients_flour_and_meal_wholesale_price_per_ton__ per ton = $__manufacturing_example_ingredients_flour_and_meal_us_dollars_supply__ __manufacturing_example_ingredients_flour_and_meal_refed_food_category__ purchased __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__: = __manufacturing_example_ingredients_shortening_and_lard_tons_supply__ tons __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__ s purchased * $__manufacturing_example_ingredients_shortening_and_lard_wholesale_price_per_ton__ per ton = $__manufacturing_example_ingredients_shortening_and_lard_us_dollars_supply__ __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__ purchased __manufacturing_example_ingredients_baking_milks_refed_food_category__: = __manufacturing_example_ingredients_baking_milks_tons_supply__ tons __manufacturing_example_ingredients_baking_milks_refed_food_category__ purchased * $__manufacturing_example_ingredients_baking_milks_wholesale_price_per_ton__ per ton = $__manufacturing_example_ingredients_baking_milks_us_dollars_supply__ __manufacturing_example_ingredients_baking_milks_refed_food_category__ purchased __manufacturing_example_ingredients_baking_yeast_refed_food_category__: = __manufacturing_example_ingredients_baking_yeast_tons_supply__ tons __manufacturing_example_ingredients_baking_yeast_refed_food_category__ purchased * $__manufacturing_example_ingredients_baking_yeast_wholesale_price_per_ton__ per ton = $__manufacturing_example_ingredients_baking_yeast_us_dollars_supply__ __manufacturing_example_ingredients_baking_yeast_refed_food_category__ purchased
Tons of Ingredients Unutilized	= Tons of Ingredient Purchased - Tons of Ingredients Utilized	__manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__: = __manufacturing_example_ingredients_herbs_spices_and_seasonings_tons_ingredients_utilized__ tons purchased - __manufacturing_example_ingredients_herbs_spices_and_seasonings_tons_ingredients_utilized__ tons utilized = __manufacturing_example_ingredients_herbs_spices_and_seasonings_tons_surplus__ tons unutilized __manufacturing_example_ingredients_flour_and_meal_refed_food_category__: = __manufacturing_example_ingredients_flour_and_meal_tons_ingredients_utilized__ tons purchased - __manufacturing_example_ingredients_flour_and_meal_tons_ingredients_utilized__ tons utilized = __manufacturing_example_ingredients_flour_and_meal_tons_surplus__ tons unutilized __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__: = __manufacturing_example_ingredients_shortening_and_lard_tons_ingredients_utilized__ tons purchased - __manufacturing_example_ingredients_shortening_and_lard_tons_ingredients_utilized__ tons utilized = __manufacturing_example_ingredients_shortening_and_lard_tons_surplus__ tons unutilized __manufacturing_example_ingredients_baking_milks_refed_food_category__: = __manufacturing_example_ingredients_baking_milks_tons_ingredients_utilized__ tons purchased - __manufacturing_example_ingredients_baking_milks_tons_ingredients_utilized__ tons utilized = __manufacturing_example_ingredients_baking_milks_tons_surplus__ tons unutilized __manufacturing_example_ingredients_baking_yeast_refed_food_category__: = __manufacturing_example_ingredients_baking_yeast_tons_ingredients_utilized__, tons purchased - __manufacturing_example_ingredients_baking_yeast_tons_ingredients_utilized__ tons utilized = __manufacturing_example_ingredients_baking_yeast_tons_surplus__ tons unutilized Total Tons Unutilized Ingredients: = __manufacturing_example_ingredients_herbs_spices_and_seasonings_tons_surplus__ tons __manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__ unutilized + __manufacturing_example_ingredients_flour_and_meal_tons_surplus__ tons __manufacturing_example_ingredients_flour_and_meal_refed_food_category__ unutilized + __manufacturing_example_ingredients_shortening_and_lard_tons_surplus__ tons __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__ unutilized + __manufacturing_example_ingredients_baking_milks_tons_surplus__ tons __manufacturing_example_ingredients_baking_milks_refed_food_category__ unutilized + __manufacturing_example_ingredients_baking_yeast_tons_surplus__ tons __manufacturing_example_ingredients_baking_yeast_refed_food_category__ unutilized = __TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ tons unutilized ingredients
Wholesale Price per Ton for each Ingredient	= Retail Price per Lb (National) * ( 100% - Grocery Retail Markup )	__manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__: = $__TODO_NOT_STORED_retail_price_per_ton__ per ton average grocery retail price * ( 100% - __TODO_NOT_STORED_retail_gross_margin_percentage_of_sales__% grocery markup ) = $__manufacturing_example_ingredients_herbs_spices_and_seasonings_wholesale_price_per_ton__ per ton average wholesale price __manufacturing_example_ingredients_flour_and_meal_refed_food_category__: = $__TODO_NOT_STORED_retail_price_per_ton__ per ton average grocery retail price * ( 100% - __TODO_NOT_STORED_retail_gross_margin_percentage_of_sales__% grocery markup ) = $__manufacturing_example_ingredients_flour_and_meal_wholesale_price_per_ton__ per ton average wholesale price __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__: = $__TODO_NOT_STORED_retail_price_per_ton__ per ton average grocery retail price * ( 100% - __TODO_NOT_STORED_retail_gross_margin_percentage_of_sales__% grocery markup ) = $__manufacturing_example_ingredients_shortening_and_lard_wholesale_price_per_ton__ per ton average wholesale price __manufacturing_example_ingredients_baking_milks_refed_food_category__: = $__TODO_NOT_STORED_retail_price_per_ton__ per ton average grocery retail price * ( 100% - __TODO_NOT_STORED_retail_gross_margin_percentage_of_sales__% grocery markup ) = $__manufacturing_example_ingredients_baking_milks_wholesale_price_per_ton__ per ton average wholesale price __manufacturing_example_ingredients_baking_yeast_refed_food_category__: = $__TODO_NOT_STORED_retail_price_per_ton__ per ton average grocery retail price * ( 100% - __TODO_NOT_STORED_retail_gross_margin_percentage_of_sales__% grocery markup ) = $__manufacturing_example_ingredients_baking_yeast_wholesale_price_per_ton__ per ton average wholesale price
US Dollars Unutilized Ingredients	= Tons of Ingredient Unutilized * Wholesale price per ton	__manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__: = __manufacturing_example_ingredients_herbs_spices_and_seasonings_tons_surplus__ tons unutilized * $__manufacturing_example_ingredients_herbs_spices_and_seasonings_wholesale_price_per_ton__ per ton = $__manufacturing_example_ingredients_herbs_spices_and_seasonings_us_dollars_surplus__ unutilized __manufacturing_example_ingredients_flour_and_meal_refed_food_category__: = __manufacturing_example_ingredients_flour_and_meal_tons_surplus__ tons unutilized * $__manufacturing_example_ingredients_flour_and_meal_wholesale_price_per_ton__ per ton = $__manufacturing_example_ingredients_flour_and_meal_us_dollars_surplus__ unutilized __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__: = __manufacturing_example_ingredients_shortening_and_lard_tons_surplus__ tons unutilized * $__manufacturing_example_ingredients_shortening_and_lard_wholesale_price_per_ton__ per ton = $__manufacturing_example_ingredients_shortening_and_lard_us_dollars_surplus__ unutilized __manufacturing_example_ingredients_baking_milks_refed_food_category__: = __manufacturing_example_ingredients_baking_milks_tons_surplus__ tons unutilized * $__manufacturing_example_ingredients_baking_milks_wholesale_price_per_ton__ per ton = $__manufacturing_example_ingredients_baking_milks_us_dollars_surplus__ unutilized __manufacturing_example_ingredients_baking_yeast_refed_food_category__: = __manufacturing_example_ingredients_baking_yeast_tons_surplus__ tons unutilized * $__manufacturing_example_ingredients_baking_yeast_wholesale_price_per_ton__ per ton = $__manufacturing_example_ingredients_baking_yeast_us_dollars_surplus__ unutilized Total US Dollars Unutilized Ingredients: = $__manufacturing_example_ingredients_herbs_spices_and_seasonings_us_dollars_surplus__ __manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__ unutilized + $__manufacturing_example_ingredients_flour_and_meal_us_dollars_surplus__ __manufacturing_example_ingredients_flour_and_meal_refed_food_category__ unutilized + $__manufacturing_example_ingredients_shortening_and_lard_us_dollars_surplus__ __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__ unutilized + $__manufacturing_example_ingredients_baking_milks_us_dollars_surplus__ __manufacturing_example_ingredients_baking_milks_refed_food_category__ unutilized + $__manufacturing_example_ingredients_baking_yeast_us_dollars_surplus__ __manufacturing_example_ingredients_baking_yeast_refed_food_category__ unutilized = $__TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ unutilized ingredients
Tons Unsold Food	= Tons Unutilized Ingredients + Tons Unshipped Product + Tons Unsold Buyer Rejections	= __TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ tons unutilized ingredients + __TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ tons finished product not shipped + __TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ tons unsold buyer rejections = __TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ tons unsold __manufacturing_example_bls_naics_category_doctored__ products manufactured in __manufacturing_example_state__ in __manufacturing_example_year__
US Dollars Unsold Food	= US Dollars Unutilized Ingredients + US Dollars Unshipped Product + US Dollars Unsold Buyer Rejections	= $__TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ unutilized ingredients + $__TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ finished product not shipped + $__TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ buyer rejections = $__TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ unsold __manufacturing_example_bls_naics_category_doctored__ products in __manufacturing_example_state__ in __manufacturing_example_year__
Tons Supply	= Sum of Ingredient Tons Purchased	= __manufacturing_example_ingredients_herbs_spices_and_seasonings_tons_supply__ tons __manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__ purchased + __manufacturing_example_ingredients_flour_and_meal_tons_supply__ tons __manufacturing_example_ingredients_flour_and_meal_refed_food_category__ purchased + __manufacturing_example_ingredients_shortening_and_lard_tons_supply__ tons __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__ purchased + __manufacturing_example_ingredients_baking_milks_tons_supply__ tons __manufacturing_example_ingredients_baking_milks_refed_food_category__ purchased + __manufacturing_example_ingredients_baking_yeast_tons_supply__ tons __manufacturing_example_ingredients_baking_yeast_refed_food_category__ purchased = __TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ tons __manufacturing_example_bls_naics_category_doctored__ product ingredients purchased in __manufacturing_example_state__ in __manufacturing_example_year__
US Dollars Supply	= Sum of Ingredient US Dollars Purchased	= $__manufacturing_example_ingredients_herbs_spices_and_seasonings_us_dollars_supply__ __manufacturing_example_ingredients_herbs_spices_and_seasonings_refed_food_category__ purchased + $__manufacturing_example_ingredients_flour_and_meal_us_dollars_supply__ __manufacturing_example_ingredients_flour_and_meal_refed_food_category__ purchased + $__manufacturing_example_ingredients_shortening_and_lard_us_dollars_supply__ __manufacturing_example_ingredients_shortening_and_lard_refed_food_category__ purchased + $__manufacturing_example_ingredients_baking_milks_us_dollars_supply__ __manufacturing_example_ingredients_baking_milks_refed_food_category__ purchased + $__manufacturing_example_ingredients_baking_yeast_us_dollars_supply__ __manufacturing_example_ingredients_baking_yeast_refed_food_category__ purchased = $__TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ __manufacturing_example_bls_naics_category_doctored__ product ingredients purchased in __manufacturing_example_state__ in __manufacturing_example_year__

Cause Calculations

Master Cause Equations:

Tons Unutilized Ingredients due to Cause = Tons Unutilized Ingredients * % Unutilized Ingredients due to Cause

Tons Unshipped Product due to Cause = Tons Unshipped Product * % Unshipped due to Cause

Tons Buyer Rejections = Tons Shipped * Buyer Rejection Rate

Table 6. Calculations Performed to Quantify the Causes of U.S. Manufacturing Surplus Food

DATA ITEM	DATA SOURCE OR CALCULATION	EXAMPLE
UNUTILIZED INGREDIENTS
% Unutilized due to Cause	Tesco Supplier Case Studies[44]	ReFED assumed that __TODO__% of unutilized ingredients were Byproducts & Production Line Waste after reviewing the supplier case studies.
Tons Unutilized Ingredients due to Cause	= Tons Unutilized Ingredients * % Unutilized due to Cause	Tons unutilized due to Byproducts & Production Line Waste: = __TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ tons unutilized ingredients * __TODO_STORE_CAUSE_DATA_GROUPED_BY_BLS_NAICS__% unutilized due to Byproducts & Production Line Waste = __TODO_GROUP_CAUSE_BY_BLS_NAICS__ tons
US Dollars Unutilized Ingredients due to Cause	= US Dollars Unutilized Ingredients * % Unutilized due to Cause	US Dollars of ingredients unutilized due to Byproducts & Production Line Waste: = $__TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ unutilized ingredients * __TODO_STORE_CAUSE_DATA_GROUPED_BY_BLS_NAICS__% unutilized due to Byproducts & Production Line Waste = $__TODO_STORE_CAUSE_DATA_GROUPED_BY_BLS_NAICS__
UNSHIPPED PRODUCT
% Unshipped due to Cause	ReFED was unable to find any data sources that quantify the breakdown of the causes of unshipped product (e.g., misprints versus discontinued product), so this cause was not broken down any further.	__TODO_STORE_CAUSE_DATA_GROUPED_BY_BLS_NAICS__% due to ‘Unshipped Finished Product’
Tons Unshipped Product due to Cause	= Tons Unshipped Product * % Unshipped due to ‘Unshipped Finished Product’	= __manufacturing_example_finished_product_tons_surplus__ tons unshipped __manufacturing_example_bls_naics_category_doctored__ products * __TODO_STORE_CAUSE_DATA_GROUPED_BY_BLS_NAICS__% = __TODO_STORE_CAUSE_DATA_GROUPED_BY_BLS_NAICS__
US Dollars Unshipped Product	= US Dollars Unshipped Product * % Unshipped due to ‘Unshipped Finished Product’	= $__manufacturing_example_finished_product_us_dollars_unshipped_product__ unshipped __manufacturing_example_bls_naics_category_doctored__ products * __TODO_STORE_CAUSE_DATA_GROUPED_BY_BLS_NAICS__% = $__TODO_STORE_CAUSE_DATA_GROUPED_BY_BLS_NAICS__
BUYER REJECTIONS
Tons Unsold Buyer Rejections	See calculation above for Tons Buyer Rejections	= __manufacturing_example_finished_product_tons_unsold_buyer_rejections__ tons unsold buyer rejections
US Dollars Unsold Buyer Rejections	See calculation above for US Dollars Unsold Buyer Rejections	= $__manufacturing_example_finished_product_us_dollars_unsold_buyer_rejections__ unsold buyer rejections

Destination Calculations

Master Destination Equations:

Tons Unutilized Ingredients sent to Destination = Tons Unutilized Ingredients * % Unutilized Ingredients sent to Destination

Tons Unshipped Product sent to Destination = Tons Unshipped Product * % Unshipped Product sent to Destination

Tons Buyer Rejections sent to Destination = Tons Buyer Rejections * % Buyer Rejections sent to Destination

Table 7. Calculations Performed to Quantify the Destinations of U.S. Manufacturing Surplus Food

DATA ITEM	DATA SOURCE OR CALCULATION	EXAMPLE
Destination Breakdown of Unutilized Ingredients (See Appendix J)	Northstar Recycling[58]	This was the destinations breakdown for __manufacturing_example_finished_product_refed_food_department__ manufacturers based on aggregated data from NorthStar Recycling: Donated: __TODO_GET_BY_FOOD_DEPT__% Animal feed: __TODO_GET_BY_FOOD_DEPT__% Trash: __TODO_GET_BY_FOOD_DEPT__% ———————————————— Total: 100%
	% of Trash that is Landfilled vs Incinerated in __manufacturing_example_state__ (Biocycle/Columbia University Survey[8]) (See Appendix Z)	% of Trash that is Landfilled = __manufacturing_example_ingredients_herbs_spices_and_seasonings_percent_of_trash_landfilled__% % of Trash that is Incinerated = __manufacturing_example_ingredients_herbs_spices_and_seasonings_percent_of_trash_incinerated__%
	Breaking “Trash” into Landfill vs Incineration:
	% Landfilled = % Trash * % of Trash that is Landfilled	% Landfilled: = __manufacturing_example_ingredients_herbs_spices_and_seasonings_percent_trash__% * __manufacturing_example_ingredients_herbs_spices_and_seasonings_percent_of_trash_landfilled__% = __TODO_SKIPPED_OVER_IN_CALCS__%
	% Incinerated = % Trash * % of Trash that is Incinerated	% Incinerated: = __manufacturing_example_ingredients_herbs_spices_and_seasonings_percent_trash__% * __manufacturing_example_ingredients_herbs_spices_and_seasonings_percent_of_trash_incinerated__% = __TODO_SKIPPED_OVER_IN_CALCS__%
Destination Breakdown of Unshipped Finished Product (See Appendix J)	Northstar Recycling	This was the destinations breakdown for Bakery manufacturers based on aggregated data from NorthStar Recycling: Donated: __manufacturing_example_finished_product_percent_donated_unshipped_product__% Animal feed: __manufacturing_example_finished_product_percent_animal_feed_unshipped_product__% Trash: __manufacturing_example_finished_product_percent_trash_unshipped_product__,% ———————————————— Total: 100%
	% of Trash that is Landfilled vs Incinerated in __manufacturing_example_state__ (Biocycle/Columbia University Survey[8]) (See Appendix Z)	% of Trash that is Landfilled = __manufacturing_example_finished_product_percent_of_trash_landfilled__% % of Trash that is Incinerated = __manufacturing_example_finished_product_percent_of_trash_incinerated__%
	Breaking “Trash” into Landfill vs Incineration:
	% Landfilled = % Trash * % of Trash that is Landfilled	% Landfilled:	= __manufacturing_example_finished_product_percent_trash_unshipped_product__% * __manufacturing_example_finished_product_percent_of_trash_landfilled__% = __TODO_SKIPPED_OVER_IN_CALCS__%
	% Incinerated = % Trash * % of Trash that is Incinerated	% Incinerated:	= __manufacturing_example_finished_product_percent_trash_unshipped_product__% * __manufacturing_example_finished_product_percent_of_trash_incinerated__% = __TODO_SKIPPED_OVER_IN_CALCS__%
Destination Breakdown of Buyer Rejections	Expert Interviews	ReFED estimated the following breakdown of buyer rejections based on expert interviews: Resale: __manufacturing_example_percent_of_buyer_rejections_sold_via_resale__% (excluded from surplus) Donations: __TODO_THEY_CUT_RESALE_WAY_EARLIER_IN_CALCS__% Animal feed: __TODO_THEY_CUT_RESALE_WAY_EARLIER_IN_CALCS__% Trash: __TODO_THEY_CUT_RESALE_WAY_EARLIER_IN_CALCS__% ———————————————— Total: 100%	Breakdown after excluding Resale: Donations: __manufacturing_example_finished_product_percent_donated_unsold_buyer_rejections__% Animal feed: __manufacturing_example_finished_product_percent_animal_feed_unsold_buyer_rejections__% Trash: __manufacturing_example_finished_product_percent_trash_unsold_buyer_rejections__% ———————————————— Total: 100%
	% of Trash that is Landfilled vs Incinerated in __manufacturing_example_state__ (Biocycle/Columbia University Survey[8]) (See Appendix Z)	% of Trash that is Landfilled = __manufacturing_example_finished_product_percent_of_trash_landfilled__% % of Trash that is Incinerated = __manufacturing_example_finished_product_percent_of_trash_incinerated__%
	Breaking “Trash” into Landfill vs Incineration:
	% Landfilled = % Trash * % of Trash that is Landfilled	% Landfilled:	= __manufacturing_example_finished_product_percent_trash_unsold_buyer_rejections__% * __manufacturing_example_finished_product_percent_of_trash_landfilled__% = __TODO_SKIPPED_OVER_IN_CALCS__%
	% Incinerated = % Trash * % of Trash that is Incinerated	% Incinerated:	= __manufacturing_example_finished_product_percent_trash_unsold_buyer_rejections__% * __manufacturing_example_finished_product_percent_of_trash_incinerated__% = __TODO_SKIPPED_OVER_IN_CALCS__%
Tons Donated	= Tons Unutilized Ingredients * % Donations_UI + Tons Unshipped Product * % Donations_UP + Tons Unsold Buyer Rejections * % Donations_BR Note: _UI means Unutilized Ingredients _UP means Unshipped Finished Product _BR means Buyer Rejections	= __TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ tons unutilized ingredients * __TODO_NO_AGGREGATE_ACROSS_INGREDIENTS_IN_CALCS__% + __manufacturing_example_finished_product_tons_unshipped_product__ tons unshipped __manufacturing_example_bls_naics_category_doctored__ products * __manufacturing_example_finished_product_percent_donated_unshipped_product__% + __manufacturing_example_finished_product_tons_unsold_buyer_rejections__ tons unsold buyer rejections * __manufacturing_example_finished_product_percent_donated_unsold_buyer_rejections__% = __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__ tons __manufacturing_example_bls_naics_category_doctored__ products donated
Tons Animal Feed	= Tons Unutilized Ingredients * % Animal Feed_UI + Tons Unshipped Product * % Animal Feed_UP + Tons Unsold Buyer Rejections * % Animal Feed_BR	= __TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ tons unutilized ingredients * __TODO_NO_AGGREGATE_ACROSS_INGREDIENTS_IN_CALCS__% + __manufacturing_example_finished_product_tons_unshipped_product__ tons unshipped __manufacturing_example_bls_naics_category_doctored__ products * __manufacturing_example_finished_product_percent_animal_feed_unshipped_product__% + __manufacturing_example_finished_product_tons_unsold_buyer_rejections__ tons unsold buyer rejections * __manufacturing_example_finished_product_percent_animal_feed_unsold_buyer_rejections__% = __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__ tons __manufacturing_example_bls_naics_category_doctored__ products sent to animal feed
Tons Anaerobic Digestion	= Tons Unutilized Ingredients * % Anaerobic Digestion_UI + Tons Unshipped Product * % Anaerobic Digestion_UP + Tons Unsold Buyer Rejections * % Anaerobic Digestion_BR	For this particular example, anaerobic digestion was __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__.
Tons Composted	= Tons Unutilized Ingredients * % Composted_UI + Tons Unshipped Product * % Composted_UP + Tons Buyer Rejections * % Composted_BR	For this particular example, anaerobic digestion was __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__.
Tons Industrial uses	= Tons Unutilized Ingredients * % Industrial uses_UI + Tons Unshipped Product * % Industrial uses_UP + Tons Unsold Buyer Rejections * % Industrial uses_BR	For this particular example, anaerobic digestion was __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__.
Tons Land Application	= Tons Unutilized Ingredients * % Land Application_UI + Tons Unshipped Product * % Land Application_UP + Tons Unsold Buyer Rejections * % Land Application_BR	For this particular example, anaerobic digestion was __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__.
Tons Sewer	= Tons Unutilized Ingredients * % Sewer_UI + Tons Unshipped Product * % Sewer_UP + Tons Unsold Buyer Rejections * % Sewer_BR	For this particular example, anaerobic digestion was __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__.
Tons Dumping	= Tons Unutilized Ingredients * % Dumping_UI + Tons Unshipped Product * % Dumping_UP + Tons Unsold Buyer Rejections * % Dumping_BR	For this particular example, anaerobic digestion was __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__.
Tons Landfilled	= Tons Unutilized Ingredients * % Landfilled_UI + Tons Unshipped Product * % Landfilled_UP + Tons Unsold Buyer Rejections * % Landfilled_BR	= __TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ tons unutilized ingredients * __TODO_NO_AGGREGATE_ACROSS_INGREDIENTS_IN_CALCS__% + __manufacturing_example_finished_product_tons_unshipped_product__ tons unshipped __manufacturing_example_bls_naics_category_doctored__ products * __TODO_CALC_DONE_DIFFERENTLY__% + __manufacturing_example_finished_product_tons_unsold_buyer_rejections__ tons buyer rejections * __TODO_CALC_DONE_DIFFERENTLY__% = __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__ tons __manufacturing_example_bls_naics_category_doctored__ products sent to landfill
Tons Incinerated	= Tons Unutilized Ingredients * % Incinerated_UI + Tons Unshipped Product * % Incinerated_UP + Tons Buyer Rejections * % Incinerated_BR	= __TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ tons unutilized ingredients * __TODO_NO_AGGREGATE_ACROSS_INGREDIENTS_IN_CALCS__% + __manufacturing_example_finished_product_tons_unshipped_product__ tons unshipped __manufacturing_example_bls_naics_category_doctored__ products * __TODO_CALC_DONE_DIFFERENTLY__% + __manufacturing_example_finished_product_tons_unsold_buyer_rejections__ tons unsold buyer rejections * __TODO_CALC_DONE_DIFFERENTLY__% = __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__ tons __manufacturing_example_bls_naics_category_doctored__ products sent to incineration
US Dollars Donated	= US Dollars Unutilized Ingredients * % Donations_UI + US Dollars Unshipped Product * % Donations_UP + US Dollars Unsold Buyer Rejections * % Donations_BR	= $__TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ unutilized ingredients * __TODO_NO_AGGREGATE_ACROSS_INGREDIENTS_IN_CALCS__% + $__manufacturing_example_finished_product_us_dollars_unshipped_product__ unshipped __manufacturing_example_bls_naics_category_doctored__ products * __manufacturing_example_finished_product_percent_donated_unshipped_product__% + $__manufacturing_example_finished_product_us_dollars_unsold_buyer_rejections__ unsold buyer rejections * __manufacturing_example_finished_product_percent_donated_unsold_buyer_rejections__% = $__TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__ __manufacturing_example_bls_naics_category_doctored__ products donated
US Dollars Animal Feed	= US Dollars Unutilized Ingredients * % Animal Feed_UI + US Dollars Unshipped Product * % Animal Feed_UP + US Dollars Unsold Buyer Rejections * % Animal Feed_BR	= $__TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ unutilized ingredients * __TODO_NO_AGGREGATE_ACROSS_INGREDIENTS_IN_CALCS__% + $__manufacturing_example_finished_product_us_dollars_unshipped_product__ unshipped __manufacturing_example_bls_naics_category_doctored__ products * __manufacturing_example_finished_product_percent_animal_feed_unshipped_product__% + $__manufacturing_example_finished_product_us_dollars_unsold_buyer_rejections__ unsold buyer rejections * __manufacturing_example_finished_product_percent_animal_feed_unsold_buyer_rejections__% = $__TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__ __manufacturing_example_bls_naics_category_doctored__ products sent to animal feed
US Dollars Anaerobic Digestion	= US Dollars Unutilized Ingredients * % Anaerobic Digestion_UI + US Dollars Unshipped Product * % Anaerobic Digestion_UP + US Dollars Unsold Buyer Rejections * % Anaerobic Digestion_BR	For this particular example, anaerobic digestion was __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__.
US Dollars Composted	= US Dollars Unutilized Ingredients * % Composted_UI + US Dollars Unshipped Product * % CompostedUP + US Dollars Buyer Rejections * % Composted_BR	For this particular example, anaerobic digestion was __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__.
US Dollars Industrial uses	= US Dollars Unutilized Ingredients * % Industrial uses_UI + US Dollars Unshipped Product * % Industrial uses_UP + US Dollars Unsold Buyer Rejections * % Industrial uses_BR	For this particular example, anaerobic digestion was __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__.
US Dollars Land Application	= US Dollars Unutilized Ingredients * % Land Application_UI + US Dollars Unshipped Product * % Land Application_UP + US Dollars Buyer Rejections * % Land Application_BR	For this particular example, anaerobic digestion was __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__.
US Dollars Sewer	= US Dollars Unutilized Ingredients * % Sewer_UI + US Dollars Unshipped Product * % Sewer_UP + US Dollars Unsold Buyer Rejections * % Sewer_BR	For this particular example, anaerobic digestion was __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__.
US Dollars Dumping	= US Dollars Unutilized Ingredients * % Dumping_UI + US Dollars Unshipped Product * % Dumping_UP + US Dollars Unsold Buyer Rejections * % Dumping_BR	For this particular example, anaerobic digestion was __TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__.
US Dollars Landfilled	= US Dollars Unutilized Ingredients * % Landfilled_UI + US Dollars Unshipped Product * % Landfilled_UP + US Dollars Unsold Buyer Rejections * % Landfilled_BR	= $__TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ unutilized ingredients * __TODO_NO_AGGREGATE_ACROSS_INGREDIENTS_IN_CALCS__% + $__manufacturing_example_finished_product_us_dollars_unshipped_product__ unshipped __manufacturing_example_bls_naics_category_doctored__ products * __TODO_CALC_DONE_DIFFERENTLY__% + $__manufacturing_example_finished_product_us_dollars_unsold_buyer_rejections__ unsold buyer rejections * __TODO_CALC_DONE_DIFFERENTLY__% = $__TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__ __manufacturing_example_bls_naics_category_doctored__ products sent to landfill
US Dollars Incinerated	= US Dollars Unutilized Ingredients * % Incinerated_UI + US Dollars Unshipped Product * % Incinerated_UP + US Dollars Unsold Buyer Rejections * % Incinerated_BR	= $__TODO_NO_SUM_ACROSS_INGREDIENTS_IN_CALCS__ unutilized ingredients * __TODO_NO_AGGREGATE_ACROSS_INGREDIENTS_IN_CALCS__% + $__manufacturing_example_finished_product_us_dollars_unshipped_product__ unshipped __manufacturing_example_bls_naics_category_doctored__ products * __TODO_CALC_DONE_DIFFERENTLY__% + $__manufacturing_example_finished_product_us_dollars_unsold_buyer_rejections__ unsold buyer rejections * __TODO_CALC_DONE_DIFFERENTLY__% = $__TODO_STORE_DESTINATION_DATA_GROUPED_BY_BLS_NAICS__ __manufacturing_example_bls_naics_category_doctored__ products sent to incineration

Data Sources and Limitations

National Value Shipped

Raw Data and Documentation:

https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Manufacturing_NationalValueShipped.xlsx

Each year the U.S. Census Bureau conducts the Annual Survey of Manufactures[3], which includes the wholesale value of product shipped from manufacturers in addition to many other data points. Every business is categorized into an industry code according to the North America Industry Classification System (NAICS). ReFED used this as the data source to determine the wholesale value of food manufactured in the U.S. on an annual basis. One of the data files specifies the percentage of manufactured food shipments that are indeed food as opposed to non-edible commercial products. This information was used to discount the total shipment values to include only edible food products. Additionally, some of the NAICS codes were too broad for ReFED’s purposes (e.g., Seafood processing). ReFED used Nielsen Point-of-sale (POS) data[38] in order to estimate the proportion of manufactured meat and seafood products that are fresh versus frozen versus canned, and therefore belong to different ReFED food departments (e.g., Fresh Meat & Seafood versus Frozen versus Dry Goods respectively).

Retail Markup

Raw Data and Documentation:

https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Manufacturing_RetailMargins.xlsx

Each year the U.S. Census Bureau conducts the Annual Retail Trade Survey[4], which includes gross margins from retail firms broken out by business types including grocery food and beverage stores. ReFED used these gross margins as a proxy for retail markup of manufactured food products. These margins were used to inflate the National Wholesale Value of manufactured food shipments to estimate the equivalent retail value of food shipments. See Appendix H for a list of retail margins over the years.

Retail Price per Lb

Raw Data and Documentation:

This is confidential data from Nielsen and cannot be shared.

Nielsen data represents over 85% coverage of grocery retail sales in the U.S. Each year top U.S. grocery retailers report item level point-of-sale sales data to Nielsen, including information about each item such as the grocery chain where it was sold, the brand name of the product, the food classification (department, category, subcategory), the weight of food and packaging, and many other attributes. ReFED used this data to quantify the retail value and weight of food sold by grocery retailers in the U.S. by year, state, and food type. For more information about the weight data, see U.S. Grocery Retail Dollar-to-Weight Conversion Factors Report[39].

The accuracy of these estimates is limited to the accuracy of the Nielsen sales and weight data. The weight data for UPC items comes directly from up-to-date product packaging images. For non-UPC items sold in eaches, Nielsen estimates weight using a weight conversion factor (e.g., the average weight of a lemon). For other non-UPC items, Nielsen is reliant on the retailer transaction data to provide the item sale weight units (e.g., lbs of apples sold).

ReFED mapped the Nielsen data to each Bureau of Labor Statistics food manufacturing NAICS code to estimate the national average retail price per lb by food manufacturing code. These prices were then used to estimate the weight of food manufactured and shipped from U.S. manufacturers after the national wholesale values shipped were inflated to equivalent retail values.

Employees

Raw Data and Documentation:

https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Manufacturing_Employees.xlsx

Each year the U.S. Census Bureau releases the number of employees working in various industry types in addition to many other data points[45]. Every business is categorized into an industry code according to the North America Industry Classification System (NAICS). ReFED used the number of employees working in each food manufacturing industry type (e.g., __food_var__ manufacturing) in each state on an annual basis to allocate national food manufacturing shipments to individual states.

The error in this approach is that the number of employees is not always proportional to the volume of production, but in absence of state-level manufacturing numbers, this was the best approach for estimating state-level food surplus. The result is that the state-level food surplus numbers may be high or low for particular manufacturing types.

Unshipped Product Rates

Raw Data and Documentation:

https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Manufacturing_UnshippedProductRates.xlsx

ReFED used data from Tesco supplier food waste case studies[44] to quantify the percentage of finished manufactured food that does not ultimately get shipped to buyers. ReFED identified specific suppliers to serve as proxies for different manufacturing types (e.g., Panelto Foods case study, a UK bakery manufacturer, was selected as the proxy for U.S. __food_var__ manufacturing). The resulting numbers from this approach are consistent with expert interviews with U.S. food manufacturers (all case studies indicated that <1% of finished product remains unshipped), so ReFED feels fairly confident in these estimates.

Buyer Rejection Rates

Raw Data and Documentation:

https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Manufacturing_BuyerRejectionRates.xlsx

Based on expert interviews, ReFED assumed that 2% of all manufactured prepared food shipments and 0.5% of all other types of manufactured food are rejected by the quality assurance teams of buyers (note that fresh produce rejections are included in the Farm sector, which were assumed to be 2%). ReFED used U.S. manufacturing shipments to estimate the weight of each food type delivered to buyers. In reality this overestimates buyer rejections for foods that are heavily exported and undestimates buyer rejections for food types that are manufactured outside of the U.S. Future iterations of this model should address this issue by accounting for imports and exports. Based on data from the USDA Global Agriculture Trade System[48] which lists import and export values, ReFED estimates that the current estimated buyer rejection tonnages in the Food Waste Monitor are not significantly affected because the overall U.S. trade deficit of manufactured food is relatively small compared to domestic production volumes. However, for specific foods with significant trade deficits (e.g., chocolate is heavily manufactured outside of the U.S.), this issue is exacerbated.

Recipes

Raw Data and Documentation:

https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Manufacturing_RecipesAndUtilizationRates.xlsx

In order to estimate the types of food ingredients and byproducts that are used (and therefore potentially wasted) at food manufacturing plants, ReFED identified a variety of recipe data sources of varying quality[__citation_Recipe___food_var__s__],[7],[9],[10],[16],[19],[20],[21],[18],[23],[24],[27],[28],[31],[33],[36],[37],[22],[57]. See the Raw Data and Documentation for a complete list of recipes and data sources. ReFED aggregated all of the category-level data to a higher level before sharing the data on the Food Waste Monitor as this data is only a rough estimate (e.g., salt and flour both become Dry Goods). ReFED was unable to find recipe data for a few manufacturing types, but these categories only represented 7.57% of value shipped. Unutilized ingredients were estimated to be zero for these categories. See Appendix I for more information.

Ingredient Utilization Rates

Raw Data and Documentation:

https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Manufacturing_RecipesAndUtilizationRates.xlsx

ReFED used data from Tesco supplier food waste case studies[44] to quantify the percentage of purchased ingredients that get utilized in finished product. ReFED identified specific suppliers to serve as proxies for different manufacturing types (e.g., Panelto Foods case study, a UK bakery manufacturer, was selected as the proxy for U.S. __food_var__ manufacturing). The resulting numbers from this approach are consistent with expert interviews with U.S. food manufacturers (all case studies indicated that 87-100% of ingredients are utilized), so ReFED feels fairly confident in these estimates. ReFED was unable to find recipe data for a few manufacturing types (only 7.57% of retail value shipped), so ingredient utilization rates were unnecessary for these categories. See Appendix I for more information.

Wholesale Ingredient Prices

Raw Data and Documentation:

This contains confidential data from Nielsen and cannot be shared.

ReFED subtracted average grocery margins[4] from the Nielsen retail price per lb data[38] to estimate wholesale prices of each manufactured food ingredient. For example, in __manufacturing_example_year__ the average retail price of eggs was $2.00 per lb. Also in __manufacturing_example_year__, the average margin for grocery stores was 26.6%. Therefore, ReFED estimated the wholesale price of eggs to be $1.56 per lb. The error in this approach is that the grocery margin data is not food type specific. While this approach likely leads to underestimation and overestimation errors for specific food types when quantifying the value of unutilized ingredients, these effects balance each other out in the total sector numbers when all food types are combined.

Unutilized Ingredient Destinations

Raw Data and Documentation:

https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Manufacturing_UnutilizedIngredientDestinations.xlsx

ReFED used custom-prepared food waste destinations data from Northstar Recycling[58] to estimate the destination breakdown of unutilized food manufacturing ingredients by food manufacturing type (See Appendix J). Northstar Recycling is a national waste and recycling company that manages waste for many food manufacturers across the U.S. and Canada. Because Northstar does not manage food waste for any meat processing facilities, ReFED assumed that 100% of unutilized ingredients at meat processing plants were sent to rendering (industrial uses). Additionally, Northstar does not have visibility to food donations data for their clients, so ReFED assumed that 1% of unutilized ingredients are donated based on data from the 2016 Food Waste Reduction Alliance survey[14] in which 9 manufacturers responded (6.2% of U.S. market share based on sales). Because these data sources are based on a single year, the data does not provide insight into changes in disposal habits over time.

The portion sent to “trash” was further broken down into landfill versus incineration on a state-by-statebasis using data from BioCycle’s 2010 “State of Garbage in America” survey[41], which was conducted in partnership with the Earth Engineering Center of Columbia University[8]. Because these surveys were discontinued in 2010 and no other state-level data sources exist, ReFED reused these estimates year over year to estimate the percentage of “trash” that is sent to incineration versus landfill facilities in each state.

Unshipped Product Destinations

Raw Data and Documentation:

https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Manufacturing_UnshippedProductDestinations.xlsx

ReFED also used the data from Northstar Recycling as described above to estimate the destination breakdown of unshipped finished product by food manufacturing type.

Retail Rejection Destinations

Raw Data and Documentation:

https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Manufacturing_RetailRejectionRates.xlsx

Based on expert interviews, ReFED assumed the following destinations breakdown for product that gets rejected by buyers: 25% sold to discount outlets, 25% trash, 25% donated, and 25% animal feed. The portion sold to discount outlets was subtracted from the surplus total. Better data is needed in this area to replace these anecdotal estimates.

The portion sent to “trash” was further broken down into landfill versus incineration on a state-by-state basis using data from BioCycle’s 2010 “State of Garbage in America” survey[41], which was conducted in partnership with the Earth Engineering Center of Columbia University[8]. Because these surveys were discontinued in 2010 and no other state-level data sources exist, ReFED reused these estimates year over year to estimate the percentage of “trash” that is sent to incineration versus landfill facilities in each state.

Data Quality Evaluation

This rubric is designed to evaluate the quality of how each data source was utilized by ReFED to estimate food loss and waste. It is not meant to rate the quality of the study itself. See Appendix AA for more information about the ReFED Data Quality Rubric.

Table 8. Data Quality Evaluation for Food Waste Monitor Manufacturing Sector

DATA	SOURCE	DATA QUALITY SCORE
		CREDIBILITY	UPDATE FREQUENCY	COVERAGE	FOOD TYPE	GEOGRAPHY	SCORE	WEIGHT
MANUFACTURING SURPLUS DATA
National US Dollars Wholesale Value Shipped	U.S. Census Bureau Annual Survey of Manufactures	5	5	5	5	3	High 23/5 = 4.6	15%
Retail Markup	U.S. Census Bureau Annual Retail Trade Survey	5	5	5	1	3	Medium 19/5 = 3.8	15%
Retail Price per Lb	U.S. Grocery Retail Dollar-to-Weight Conversion Factors Report	4	5	5	5	3	High 22/5 = 4.4	15%
Employees	U.S. Bureau of Labor Statistics Employee Levels	5	5	5	5	5	Very High 25/5 = 5.0	15%
Buyer Rejection Rates	Expert Interviews	1	1	1	3	3	Very Low 9/5 = 1.8	5%
Unshipped Product Rates	Tesco Supplier Case Studies	3	1	1	3	1	Very Low 9/5 = 1.8	5%
Recipes	Multiple Data Sources	1	1	1	5	1	Very Low 9/5 = 1.8	10%
Ingredient Utilization Rates	Tesco Supplier Case Studies	3	1	1	3	1	Very Low 9/5 = 1.8	20%
4.6 * 15% + 3.8 * 15% + 4.4 * 15% + 5.0 * 15% + 1.8 * 5% + 1.8 * 5% + 1.8 * 10% + 1.8 * 20% = 3.39							Medium
MANUFACTURING CAUSES DATA
Ingredient Utilization Rates	Tesco Supplier Case Studies	3	1	1	3	1	Very Low 9/5 = 1.8	80%
Unshipped Product Rates	Tesco Supplier Case Studies	3	1	1	3	1	Very Low 9/5 = 1.8	10%
Buyer Rejection Rates	Expert Interviews	1	1	1	3	3	Very Low 9/5 = 1.8	10%
1.8 * 80% + 1.8 * 10% + 1.8 * 10% = 1.8							Very Low
MANUFACTURING DESTINATIONS DATA
% Destination Breakdown of Unutilized Ingredients	Northstar Recycling	4	1	2	1	3	Low 11/5 = 2.2	78%
% Destination Breakdown of Unshipped Finished Product	Northstar Recycling	1	1	1	3	3	Very Low 9/5 = 1.8	8%
% Destination Breakdown of Buyer Rejections	Expert Interviews	1	1	1	1	3	Very Low 7/5 = 1.4	8%
% of Trash Landfilled vs Incinerated	Biocycle/Columbia University Survey	5	2	4	1	5	Medium 17/5 = 3.4	6%
2.2 * 78% + 1.8 * 8% + 1.4 * 8% + 3.4 * 6% = 2.18							Low