Determining Root Causes - Why and How to Measure Food Loss and Waste

It is difficult to reduce FLW without understanding what causes it. For example, after performing a waste composition analysis, a restaurant may discover that it is discarding a large amount of tomatoes each week, but the waste data do not tell it why those tomatoes are being discarded. This module describes how to track causes of FLW when the information is not obvious in the quantification method.

Defining Causes and Drivers

There are two layers to identifying the cause of FLW – the immediate reason why something became FLW, and the underlying factor that led to the waste. The FLW Standard uses the terms “causes” and “drivers.” A cause is defined as the proximate or immediate reason for FLW, while a driver is defined as an underlying factor that played a role in creating that reason (FLW Protocol 2016a). Tables 4 and 5 list some possible causes and drivers by stage in the food supply chain.

Table 4. Some Causes of FLW by Stage of the Food Supply Chain

Primary Production	Processing and Manufacturing	Distribution and Wholesale	Retail	Food Service/ Institutions	Household
Spillage Cosmetic or physical damage Damage from pests or animals Not harvested Unable to sell due to quantity or size Unable to reach market	Spillage Trimming during processing Rejected from market	Cosmetic or physical damage Spoilage Past sell-by date Rejected from market Unable to reach market	Product recall Food prepared improperly Food cooked but not eaten Cosmetic damage Spoilage Past sell-by date	Product recall Food prepared improperly Food cooked but not eaten Cosmetic damage Spoilage	Product recall Food prepared improperly Food cooked but not eaten Cosmetic Damage Spoilage Past sell-by or use-by date

Source: FLW Protocol 2016a, CEC 2017.

Table 5. Some Drivers of FLW by Stage of the Food Supply Chain

Primary Production	Processing and Manufacturing	Distribution and Wholesale	Retail	Food Service/ Institutions	Household
Premature or delayed harvesting Poor harvesting technique/ inadequate equipment Lack of access to market or processing facilities Poor access to farming equipment Price volatility Stringent product specifications Overproduction Improper storage	Outdated or inefficient equipment and processes Stringent product specifications Human or mechanical error resulting in defects	Excessive centralization of food distribution processes Lack of effective cold-chain management Stringent product specifications Poor transportation infrastructure Failure in demand forecasting Ineffective packaging or storage conditions	Regular replenishment of stocks to evoke abundance Package sizes too large Failure in demand forecasting Too many products offered Lack of system for food donation	Regular replenishment of buffet or cafeteria to evoke abundance Portion sizes too large Failure in demand forecasting Too many products offered Lack of system for food donation Improper training of food preparers	Overpurchase Inadequate planning before shopping Lack of cooking knowledge Confusion over date labels Inadequate or improper storage of food Desire for variety, resulting in uneaten leftovers Overcooking

Source: FAO 2014, FLW Protocol 2016a, CEC 2017.

If a restaurant discards a large amount of tomatoes, the immediate cause might be that the tomatoes spoiled after sitting unused in the kitchen. The underlying driver may be that the restaurant is incorrectly forecasting the amount of tomatoes it needs each week. Perhaps a previously popular dish that requires tomatoes is not selling as much as anticipated, but the restaurant is continuing to order tomatoes based on prior rather than current demand.

In this example, simply knowing that a large amount of tomatoes was being disposed of was not enough to determine the correct course of action to reduce waste. However, once the tomato FLW was linked to a cause (e.g., spoilage after not being used) and an underlying driver (e.g., failure of demand forecasting), the restaurant is now able to take action to reduce the FLW (e.g., reduce the weekly order for tomatoes or adjust the menu to remove the dish not being ordered).

In more complicated cases, the causes and drivers may not be clear. Meeting with an outside waste-reduction consultant may be beneficial. Numerous firms make detailed sustainability audits of facilities and organizations to address root causes of inefficiencies and unsustainable practices.

Incorporating Causes into FLW Quantification Methods

The methods described in this guide differ in how well they track the causes and drivers of FLW. Table 6 provides a list of methods, whether they can track causes and how to best do so.

Table 6. Tracking Causes by Method

Method	Can it track causes?	How to track causes with the method
Direct weighing	Yes	Although direct weighing provides only numerical data, staff can be instructed to log causes while weighing the FLW. This will provide an additional data point about how the FLW occurred.
Waste composition analysis	No	A waste composition analysis will not directly provide information on causes of FLW, since the waste is being analyzed after it has been discarded. For this reason, waste composition analyses are often paired with a survey or process diary to generate qualitative data on causes and drivers assessed in tandem with the waste analysis.
Records	Not usually	Because records are kept for purposes other than FLW quantification, they are less likely to contain information relating to FLW causes and drivers. However, some records will have information that can help identify causes. (For example, a repair record for a piece of faulty equipment may help identify a cause of food waste.) Usually, a diary or survey will likely need to be implemented to generate qualitative data.
Diaries	Yes	A diary can be used to determine causes and drivers of FLW. The diarist can be asked to provide information on why the FLW occurred while recording it.
Interviews/Surveys	Yes	A survey can be used to determine causes and drivers of FLW. The respondent can be asked to provide information about why FLW occurs within their boundaries.
Proxy data/mass balance	No	Because inference by calculation is a mathematical operation based on material flows and proxy data, it will not provide information about causes and drivers of FLW. It provides only a quantitative estimate of the amount of FLW occurring within a given sector or commodity type. An additional analysis of the relevant sector or commodity will be necessary to understand the causes of FLW.

Source: Authors.

How to Track Causes and Drivers

Causes and drivers can be tracked simply by capturing information on causes while numerical estimates of FLW are being logged. In most cases, only the immediate cause will be available at first and additional research may be needed to detect the driver. Table 7 shows an example of how causes and drivers can be tracked alongside numerical estimates of FLW.

Table 7. Tracking Causes and Drivers

Food Type	Amount	Stage of the Supply Chain	Cause	Driver
Wheat	1000 kg	Primary production	Eaten by pests	Improper storage on the farm
Apples	10 kg	Processing	Trimmings	Inefficient equipment trims more than necessary
Strawberries	40 kg	Distribution and wholesale	Spoilage / Damage during transport	Lack of effective cold-chain management / Improper packaging / Excessive centralization of distribution processes
Beef	100 kg	Retail	Spoilage	Improper refrigeration
Fish	34 kg	Food service/ institution	Spoilage	Failure in demand forecasting
Milk	500 g	Household	Past sell-by date (but not spoiled)	Confusion over meaning of date labels

Note: the information in this table is illustrative.

Source: Authors.