Domain: Waste Management & Environmental Services
Question:
A property management company oversees a portfolio of office and institutional buildings and uses service-based waste estimates to compare buildings and identify opportunities for improvement. Some buildings show large differences in waste generation that may be driven by occupancy patterns, compaction practices, or hauling arrangements rather than behavior. 
 
 How should estimated waste data be interpreted to decide where intervention is needed, and where differences may reflect structural or service factors rather than poor waste practices?

Expected points in a correct answer:
States that service based waste data should be treated as a screening a prioritization tool
States that estimates are valuable for identifying where to look, but large differences should be treated as signals to investigate, not automatic proof of poor behavior or failed programs
States the need to normalize the data so it is not affected by scale of the sites
Explains that normalization helps separate behavioral differences from simple differences in scale and usage
Mentions normalization denominators such as per occupant/occupant-day, per 1,000 sq ft, per event-day/attendee, or per operating day
States the need to compare like for like based on service configuration
Gives an explanation of why the buildings should be compared by waste service as that drives the estimates
Provides list of key waste service arrangements to compare the sites by compactors vs dumpsters, hauling frequency, shared vs dedicated infrastrucure, and container size differences
Emphasizes that sites may be different due service arrangements, not the behaviors of the employees at the site
States that outliers should be treated as investigation targets, not conclusions
States to confirm that the outlier exists even after normalization of the data
States to check if the service factors are the reason for variance if it is still an outlier after normalization
Provides examples of structural/service factors to investigate such as unusual occupancy patterns, compaction practices, event schedules, hauling frequency changes, shared hauling arrangements, or special cleanouts/renovations
Explains to target service intervention or solutions if that is the driving factor for the variance in waste estimates, not behavioral reasons
Defines magnitude as how high waste generation is after normalization, and confidence as how reliable the estimate is given service structure and assumptions
Specifies distinct actions for different magnitude/confidence combinations, such as intervening for high-waste/high-confidence sites and validating first for high-waste/low-confidence sites
States to validate high impact sites prior to spending any money
States verification of the waste log and targeted audits
Mentions specific verification activities such as short Waste Log windows (pickup count, conditions, extra pulls) or targeted audits where compaction or shared services reduce estimate reliability
References or describes a workbook/tool structure for tracking and calculating total waste estimates across sites
States that step 1 of the workbook calculates consistent waste estimations for each site and can to use final tons/month outputs (primary numbers): Calculator!G9:G23
States that step 2 of the workbook needs to calculate with the same conversion assumptions applied using the conversion factors applied in the Calculator: Calculator!K9:K23 and the source conversion table: Background Data!B9:F15
Step 3 states to normalize the data outside of the workbook as the common denominators are not supplied within the given workbook using occupant count, sqft, event days, operating days
Step 4 states to validate the outliers using the populated waste log in Waste Log!B5:F7
States that service-based waste data should not be used as a scorecard
States that large differences should not be treated as automatic proof of poor behavior or failed programs
Mentions that if the headline number is driven by service mismatch, interventions should target service configuration or measurement method specifically
States that low diversion combined with contamination signals should lead to prioritizing segregation controls and education
States that large swings tied to service changes should prompt fixing service assumptions before making program changes
States that validation keeps interventions targeted and prevents chasing noise