Domain: Waste Management & Environmental Services
Question:
Facilities has been confident that recycling is working in a municipal office building because the internal program looks solid. Good signage, staff engagement, and plenty of bins. Then the hauler's numbers come in and tell a very different story: trash is much higher than expected and recycling is lower. Now leadership is asking whether the building is underperforming or whether the data is unreliable. Before recommending any operational changes, lay out how you would investigate the discrepancy. Include what you would validate first (and why), how you would check whether the hauler estimates reflect actual loads and service conditions, what on-site observations or spot checks you would run to confirm what is really happening and how you would decide whether the issue is data quality, collection practices, contamination, or a breakdown in internal separation behavior.

Expected points in a correct answer:
States to handle the situations as an internal check of system, not point fingers of who is actually wrong (hauler or site)
States to verify what the haulers numbers actually represent whether estimates or measured
States actual scale weights as a basis of haulers numbers
States service based estimates as basis of hauler numbers
States most hauler use estimates based on service pick ups, so confirm their method before interpreting outcomes
States to confirm service inputs are correct as the next step in the process
Lists the core variables that impact estimated tonnage (container type/size, pickup frequency, number of containers, extra pickups)
States that even small errors in service input variables can make trash appear significantly higher or recycling appear artificially low
States to check operational explanations before assuming any behavioral issues
States to check for any extra pickups that are not obvious to staff
States to check for event spikes that may cause waste increase
States that compaction drastically changes density which can impact your total waste volumes
States recycling contamination that results in being treated as trash downstream as and operational deficiency
States consolidation and mixing at storage areas as operational impact
States that site should run a short validation period to ensure the issue was actually identified correctly
States to run validation for at least 3-5 service cycles PER major waste stream
States that the validation period should confirm: service frequency, container fullness levels, occurrence of extra pulls, and actual contents of each waste stream
States that after the operational checks have been validated, then leadership can pursue other means of the waste discrepancy
Identifies the three possible root cause categories: program effectiveness (true behavior issue), service configuration (pickup patterns, compaction), or misunderstanding/limitations in estimation methodology
References or describes a structured tool/workbook for tracking and validating waste data inputs and outputs
States Step 1 is to confirm inputs driving estimates by filling in the service inputs fields in the calculator tab cells C9:F23
States Step 2 is to confirm conversion assumptions by using the conversion factors applied in the calculator tab cells K9:K23
States the source conversion table is located in the background data tab in cells B9:F15
States Step 3 is the final tons/month outputs is locating in the calculator tab in cells G9:G23
States step 4 is to validate in the in the Waste Log tab in cells B5:F7
States that the service-based estimate method uses container size × pickup frequency × assumed density/fullness
States that actual scale weights are the most defensible measurement method
States that conversion factors/assumptions are part of the validation process (checking how service data is converted to tonnage)
States that the investigation should confirm the basis of the data, verify inputs/assumptions reflect reality, and validate whether the issue is measurement, service configuration, downstream handling, or true program performance