Invoice Analytics

Advanced Invoice Analytics Reveals Opportunities to Save Big on Hauling Costs

February 1, 2021

Case for Zero Waste

As a Zabble intern earning my master’s degree in business analytics, I built an advanced algorithm to identify opportunities to reduce waste hauling costs. 

Looking at one city in California, my model found significant savings potential for large commercial or institutional buildings:

  1. 10% average savings simply by optimizing pick-up frequency along with the number and size of containers for each stream. 
  2. 32% average savings resulted from boosting both recycling and composting rates by 10%


Building managers need to make multiple decisions when selecting their waste servicing levels from haulers: the size and number of containers, along with frequency of pickups for each waste stream. 

Ideally, managers will choose a level of service that matches the building’s waste generation, while not paying for excess bin volume or pickups. Readily available tools to help make the best decision have been limited thus far, and managers may spend 10+ hours per month trying to understand and improve their service levels. 

This is where decision modeling can help. Using an advanced algorithm, the model finds the best solution from a set of potential decisions, having been programmed to find the lowest costs while still providing the minimum bin capacity needed to handle the building’s waste volume. 


My model looked at one city with its own specific regulatory and pricing environment, and only focused on containers smaller than 10 yards. Results in other regions may vary, but the model can be modified for use in a variety of hauler markets. 

Landfill fees were high in the region I studied, so diverting more waste to compost or recycling led to higher savings for customers, as I will demonstrate later. 

Using real data from one of Zabble’s client partners, my model found an average of 10% cost savings for a building, even without taking extra steps to divert or reduce waste: 

In the example above, the building could save $7200+ in annually by simply adjusting service levels. These savings largely resulted from choosing fewer, larger containers that were picked up less frequently for the compost stream (green bar). A small portion of savings was also identified by changing the landfill (gray bar) subscription. 

Across a set of sample buildings, I looked at three different scenarios to compare their savings potential: 

  1. Status quo - i.e. no change in diversion rates or total waste volume
  2. Waste volume reduction of 20% across all streams
  3. 10% increase in recycling and compost rates

Each scenario and its range of potential savings is displayed in the chart below:

Scenario 1: Status Quo

Average savings: 10%

Savings range: 0% - 26%

Analysis: Many organizations can save just by opting for fewer, larger containers that get picked up less often. 

Scenario 2: Waste Reduction of 20%

Average savings: 22%

Savings range: 0% - 36%

Analysis: If a building reduces waste volume by 20% across all waste streams, it can expect to save approximately an equivalent amount on hauling costs. 

Scenario 3: Diversion Rate Boost of 10%

Average savings: 32%

Savings range: 14% - 56%

Analysis: Major savings are possible when a building increases its diversion rates, showing that zero waste initiatives aimed at boosting diversion can actually save money. However, these results depend on the hauler’s rate structure, and won’t apply everywhere. 

To explore how decision modeling and analytics can optimize your building’s service levels, please contact the Zabble team.

Kelley Nelson has over ten years of experience in operations and fundraising with science-led environmental organizations, including the ClimateWorks Foundation and the National Audubon Society. She recently interned with Zabble as part of San Francisco State University's Master of Science in Business Analytics program. She is currently a full-time senior data scientist at Zabble. Find her on Linkedin.

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