Poor waste management has a devastating impact on the environment, especially in commercial areas. The awareness may not be sudden, but it is certainly gaining momentum, as more companies now seek to minimize their impact on the environment. Of course, that’s easier said than done. Since the dawn of the 21st century, the volume and pace of business growth has been unprecedented. But the fact remains that smart waste management is both a business and social responsibility.
Importance of commercial waste management
The commercial waste management life cycle includes collection, transportation and disposal. Ensuring that all waste is properly handled throughout the process is a matter of compliance. After all, multiple environmental regulations dictate how waste management protocols should be implemented and monitored. Having good waste management guidelines in place also helps companies meet their ethical and legal responsibility to maintain appropriate health and safety standards in the workplace.
Additionally, with climate change increasingly recognized as a serious threat to the environment, the impact of inefficient commercial waste management is also being felt in the minds of employees and customers. Consequently, companies are both motivated and under pressure to champion sustainability at the sites where they operate.
For example, all waste generated in commercial buildings is stored in bins placed at strategic locations. If companies don’t use them effectively, it will lead to bin overflows with serious financial, reputational and legal repercussions.
Impact of data analysis on commercial waste management
Data analytics eliminates compliance issues that arise from overflowing bins by closing operational gaps. Additionally, it provides the precise know-how to create smart waste management workflows. With high-quality video cameras built into the chassis of their waste collection trucks, image-based analytics can be captured and shared through a cloud-hosted platform for real-time visual detection. From these, data insights can be extracted to assess when bins are filled and plan collection to minimize transport, fuel and labor expenses. They can also determine the right collection frequency, streamline collection routes and optimize vehicle loads.
By monitoring real-time data from video cameras, the flow of waste through each bin can be managed quickly to avoid compliance repercussions. The trucks also receive real-time data on the location of empty bins, which helps them plan optimal routes and be more fuel efficient.
Ultimately, using sophisticated data analytics helps create a leaner, greener waste management system. Additionally, it can improve operational efficiency while taking an uncompromising stance on environmental and health sustainability.
Tiger Analytics waste management modeling use case for a large manufacturer
Overflowing bins are a major impediment to the waste management process as they increase the time needed to process waste. Waste collection trucks will have to spend more time than expected to ensure they deal with overflowing bins effectively – without any spillage in and around the premises. It is also difficult for them to complete their trips in time. When it comes to commercial trash cans, the situation is even more complicated. The size and contents of commercial trash cans vary depending on the businesses unique waste disposal requirements.
Recently, Tiger Analytics worked with a leading waste management company to leverage advanced data analytics to improve commercial waste management compliance.
Previously, the customer had to record videos of the waste collection process and send them for manual review. The videos were used to identify commercial establishments that did not meet prescribed standards on how much waste can be stored in a bin. However, their video review process was inefficient and cumbersome.
When the pickup takes place, the manual examiner must watch hours of video clips and images captured by each truck to determine the offenders. Thus, there was a need for uncompromising accuracy since overflowing bins resulted in compliance violations and potential penalties.
Tiger Analytics has developed a solution that leverages video analytics to help determine whether a particular bin in an image was overflowing or not. Using state-of-the-art deep learning algorithms, the solution enabled a high level of accuracy and eliminated all manual video review activities and associated costs.
Tiger Analytics’ solution was based on a new data classification algorithm that increased the efficiency of waste collection trucks. Based on sensor data collected from the chassis, we allowed the customer to predict collection time when the truck was within five seconds of being near a trash can. Additionally, through advanced monitoring analytics, we reduced the review process time from 10 hours to 1.5 hours, which also improved labor efficiency.
As a result, the client could effortlessly reduce the risks of their waste management approach and prevent overflow into commercial trash cans. Some of the business benefits of our solution were:
- More operational efficiency by streamlining pickup scheduling
- Smarter asset management through improved fuel efficiency and lower vehicle operating costs
- Improved labor productivity – with accelerated critical processes like video playback to confirm pickup
- Rapid risk mitigation of any overspill negligence that leads to compliance violations
New avenues for leveraging advanced analytics continue to pave the way for eco-responsible and sustainable business practices. Especially in a highly regulated industry like commercial waste management, it delivers the precision, convenience, and speed needed to bolster day-to-day operations and prevent compliance issues.
Day by day, commercial waste management is becoming an increasingly important catalyst for societal progress. As mentioned earlier, more and more companies are becoming aware of their impact on the environment. Additionally, the scale of infrastructure development has taken its toll, exponentially increasing the need to optimize waste disposal and collection methods. Either way, the data provides valuable insight into how it should be done.