The impact of our waste on the planet is profound, and of the 100 billion tons of resources that enter the economy annually, more than 60% end up as greenhouse gas emissions or waste. Recycling statistics are equally disappointing, with as little as 9% of plastic ever produced being recycled. We also know that even unavoidable waste is not always efficiently disposed of and that many of its negative impacts could be avoided with better waste management. This is where big data comes in.
By harnessing the power of data via new digital technologies we can identify problems and solutions that were hitherto invisible. Data analytics can help to make sure that waste streams are minimized, properly separated and diverted towards the most effective destinations. This doesn’t just mean sending less solid waste to landfill, although that is a good thing, but it also means that food waste could be repurposed to make biofuels that power waste trucks among other things, or that recyclable plastic can be harnessed as feedstock for new products.
Together these efficiencies add up to a leaner, more profitable economy, a healthier population, and a more sustainable planet.
The Growth of Waste Data Tracking
The early benefits of data analysis in waste management were realized in the logistics of waste collection. The improved organization and scheduling of humans and waste trucks within waste collection frameworks were vastly improved through data analytics that followed digitization. As well as improved forward planning, adding a real-time element to traditional waste management strategies allowed for efficiencies in responding to unforeseen incidents. Vehicle tracking and satellite navigation also led to cost efficiencies in collection and transportation.
From there, the application of data and digital technology has expanded to encompass almost every aspect of waste management such that the concept now applies throughout a product’s life cycle. In turn, the goal of improving traditional systems has been usurped by the loftier objective of moving towards a circular economy. This realignment touches on everything from raw material sourcing, waste created during production and product use, right through to end of life disposal.
What Does Waste Data Tracking Consist Of Today?
A variety of factors are pushing waste management even further toward digitization and data analysis, including customer expectation, ongoing urbanization, population growth and the increasing amounts of waste that come with those trends.
Data can help reduce how much waste is generated in the first place by streamlining stock processes and allowing businesses to make better informed decisions about production schedules. For example in industries that deal with perishable products, such as pharmaceuticals, the use of data tracking can more accurately predict demand and address production appropriately to avoid excess waste. Within production itself, data analysis can help identify patterns of waste hitherto overlooked, which can then be used to alter production processes for greater efficiencies. In terms of waste collection services, technologies like innovative RFID sensors in bins can provide live information to waste management companies and permit more targeted and efficient pickup schedules. And with data available at every stage of the waste cycle it becomes possible to troubleshoot problematic areas such as bottlenecks in disposal or discrepancies in payment.
Strategic benchmarking can be more accurately achieved with consistent data on waste, allowing companies and municipalities alike to set quantifiable targets and measure their success or failure more accurately than ever. Similarly, reporting to stakeholders becomes a quantitative endeavor rather than an exercise in declaring your best intentions. Transparency is a knock-on benefit of ubiquitous data. Such data analysis will help to improve climate reporting and thus make businesses more accountable to their stakeholders on their environmental efforts. On a socioeconomic level, increased digitization provides employment in higher value parts of the waste management chain as this traditionally labor intensive industry upgrades its skillset.
Challenges of Waste Data Tracking
The waste management sector operates with low margins and so investing in a costly new digital infrastructure and all the training and appendant costs it entails is a risk, even though we know it offers scale efficiencies. There are also environmental trade offs that come with increased digitization that ought to be part of the discussion. These include the increased energy consumption that new digital processes bring. Add to this the cost of producing all the digital infrastructure required to run new systems and the entirely new stream of waste created from that infrastructure when it reaches the end of its life. It is thought that the benefits will far outweigh the costs in this line of thought, however more studies are being called for.
Other Digital Technologies In Play
As well as data analysis, the digitization of waste management encompasses other connected technologies. One promising avenue of exploration is artificial intelligence (AI). There are now AI machines that can sort recycling and waste to avoid contamination of recycling streams; hitherto a highly labor intensive process. AI can achieve cost saving efficiencies as well as the goal of avoiding sending waste to landfill where it is left to emit harmful greenhouse gasses.
Advances in robotics pertaining to the sorting of waste streams mean that higher purities of waste streams can be achieved (over 90%). Robotics can be coupled with AI in order to identify and physically sort materials using patterns, texture, or QR codes in waste streams such as used electronics.
How RTS Approaches Waste Data
At RTS data is integral to every part of our waste management approach and is specifically designed to help businesses set and meet sustainability goals. RTS uses data in three stages. First of all we measure your waste in order to gain a clear and thorough picture of where you stand today. Using that data a plan is formed to improve your waste management. The second stage is monitoring; implementing the plan and drawing on a continual stream of data from your day to day operations to make sure your goals are being met.
The third step is management, which entails tweaking your waste management strategy as per the incoming data. This innate reactivity leads to our on-demand and customized services, designed to supplement your overall plan as and when required. RTS harnesses data to help businesses react to external pressures such as legislation and customer pressure, while exploiting the operational benefits of data-driven waste management.
For more information on how both individuals and businesses can harness data analysis to optimize waste management, subscribe to the RTS blog today. Additionally, for tailored insights into your waste management practices, contact one of our LEED-accredited advisors today.