Digitalization of everything has ushered us to a new era where machines, robots, and algorithms play a major role. This, in turn, has led to the creation of a new economy, the adoption of new skills, and the transformation of industries in unbelievable ways.
One such industry that has garnered attention in the recent past is the waste management industry. The industry defines new challenges to technology as the applications for real-life examples are limited. However, to have an estimate about the size of the domain, the industry leaders employ more than 43,700 individuals, have more than 20 million customers, and 17,500 trucks. Furthermore, a total of $8 billion is infused to achieve digital transformation in the process.
However, pilot tests and innovative ideas spurring all across the globe show promising signs of growth in the future and hence could not be ignored at all costs.
Some of the important key take-aways are as follows:
- After a profound use of digital technologies, it was observed that the technologies improved recycling facilities, enabled the sound purchase and sorting decisions among consumers while being an agent for the improvement of waste sourcing options for recyclers.
- Digital transformation in the wastage industry is still in its nascent stages, and newer business models are expected to pop up in years to come.
- Every step in the waste management space could be digitalized, making digital transformation the need of the hour. Currently, a majority of countries follow a heterogeneous process to make the entire system robust.
Functions of Advanced Technologies in Waste Disposal and Management
Modern-day technologies have been instrumental in the replacement of legacy systems vastly. As a result, companies now seek solutions that are miniaturized, advanced, and easy to set up and operate.
Some of the examples are as follows:
a) Robotics
Process: Better pneumatic sorting process
Examples: Using robots that are enabled by machine vision to sort one item from another.
b) Artificial Intelligence
Process: Usings machine learning that works with tandem computer vision to recognize patterns and improve sorting abilities.
Examples: Independent, self-driving street sweepers
c) Internet of Things
Process: With multiple gadgets being connected to private networks or the internet, one can use the sensors for collecting, sharing, and processing data for improvement in systems.
Examples: Smart waste bins with a wide array of sensors for simplifying logistics.
d) Cloud Computing
Process: Cloud solutions for storing and processing all data collected from sensors while keeping a tab on the costs.
Examples: Connecting and standardizing different internal processes.
Thinking Automation Beyond Traditional Norms
Waste management is a tiring process that involves a lot of manual intervention. These age-old techniques, such as manual sorting, increase the cost and time. However, with automation in place, things have come far along.
Waste is no longer manually sorted in major recycling plants. The robots or the mechanical arms are well equipped to carry out these processes with utmost efficiency. On the other hand, this opens up employment opportunities in the higher levels of hierarchy.
Other notable processes include labeling products using digital watermarks, QR codes, or any other digital ink that provides adequate information about the composition of the waste and if any high-value material finds its way into the waste.
One such instance that lies above the hierarchy, and yet has proven useful in the waste sorting process is Daniel Newman’s article on Forbes. He states, “ We are scaling back on the use of paper - books, files, magazines, contracts - instead of digital communication and digital file management. Cloud storage helps eliminates paper waste and the overhead costs of traditional storage and secure shredding It also makes accessing documents from anywhere even easier ”
Furthermore, robotic sorters can then generate adequate information about the waste and optimize the process accordingly. This also means making improvements in AI. For instance, data load in the incoming loads of waste could be identified and sorted by the robots using machine vision. Any additional information such as raw material cost could lead to further classification of waste for long-term end solutions.
Drivers and Inhibitors
The waste management industry operates at very nimble margins and is constantly subjected to intense pressure to reduce its expenditures. To add on top of it, the digital transformation required in the industry is capital intensive and faces modernization challenges. Michael Kelley Of The Fourth Wave goes in-depth about digital transformation in waste management and hence could not be ignored.
The report on digitalization by the European Topic Center highlights the key elements in the process and hence could not be ignored at all costs.
Some of the major forces are as follows.
Drivers:
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Customer expectations - Customers expect state-of-the-art technologies and are willing to invest in them.
- An increasing amount of waste - The sheer amount of waste produced every day needs automation to deal with it.
- Climate crisis - Landfills have been destroying the planet and accelerating the climate crisis.
- Urbanization - The intensive amount of waste produced every day and the creation of new hubs need solutions that can drive change.
- Business models - Evolving business models in and around waste management pushes entrepreneurs to explore opportunities.
Inhibitors:
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Lack of digital literacy - Reluctant to changes and lack of digital literacy has emerged as a leading inhibiting factor across geographies.
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Invest costs - The tools used in the digital transformation of waste management systems call for solutions. However, the cost of the equipment acts as a major roadblock.
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Missing digital ecosystem - Digital transformation needs solutions that encompass both the citizens and the waste management bodies. However, lack of funds or underdeveloped technologies acts as a hindrance.
Despite the cons, investors and governments see the long-term vision and hence provide adequate support to run and keep up the entire setup.
Trade-Offs: Balancing the Positive With the Negative
While there are many things through which digitization could outpace any of the flaws, the entire scenario faces some grave challenges. Digitalization could help nations in achieving circular economies across the waste management cycles. These factors spread across material outsourcing to production to reuse resources and hence could not be ignored at all costs.
However, these advantages come with costs, the first being energy trade-off, assisting technologies to require substantial energy to operate, thus stressing resource consumption.
Second, the material involved in producing infrastructure, sorting robots, computing machines, and several other elements requires intense capital resources.
Finally, all infrastructure has a lifespan of itself beyond which it is inoperable. Capital intensive such setups cannot be easily discarded, thus posing a concern for the companies and the state.
References
[1].https://www.cio.com/article/3433560/waste-management-dumps-legacy-processes-drives-digital-change.html
[2].https://www.eea.europa.eu/themes/waste/waste-management/digital-technologies-will-deliver-more
[3].https://medium.com/the-fourth-wave/what's-now-and-next-for-digital-transformation-in-environment-innovation-a672def48d22
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