Going beyond the last mile delivery
3 Ways delivery gig workers could help solve some unique urban problems and move up the value chain.
UN has projected that 68% of the world population could live in urban areas by 2050. The urban population of the world has grown rapidly from 751 million in 1950 to 4.2 billion in 2018. Asia, despite its relatively lower level of urbanization, is home to 54% of the world’s urban population, followed by Europe and Africa with 13% each.
The advent of delivery based gig workers solved some of the major problems in last mile delivery of goods and services at the same time creating more job opportunities. However exploitation of delivery gig workers is a cause of concern including lower income when compared to the efforts invested due to basic skills being currently utilized. Hence improving the income by incorporating additional skills to create more value without disrupting the current ways of working needs to be explored to move up the value chain.
Delivery based gig workers are typically distributed in any major city and they are all equipped with smart phones hence well connected and tech-savvy. Could this distributed somewhat resilient network hold potential to solve some of the unique problems currently faced in urban areas ?
Below are 3 examples which could trigger your ideas on how delivery based gig workers could further move up the value chain.
- A digital network or marketplace for informal waste sector
“There is enough for everybody’s need and not for everybody’s greed”. — Mahatma Gandhi
Could delivery gig workers help integrate informal waste sector for recovering valuable e-waste ?
Today’s e-waste problem is massive.
- 80% of electronics is never recycled and ends up in land fills.
- It’s estimated a 40% increase in e-waste by 2030
- It costs 13 times more to obtain gold, copper and other metals from mining the ore than from urban mining.
Adding to the e-waste problem is the advent of newer technology:
- Impact of COVID-19 pandemic is that almost a quarter (23%) of PCs in Europe won’t be needed again as a result of home working .
- Carbon impact of Information & Communication Industry (ICT) items including cell phones, computers, servers, etc., will go from 1 percent of the overall carbon footprint in 2007 to 14 percent by 2040.
- It’s estimated 3.5 billion 5G subscriptions by 2026
- Advent of electric vehicles mean more lithium ion batteries reaching end-of-life. The recycling market is likely to see more generation of end-of-life batteries from 2022–23 onwards as the annual lithium-ion battery market is expected to grow at a compounded annual growth rate of 37.5%, according to a research report.
General model currently used to recovery of e-waste flows from electronic consumers — Aggregators — Recyclers — Producers,
This general model is somewhat disconnected where individual parties work in silos , for example consumers are not well connected to the aggregators . The e-waste collection process is largely disorganized or an one-time collection drive hence not sustainable.
- It’s observed 94% of the enterprises do not have proper e-waste collection system.
- Almost all enterprises are willing to pay for the disposal of their e-waste through authorized collection centers and were in demand of adequate training and capacity building on e-waste management.
- About 26% of the e-waste is stored in premises, without knowing what must be done with it due to lack of proper e-waste policy in the organization.
- About 31% of the e-waste is disposed to scrap dealers without knowing the impacts of informal recycling
Though almost all firms are willing to pay for the disposal of their e-waste through authorized collection centers, still bulk of the e-waste is disposed to informal sector like scrap dealers. Here unscientific method related to dismantling of e-waste results in high wastage of precious metals which are never recovered and ends up in landfills. Most of the recyclers are not equipped to deal with e-waste which aggregators collect. Producers are not sure of the quantity of recycled raw materials they can source from the recyclers.
Small appliances typically disposed off are regarded as the most valuable part of the e-waste stream, containing IT as well as consumer electronics with a higher content of non-ferrous and precious metals. Printed circuit boards contain high concentrations of copper and higher concentrations of precious metals than any other category, which are mainly found in information and communication technology equipment (ICT), e.g. computers, mobile phones, printers, etc., and consumer electronics, such as audio equipment, SAT receivers etc.
Energy savings from using recycled materials as compared to virgin raw materials in production is very high. For example recycled aluminum saves 95% energy hence costs when compared to virgin raw materials.
There is a need to connect the silos with a reliable network where Consumers — Aggregators — Recyclers — Producers are connected in real-time via an effective application ecosystem.
Delivery gig workers could play a vital role in plugging the last-mile gap between the electronic consumers and aggregators or recyclers leveraging a connected application ecosystem. Gig workers could even explore initial treatment or dismantling of e-waste with proper equipment and training.
Treating e-waste includes dismantling, segregating and sorting so as to increase value . Information on composition of products to enables local treatment and segregation. For example iron and steel , aluminum and copper have high energy savings when recycled when compared to virgin materials hence cost savings to the manufacturers.
Gig workers could also help carry out initial assessment, classification or even dismantling ( with proper training ) of the e-waste which could help efficiently and sustainably organize , segregate and plan for effective recycling such that maximum value is realized down the chain whilst recycling.
Since the activity is not real-time like food delivery, there is less pressure in terms of collection of e-waste and is more driven from the point of generating additional income.
Bringing it all together an end-to-end platform or ecosystem with integrating payment through the reverse chain enables connecting the demand of materials by the manufacturers to the e-waste recovery by the delivery gig workers . Hence delivery gig workers here could play a vital role in the last-mile connectivity by upskilling on e-waste collection and even initial treatment of e-waste with the help of an application ecosystem.
2. Collecting and organizing near real-time local Intelligence
Despite many advancements in weather predictions, automated measurements and technologies (like google maps where one could almost in real-time know the traffic conditions), there are a lot of gaps in terms of accuracy and the breadth of local intelligence needed to address everyday urban problems.
For example there are challenges with accurately classification of different types of precipitation automatically with commercial microwave links ( on cell towers ) to detect rain (liquid), sleet, hail, or snow (all solid). Currently artificial intelligence and machine learning models are used in an experimental phase to classify with reasonable confidence hence measure the amount of rainfall in populated areas which is useful in planning. However such models are still not very accurate. Also there are challenges in automatically detecting unpredictable weather conditions like fog, heatwaves etc. Also IOT sensors needed to collect variety of data may not be as pervasively installed to cover a wide geographical area.
Other examples could be like unplanned events, noise levels, air quality, local disruptions in an area which could disrupt our daily schedule or impact our health whilst commuting in the urban landscape.
Delivery gig workers here could play and vital role in plugging such gaps due to the advantage of being geographically spread out. With a connected application via smartphones gig workers could now periodically update various parameters like weather, local events etc. which could be classified as valuable local intelligence and most in-demand.
The data quality and reliability could be a challenge , however effective data quality checks and mechanisms to improve reliability of data could be employed to offset this risk. For example if 7 in 10 mark smog at a specific location then there is high likelihood of the actual smog conditions experienced at a specific location in the city so subscribers of this data with certain health conditions could potentially avoid such areas in real-time.
The sustainability will depend on the incentives which gig workers receive periodically for their participation in the application ecosystem . Hence with upskilling on how to capture specific parameters or data in-demand at various points in the city the gig workers could potentially offer more value and earn additional income.
3. Mitigating food insecurity
Zero hunger is 2nd of the UN sustainability development goals. In urban areas the contrast between deprivation and excess can be easily observed. It’s estimated 1 in 9 people face chronic food deprivation. At the same time as per UN reports nearly half of all fruit & vegetables produced globally are wasted each year and roughly one-third of the food produced in the world for human consumption every year gets lost or wasted. Food insecurity is becoming a major problem for the local governance.
The problem stems from lack of reliable real-time data about the needy in the neighborhood and connecting them to the donors who are willing to immediately help them connect to a grocery store or restaurant nearby where perishable food like fruits and vegetables are about to be wasted or near expiry. However connecting all parties is tricky and currently not sustainable.
Not all grocery store or restaurant personnel are able to make the decision about deciding at what point before expiry they are willing to sell for discounted a price. It’s a difficult decision as it impacts the profitability and sustainability of the store or restaurant owners. But if they do make that decision, it will in real-time and tends to be towards a limited audience. If an application ecosystem is designed so that it supports this decision and helps the store / restaurant owners connect in time with the donors who are willing to sponsor the food delivery to the needy, the problem of food security could be somewhat mitigated in urban areas.
Here delivery gig workers could play a vital role in connecting the needy and the store / restaurant personnel via a suitable application ecosystem. If a reliable mechanism through which the needy could be identified in near real-time in the neighborhood, same could be informed to the donors and restaurants /store personnel.
Sometimes the communication with the store or restaurant personnel may need to be face to face ( based on past transactions). Also identification of needy may need a human touch. Here delivery gig workers could play a vital role in helping identify the needy in a verifiable manner and at the same time talk to restaurants / store personnel to understand the situation on the ground.
Hence by upskilling delivery gig workers could play a larger role in mitigating the problem of food insecurity in urban areas. To make this sustainable the local government bodies, willing donors, NGOs could support the delivery gig workers with the additional income for connecting the needy to the local stores or restaurants with a well designed application ecosystem.
Summary
Delivery gig workers have solved one of the major problem of last mile connectivity in urban areas. However there are still areas where delivery gig workers could play a major role by employing additional skills hence moving up the value chain.
A well engineered application ecosystem could provide a platform where such additional skills could help solve some of the unique problems plaguing the urban areas. E-waste management, maintaining local data points and effective communication to help mitigate urban food insecurity are some of the ideas which could help delivery gig workers move up the value chain by upskilling.
