In the previous article, we mentioned that last-mile delivery is one of the most challenging logistics problems in B2C e-commerce. Unlike traditional shopping, when users choose online shopping, they can easily compare different platforms. It is also very easy for them to switch shopping platforms when they want to. This has forced e-commerce companies to offer a higher level of service to retain customers. Besides, the small size of e-commerce orders makes us have to pay a high price in the last mile of delivery. The combination of circumstances made it necessary to find a better solution.
Perhaps you have often heard the term “sharing economy” recently. Among the companies that are using it, the most famous is Uber, a ride-hailing company, and Airbnb, a lodging specialist. They have been developing very rapidly in recent years, occupying a significant market share. Their success is due to the 21st-century cooperative consumption trend, which generates a sharing or platform economy (Chase, 2015). In a particular system, services or assets are usually free or charged sharing between private individuals and companies via the Internet. (Ossewaarde & Reijers, 2017).
It also has another way of saying: “crowdsourcing.”
Crowdsourcing is the practice of individuals, institutions, nonprofits, or companies sourcing tasks from a diverse group of individuals through flexible, public means, such as phone calls, software, websites, etc. The task that an individual may voluntarily undertake.
(Estellés-Arolas and González-Ladrón-de-Guevara 2012)
We can study the service requirements of online customers. First, they focus on how they interact with us. In terms of express delivery, this is reflected in whether the Courier meets their requirements of time, place and service attitude when delivering the goods. Also, they pay special attention to time-related performance indicators (Davarzani and Norrman 2015). Crowdsourcing logistics, as a new terminal logistics distribution mode, is an effective solution if it can meet these requirements. This could turn out to be a promising option. (Wang et al. 2016).
The last-mile delivery process includes outsourcing the delivery of goods to “ordinary people” so that they can pick up packages from a collection point, usually a collection point, warehouse or store brought to a delivery point. When the number of people enough can naturally meet the customer for the delivery time and location of all requirements. At the same time, assigning the right people quickly can also improve time.
From the people providing these services, crowdsourced logistics can make money from tasks that do not require much effort because they usually have to move on the same route for personal or work reasons (Asdecker, 2020).
Uber integrates private car resources in the market to help us solve travel problems. In contrast, crowdsourcing logistics integrates idle “human resources” outside of work in the market and based on the Internet platform. It will allocate distribution work to full-time delivery personnel Subcontracting to non-professional groups outside the enterprise.
The difference between traditional couriers and crowdsourcing couriers is that the delivery information is sent from the logistics company to grab the order from the mobile APP directly. Crowdsourcing is still a decentralized model that accelerates the development of the industrial chain. As the bottom-level personnel’s distribution personnel through “part-time” crowdsourcing logistics, they will have the opportunity to double their total monthly income.
Amazon has already started to use this model. Download Amazon Flex and you can receive orders assigned to you by Amazon, and you will get a lot of commission for completing them. The existence of Amazon Flex proves the interest of practitioners (Arslan, 2018).
Here are some introduction videos of users:
In this mode, we can find the following advantages:
The deployment of personnel is extremely free
Using crowdsourcing express is equivalent to having an infinite number of “employees” and are fully deployed on demand. This means that no matter the peak period or the trough period, the number of employees will always be the right number. The staff cost of each package is fixed and low at all times. Of course, during peak periods, we can increase commissions to get more personnel.
Freedom of time and place
Because we have “massive employees”, theoretically, customers can send an order request for delivery at any time, such as “delivery from 10 am to 12 am on Saturday”. Customers can also propose other customized services, which are all possible. For example, the customer may be busy, but he can pick up the goods on the way home from getting off work. Then the person accepting the order only needs to put the courier at home and wait.
Environmental protection and benefits brought by the sharing economy
Establish customer networks and local communities to make more effective use of existing capabilities, reduce the acquisition and maintenance costs of expensive investments and transportation costs themselves, thus minimizing the environment’s negative impact.
At the same time, this mode can also bring the following benefits to the deliverers:
1. A new level of customer engagement
2. Additional earning opportunities
3. Reduce transportation costs
4. Flexible services and job opportunities.
(Mladenow, 2015)
Reduce costs
Although the cost of logistics will increase at certain times after the introduction of crowdsourcing, such as delivery within 2 hours. But in Seghezzi’s research report, we can also find that “crowdsourcing” express delivery costs are lower than normal express delivery in some cases (Seghezzi, 2020).
In Devari’s research, it was found that the cost savings due to the use of crowdsourcing to reduce the number of trucks was considerable. (Devari, 2017)
Business expansion
When we introduce crowdsourcing express delivery, we will find that it has brought us new company development opportunities due to its advantages. Let us provide better service quality while expanding business types. This is a bit similar to uber’s current development model, such as uber food delivery and so on.
I will discuss this part in the next article
Of course, besides the advantages, he also has some problems that we need to solve:
Courier safety & standard management
It used to be difficult to manage non-employed employees to work. Due to the advancement of information and communication technologies (ICT), we can already carry out people’s specific status through mobile phones. We can accurately know each courier’s location and current progress and give appropriate instructions at the right time (Chatzimilioudis, 2012). Implementing the network supervision mechanism also allows us to authenticate users with real names just by talking to the mobile phone, which has improved the management standard and the safety of express delivery. And very fortunately, many companies that use the sharing economy are already exploring solutions to these problems and have achieved results. We can learn from their experience when we start this model.
Reasonable distribution route
Due to advances in artificial intelligence, many fields have begun to deal with more complex and practical problems. On the issue of package crowdsourcing delivery, in the early years, the algorithm contributed a lot of research in complex and practical situations such as multiple delivery vehicles, on-time pick up and delivery, minimal fuel consumption, and maximum profit (Mladenow, Bauer, & Strauss, 2015). Later, some learning-based logistics planning and scheduling (LLPS) algorithms were proposed. The algorithm controls the permission of order requests and dispatches the routes of multiple vehicles. (Kang, 2019). The current research adds time and space factors, which help crowdsource and assign couriers in time and space to efficiently deliver daily orders and improve last-mile logistics’ service quality (Huang, 2021).
Increase customer willingness to participate
In Guo’s study, the results show that participants’ income expectation is positively correlated with their willingness to participate. Therefore, to attract more social groups to participate in crowdsourcing logistics, it is necessary to design a set of operating rules to enable participants to obtain sufficient benefits. Theoretically, the higher the interest, the more participants can live.
Meanwhile, there is a significant positive correlation between social influence and users’ willingness to participate. Therefore, if we can make “participating in crowdsourcing logistics” a more positive activity in the minds of the public, then their willingness to participate will increase. For entrepreneurs, that means putting a lot of effort into pitching an idea. It’s not hard to see crowdsourcing companies doing something similar. (Guo, 2019)
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In addition to improving the service experience, crowdsourcing express delivery may bring more business development opportunities in the future. In the next article, we will look forward to what breakthroughs we can make after successfully applying crowdsourcing logistics.
Continue reading: After successfully applying crowdsourcing logistics, what opportunities and prospects the company may have?
Reference:
Seghezzi, A., Mangiaracina, R., Tumino, A., & Perego, A. (2020). ‘Pony express’ crowdsourcing logistics for last-mile delivery in B2C e-commerce: an economic analysis. International Journal of Logistics Research and Applications, 1-17.
Arslan, A. M., Agatz, N., Kroon, L., & Zuidwijk, R. (2019). Crowdsourced delivery—A dynamic pickup and delivery problem with ad hoc drivers. Transportation Science, 53(1), 222-235.
Chase, R. (2015). Peers Inc: how people and platforms are inventing the collaborative economy and reinventing capitalism. PublicAffairs.
Ossewaarde, M., & Reijers, W. (2017). The illusion of the digital commons:‘False consciousness’ in online alternative economies. Organization, 24(5), 609-628.
Davarzani, H., & Norrman, A. (2015). Toward a relevant agenda for warehousing research: literature review and practitioners’ input. Logistics Research, 8(1), 1-18.
Estellés-Arolas, E., & González-Ladrón-de-Guevara, F. (2012). Towards an integrated crowdsourcing definition. Journal of Information science, 38(2), 189-200.
Kang, Y., Lee, S., & Do Chung, B. (2019). Learning-based logistics planning and scheduling for crowdsourced parcel delivery. Computers & Industrial Engineering, 132, 271-279.
Mladenow, A., Bauer, C., & Strauss, C. (2015, December). Crowdsourcing in logistics: concepts and applications using the social crowd. In Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services (pp. 1-8).
Anwar, S. T. (2018). Growing global in the sharing economy: Lessons from Uber and Airbnb. Global Business and Organizational Excellence, 37(6), 59-68.
Asdecker, B., & Zirkelbach, F. (2020). What drives the drivers? a qualitative perspective on what motivates the crowd delivery workforce.
Rodrigue, J. P. (2020). The distribution network of Amazon and the footprint of freight digitalization. Journal of transport geography, 88, 102825.
Huang, B., Zhu, H., Liu, D., Wu, N., Qiao, Y., & Jiang, Q. (2021). Solving Last-Mile Logistics Problem in Spatiotemporal Crowdsourcing via Role Awareness With Adaptive Clustering. IEEE Transactions on Computational Social Systems.
Guo, J., Wang, J., & Yan, Z. (2019, January). Motivation and factors effecting the participation behavior in the urban crowdsourcing logistics: evidence from China. In Proceedings of the 10th International Conference on E-Education, E-Business, E-Management and E-Learning (pp. 334-341).
Chatzimilioudis, G., Konstantinidis, A., Laoudias, C., and Zeinalipour-Yazti, D. (2012). Crowdsourcing with smartphones. Internet Computing, IEEE, 16(5), 36-44.
Mladenow, A., Bauer, C., & Strauss, C. (2015, December). Crowdsourcing in logistics: concepts and applications using the social crowd. In Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services (pp. 1-8).
Seghezzi, A., & Mangiaracina, R. (2021). Investigating multi-parcel crowdsourcing logistics for B2C e-commerce last-mile deliveries. International Journal of Logistics Research and Applications, 1-18.
Devari, A., Nikolaev, A. G., & He, Q. (2017). Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers. Transportation Research Part E: Logistics and Transportation Review, 105, 105-122.
