After successfully applying crowdsourcing logistics, what opportunities and prospects the company may have?

In the previous article, we discussed the benefits of crowdsourcing logistics. Based on these benefits, enterprises may also usher in a series of opportunities. Today we’re going to do some thinking about the future. Hope to bring you new inspiration.

The realization of these possibilities is also a very interesting topic, which can be further studied in the future.

Performance improvement

Improved quality of logistics services will improve the performance of online retailers (Rao, 2014).

Besides, the reduction in order fulfilment cycle time will enhance customer recommendation behaviour, which leads to improved corporate performance (Griffis, 2012).

Delivery in remote mountainous areas

When only a few people in an area use the courier service, assigning a courier to them individually is very expensive. Using the crowdsourcing logistics model, people in this area can take orders and deliver by themselves. Even if there is only one order, they can take it by the way and earn commissions.

City delivery within 2 hours

In the past, if a courier was hired to complete the work, it was difficult to meet such order requirements. Because the order time is not fixed, the location is not fixed, which means that the courier company must prepare enough couriers to meet such demand. That requires a very high cost. In crowdsourcing mode, it can be completed efficiently and at a low cost.

B2B logistics distribution

When we complete the crowdsourcing logistics, it is relatively easy to extend it to B2B delivery. Their model is the same, and the only difference is that the person who takes the order may need to have a larger transport vehicle.

Fresh food delivery

In the past, it was difficult to use logistics and distribution due to the extremely perishable quality of fresh products. But after the introduction of crowdsourcing, when enough people participate, we can carry out express delivery in the urban area at any time.

Pickup/delivery at a fixed time

Similarly, based on a large number of participants, we can provide customers with more detailed services—even some more special private customizations. Customers can place orders freely, and couriers can take orders according to their circumstances.

Serving multiple logistics companies

The first article mentioned cooperative urban logistics, a courier company that focuses on last-mile delivery. The problem that more difficult to solve is parcel delivery’s priority ranking when serving multiple companies. The use of crowdsourcing will solve because, for those involved in crowdsourcing delivery, any company’s express delivery is the same. Compared with the traditional model, it is easier to gain the trust of the express company.

Drone delivery

As the current hot spot, drone delivery may replace manual delivery in the future. For this kind of change, will the realization of crowdsourcing logistics waste energy? In my opinion, it is also beneficial because drone delivery is a crowdsourcing participant with higher trust and stronger capabilities. The delivery routes and distribution principles we designed for crowdsourcing can all be used.

Conclusion

Crowdsourcing logistics has received some recognition from the market, and many companies have opened related businesses. In the past, there were two motivations for the Internalization of express companies. The first is the cost composition of express companies and the demands of reducing costs and improving efficiency, prompting enterprises to spontaneously Internet; the second is the various pain points faced by users when using express services, and the multiple causes an innovative attempt at the Internetization of express delivery services promoted by organizations outside the express delivery industry. From this point of view, crowdsourcing logistics is an attempt and revolution of terminal distribution based on mobile Internet and big data system. Crowdsourcing logistics mode can be promoted quickly because it is conducive to integrating social resources, reducing logistics and distribution costs, improving logistics and distribution efficiency, and improving serviceability.

But in addition to these benefits, the operation of the crowdsourcing logistics model requires strong big data computing capabilities and risk control capabilities. Big data computing is needed to provide strong support for crowdsourced logistics, and provide instant and accurate positioning information, the most reasonable distribution route information and the most real-time road condition information for crowdsourced logistics. Without the support of big data, crowdsourcing logistics will not be able to take advantage of the efficiency of distribution. Not only is it a little absent but it creates a lot of problems.

On the other hand, crowdsourcing logistics couriers’ qualifications are not complete, and there are some hidden risks. Generally, you only need a smartphone, and you are over 18 years old to sign up. In this regard, there is currently no relatively unified and reasonable risk management model and system, risk control methods are not perfect, and risk control capabilities are limited.

As an innovative application and attempt of Internet-based distribution, crowdsourcing logistics has now been recognized by capital. Now, crowd-sourced logistics needs more support from relevant government policies, and at the same time, it needs to improve its ability of big data calculation and risk control. When the crowdsourcing logistics model solves the inherent problems, it is believed that the social recognition of the crowdsourcing logistics model will be greatly improved, and the speed of development and promotion will also be greatly accelerated.

Reference:

Rao, S., Rabinovich, E., & Raju, D. (2014). The role of physical distribution services as determinants of product returns in Internet retailing. Journal of Operations Management32(6), 295-312.

Griffis, S. E., Rao, S., Goldsby, T. J., & Niranjan, T. T. (2012). The customer consequences of returns in online retailing: An empirical analysis. Journal of Operations Management30(4), 282-294.

css.php