How Artificial Intelligence Is Reshaping The Secondary Phone Market (Mobile Refurbishment)

The secondary market has grown globally with increasing trade, marking its impact on the global economies. The secondary market for cell phones in the United States alone had a market cap of $8.09 Billion and is expected to grow to $30 Billion by 2030. That is a growth rate of 12.9% CAGR [1]. With such an excellent growth rate, the industry surely will require systems and processes that cater to the scale that businesses would achieve while working towards fulfilling the market demand.

This catering would come in the form of automation solutions with artificial intelligence and machine learning capabilities. A combination of robotics, data analytics, and cloud computing would shape the way the secondary market functions in the coming years. Businesses in the secondary market would achieve economies of scale only when they can deliver large quantities of processed goods to the market to fulfill its growing demands.

Let us dive into how Artificial Intelligence is poised to take over the secondary market and help businesses grow.

Understanding the Challenges of the Secondary Market

The refurbishment market has seen promising growth in the recent years with the e-commerce boom. As sales of brand-new products have increased, so has the number of returns and involvement of the reverse logistics industry in the same.

The very core of existence of the secondary market is an array of cost-effective goods available at lower prices than what one would pay for brand new. As such, procuring used devices at low prices may not be enough for a business to thrive since there would be a limit to how highly a refurbished phone or an open-box item can be priced. This leads to the need for a refurbisher to reduce the processing costs if they are to maintain a healthy margin.

Another challenge for the secondary market comes with growth. As demand grows, so does the need to employ more resources and process devices in large quantities without hampering the accuracy and consistency of the grading results. Subjectivity due to human errors could cause a product to be priced inaccurately when listing on the market, leading to loss of revenue.

Simplification and customization of grading criteria marks another test for a business. As the market changes, so does consumer perception. What could be considered a good grade of a used device may not be same in the following months. As such, being able to personalize the grading criteria as per the target market becomes cumbersome, especially at a large scale.

Challenges such as the need to reliably predict demand and making appropriate preparations are also opportunities to employ rapidly growing technological improvements such as Artificial Intelligence and data analytics. With the power of AI, businesses and consumers can both benefit tremendously.

AI and Economies of Scale

A business that is growing will soon find itself needing more space to expand. As large quantities of devices of various makes and models come in, the need to process them accurately and with consistency becomes paramount to achieve economies of scale.

The limits of manual work are the starting point for machines doing the tasks, reducing costs of processing drastically at scale. With automation and the power of AI, these machines would learn the ways of the market, and adjust their functions accordingly to always offer accurate data, while even working 24/7 to keep processing devices.

Add to it the need to store devices and retrieve them quickly for dispatch when orders come through. Efficient storage and retrieval systems, which can remember the position of each device, while having similar devices sorted in advance would mean quick packing and dispatch into the market. Using AI to store a device with higher chance of sale in accessible places is a good solution. Combine it all with an innovative way to keep the processed devices and you have a winning combination of efficiency and scale.

AI and Process Personalization

Since every business works differently, everyone’s requirements and expectations from how their processing should happen would also differ. Considering that business would be operating at scale in the coming years, the ability to adjust operations quickly could be the differentiating factor between growth and loss of business.

Having a standard set of rules for grading devices and arriving at a disposition, especially when the consumer perception is subject to change over time may render the entire automated line obsolete in no time. Therefore, a system that can adjust its working and rules based upon analytical data along with quick, data driven decisions from the management could keep margins healthy while maintaining the quality of grading and costs low.

For example, should the market data show that a certain grade of phones requires a lower cost of repair, the business can spend fewer dollars behind every such device, leading to improved margins. With automation, the machines would simply adjust their processing and everything is good to go. This would happen based upon simplified decisions that are based upon market data processed using AI.

AI and Smart Lotting

One more aspect of process personalization, that may at times get ignored is that of bulk orders. Many businesses procure cell phones for a variety of reasons. And when commercial use of communication devices comes into the picture, artificial intelligence helps the supplier to lot their inventory accordingly. Smart lotting is a way to achieve order fulfilments for bulk orders. As discussed previously, efficient storage and retrieval systems can help in quickly accessing popular orders and dispatch them.

With help from AI-driven data analytics, market data and order fulfilment history of a supplier would help managers create lots in advance anticipating an order to come through. These lots could be of any size and comprise of any types of devices the business chooses. The software would help the managers in predicting the size and compositions of these lots to quickly fulfil large orders.

AI and Pricing Strategies:

Another example of an AI-powered solution to a business problem is auction management of cell phones that have been processed and marked as being good to be sold on the marketplace. Retailers operating in the secondary market place their orders with a warehouse that processes such devices.

Using the power of AI-based auction management, the retailer can now see which phones were sold in most quantities on the market, at their shop, and what price each phone fetched. This gives valuable understanding of the current conditions of secondary market to the retailer, who can then price their offerings accordingly to earn a healthy profit, while also giving their customers a phone that was graded objectively and without errors.

Dynamic pricing algorithms that adjust prices based on demand, inventory, and market trends is yet another useful tool in the digital toolbox of the re-seller of the 21st century. Using this tool, the seller can simply let the AI tell how to adjust pricing as per the market conditions, taking a huge load off their shoulders.

AI and Supply Chain Optimization:

Use of AI-powered software systems aids in improving not only the reliability of the test results along with improved operations, but its use is also well-suited to optimize the entire reverse supply chain.

These software systems can understand when the optimal time is to dispatch products into the market, helping rapid transportation at the right cost. Not only that, but AI enhances logistics efficiency by optimizing delivery routes and improving maintenance schedules. Your software would calculate the most efficient routes based on real-time traffic and weather data, reducing fuel consumption and delivery times. Predictive maintenance ensures vehicles and equipment remain operational, minimizing downtime.

AI and Sustainability and the Circular Economy:

As AI keeps improving, its roles in promoting sustainable business practices grows. Responsible business practices and regulation compliance are another set of fields where the AI empower businesses in the secondary market. From minimizing waste and reducing environmental impact to active contribution to a more sustainable future, AI-driven practices will surely help businesses align profitability with responsible environmental stewardship, benefiting both their bottom lines and the planet.

Here are three ways AI can help a business be sustainable:

1. Waste Reduction: By optimizing the refurbishing, recycling, and disposal of used and returned products, a warehouse can reduce waste and extend the life of products, contributing to a circular economy.
2. Efficient Recycling: AI assistance in the sorting and categorization of recyclable materials enhances recycling facility operations through automation of identification and separation of materials, ensuring effective reclamation of valuable components.
3. Sustainable Sourcing: AI helps businesses make informed decisions regarding sourcing and procurement, favoring sustainable materials and ethical practices, thus reducing the environmental impact of product manufacturing and refurbishment.

Conclusion:

The integration of artificial intelligence into responsible and efficient business operations is poised to redefine the dynamics of the secondary market in the forthcoming years. As markets and consumption expand, the imperative to implement streamlined business methodologies will correspondingly increase.

The future of a sustainable and efficient secondary market, catering to both consumers, businesses, and the environment, will be shaped by AI-driven personalized experiences, finely-tuned pricing, predictive data analysis, streamlined supply chain management, and responsible business practices.

Custom Market Insights. " U.S. Refurbished and Used Mobile Phones Market Size, Trends and Insights By Type (Refurbished phones, Used phones), By Price point (Low-priced, Mid-priced, Premium), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2023–2032." Accessed September 26, 2023. [https://www.custommarketinsights.com/report/u-s-refurbished-and-used-mobile-phones-market/].