Process automation, machine learning, and IoT (Internet of Things) are playing a larger role in device lifecycle management, electronics recycling, and enterprise device trade-ins. Today, HYLA Mobile (a technology company focused on maximizing the residual value of mobile devices through optimized re-use working with carriers, retailers, OEMs and other channels) continues to evaluate the ways emerging technologies and next generation solutions can support efficiencies, reduce manual labor, improve accuracy levels, and build intelligence into the company’s overall operations.
There are 4 primary areas where IoT and machine learning are playing a major role in supporting HYLA’s efforts.
1. Machine vision for cosmetics evaluation of devices
2. Big data analytics, garnering intelligence through IoT
3. Process & Operations Automation
4. Automatic Decision-making
Specific to machine vision, the industry currently utilizes somewhat of a manual approach for cosmetic evaluation of devices after they are traded-in or recycled. Once they get to our warehouse, the goal is to shorten the time it is tested, evaluated, and decisions are made to whether it gets prepared to be sold back into the market or it is recycled for parts. Once HYLA receives the devices, we can automatically test for functionality, remove all personal data and grade them, but also need to have a cosmetic evaluation of the device. The cosmetic evaluation of the device is somewhat subjective and we have a grading process to determine its overall condition, after the technician visually looks at the front and back of the devices (looking for scratches, cracks, dents). HYLA process millions of devices annually, and this is where machine vision plays a vital role. Due to the sheer number of devices coming into our warehouse, we seek to eliminate subjectivity and manual errors as much as possible through process automation. The risk of devices being evaluated incorrectly could mean loss of revenue or loss of reputation and client dis-satisfaction.
" There is an increasing awareness that there is a potential to monetize the capability, but we must continue to communicate this to corporations, CIOs, and other IT managers "
HYLA technicians also leverage internal and supply partner databases of devices to properly make intelligent decisions about what happens to the device after evaluation. Currently this is a partially automation process as the tech has visual review of the front and back. Down the road, robotics could be used to improve the process but that is not an option as this time due to timing, investment, and available technology.
The other industry factor is insurance and warranty of devices. Consumers and businesses generally invest upfront at the time of the initial purchase to insure their devices. Customers generally have to go the store location or mail their devices for repair and insurance claims, which can be cumbersome and leave customers without devices. In the context of automation and artificial intelligence (AI), HYLA offers a full suite of remote diagnostics, and can walk customers through tests and easy prompt screens for testing, warranty, and insurance review. This removes the need to go to a brick and mortar store and be without the device if unnecessary. This self-diagnostic solution can be used in many industries where electronics need to be tested, evaluated, and a decision has to be made, especially in a network of unmanned sensors in the IoT world, where decision criteria relies heavily on the functioning and accuracy of these sensors. Risk calculation and decision-databases are now being used in many industries to remove complexity and risk, as well as offer big operational efficiencies.
Another area is big data analytics and automated decision making. HYLA uses a “policy engine” to make decisions on the residual value of the device, what to do with the device next (sell or recycle), and other intelligence data. This same data can be used by our customers to help their sales and customer service teams make instant decisions on customer retention and automate satisfaction efforts. We use IoT process improvement to reduce the time metric and maximize the sale value of electronics and devices. Of course the IoT dream is when devices come in, we place them on a conveyor belt with a scanner, and the scanner automatically routes to recycling bin if older model or route to the area to process for sale. This is where machine to machine technology could greatly benefit the electronics recycling and even other processing industries.
We procure and use 350K+ points of data from all over the world and run analytics using this data combined with social sentiments, geo-political events, as well as new technology events to understand the going rate of devices, and we have to get it right the first time.
Now I want to shift to the areas of corporate responsibility and sustainability. As in our industry, outside of working with the wireless carriers directly, we also work with corporations. Large corporations are becoming very focused on reducing e-waste, implementing sustainability efforts, and tracking these efforts to promote green successes. CIOs and IT organization have the responsibility of the proper disposal and recycling of devices. Most of those are not monetizing the value of the devices and are missing a big opportunity to support IT budgets and find new resources for upgrades and IT purchases. Once the devices come back into the hands of the IT organization (or many sit in drawers) and are no longer in use, businesses could get up to $200 for the value of the devices and this should not be ignored as the values degrades with time. There is an increasing awareness that there is a potential to monetize the capability, but we must continue to communicate this to corporations, CIOs, and other IT managers. Both carriers and B2B channels are sometimes handling these transactions, and this should be a conversation as part of business’ overall mobility and workforce strategy. On a final note, there are a few recommendations I have to CIOs and other IT managers:
• Initiate corporate-wide buy in through marketing or sustainability departments
• Track your progress and green efforts (Repurposing X number of devices every year, this eliminated X number of lbs. of e-waste going into landfills and reduces pollution in X million gallons of ground water)
• Ensure data privacy (wiping), security, and removal as part of your recycling efforts
• Understand the economics, monetize or repurpose your idle and unused devices
• Leverage green taxes and credits to add credibility to the program.