Device Circularity Needs Data Collaboration

In recent years, the transition from a linear relationship with materials to a circular economy—where products stay in use to their highest utility for as long as possible—has become imperative across all industries and sectors. This is driven by the need to manage the increasing risks of environmental degradation and resource depletion—specifically rare earth minerals critical in the manufacturing connected devices.

Despite this increased awareness, thousands of companies across the connected device supply chain have yet to capture the extraordinary business value that is achievable through a circular business model. With a circularity-based framework, great potential exists to achieve top-line and bottom-line growth, as value is derived all along the device lifecycle—from design and manufacturing, to deployment and management, and eventually onward to reverse logistics and intelligent disposition. Ultimately, it enables more connected devices to remain in circulation longer, extracting the maximum value from deployed assets, reducing carbon footprint and, in turn, preventing e-waste.

A key barrier to achieving circularity is the reluctance of players to manage and share with each other distinctive data generated by their individual device processes and programs, both inter and intra- company. What’s needed is data maturity within an organization and data maturity across the circular ecosystem.

The Role of Data Maturity in Achieving Circularity

The current approach to creating, using, and disposing of connected devices is overwhelmingly linear. As a result, value is lost, and waste is created—even when physical recycling is utilized to its fullest potential. This waste goes beyond the tangible and takes the form of carbon emissions, delays and inefficiencies, unrealized revenue, and increased costs.

At the heart of these problems is one or more issues related to data. Organizations within the connected device industry have an opportunity to achieve circularity and address these data issues by understanding their capabilities, strengths, and weaknesses along a data maturity spectrum.

At the preliminary stage (Figure 1) of data maturity, most of an organization’s data is unstructured, making it hard to aggregate and use, especially since data is tracked and shared primarily through spreadsheets and email attachments. Data is typically incomplete, lacks sufficient granularity, or is disparate and exists in siloed systems, departments, or teams. Often, no documented data strategy is in place, so data is stranded across the organization, preventing a holistic view. Limited data sharing prevents enhancing processes or improving how work gets done. The status quo remains intact, preventing granular, device-specific inquiries such as, “What should I do with this device right now, to maximize its value and minimize waste?”

As an organization progresses through improving stages of data maturity, internal data becomes more structured and accessible. Data sets are normalized for defined use cases. Product hierarchy and classifications are established to assist with data parsing and aggregating. External data is used to inform processing and workflow decisions. While data sharing is primarily internal at this stage, increased visibility and cooperation across internal teams and departments demonstrate the value of internal collaboration. For example, benchmarking to create an annual sustainability or ESG report is improved, but the creation of realistic, informed goals is limited. Integrations across internal systems exist, but achieving “one source of device truth” remains challenging because each team interprets and utilizes data differently.

As organizations advance through later stages in their data maturity, the first movers emerge, evidenced by the minimization of assumptions and the increase in new insights gained. Here, external data from third-party partners is being used more prominently. Legacy device data is enriched and enhanced with external sources to create a more holistic view of device destination alternatives, enabling a second or third life. The layering of data sets creates opportunities for deeper intelligence. Data governance is shared across teams. Preliminary instances of artificial intelligence (AI) are in place. At this stage, an organization has leveraged data to identify and pursue adjustments that improve the circularity of their product or process within their control, but system-level constraints persist and limit broader adoption.

Reaching this level of data maturity is significant and has many operational benefits that enable improved circularity, but it falls short of achieving true industry-wide circularity. Collaboration with other players in the connected device ecosystem is the critical maturity step because the strategic sharing of data between players increases transparency, facilitates trust, and enables a wider perspective that inspires system-level problem-solving with far-reaching benefits.

Activating the Vision

To illustrate this point, imagine that every connected device has the potential for a second life, provided it still performs and operates as expected after its first life. Market research demonstrates that some device models are more desirable than others in the secondary market. In the early stages of data maturity, a device reseller* may bundle together all device models into auction lots of high-end and low-end models, and the low-end models may be sold two to three more times before they find a second end user. Under-utilizing the market research data produces inefficiency in both financial and carbon terms for the seller.

In the middle stages of data maturity, a device reseller utilizes this external market data to justify improved grading and sorting processes, which facilitates the disposition of highly desirable models to buyers willing to pay more for top-tier models and the redirection of less desirable models to alternate, more suitable second owners. This approach not only improves processing efficiency and revenue for the seller, but it also reduces wasted emissions from excess transportation, improves transparency for buyers, and maximizes the useful life of all the devices.

Now consider a player within the connected device industry who is reselling devices and has a more holistic view of the available options to answer the question, “What should I do with this device, right now, to maximize its value and minimize waste?” Let’s say this device reseller utilized internal processing data to identify a significant quantity of devices with low value based on secondary market research revealing that refurbishment is cost-prohibitive but that, in the device’s current state, it can retain 12-18 months of additional lifespan for specific use cases, as determined by a proprietary AI-enabled predictive model. They then leverage this data by matching the specifications of devices to the needs of a partner who specializes in facilitating the distribution of secondary devices—via API through their source-of-device truth—to nonprofit organizations within a limited radius of where the devices were recently processed. As a result, devices that likely would have become scrap for physical recycling are instead routed directly to temporary migrant workers in the next state over to enable personal financial transactions, assist with language learning skills, or provide educational access to elementary school children. The positive impacts realized here contribute to the triple bottom line of people, planet, and profit while improving the circularity of the entire system.

The example above illustrates how collaboration focused on strategic data sharing expands the possible answers to the question of, “What should I do with this device, right now?” from limited to expansive. Communication between partners around the connected device lifecycle not only increases access to available data but also enables the identification of an overlap between demand and supply and surfaces mutually beneficial collaborations.

At this most mature phase of data sophistication, new relational infrastructure is built to support a broader perspective that benefits all players and drives industry-wide improvements with measurable environmental and social impact. Shared device data across ecosystem players prevents greenwashing and raises confidence in sustainability metrics and reporting. Data sharing and collaboration with Original Equipment Manufacturers (OEMs) increases trust throughout the entire device supply chain, resulting in devices being designed for durability and longevity, moving quickly and efficiently between the stages of the device lifecycle, and high percentages of devices being recovered at the end of their useful life for repair and/or reuse in subsequent lifecycles.

Start Small or Start Big, Just Start

Companies are already making data-driven decisions today, of course, but greater power comes from using data in conjunction with robotics, AI models, and other technologies to automate decisions and create scenarios for consideration. Applying a data maturity model to players in the connected device lifecycle allows for improved decision-making, efficiency, and transparency that enables connected device circularity through the physical design of devices and the design of the processes involved in the motions connected devices make throughout their lifetime.

By embedding data within the automation solutions, a platform like Apkudo’s can be the common thread where data can be shared and utilized without players having to share it directly with each other. A managed connected device supply chain platform serves as a bridge that brings all the parties and their data together in a secure and mutually beneficial way.

Each player’s willingness to share data is crucial for receiving high-quality data within the circular supply chain. Sharing data benefits all players operating in a connected device lifecycle and allows everyone to contribute to circularity.

To share data, companies need to know the current quality and depth of their data; these insights will inform them of what steps they should take to improve that maturity. Additionally, due to the essential nature of data management and strategy in the circular supply chain, companies must take action before they fall behind. You can easily get started by conducting an assessment to understand your current status along the data maturity spectrum. You might be surprised and find that your company is in a good position, but you might also realize there’s room for improvement in a particular area. Either way, now is the time to take your first steps toward data maturity and better collaboration across the connected device lifecycle.

Circularity in the connected device industry is not achievable alone; collaboration amongst the players is critical for leveraging the insights gained by a bigger-picture perspective that enables efficiency, increased revenues, and reduced waste. The positive impact for people, planet, and profit makes this a vision worth pursuing by all.