Managing component obsolescence and second-source risk in an AI era
KEY TAKEAWAYS:
- AI demand is pulling lifecycle risk into the design phase
- Product teams should implement structured BoM analysis
- Proactive planning is the best defense against procurement disruptions
After a few relatively stable years a familiar nervousness is creeping back into the electronics industry. The combination of AI build‑out and a normal semiconductor cycle upswing is distorting demand, stretching lead‑times and long‑term lifecycle promises.
Engineers who thought they had put the worst of the pandemic shortages behind them are discovering that lifecycle and second‑source strategy have quietly become front‑rank design parameters, not optional extras bolted on by procurement at the end.
The shape of demand is different this time. Instead of broad‑based shortages, we are seeing intense spikes around specific device classes: accelerators, high‑bandwidth memory, advanced power stages and the infrastructure silicon that feeds AI data centers. Those products live on fast, cloud‑driven refresh cycles measured in a handful of years, while many industrial, medical and transport designs expect components to remain available and supportable for a decade or more. When those two worlds collide, the risk is simple: a part can go not‑recommended‑for‑new‑designs (NRND) or even end‑of‑life (EOL) before your product has finished its ramp.
More design reviews now include questions about lifecycle status, second‑source options and exit plans for sole‑sourced parts. From Avnet’s vantage point across many sectors and cycles, this is fundamentally a continuity‑of‑supply challenge. The good news is that there are practical ways to grade obsolescence risk at design time and to bake mitigation into the hardware, firmware and supply‑chain architecture from the start.
“Our customers’ #1 focus is continuity of supply, which is the value Avnet brings. We act as an extension of our customer’s business, improving their visibility into the supply chain and ensuring they have a healthy supply chain.”
—Alex Iuorio, senior vice president, Supplier Management and Business Development
Source: Supply Chain Connect
Obsolescence risk assessment for electronic components
The starting point is to accept that not all components carry the same lifecycle risk. Passive components, commodity logic and long‑life industrial microcontrollers typically behave very differently from AI accelerators or cutting‑edge memory. A simple framework that scores each bill of materials (BOM) line against a few key dimensions can reveal where attention is really needed.
The first dimension is technology maturity. Parts built on long‑established processes with well‑understood failure modes and broad application are generally more stable. Families used for automotive and industrial control, for example, often come with explicit longevity commitments. At the other end of the spectrum sit devices on the newest process nodes, or in novel packages, where suppliers are still feeling out the economics. These are more likely to be tweaked, replaced or rationalized as fabs are re‑tooled for higher‑margin designs.
Supplier posture is the second important factor. Some manufacturers are explicit about lifetime support, publishing longevity programs or “available for 10+ years” pledges for selected ranges. Others are closer to the consumer or mobile phone business, where product churn is accepted and device families may be superseded quickly. Looking at the supplier’s historic behavior around EOL notices, PCN (product change notification) discipline and industrial support tells you a lot about how they will behave when capacity is tight.
A third dimension is the component’s wider market demand profile. Parts that serve a wide variety of end markets with no single dominant segment are naturally more resilient. By contrast, a device whose primary volume comes from AI servers or premium smartphones will be managed to suit those customers first. When demand spikes, industrial and embedded users often find themselves on allocation or facing sharp lead‑time extensions.
Finally, engineers should pay attention to explicit lifecycle indicators available at the time of design. Many distributors and data‑platforms now tag parts as active, NRND or EOL and show predicted lifecycle curves derived from manufacturer data, shipment histories and PCN activity. That information is too valuable to ignore when committing to a new design.
What is BoM risk analysis?
A pragmatic way to use this framework is to score each BOM line across these dimensions and then classify the results into green, amber and red bands. Green parts are mature, broadly used, with supportive suppliers and clean lifecycle flags. Amber components are acceptable but deserve a second‑source plan and periodic review. Red parts are those whose loss would stop the product in its tracks, and which have no obvious replacement or present clear lifecycle warning signs. The table below illustrates how this might look.
Component lifecycle‑risk matrix
| Risk dimension | Green (low risk) | Amber (moderate risk) | Red (high risk) |
|---|---|---|---|
| Technology maturity | Long lived industrial MCU or logic on stable process | Newer low power node with limited field history | First generation AI accelerator or HBM memory on leading edge node |
| Supplier posture | Vendor with published longevity / industrial roadmap | Mixed consumer/industrial focus, patchy longevity statements | Supplier focused on fast moving consumer or cloud markets |
| Market demand profile | Commodity passives, generic regulators, broad usage | Mid speed memory shared with consumer designs | Device family primarily sold into AI servers or premium smartphones |
| Lifecycle indicators | Active status, no NRND/EOL flags, stable lead times | “Preferred alternative available” or mild lead time volatility | NRND/EOL notifications, shrinking stock, rapidly extending lead times |
Once each item has a risk color, the design team can make deliberate decisions. Green parts are essentially “fit and forget”. Amber devices require at least one documented second‑source or a clear redesign path. Red items demand an explicit sign‑off explaining why they are unavoidable and what the exit strategy will be if they move towards EOL sooner than expected.
Second‑source strategies for critical components
The most attractive route to risk reduction is a genuine pin‑compatible second source. Two or more suppliers share a footprint and pinout and keep functional behavior close enough that the system does not have to change when you swap between them. Within some analog families, regulators, logic and microcontrollers this is still realistic, although it takes work to verify.
Engineers should not accept the datasheet at face value. A meaningful second‑source evaluation compares not only the high‑level parameters but also the edge‑case behaviors that can cause grief later: power‑on reset timing, brown‑out thresholds, ADC linearity, EMC behavior and temperature margins, to name a few. Building a simple compliance matrix that lists each critical characteristic and highlights differences is good discipline. Once candidate alternates are identified, they should be qualified through bench testing and, ideally, at least limited field exposure in non‑critical builds so that surprises are flushed out early.
Where true pin‑compatibility is not available, it is still possible to design for functional compatibility. This usually means abstracting interfaces and allowing enough design margin that several different device families could serve the same role with minimal changes. At the board level, this might look like choosing standard communication buses such as I²C, SPI, Ethernet or PCIe rather than proprietary interfaces wherever possible. In power, it might mean modularizing the architecture so that a different vendor’s module could be dropped in without re‑engineering the entire system.
Firmware and software have a role to play, too. If the code that interfaces with a given device is cleanly layered and parameterized, migrating to a similar device from another supplier is far easier. Engineers can go further by pre‑qualifying at least one functionally equivalent part, even if it uses a different footprint, and by sketching what a board re‑spin would entail. On high‑value or safety‑critical programs, some organizations may plan a mid‑life design refresh as part of the product strategy, deliberately scheduling a board update that removes the highest‑risk devices before they become a threat.
Using supply‑chain data to predict EOL and shortages
Ten years ago, obsolescence management was driven mainly by formal notifications from manufacturers and by the experience of seasoned buyers. Today, there is far more data to work with. Distributors and specialist analytics providers combine shipment histories, pricing and lead‑time trends, PCN flows and public roadmap statements into dashboards that can act as an early‑warning system.
Design teams can take advantage of this right from the concept stage. Before committing to a new device, check not only that it is currently active but also how its lead‑time curve has been behaving and whether stock is consolidating into a few global hubs. A part that has suddenly moved from eight to twenty‑six weeks, or that shows repeated bouts of allocation, may be signaling either explosive demand from a strategic segment such as AI or a quiet run‑down ahead of EOL.
As Avnet has asserted previously, “Supply chain resilience demands more than assurance of supply.” Agility and visibility are also critical, enabled by techniques like scenario planning, value at risk calculations, deep tier supply chain mapping and alternate source identification. Working with a supply chain partner, OEMs can more quickly and cost‑effectively outmaneuver disruptions that threaten their business continuity. The same tools and disciplines are directly applicable to grading obsolescence risk and qualifying second sources before problems hit production.
That breadth of focus is echoed in Avnet’s commentary around the industry’s recent shortages, which stresses the need to combine stocking strategies with intelligent logistics and information sharing. As one article summarizes, the company is helping customers with “assurance of supply, the agility to move that supply around the world, and the visibility into the supply chain needed to make informed decisions.” For design and procurement teams wrestling with AI‑era uncertainty, those three themes of assurance, agility and visibility are an effective shorthand for what good lifecycle management should deliver.
Avnet supports lifecycle and obsolescence management with BOM risk assessment, EOL and product-change notifications, alternative-component identification, and supply-chain analytics. Our teams can also help plan mitigation actions such as last-time-buy, safety-stock and inventory strategies, using visibility tools and real-time data to reduce disruption risk.
Once a design is in production, ongoing monitoring becomes at least as important as the initial check. Automated alerts tied to your BoM can flag NRND/EOL status changes, new recommended replacements and unusual shifts in availability. This gives engineering and procurement time to act, qualifying a second source, scheduling a controlled redesign, or placing a last‑time‑buy order sized to cover realistic service and spares requirements. In some cases, where regulations permit, there may also be options to use authorized refurbishment or licensed remanufacture to extend support for critical legacy systems.
Design‑for‑lifecycle: checklist for AI‑driven markets
The practical message for design teams is that lifecycle and second‑source strategy can no longer be left to the tail end of a project. They belong alongside performance, safety and cost in the early architecture discussions. A short, structured exercise at concept and schematic freeze can pay for itself many times over.
A simple design‑for‑lifecycle checklist for an AI‑era project might ask:
- Have we identified any red‑risk components and either eliminated them or documented an exit plan?
- Do all amber‑risk devices have at least one pin‑compatible or functionally viable alternative, with a test plan in place?
- Have we captured supplier lifecycle commitments and set up automated alerts for changes?
- Are we prepared, organizationally, to act on those alerts with controlled redesigns or last‑time buys when necessary?
Seen in this light, managing obsolescence and second‑source risk is less about reacting to bad news and more about building resilience into the product from the outset. The AI wave will continue to pull the industry in new directions, and cycles of tightness and oversupply will remain a fact of life. Designs that treat lifecycle as a first‑class constraint, and that exploit the growing pool of data and services available through the supply chain, will be the ones that keep shipping smoothly while others scramble for parts.