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Tuesday 21 July 2026
Electronics Supply Chain Outlook: Where H2 2026 Momentum Is Heading
Six months into 2026, the story isn't whether the electronics supply chain has stabilized. It has, broadly. The real story is that it has split into two distinct markets moving in opposite directions, and most procurement teams are still planning as if there's just one. The AI-driven leading edge remains capacity-constrained and pricing-positive, while the mature-node segment is loosening into a genuine buyer's market. Knowing which side of that line each line item on your BOM sits on is the single most useful thing you can do heading into the second half of the year.The Two-Speed Market, BrieflyTSMC is reportedly preparing to raise prices 3–10% on its sub-5nm offerings, and with Nvidia and Apple having already locked in large blocks of capacity through year-end, second-tier buyers are increasingly competing for allocation that may not exist in H2. Memory tells a similar story. Combined output from Samsung, SK Hynix, and Micron is expected to grow sharply by 2030, led by a projected rise in HBM production, but that new capacity doesn't meaningfully arrive until 2027. In the meantime, DRAM and HBM remain the tightest categories in the entire component ecosystem.Mature-node wafer pricing has actually returned to pre-pandemic levels, down 5–8% year-over-year, as Chinese fab capacity comes online and automotive/industrial utilization climbs into the 80–85% range. Consumer electronics demand is stabilizing too, which is easing pressure on mature-node semiconductors and passives.What This Actually Means for Your Procurement StrategyThis is where most H2 outlooks stop short. Here's what to actually do with this picture, category by category.1. Segment your BOM by risk profile, not just by part number.Advanced logic tied to AI-adjacent applications faces a fundamentally different supply reality than commodity discrete or mature-node passives. Treat these as two separate procurement strategies, not one blended approach. A BOM review that groups parts by constrained, stable, or loosening rather than by function or supplier will surface where your actual exposure sits, and it's often not where teams assume.2. Use the mature-node buyer's market now, not later.If you were forced into single-sourcing during the 2021–2023 shortage years, H2 2026 is the window to qualify second sources for those mature-node components while pricing and availability both favor you. This window won't stay open indefinitely. As automotive and industrial utilization keeps climbing toward capacity, the leverage shifts back to suppliers.3. Be strategic, not reactive, on memory buys.Memory is not one uniform story. Leading-edge densities tied to HBM demand carry a real price premium, but previous-generation DDR4 and LPDDR4 may still offer value while pricing is structurally supported mainly at the high end. If your designs can tolerate a prior-generation module, this is the year to lock it in rather than wait and hope for relief that isn't coming until 2027.4. Watch for new procurement categories forming in real time.Optical networking components for AI data centres are moving from niche to mainstream as AI clusters push toward much higher bandwidth per rack. If your roadmap touches high-bandwidth AI infrastructure at all, get ahead of this now. Procurement categories that don't exist yet in your sourcing playbook have a way of becoming urgent overnight once a design win locks them in.5. Rebuild safety stock around true risk, not blanket buffers.Broadly increasing inventory across the board is expensive and imprecise. The more effective move is targeting buffers at the small percentage of components, often a single connector, capacitor, or legacy memory module, that actually drive downtime risk if they disappear. Combine that with forecasting discipline: suppliers increasingly prioritize allocation and pricing based on how credible and consistent your rolling forecasts are, which means forecast accuracy is now a negotiating asset, not just a planning exercise.6. Don't sleep on non-obvious demand drivers.Rising defense spending across Asia-Pacific is quietly adding a new, non-cyclical source of demand for industrial and mature-node electronics, one that doesn't show up in most consumer-electronics-driven forecasts. If your end markets touch defense, aerospace, or industrial automation, factor this in as upside demand pressure, not background noise.The Bottom Line for the Rest of 2026H2 2026 doesn't call for a single supply chain strategy. It calls for two, running in parallel. Where you're constrained, the priorities are allocation planning, forecast credibility, and locking in what capacity you can. Where you're not, the priority is using the current leverage to diversify, qualify alternates, and rebuild resilience before the window closes. Teams that treat this as one undifferentiated “stabilizing market” will miss both opportunities.If you're navigating either side of this, securing allocation on constrained parts or taking advantage of loosening mature-node availability, that's exactly the kind of sourcing challenge our team works through with customers every day. Reach out to your Fusion Worldwide representative and let's talk through your BOM.(Article Sponsored by Howard Tan, Director of Purchasing, China Fusion Worldwide)
Tuesday 21 July 2026
Taiwan launches AI competition to tackle marine debris with 20,000-image dataset
Marine debris management is entering a new phase of data-driven applications. Converting years of accumulated coastal imagery into actionable tools for surveying, identification, and monitoring has emerged as a critical challenge in the digitalization of ocean governance.Under the guidance of Taiwan's Ocean Affairs Council (OAC), the National Academy of Marine Research (NAMR) is hosting the "2026 International Marine Debris Image Recognition AI Challenge." Featuring a dataset of over 20,000 real-world marine debris images, the competition invites AI, data science, computer vision, and marine science teams from Taiwan and abroad to participate.The competition is supported by Amazon Web Services (AWS) as the AI technology partner, with model evaluation and competition operations managed through the Industrial Technology Research Institute's (ITRI) AIdea AI Co-Creation Platform. Registration is now open.NAMR sets the challenge: bringing AI to the frontlines of ocean governanceMarine debris has long been a fundamental issue in coastal environmental governance - and one of the most difficult to address in the field. Coastal debris is diverse in type, scattered in distribution, and frequently degraded by sun exposure, seawater erosion, sand burial, and physical damage, making manual surveys and image interpretation highly labor- and time-intensive.To accelerate digital transformation, NAMR has established MDImageNet, an AI-Ready marine debris image dataset covering the ICC19+1, NAMR26+1, and NAMR33+1 marine debris category schemes. The competition draws on NAMR's existing marine debris image dataset, comprising over 20,000 images annotated with YOLO-format bounding boxes. The dataset covers common coastal waste categories including plastic litter, fishing-related debris, and other anthropogenic waste.A "post-mapping" strategy is adopted: participants first train models using the original class labels provided, then map predictions into 20 official recognition categories during inference, with final scoring based on 19+1 primary marine debris target classes.The competition design confronts teams with the real-world constraints of field data - cluttered backgrounds, diverse object classes, and significant appearance variations. By requiring quantitatively evaluated object detection models, the challenge goes beyond open data sharing: it validates whether AI can be transformed into deployable tools built upon existing survey infrastructure.The competition comprises preliminary and final rounds, evaluated primarily on mean Average Precision (mAP) at an IoU threshold of 0.5. A "Best Lightweight Optimization Award" is also offered, using NetScore to balance detection accuracy, model size, and computational complexity-encouraging models suitable for practical deployment in coastal patrols, UAV-based image analysis, and long-term environmental monitoring.AWS cloud resources and AIdea platform power hands-on AI developmentThe competition integrates resources from AWS and ITRI's AIdea AI Co-Creation Platform to support teams throughout model development, training, testing, and evaluation. AWS provides the cloud development environment, offering Amazon SageMaker AI and computing resources for machine learning workflows. Technical workshops are also scheduled to familiarize participants with cloud-based AI development tools and model training pipelines.ITRI's AIdea Platform handles competition execution, dataset support, and automated scoring. The standardized cloud environment and evaluation framework ensures all teams operate under identical conditions, maintaining fairness and reproducibility.The competition is open to high school students, college students, and professionals, with teams of two to five members. Cross-institutional and interdisciplinary collaboration is encouraged, combining expertise in AI, computer vision, data science, and marine science to produce evaluated model outcomes for marine debris recognition.Registration for the "2026 International Marine Debris Image Recognition AI Challenge" is open until August 10. The total prize pool is NT$300,000, with two additional Best Lightweight Optimization Awards.NAMR aims to bring together the AI community, research institutions, academia, and industry to convert marine debris imagery into deployable environmental monitoring models-building Taiwan's AI application experience and marine data foundation for ocean governance.For competition details and registration, visit the official website (link).
Tuesday 21 July 2026
Eko Agentic: AI-driven data analytics for optimising retail performance
Artificial intelligence is just an interesting theoretical problem for many scientists and engineers, but it is at its most useful when it directly responds to the needs of its users.That's why Amity Solutions, a component company of Thailand-based Amity Group, developed Eko Agentic, a data analyst for store managers. They were long-standing consultants to one of the biggest retail chains active in Thailand and Malaysia with thousands of stores in the region. Executives at this retailer told Amity Solutions that they had tried to use various AI tools to improve the efficiency of their store management, but that these tools were not adequate for their needs.Store managers, stock replenishers, and other frontline workers on the store floor have to handle numerous disconnected tasks on a daily basis. They might use dashboards to monitor various store performance metrics, but synthesising the disparate information into business decisions can be complicated, with store managers resorting to past experience and guesses. Inexperienced store managers, in particular, might be unable to respond effectively to new situations or to best implement requests from headquarters.What if AI could take over the data analysis from store managers? Amity Solutions developed Eko Agentic to do just this: It is trained with data on how the top-performing store managers across the retailer's large network would respond to various business situations, and then rolled out across other stores, taking into account each store’s particular characteristics. The goal is to reduce extra, unsellable stock; to avoid empty shelves; and to better time and set up promotions. This way, the retailer tries to make all stores as efficient as those run by the best store managers.In its first iteration of Eko Agentic, Amity Solutions identified those stores that consistently outperformed the average, both through looking at store performance metrics and by talking to headquarters. Positive outliers were also identified in different environments – for example, the best inner-city markets (which tend to be smaller) and the best rural hypermarkets (which tend to be larger) – in order to get the widest possible range of data.Amity Solutions then sent teams to perform interviews at each of these stores, asking frontline workers to explain how they would think through various situations. What would they do if sales dropped by 5 per cent year-on-year? Perhaps the store manager would first check the basket size, then check the average value of each item in the basket, and then check for the use of special promotions.AI – and in particular, a technique developed by Amity Solutions called reflective optimisation via automated debugging (ROAD) – then structured these interviews into decision trees that visualised the store managers' train of thought. Most optimisation methods so far rely on large data sets for testing and calibration, but these interviews with store managers at Lotus's produced a smaller data set, something that ROAD's algorithm could work with. This was especially important in the Thai context because most large language models are trained on Western datasets, but differences in culture and the business environment between the West and Thailand (e.g., in the availability of parking lots) meant that other models, trained on larger data sets, weren't necessarily immediately applicable.The model was then applied to each individual store, generating a strategy that had been optimised for each one. Reinforcement learning (a paradigm within machine learning that seeks to optimise the impact of an agent's actions based on continual feedback to that agent from those impacts) is then used to further optimise store managers' strategies.Despite this rather simplistic description (and its correspondingly smaller size), Eko Agentic has been remarkably effective in data analytics. It is cheaper than many other AI tools (such as Claude and ChatGPT), and outperforms other state-of-the-art LLM and AI data analysis agents in an industry-standard set of real-world problems, the Data Agent Benchmark for Multi-step Reasoning (DABStep). It achieved 41 per cent accuracy in resolving DABStep tasks – the highest amongst all such agents – whilst its nearest competitor, Microsoft, only achieved 32 per cent accuracy; Anthropic's, OpenAI's, and Google's systems lagged even further back.Eko Agentic is also now able to outperform human analysts working at the Thai retailer. A blind test was conducted, wherein both Eko Agentic and a human analyst performed an analysis on various real business problems. Store managers then select the better of the two responses, without knowing who composed each one. The first versions of Eko Agentic still performed below a human analyst, but the latest version – the fifth – gives, on average, suggestions that are favoured over those from a human analyst. There are only a few supermarket chains in Thailand, and Amity Solutions is, of course, unable to work with the competitors to the retailer it currently works with. However, their methodology is applicable to other retail applications – and in fact, Amity Solutions is currently using Eko Agentic to help a telecommunications giant in Thailand manage its mobile phone shops. Amity Solutions is also looking for opportunities to apply Eko Agentic to supermarket chains in other Southeast Asian countries.A potential limitation with basing decisions on what the best store managers would do is that one might be limited to – and thus not be able to improve on – how well the best store managers do. In other words, you can interpolate performance but it is uncertain whether you can extrapolate to even superior strategies.Thus, as one of its next steps, Amity Solutions is creating a large behavioural model (LBM) that serves as a stand-in for customers. It is a digital twin that simulates customer behaviour, and models that respond to this LBM can potentially outperform the current best store managers.Amity Group, with offices in Thailand, Malaysia, Singapore, Australia, India, the United Kingdom, and the United States, employs 800 staff members over five companies in various realms of AI. Amity Solutions, the business unit that commissioned Eko Agentic through its long-standing collaboration with the aforementioned retailer, is based in Bangkok and employs 150 employees. However, it was Amity's AI Research and Application Center (ARAC), whose small team of just 15 staff members deploys generative AI solutions across all of Amity's daughter companies, that developed the technology behind Eko Agentic. Currently based in Thailand, they aim to stay at the forefront of developments in AI, says Touchapon Kraisingkorn, the chairman of ARAC and the executive director of Amity – and thus they have plans to expand ARAC to Singapore.Winning at the Best AI Awards is, says Kraisingkorn, validation that they are "one of the world-class labs that creates an effective product and solves real-world problems". The earnings from this award will help them jump-start hiring in Singapore. They are also open to opportunities for collaboration with Taiwanese companies in chips and robotics. The Best AI Awards celebrate global excellence in artificial intelligence and IC design, welcoming submissions from innovative companies and brilliant student teams. Following the success of the 2026 edition—advised by the MOEA, organized by DoIT, and executed by TCA—the prestigious competition is officially transitioning into an annual tradition.Offering substantial grand prizes and unmatched industry exposure, the countdown to Best AI Awards 2027 has already begun. Details on the next submission cycle, prize tiers, and eligibility rules will be released soon. Connect with us on LinkedIn for the latest official updates and application alerts.