Supply Chain Management

🚛 WHEN TRANSPORT LEARNS TO THINK GREEN I came across a concept today that stopped me — an autonomous hydrogen truck-trailer drone designed for long-distance freight. At first, it looked like another futuristic vehicle. But then it hit me: this isn’t just transport evolving — it’s intent evolving. For decades, we’ve designed logistics around speed and scale. Now we’re finally designing around sustainability. This new concept merges autonomy, aerodynamics, and hydrogen power to do something radical: → Eliminate carbon emissions in heavy freight. → Cut operational energy costs through intelligent routing. → Reduce highway congestion with coordinated drone convoys. It’s not just engineering — it’s a shift in philosophy. A move from moving faster to moving responsibly. We often talk about “green tech” as a feature — but the real shift happens when sustainability becomes the invisible infrastructure behind innovation. It’s not an addition to progress. It is progress. What’s needed now isn’t more invention — it’s integration. We need to: ✅ Build networks where clean energy and automation reinforce each other. ✅ Redefine “efficiency” to include environmental balance. ✅ Shift from carbon offsetting to carbon prevention at design level. Because the next breakthrough won’t come from faster engines — but from systems that make waste impossible by design. That’s when technology stops being an experiment in innovation… and becomes an expression of intelligence. So here’s the question I keep returning to — 👉 Will the next era of transport be powered by fuel — or by foresight? #Innovation #Sustainability #Hydrogen #AutonomousVehicles #GreenTech #Logistics #FutureThinking

📣 Great new report comparing 7 leading nature-related assessment and disclosure approaches! A joint collaboration between UNEP-WCMC and United Nations Environment Programme Finance Initiative (UNEP FI), this work explores: 1. CDP 2. EFRAG's Sustainability Reporting Standards (ESRS) 3. Global Reporting Initiative (GRI) Standards 4. International Sustainability Standards Board (ISSB) 5. Capitals Coalition's Natural Capital Protocol 6. Science-Based Targets Network (SBTN) target-setting guidance 7. Taskforce on Nature-related Financial Disclosures (TNFD) The report also dives into key trends in assessment methodologies and disclosure requirements, highlighting several areas of increasing alignment across the approaches. 📰 Have a read here! https://lnkd.in/eRz8ZpwP #nature #tnfd #biodiversity #issb #esrs #csrd #gri #naturefinance #sustainabilitydisclosures #sustainabilityreporting #esg #esgreporting #sustainablefinance #climate

The European Parliament has officially passed Extended Producer Responsibility (EPR) legislation that fundamentally shifts the responsibility for textile waste management to fashion brands and retailers – with far-reaching global implications. This new law requires all producers, including e-commerce platforms, to cover the full cost of collecting, sorting, and recycling textiles, regardless of whether they are based within or outside the EU. The financial burden of Europe's textile waste now falls squarely on the brands that create it. What are the critical business implications? UNIVERSAL SCOPE: The legislation applies to all producers selling in the EU market, including those of clothing, accessories, footwear, home textiles, and curtains. No company is exempt based on location. FAST FASHION PENALTY: Member states must specifically address ultra-fast and fast fashion practices when determining EPR financial contributions, creating cost penalties for unsustainable business models. GLOBAL SUPPLY CHAIN DISRUPTION: As the world's largest textile importer, the EU's new rules will ripple across global supply chains, particularly impacting exporters from Bangladesh, Vietnam, China, and India who supply much of Europe's fast fashion. TIMELINE PRESSURE: Officially adopted September 2025, this creates immediate operational and financial planning requirements. COMPETITIVE RESHAPING: Brands and retailers will inevitably pass increased costs down their supply chains, fundamentally altering supplier relationships and pricing structures globally. What are the implications for various stakeholders? For CEOs and board members: This represents more than regulatory compliance – it's a complete business model transformation. Companies must now integrate end-of-life costs into product pricing, rethink supplier partnerships, and accelerate circular design strategies. For sustainability and decarbonisation executives: This creates unprecedented opportunities for circular economy solutions, sustainable material innovation, and traceability system development across global supply chains. Link: https://lnkd.in/dTyHtHuD #sustainablefashion #circulareconomy #textilwaste #epr #fashionindustry #sustainability #supplychainmanagement #fastfashion #environmentalregulation #businessstrategy #decarbonisation #textilerecycling #fashionceos #boardgovernance #climateaction #wastemanagement #producerresponsibility #fashionsustainability #textileindustry #greenbusiness

𝗢𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗠𝗢𝗦𝗧 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗲𝗱 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: 𝗛𝗼𝘄 𝘁𝗼 𝗽𝗶𝗰𝗸 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗟𝗟𝗠 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲? The LLM landscape is booming and choosing the right LLM is now a business decision, not just a tech choice. One-size-fits-all? Forget it. Nearly all enterprises today rely on different models for different use cases and/or industry-specific fine-tuned models. There’s no universal “best” model — only the best fit for a given task. The latest LLM landscape (see below) shows how models stack up in capability (MMLU score), parameter size and accessibility — and the differences REALLY matter.  𝗟𝗲𝘁'𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻: ⬇️ 1️⃣ 𝗚𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘀𝘁 𝘃𝘀. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁: - Need a broad, powerful AI? GPT-4, Claude Opus, Gemini 1.5 Pro — great for general reasoning and diverse applications.   - Need domain expertise? E.g. IBM Granite or Mistral models (Lightweight & Fast) can be an excellent choice — tailored for specific industries.  2️⃣ 𝗕𝗶𝗴 𝘃𝘀. 𝗦𝗹𝗶𝗺:  - Powerful, large models (GPT-4, Claude Opus, Gemini 1.5 Pro) = great reasoning, but expensive and slow. - Slim, efficient models (Mistral 7B, LLaMA 3, RWWK models) = faster, cheaper, easier to fine-tune. Perfect for on-device, edge AI, or latency-sensitive applications.  3️⃣ 𝗢𝗽𝗲𝗻 𝘃𝘀. 𝗖𝗹𝗼𝘀𝗲𝗱   - Need full control? Open-source models (LLaMA 3, Mistral, Llama) give you transparency and customization.   - Want cutting-edge performance? Closed models (GPT-4, Gemini, Claude) still lead in general intelligence.  𝗧𝗵𝗲 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆? There is no "best" model — only the best one for your use case, but it's key to understand the differences to make an informed decision: - Running AI in production? Go slim, go fast. - Need state-of-the-art reasoning? Go big, go deep. - Building industry-specific AI? Go specialized and save some money with SLMs.  I love seeing how the AI and LLM stack is evolving, offering multiple directions depending on your specific use case. Source of the picture: informationisbeautiful.net

Yesterday, the U.S. International Trade Commission (USITC) issued an affirmative injury determination in the Solar III AD/CVD investigations. For those who have not been following, this investigation focused on solar cell and module imports from Cambodia, Malaysia, Thailand, and Vietnam. The USITC was trying to determine whether the domestic cell, module, laminate, and panel industry is experiencing “material injury” (economic harm) by reason of dumped and subsidized imports.    The investigation found material injury. Therefore, tariffs on solar cells and modules from the subject countries will continue. Cash deposits at final AD/CVD rates will resume when the ITC’s final report is published in the Federal Register which we estimate will happen around June 16th.   To take a step back, yesterday's decision adds an additional layer of tariffs that will raise costs for the solar products American companies need to build projects and grow domestic manufacturing.   While U.S. solar cell manufacturing is growing for the first time in years, it is still not at the scale needed to meet demand. So, this determination especially harms U.S. solar module producers that depend on access to imported solar cells as we ramp up domestic cell manufacturing capacity. Building out a domestic supply chain takes time and focus, and this determination represents yet another hurdle.   After today’s decision, it is more important than ever for Congress to support American solar manufacturing. Tax credits for domestic production must be preserved and strengthened as lawmakers consider the budget reconciliation bill. These policies are the key to building a resilient, competitive solar manufacturing base here at home.   You can find SEIA’s statement here: https://lnkd.in/eeC4aM4r   The bulletin notice is here: https://lnkd.in/ehFRNmXz   And the Reuters article is here: https://lnkd.in/et97v7RV

Can we use AI agents for stock market prediction? 😮 Recently, LLM-based agents have demonstrated remarkable advancements in handling multi-modal data, enabling them to execute complex, multi-step decision-making tasks. This research introduces a multi-modal multi-agent system designed specifically for financial trading tasks. The framework employs a team of specialized LLM-based agents, each adept at processing and interpreting various forms of financial data, such as textual news reports, candlestick charts, and trading signal charts. The framework comprises four primary components: the Summarize Module, the Technical Analyst Module, the Prediction Module, and the Reflection Module. The Summarize Module condenses large volumes of textual news data into concise summaries that highlight factual information influencing stock trading decisions. The Technical Analyst Agent leverages the visual reasoning capabilities of LLMs to analyze candlestick charts with technical indicators, providing interpretations for next-day trading strategies. The Reflection Module consists of two parts: one assesses the short-term and medium-term performance of previous trades, while the other plots past trading signals, generates charts, and offers insights into the effectiveness of trades. The Prediction Agent integrates information from these components to forecast trading actions, determine position size as a percentage of the portfolio, and provide a detailed explanation of the decision. Based on the Prediction Agent’s output, the Reward Agent executes trades and calculates performance metrics. These metrics are then used by the Reflection and Prediction Agents in the subsequent iterations. The detailed flow of our framework is illustrated in the Figure. Know more about the framework in this practical research paper: https://lnkd.in/gxvEUGAA Here is my simple video explaining how AI agents work: https://lnkd.in/d_V9DqbH This is my practical hands-on guide on building multi-agent AI system: https://lnkd.in/gdaA5s3Z

As we move into 2024, I find myself in an unusual position. In 2023, I was vocal in saying that we are in a freight recession but that the economy overall wasn’t in a recession (a forecast that proved correct). I continue to believe the economy is unlikely to fall into a true recession in 2024, but I’m now finding I’m one of the more bearish voices on the trucking sector’s outlook in 2024 (e.g., my best-case scenario is for a tepid rebound in seasonally adjusted volumes that won't take place till the second half of the year). As such, I wanted to share more data as to why I’m bearish on trucking demand over the next several months. These data are the total hours worked by all employees in the general freight trucking, long-distance, less-than-truckload. This is a great proxy for output from that sector. Thoughts: •These data show that LTL output is down about 12% from where is was in 2017 and a staggering 19% from where it was in 2021. This represents more than just Yellow’s departure, as they were about 7% of output at the time of failure. This figure aligns closely with what the public LTL carriers have reported for tonnage per day figures. •Consistent with my claims that trucking demand, especially from industrial customers, fell steadily through 2023 and is likely now just in a trough, we see the same pattern for LTL hours worked (note, data extend through October). Manufacturing activity tends to take a long time to rebound. •Not pictured, but hours per worker are well below pre-COVID levels (it used to be 43, it is now 38). This means that the low total hours worked isn’t because the LTL carriers are up against some capacity threshold. Rather, it is because demand is low. Implication: trends in total LTL hours worked tend to be quite procyclical to demand conditions in trucking. We aren’t seeing signs yet of demand improving significantly. Some not welcome news as we enter 2024. #supplychain #supplychainmanagement #freight #trucking #transportation #logistics

𝟓 𝐏𝐫𝐨𝐜𝐮𝐫𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐬𝐬𝐨𝐧𝐬 𝐟𝐫𝐨𝐦 𝐚 ₹𝟏𝟗,𝟕𝟎𝟎 𝐂𝐫 𝐌𝐢𝐬𝐭𝐚𝐤𝐞 What every procurement leader must learn from the JSW–Bhushan Steel chaos 𝗕𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱 : In 2019 JSW Steel won the bid to acquire Bhushan Power & Steel under India’s Insolvency & Bankruptcy Code (IBC). Deal value : ₹19,700 Cr. Funds were infused. Operations taken over. 𝘊𝘰𝘯𝘵𝘳𝘰𝘭 𝘢𝘴𝘴𝘶𝘮𝘦𝘥 𝗕𝘂𝘁 𝗜𝗻 𝗠𝗮𝘆 𝟮𝟬𝟮𝟱: The Supreme Court has canceled the entire deal ❌ Declares the process illegal Orders liquidation instead That’s true a company that JSW ran for years is now off its books Overnight 𝗕𝘂𝘁 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗛𝗮𝗽𝗽𝗲𝗻𝗲𝗱 ? ⛔ 𝐓𝐢𝐦𝐞𝐥𝐢𝐧𝐞 𝐛𝐫𝐞𝐚𝐜𝐡: IBC allows 270 days. This deal dragged on for over 500 ⛔ 𝐏𝐫𝐞𝐦𝐚𝐭𝐮𝐫𝐞 𝐜𝐨𝐧𝐭𝐫𝐨𝐥: JSW took over without full legal closure ⛔ 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐠𝐚𝐩𝐬: The resolution process lacked statutory rigor 𝘛𝘩𝘦 𝘊𝘰𝘶𝘳𝘵 𝘳𝘶𝘭𝘦𝘥 𝘵𝘩𝘢𝘵 𝘵𝘪𝘮𝘦 𝘦𝘹𝘵𝘦𝘯𝘴𝘪𝘰𝘯𝘴 𝘤𝘢𝘯𝘯𝘰𝘵 𝘫𝘶𝘴𝘵𝘪𝘧𝘺 𝘱𝘳𝘰𝘤𝘦𝘥𝘶𝘳𝘢𝘭 𝘷𝘪𝘰𝘭𝘢𝘵𝘪𝘰𝘯𝘴 𝐖𝐡𝐚𝐭 𝐓𝐡𝐢𝐬 𝐌𝐞𝐚𝐧𝐬 𝐟𝐨𝐫 𝐘𝐨𝐮 𝐀𝐬 𝐀 𝐏𝐫𝐨𝐜𝐮𝐫𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐝𝐞𝐫 : This is a brutal real-world case study in contractual discipline and risk management. 1️⃣ 𝗗𝗲𝗮𝗱𝗹𝗶𝗻𝗲𝘀 𝗮𝗿𝗲 𝗟𝗔𝗪 — 𝗡𝗼𝘁 𝗦𝘂𝗴𝗴𝗲𝘀𝘁𝗶𝗼𝗻𝘀 Contracts with statutory or regulatory timelines must be treated as non-negotiable. 2️⃣ 𝗟𝗲𝗴𝗮𝗹 𝗖𝗹𝗼𝘀𝘂𝗿𝗲 > 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 Never take charge of suppliers, assets, or projects until contracts are 100% sealed and validated. 3️⃣ 𝗣𝗮𝗽𝗲𝗿 𝗧𝗿𝗮𝗶𝗹𝘀 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗬𝗼𝘂 Assumptions don’t win in court. Documentation does. 4️⃣ 𝗜𝗻𝗰𝗹𝘂𝗱𝗲 ‘𝗪𝗵𝗮𝘁-𝗜𝗳’ 𝗖𝗹𝗮𝘂𝘀𝗲𝘀 𝗶𝗻 𝗛𝗶𝗴𝗵-𝗦𝘁𝗮𝗸𝗲 𝗖𝗼𝗻𝘁𝗿𝗮𝗰𝘁𝘀 Always draft contingency, rollback, and reversal clauses — especially in M&A, Capex, or long-term supply contracts. 5️⃣ 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗡𝗼𝘁 𝗕𝘂𝗿𝗲𝗮𝘂𝗰𝗿𝗮𝗰𝘆 — 𝗜𝘁’𝘀 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 Be the leader who slows down when it matters. Because speed without structure kills deals. Have you seen contract risks like this in your industry? #JSW #bhushansteel #India #Contract #Contractdrafting #Purchsing #Procurement #Industry #Mergerandaquisition #Riskmanagement #Leadership #CXOinsights #SMARTProcurement #ContractRisk #SCM

🚨 BREAKING: The EU Commission announces that X is likely breaching the Digital Services Act. Elon Musk then accuses the EU of attempting to CENSOR speech. Here's what happened: ➡ Today, the EU Commission informed X of its preliminary view that the company is in breach of the Digital Services Act (DSA) regarding dark patterns, advertising transparency, and data access for researchers. ➡ According to the official press release (link below), these are the main issues that have motivated the preliminary findings: "➵ First, X designs and operates its interface for the 'verified accounts' with the 'Blue checkmark' in a way that does not correspond to industry practice and deceives users. Since anyone can subscribe to obtain such a 'verified' status, it negatively affects users' ability to make free and informed decisions about the authenticity of the accounts and the content they interact with. There is evidence of motivated malicious actors abusing the 'verified account' to deceive users. ➵ Second, X does not comply with the required transparency on advertising, as it does not provide a searchable and reliable advertisement repository, but instead put in place design features and access barriers that make the repository unfit for its transparency purpose towards users. In particular, the design does not allow for the required supervision and research into emerging risks brought about by the distribution of advertising online. ➵ Third, X fails to provide access to its public data to researchers in line with the conditions set out in the DSA. In particular, X prohibits eligible researchers from independently accessing its public data, such as by scraping, as stated in its terms of service. In addition, X's process to grant eligible researchers access to its application programming interface (API) appears to dissuade researchers from carrying out their research projects or leave them with no other choice than to pay disproportionally high fees." ➡ X can now defend itself. If the EU decides that X is breaching the DSA, the fine can be up to 6% of X's total worldwide annual turnover. ➡ In response to the EU Commission's announcement, Elon Musk posted: "The European Commission offered 𝕏 an illegal secret deal: if we quietly censored speech without telling anyone, they would not fine us. The other platforms accepted that deal. 𝕏 did not." ➡ It will be interesting to follow this legal battle - one more for Musk's collection. ➡ To stay up to date with the latest developments in AI & Tech policy & regulation, join 28,500 people who subscribe to my weekly newsletter (link below). #DigitalServicesAct #DSA #X #darkpatterns #techregulation #ElonMusk

⛈️ 𝐂𝐥𝐢𝐦𝐚𝐭𝐞 𝐑𝐢𝐬𝐤 𝐌𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐲 𝐁𝐚𝐬𝐞𝐝 𝐨𝐧 𝐎𝐩𝐞𝐧-𝐀𝐜𝐜𝐞𝐬𝐬 𝐓𝐨𝐨𝐥𝐬 🗺️ Over the past months, I shared lists of open-access climate and nature risk assessment tools. They sparked quite some interest. Here’s how I thought I might provide additional value: ➡️ A practical Excel methodology for assessing climate risk based on open-access geospatial tools. For every risk category required by the EU Taxonomy, the Excel links to the best assessment tool. 🔥🌡️ This initial release focuses on temperature-related physical risks like heat stress and wildfires. Updates on additional risk categories are forthcoming. 𝐖𝐡𝐚𝐭’𝐬 𝐢𝐧𝐬𝐢𝐝𝐞: 🗺️ Open-access geospatial tools for assessing each temperature-related risk 📊 A conclusive methodology to assess company sites and supply chains 📝 Additional guidance for smooth assessment and reporting in line with EU Taxonomy and CSRD, including descriptions and instructions for each tool 📈 Based on the latest climate models and data by organizations like the IPCC. I hope this will save ESG teams substantial time and money in their search for adequate data and methods. 𝐈𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐞𝐝 𝐢𝐧 𝐭𝐡𝐞 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞? Comment below, and I’ll send it your way. (Please connect so I can message you directly.)