Supply Chain Management

𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. Because most people explain it from the inside out: policies, councils, standards, stewardship. But the business does not buy any of that. The business buys outcomes: → trustworthy KPIs → vendor and partner data you can actually use → faster financial close → fewer reporting escalations → smoother M&A integration → AI you can deploy without creating risk debt Most AI programs fail for boring reasons: nobody owns the data, quality is unknown, access is messy, accountability is missing. 𝗦𝗼 𝗹𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁. 𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗴𝘀: → ownership → quality → access → accountability 𝗔𝗻𝗱 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘃𝗲𝗿𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝟰 𝗹𝗮𝘆𝗲𝗿𝘀: 1. Data Products (what the business consumes) → a named dataset with an owner and SLA → clear definitions + metric logic → documented inputs/outputs and intended use → discoverable in a catalog → versioned so changes don’t break reporting 2. Data Management (how products stay reliable) → quality rules + monitoring (freshness, completeness, accuracy) → lineage (where it came from, where it’s used) → master/reference data alignment → metadata management (business + technical) → access controls and retention rules 3. Data Governance (who decides, who is accountable) → data ownership model (domain owners, stewards) → decision rights: who can change KPI definitions, thresholds, and sources → issue management: triage, escalation paths, resolution SLAs → policy enforcement: what’s mandatory vs optional → risk and compliance alignment (auditability, approvals) 4. Data Operating Model (how you scale across the enterprise) → domain-based setup (data mesh or not, but clear domains) → operating cadence: weekly issue review, monthly KPI governance, quarterly standards → stewardship at scale (roles, capacity, incentives) → cross-domain decision-making for shared metrics → enablement: templates, playbooks, tooling support If you want to start fast: Pick the 10 metrics that run the business. Assign an owner. Define decision rights + escalation. Then build the data products around them. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

📦 BMW had over $700M invested in returnable containers. And no idea where most of them were, until it implemented a simple passive RFID solution. Here is how the cycle works: 🏭 Suppliers fill containers with parts 🚛 Containers ship to the assembly line 🔧 Parts are consumed on the line ↩️ Empty containers return to a warehouse for cleaning 🔁 Then it repeats The problem? ✅ 10-15% of containers disappeared every year ✅ Replacements cost 3x the original price ✅ Roughly $300M in annual spend just to keep the cycle running ✅ Up to 30% were excess, sitting idle and invisible One senior manager found his own containers stacked above the walls of a competitor's plant. Not stolen. Just lost in a system with no visibility. The fix? RFID readers at the empties warehouse only. When a container did not return, BMW knew who had it and could charge for it. The mere threat of being charged established near-perfect compliance across the entire supplier network. Results: ✅ 30% reduction in total container inventory ✅ 75% reduction in reconciliation costs ✅ 65% reduction in substitute container costs ✅ 20% improvement in container turnaround time We designed and deployed this solution nearly 20 years ago. Total implementation cost: under $1M. The technology works. The ROI is clear. And there surely are lots of great success stories like this by now. Visibility is about making the right decisions, not about seeing everything, everywhere. 💬 What are your biggest supply chain visibility wins? #SupplyChain #RFID #Logistics #Innovation #Truckl

I’ve had 4 legal battles since starting my business. Could I have avoided them? Probably. But to be honest, I didn't have the funds to pay a proper lawyer, or the network of founders to ask the right questions to. I don't want that to happen to you. Here are 5 clauses I put in my contracts that might help you protect your work, your business and most importantly.. your sanity ↓ #1 Non-cancellable, non-refundable contracts. This shouldn’t even be an issue if you qualify your clients properly. BUT if someone signs, onboards, and then ghosts? We still get paid. And so should you 🤗 #2 Immediate or short payment terms Most businesses accept 30-to 90-day payment terms. I don’t. You wouldn’t work for 3 months without pay—so why should your business? Cash flow is your business’s lifeline. Protect it. #3 While we’re on payment terms… Your contract should include: → Interest on late invoices. → A clause that stops work if invoices aren’t cleared. → A guarantee that if a client delays the project, you still get paid. Your time isn’t free! #4 Your IP stays YOURS. Anything we bring into the agreement at Klowt stays ours. Anything we create for you is yours. Simple. I once ran a training session, and the client recorded it—then tried to sell it behind a paywall. Now, our contract states a £10,000 fine per breach. (And for that particular case, per breach = per view. 😅) #5 Don't work with d*ckheads. This isn't a legal clause, more legal... advice? 🤣 If someone is giving you red flags in any way at the beginning of your relationship, do not work with them. This could include but not limited to: - Focusing on immediate ROI. - Cost or discounts being a primary concern. - Pushing for work to kick off before contracts or payments. - Reaching out at inappropriate times - or in inappropriate ways. - Delaying initial payments. Legally binding contracts are a good insurance policy, but they're lengthy and expensive to implement if you actually have to go to court. So the best LEGAL advice I can give you as a 2x founder is, don't work with d*ckheads. And learn from my mistakes. It's a lot cheaper than learning from your own... trust me 😂. Was this helpful? 💜 I write a 2x weekly newsletter for founders and freelancers on topics like this. Join us here: https://lnkd.in/ejDbD94R

We've all had nightmare supplier stories. Missed deadlines, poor quality, and a lack of accountability - these issues can literally threaten the existence of your company. After starting my own business and working professionally for over 20+ years, I've learned: Having the right suppliers can make or break your operations. I've found that asking the right questions can reveal so much about a supplier's capabilities, values, and more. It allows me to weed out bad fits early and lay the groundwork for a strong partnership. Here are the top 8 questions I always ask: 1️⃣ Walk me through your quality control processes from start to finish. I need to know they have legitimate procedures in place, not just words on paper. 2️⃣ What's your contingency plan if there's a major supply disruption? Their answer shows how proactive they are about risk mitigation. 3️⃣ How do you ensure you remain compliant with all industry regulations? Compliance is non-negotiable. I want to see their commitment baked in. 4️⃣ If I have an issue, what's the process for getting it resolved properly? A solid system for addressing problems is crucial before they snowball. 5️⃣ Can you provide some customer references I could speak with? Hearing directly from others about their capabilities and partnership is telling. 6️⃣ What makes you different or better than your competitors? I'm looking for a clear value proposition beyond just low costs. 7️⃣ Where do you want to take your company in the next 3-5 years? Gauging whether our longer-term visions remain aligned is important. 8️⃣ How do you stay innovative and keep improving your operations? The status quo isn't good enough. I need a supplier committed to continuous evolution. These questions have been indispensable for vetting suppliers over the years. If you can't get clear, trustworthy answers, it's probably not going to be a good partnership. I'd love to hear any other key questions my fellow entrepreneurs like to ask suppliers! Let's discuss in the comments. #suppliers #fashion #leadership

Procurement: Treat suppliers as extensions of your enterprise, not transactions. Procurement Excellence | 23 NOV 2025 - In complex global markets, resilient supply chains demand partnerships built on shared destiny, not just contracts. Here are 9 Steps to Create Long-Term Supplier Partnerships: #1. Transparent Communication ↳ Co-develop comms protocols e.g. QBR ↳ Clearly share expectations, goals & challenges #2. Long-Term Contracts ↳ Replace short-term with multi year agreements. ↳ Share long-term roadmaps & cost-savings initiatives. #3. Shared Performance Metrics ↳ Jointly agree and track SMART KPIs. ↳ Define escalation paths & RCA templates #4. Early Supplier Involvement ↳ Involve and recognize vendor’s contributions. ↳ Include key suppliers in product development cycles. #5. Guarantee Timely Payments ↳ Automate payment & consider early payment discounts. ↳ Audit internal processes for bottlenecks. #6. Co-Create Innovation ↳ Create supplier ideation portals & protect IP collaboratively. ↳ Fund joint proof-of-concept projects. #7. Recognize & Reward Excellence ↳Formally acknowledge & reward outstanding suppliers. ↳Bronze (Operational Excellence), Silver (Innovation), Gold (Strategic Impact). #8. Uphold Fairness & Ethics ↳ Interactions & contractual terms are mutually beneficial. ↳ Ensure cost pressures don't force unethical labor. #9. Jointly Manage Risks ↳ Jointly identify risks & develop contingency plans. ↳ Map tier-2/3 suppliers collaboratively. In today's volatile market, Resilient supply chains are built on deep, strategic supplier partnerships. Achieving lasting, mutually beneficial supplier partnerships requires: ✅️ Deliberate strategy ✅️ Centered on trust ✅️ Shared objectives ✅️ Continuous collaboration ♻️ Repost if you find this helpful. ➕️ Follow Frederick for Procurement insights. #ProcurementExcellence #SupplierCollaboration

It's astonishing that $180 billion of the nearly $600 billion on cloud spend globally is entirely unnecessary. For companies to save millions, they need to focus on these 3 principles — visibility, accountability, and automation. 1) Visibility The very characteristics that make the cloud so convenient also make it difficult to track and control how much teams and individuals spend on cloud resources. Most companies still struggle to keep budgets aligned. The good news is that a new generation of tools can provide transparency. For example: resource tagging to automatically track which teams use cloud resources to measure costs and identify excess capacity accurately. 2) Accountability Companies wouldn't dare deploy a payroll budget without an administrator to optimize spend carefully. Yet, when it comes to cloud costs, there's often no one at the helm. Enter the emerging disciplines of FinOps or cloud operations. These dedicated teams can take responsibility of everything from setting cloud budgets and negotiating favorable controls to putting engineering discipline in place to control costs. 3) Automation Even with a dedicated team monitoring cloud use and need, automation is the only way to keep up with the complex and evolving scenarios. Much of today's cloud cost management remains bespoke and manual, In many cases, a monthly report or round-up of cloud waste is the only maintenance done — and highly paid engineers are expected to manually remove abandoned projects and initiatives to free up space. It’s the equivalent of asking someone to delete extra photos from their iPhone each month to free up extra storage. That’s why AI and automation are critical to identify cloud waste and eliminate it. For example: tools like "intelligent auto-stopping" allow users to stop their cloud instances when not in use, much like motion sensors can turn off a light switch at the end of the workday. As cloud management evolves, companies are discovering ways to save millions, if not hundreds of millions — and these 3 principles are key to getting cloud costs under control.

Killer graph. Out of the £130 billion online non-food purchases we make in the UK, £27 billion of them get sent back to retailers. Our research with ZigZag Global shines a spotlight on the significant challenge online returns cause in the industry, focusing on those consumers who consistently and intentionally over-order - the "serial returners". Key stats ➡️ Around 11% of online shoppers are serial returners (frequently over-ordering with the intention of returning many items) ➡️They account for 24% of all online returns ➡️Serial returners send back, on average, £1,400 worth of online orders per year, compared with an average of £650. ➡️ This amounts to £6.6 billion of returns. ➡️ Almost three-quarters of serial returners are under the age of 45, and they return more than 42% of all their orders. A 1/4 of serial returners admit to over-ordering just to reach a minimum order value (often to trigger free delivery) only to return goods they had no intention of keeping. The same proportion also said they had returned items after finding them cheaper elsewhere or on promotions. While 18% admitted to returning items having already used them for a short period. There is no silver bullet here that is going to fix this issue for retailers. A nuanced understanding of specific triggers and barriers is essential to effectively target returners through pricing and returns options. 💥 For many boardrooms debating whether they should charge for returns, my thoughts are: 💥 The returns equation transcends simple binary choices between free or paid. Retailers must architect differentiated returns propositions that align commercial realities with customer lifetime value. Smart retailers will segment their returns strategy by customer profitability metrics, leveraging AI to identify purchase patterns that predict long-term value. This enables dynamic returns pricing that protects margins while fostering relationships with truly valuable customers. The goal isn't to punish returns – it's to price them according to their true cost to serve, while rewarding profitable shopping behaviours. There's also a paradox at play where customer acquisition costs are optimised but customer profitability is compromised. Many retailers are essentially subsidising unsustainable shopping behaviours at the expense of margin, unknowingly targeting customers they could do without. The real opportunity lies in leveraging returns data as a predictive indicator of customer profitability. By applying advanced analytics to returns patterns, seasonal purchasing behaviours, and cross-category browsing and mining deep behaviour insights, retailers can enable proactive intervention before profitability erodes. This shifts the conversation from universal policies to personalised solutions that can turn returns from a pure cost centre into a strategic lever for customer engagement and loyalty. Full research is available to download here ⬇️ https://lnkd.in/e5paRNWC

GenAI’s black box problem is becoming a real business problem. Large language models are racing ahead of our ability to explain them. That gap (the “representational gap” for the cool kids) is no longer just academic, and is now a #compliance and risk management issue. Why it matters: • Reliability: If you can’t trace how a model reached its conclusion, you can’t validate accuracy. • Resilience: Without interpretability, you can’t fix failures or confirm fixes. • Regulation: From the EU AI Act to sector regulators in finance and health care, transparency is quickly becoming non-negotiable. Signals from the frontier: • Banks are stress-testing GenAI the same way they test credit models, using surrogate testing, statistical analysis, and guardrails. • Researchers at firms like #Anthropic are mapping millions of features inside LLMs, creating “control knobs” to adjust behavior and probes that flag risky outputs before they surface. As AI shifts from answering prompts to running workflows and making autonomous decisions, traceability will move from optional to mandatory. The takeaway: Interpretability is no longer a nice-to-have. It is a license to operate. Companies that lean in will not only satisfy regulators but also build the trust of customers, partners, and employees. Tip of the hat to Alison Hu Sanmitra Bhattacharya, PhD, Gina Schaefer, Rich O'Connell and Beena Ammanath's whole team for this great read.

As an MLOps platform, we started by helping organizations implement responsible AI governance for traditional machine learning models. With principles of transparency, accountability, and oversight, our Guardrails enabled smooth model development. However, governing large language models (LLMs) like ChatGPT requires a fundamentally different approach. LLMs aren't narrow systems designed for specific tasks - they can generate nuanced text on virtually any topic imaginable. This presents a whole new set of challenges for governance. Here are some key components for evolving AI governance frameworks to effectively oversee large language models (LLMs): 1️⃣ Usage-Focused Governance: Focus governance efforts on real-world LLM usage - the workflows, inputs and outputs - rather than just the technical architecture. Continuously assess risks posed by different use cases. 2️⃣ Dynamic Risk Assessment: Identify unique risks presented by LLMs such as bias amplification and develop flexible frameworks to proactively address emerging issues. 3️⃣ Customized Integrations: Invest in tailored solutions to integrate complex LLMs with existing systems in alignment with governance goals. 4️⃣ Advanced Monitoring: Utilize state-of-the-art tools to monitor LLMs in real-time across metrics like outputs, bias indicators, misuse prevention, and more. 5️⃣ Continuous Accuracy Tracking: Implement ongoing processes to detect subtle accuracy drifts or inconsistencies in LLM outputs before they escalate. 6️⃣ Agile Oversight: Adopt agile, iterative governance processes to manage frequent LLM updates and retraining in line with the rapid evolution of models. 7️⃣ Enhanced Transparency: Incorporate methodologies to audit LLMs, trace outputs back to training data/prompts and pinpoint root causes of issues to enhance accountability. In conclusion, while the rise of LLMs has disrupted traditional governance models, we at Katonic AI are working hard to understand the nuances of LLM-centric governance and aim to provide effective solutions to assist organizations in harnessing the power of LLMs responsibly and efficiently. #LLMGovernance #ResponsibleLLMs #LLMrisks #LLMethics #LLMpolicy #LLMregulation #LLMbias #LLMtransparency #LLMaccountability