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How scrum can help improve dead inventory improving supply chain - apparel example

Problem Statement: Decide on the product and sprint backlog and develop a Scrum framework that significantly reduces dead inventory. Clearly define the roles and responsibilities of the Scrum team. Clearly define the events and artefacts of Scrum. Explain how the Scrum framework helps reduce dead inventory and increase supply chain efficiency. Product Decision: Reposition the core offering as “Fast-Moving Modular Shirt Collections.” Instead of full-season bulk production, launch smaller capsule collections (20–25 designs per drop) refreshed every 8–10 weeks based on real-time sales data. Keep formal pure cotton shirts as the anchor category, with limited data-driven expansion into weekend wear. Adopt a 60:40 production model — 60% controlled initial batch, 40% reserved for rapid replenishment of fast sellers. Product Backlog (High-Level): Reduce design-to-store cycle from 12 months to 6 months. Shift from forecast-driven to sell-through-driven production. Implement smaller batch manufa...
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Zara – “The Cube” Advantage in supply chain

  Zara – Why “The Cube” Creates Structural Advantage “The Cube” (Zara’s central distribution hub in Arteixo, Spain) is not just a warehouse. It is the physical backbone of a tightly integrated, demand-driven supply chain. Handling 11,000+ SKUs efficiently is a consequence of specific design choices: 1. Vertically Integrated Control Unlike competitors who outsource most production, Zara retains control over: Design Fabric sourcing Cutting High-value manufacturing steps Distribution This reduces coordination friction and eliminates multi-layer supplier delays. Advantage: Faster decision cycles; fewer forecast errors. 2. Proximity Manufacturing (Nearshoring) A large share of fashion-sensitive items is produced in: Spain Portugal Morocco Basic items may be offshore, but trend-driven SKUs stay geographically close. Advantage: 2–3 week design-to-store cycle vs 3–6 months for competitors. 3. Centralized Distribution Model All finished goods pass through “The Cube.” Characteristics: No i...
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Beyond the Cognitive Arms Race: The Real Fight for Enterprise Memory

A Strategic Analysis of AI's Infrastructure Phase OAI = open AI, AIC = anthropic, DSK = deepseek. The cognitive arms race narrative has dominated AI discourse for two years. It is seductive: three labs, each with distinct ideologies, racing toward superhuman intelligence. OAI the defector, AIC the conscience, DSK the insurgent. But this framing is becoming obsolete. What we are witnessing is not a race to AGI. It is a race to become operational infrastructure . The distinction matters profoundly for strategy, investment, and market outcomes. I. Reframing the Battlefield Distribution, cost structure, and trust are the true axes of competition. But stress-testing those assumptions reveals something deeper: each competitor is playing a different game, on a different board, with different victory conditions. This is not a symmetrical war. It is three asymmetric campaigns. II. OAI: The Infrastructure Ambition The Misread Signal OAI is frequently criticized for incremental model impro...
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Career advice for coders by Anthropic Claude AI (claude.ai) which has caused the crash of AI stocks ( mainly due to its agentic AI solution Cowork AI)

 Q1: with ai systems being capable of creating software, what's the advice for coders for next 5 to 10 years for a good career ? Ans 1:  The software development landscape is shifting rapidly, but there's still tremendous opportunity for human developers who adapt strategically. Here are the key areas I'd focus on: Move up the stack to judgment and architecture . AI excels at implementing well-defined specifications but struggles with ambiguous requirements, making strategic decisions, and understanding what should be built. Skills in system design, technical leadership, product thinking, and translating messy business needs into technical solutions will become more valuable, not less. Develop deep domain expertise . The combination of coding ability plus deep knowledge in healthcare, finance, climate tech, manufacturing, or other complex domains is powerful. AI can generate code, but it can't replace someone who understands both the technical and domain-specific nuanc...
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Data migration: Transferring work items (like AMS tickets) from Azure DevOps to ServiceNow.

Process based on established methods and tools commonly used for transferring work items (like AMS tickets) from Azure DevOps to ServiceNow. This includes handling historical data such as the last 6 months. Many organizations achieve this through APIs, built-in integrations, or third-party tools to ensure data fidelity. If you're looking for someone with hands-on experience, I recommend checking ServiceNow's community forums servicenow.com +2  or LinkedIn groups focused on DevOps and ITSM migrations, where users often share case studies. Tools like OpsHub, Exalate, or ZigiOps also have support teams that can guide you through custom setups marketplace.visualstudio.com +2 .Steps for MigrationThe process typically involves exporting data from Azure DevOps, mapping it to ServiceNow's structure (e.g., incidents, change requests, or custom tables), and importing it. You can use ServiceNow's native Azure DevOps integration for ongoing sync, but for a one-time historical t...
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Planning a career as a programmer ? two learning tracks

  Track A: Language-first approach. Start by mastering a general-purpose language such as Python. This builds core programming logic and problem-solving skills. Once comfortable, the learner can branch into adjacent domains—web development (Django/Flask), data analysis, automation, or basic machine learning. For example, a Python-first learner can move into backend APIs, data pipelines, or scripting roles across startups, healthcare IT, fintech, and enterprise automation. This track offers flexibility and faster entry into diverse roles. Track B: Full-stack-first approach. Begin with full-stack development, such as the MEAN stack (MongoDB, Express, Angular, Node.js). This provides an end-to-end understanding of building real-world applications—frontend, backend, databases, and deployment. After this, the learner should systematically study Data Structures and Algorithms (DSA) : arrays, stacks, queues, linked lists, trees, and searching/sorting algorithms. For example, a full-sta...
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For a bright career in the AI age, key skills programmers should develop

 In a post on X that has sparked widespread debate, Karpathy confessed: “I’ve never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between.” Earlier, programmers wrote most of the software themselves. They planned the code, wrote every line, and fixed problems manually. Now, AI tools are doing many of these tasks. Because of this, the amount of code written directly by humans is reducing. Ref https://www.financialexpress.com/life/technology-teslas-former-ai-chief-andrej-karpathy-warns-software-engineers-says-never-felt-this-far-behind-as-a-programmer-4092061/ Based on the article about Andrej Karpathy's warning and the broader trends in AI-driven software development, programmers should focus on developing a blend of **technical, cognitive, and strategic skills** to stay relevant. The era of simply writing lines of code is evolving into one where programmers act as **archit...
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