Supply Chain Collaboration Techniques

All planning is NOT the same. This infographic shows demand vs supply vs capacity planning: Main Objective ↳ Demand: forecast customer demand ↳ Supply: plan how to meet forecasted demand ↳ Capacity: ensure resources can meet the supply plan Type of Planning ↳ Demand: unconstrained ↳ Supply: constrained by materials, suppliers, production ↳ Capacity: constrained by labor, equipment, shifts, plant availability When in the S&OP Cycle ↳ Demand: demand review ↳ Supply: supply review ↳ Capacity: supply review Input ↳ Demand: sales data, market trends, promotions, historical demand ↳ Supply: demand forecast, inventory levels, supply constraints ↳ Capacity: supply plan, production rates, shift schedules, resource calendars Output ↳ Demand: forecasted demand  ↳ Supply: supply plan including procurement and production schedules ↳ Capacity: capacity plan (available vs. required capacity by period) Key Deliverable to S&OP ↳ Demand: aligned consensus forecast ↳ Supply: feasible supply plan ↳ Capacity: confirmation of capacity readiness or gaps Metrics ↳ Demand: forecast accuracy (MAPE, WMAPE), bias ↳ Supply: OTIF, inventory turns, service level ↳ Capacity: capacity utilization %, available hours, OEE Any others to add?

Modern corporations are creating innovation ecosystems where internal teams work directly with portfolio companies, sharing resources, expertise, and market access. This integration goes far beyond traditional corporate-startup partnerships: ➡️ Shared Technology Platforms: Portfolio companies gain access to proprietary corporate platforms and APIs, while corporations benefit from rapid external innovation cycles. ➡️ Cross-Pollination of Talent: Employees move between corporate R&D teams and portfolio companies, creating knowledge transfer and cultural bridges. ➡️ Collaborative Product Development: Joint development projects between corporate teams and startups are becoming more common, leading to products that neither could create independently.

🚀 AI is Reshaping Supply Chain Consulting – Adapt Now or Risk Disruption The supply chain industry is at an inflection point. AI isn’t just optimizing logistics, it’s redefining how consulting delivers value. Big firms are deploying AI for everything from demand forecasting to autonomous procurement strategies. Meanwhile, agile AI-native consultancies are delivering end-to-end supply chain diagnostics in days, not weeks, using automated analytics and real-time scenario modeling. As one industry leader put it: “Supply chain consulting without AI is like navigation without GPS, possible, but dangerously inefficient.” 6 Critical AI Skills for the Next-Gen Supply Chain Consultant To lead in this new era, professionals must master: 🔹 AI-Driven Network Optimisation – Orchestrate multi-agent systems to simulate and optimize end-to-end supply chain flows. 🔹 Predictive & Prescriptive Analytics – Leverage AI to anticipate disruptions, model trade-offs, and prescribe resilient actions. 🔹 Agile Process Reinvention – Continuously adapt workflows as AI unlocks new efficiencies in procurement, warehousing, and logistics. 🔹 Domain-Specific Prompting – Engineer precise queries to extract actionable insights from supply chain data lakes. 🔹 Responsible AI Deployment – Ensure ethical sourcing, bias-free algorithms, and transparent AI-driven decisions. 🔹 Automation at Scale – Deploy bots for repetitive tasks (e.g., PO processing, carrier selection) while focusing human expertise on strategic pivots. 3 Urgent Actions for Supply Chain Leaders To future-proof your operations and advisory services: ✅ Conduct an AI Workforce Gap Analysis – Identify where AI will augment planners, analysts, and strategists and where roles must evolve. ✅ Define an AI-Powered Supply Chain Vision – Reimagine everything from inventory algorithms to supplier risk scoring with AI as the core enabler. ✅ Build a Hybrid Talent Pipeline – Upskill teams in AI fluency while recruiting data-savvy supply chain engineers. The future belongs to firms that embed AI into every layer of supply chain consulting from diagnostic to execution. Is your team leading the transformation or playing catch-up? Let us help you achieve Real results, together https://lnkd.in/gXnZT_r #SupplyChain #Resilience #DigitalTransformation #ArtificialIntelligence #Logistics #ManagementConsulting

Sales and Operations Planning is my favorite domain for data analysts. Here is why analytical skills are in high demand in S&OP teams. In Sales and Operations Planning (S&OP) it’s all about aligning the company’s sales, marketing, finance, and supply chain functions to ensure that demand and supply are balanced, and business goals are met efficiently. S&OP helps organizations make better decisions by integrating data, forecasting, and cross-functional collaboration to optimize resources and meet customer needs. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 𝗧𝗮𝘀𝗸𝘀 𝗶𝗻 𝗦&𝗢𝗣: 1. 𝗗𝗲𝗺𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: You’ll analyze historical sales data and market trends to forecast future demand. This helps the business align its marketing campaigns, inventory, and work force with customer needs. 2. 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: By analyzing data across the supply chain, you’ll identify bottlenecks, inefficiencies, and opportunities to improve operations, by ensuring that products are delivered on time and within budget. 3. 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: S&OP requires constant adjustments. You’ll create "what-if" scenarios to predict the impact of changes in demand, supply, or market conditions, enabling the business teams to make better decisions. 4. 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: You’ll work closely with sales, marketing, finance, and operations teams to ensure everyone is aligned and working towards common goals. Your ability to translate data into recommendations for the different stakeholders is important in this cross-functional collaboration. 5. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴: Tracking KPIs and metrics is the baseline for continuous improvement. You’ll provide the data-driven insights that help the business stay on track and adapt to changing conditions. I worked on the network planning team responsible for the S&OP process of Zalando's fast-growing network and built the first steps of S&OP processes for multiple companies as a consultant at RizonX. In every project, data analysts have played an important role in bridging the gaps between the different teams by providing aligned reports, dashboards, and forecasts. Does your company run a structured S&OP process utilizing data analytics to its full potential? Have you worked on S&OP teams or are considering it for your future career? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #supplychainmanagement #salesandoperationsplanning #demandplanning #careergrowth

𝗔𝗿𝗲 𝗬𝗼𝘂 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗕𝗶𝗴𝗴𝗲𝗿 𝗣𝗶𝗰𝘁𝘂𝗿𝗲? Many sales and marketing leaders focus on metrics that matter to their individual teams. While tracking website traffic, lead volume, or pipeline velocity is common, have you stepped back to see how these numbers fit into your overall revenue engine? Below is a snapshot of the key metrics each function typically tracks—and the revenue engine metrics you should monitor together for a complete picture: 𝗙𝗼𝗿 𝗦𝗮𝗹𝗲𝘀 𝗟𝗲𝗮𝗱𝗲𝗿𝘀:  • 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆: How quickly deals move through your funnel. Faster velocity means efficient conversion.   • 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗥𝗮𝘁𝗲𝘀: The percentage of leads that turn into opportunities and closed deals.   • 𝗔𝘃𝗲𝗿𝗮𝗴𝗲 𝗗𝗲𝗮𝗹 𝗦𝗶𝘇𝗲 & 𝗪𝗶𝗻 𝗥𝗮𝘁𝗲𝘀: Indicators of deal quality and sales effectiveness. 𝗙𝗼𝗿 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗟𝗲𝗮𝗱𝗲𝗿𝘀:  • 𝗪𝗲𝗯𝘀𝗶𝘁𝗲 𝗧𝗿𝗮𝗳𝗳𝗶𝗰 & 𝗦𝗼𝗰𝗶𝗮𝗹 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Although often seen as vanity metrics, they offer a glimpse of initial interest.   • 𝗟𝗲𝗮𝗱 𝗩𝗼𝗹𝘂𝗺𝗲 & 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: Focus on not just the number, but the qualification of leads (e.g., MQLs).   • 𝗟𝗲𝗮𝗱 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆 𝗥𝗮𝘁𝗲 (𝗟𝗩𝗥): The growth rate of qualified leads, hinting at future sales potential.   • 𝗔𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 & 𝗥𝗢𝗜: Which campaigns are truly driving valuable leads and revenue. 𝗙𝗼𝗿 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗟𝗲𝗮𝗱𝗲𝗿𝘀:  • 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 & 𝗖𝗵𝘂𝗿𝗻 𝗥𝗮𝘁𝗲𝘀: High retention and low churn show that your team is building lasting, profitable relationships.   • 𝗨𝗽𝘀𝗲𝗹𝗹 & 𝗖𝗿𝗼𝘀𝘀-𝗦𝗲𝗹𝗹 𝗥𝗮𝘁𝗲𝘀: Measure success in generating additional revenue from existing customers.   • 𝗡𝗣𝗦 & 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗛𝗲𝗮𝗹𝘁𝗵 𝗦𝗰𝗼𝗿𝗲𝘀: Gauge customer satisfaction and loyalty. 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 𝗘𝗻𝗴𝗶𝗻𝗲 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝘁𝗼 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗧𝗼𝗴𝗲𝘁𝗵𝗲𝗿:  • 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗙𝘂𝗻𝗻𝗲𝗹 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻: Track the seamless movement from MQL to SQL to closed deal.   • 𝗖𝗔𝗖 𝘃𝘀. 𝗖𝗟𝗩: Compare the cost of acquiring customers with the revenue they generate over their lifetime.   • 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗻𝗲𝘀𝘀: Assess how well customer data is shared and used across teams for smarter targeting and personalization. Shifting your focus from isolated metrics to these holistic KPIs gives you clarity on where your revenue engine excels—and where it needs improvement. Together, these indicators provide a comprehensive view of how effectively your organization drives sustainable revenue growth. Are you ready to break down silos and embrace a holistic view of your performance metrics -  to unlock the full potential of your revenue engine?

Strategy isn’t a slide. It’s a fight worth having. I’ve been quiet here for a day because I just came out of two intense, energizing sessions with our extended strategy team. And I’m still fired up. 💥 We pushed each other hard. We challenged assumptions. We laughed a lot. And we left with crystal-clear alignment and a shared determination to think bigger, move faster, and win as one team. Our focus: ✅ Think Big, Go Fast Not in months and quarters. In days and weeks. ✅ Win Every Key Moment in the Customer Journey Especially the ones that define value and long-term loyalty. ✅ Win as One Team Not your team, not my team. Our team. Rooted in shared goals, not personal preferences. For some reason, I usually get to help moderate these sessions. That’s no small task with 25 to 30 strong leaders in the room from every department. But it gives me a front-row seat into how we build alignment that lasts. Here’s what works for us and might work for you: 1️⃣ Be clear up front Why are we meeting? What are the most important objectives? And how exactly are we going to win together? Set the tone early. Remove ambiguity. Drive purpose. 2️⃣ Bring the voice of the customer into the room 🎤 The most substantial alignment starts with empathy and clarity around what matters most to our customers. When we anchor the conversation in value needed and delivered, priorities become clearer and conflict becomes productive. Customer insights create unity. 3️⃣ Make cross-functional ownership real 🤝 Everyone says “we’re one team.” But real alignment means we walk out with shared KPIs, not siloed tasks. Product, Sales, CS, Ops, we all succeed only when we move together. 💬 So here's my call to action for you today: If you’re leading in CS, CX, Product, or Revenue, and you’re halfway through Q3, ask yourself: Are you chasing alignment? Or are you building it through purpose, participation, and shared accountability? The next level doesn’t arrive by accident. We create it. Together. #CreateTheFuture #LeadershipInAction #CustomerSuccess #StrategyExecution #CrossFunctionalAlignment #OneTeamOneMission #Q3Momentum

If you're serious about mastering the complexities of supply chain and operations, I can't recommend Manufacturing Planning and Control for Supply Chain Management by F. Robert Jacobs, William L. Berry, D. Clay Whybark, and Thomas E. Vollmann enough Manufacturing Planning and Control for Supply Chain Management. This breakdown can help you focus your reading or use it as a study guide, especially if you're preparing for the CPIM exam or applying it in your organization. Key Chapters & Core Topics (Deep Dive). 1.-Introduction to Manufacturing Planning and Control (MPC). Overview of MPC systems in modern supply chains. Strategic vs tactical vs operational planning. Importance of demand-supply alignment. 2.-Demand Management. Forecasting techniques (qualitative & quantitative). Customer order servicing strategies. Collaborative Planning, Forecasting, and Replenishment (CPFR). 3.-Sales and Operations Planning (S&OP). Balancing demand and supply at the volume level. Cross-functional planning processes. Aggregate planning and rough-cut capacity planning. 4.-Master Production Scheduling (MPS). Transitioning from volume to mix. Priority setting, time fences, and ATP (Available to Promise). Stability and flexibility in MPS. 5.-Material Requirements Planning (MRP). BOM (Bill of Materials) structures. Netting, lot sizing, and lead time offsets. Regenerative vs net change planning. 6.-Capacity Planning. Capacity Requirements Planning (CRP). Rough-Cut Capacity Planning (RCCP). Load vs capacity analysis. 7.-Production Activity Control (PAC). Shop floor control systems. Sequencing, dispatching, and monitoring. Lean scheduling methods. 8. Advanced Planning and Scheduling (APS). Real-time planning systems. Constraint-based scheduling. Integration with ERP systems. 9.-Just-in-Time (JIT) and Lean Manufacturing. Pull systems, kanban, and takt time. Waste elimination and continuous flow. Cultural and organizational enablers. 10.-Distribution Requirements Planning (DRP). Planning for finished goods distribution. Inventory positioning across the network. Multi-echelon supply chains. 11.-ERP Systems and Supply Chain Integration. Role of ERP in MPC. Data structures and integration across functions. Real-time visibility and decision-making. 12.-Strategy and Performance Measurement. Aligning MPC with corporate strategy. KPIs for planning and execution. Continuous improvement through metrics.

I’m pleased to share our new report on what miners need to know when entering joint ventures (JV) and other partnerships with players in other industries.    As miners seek to decarbonize operations, reduce their environmental footprint and generally evolve to become more sustainable, they are coming to us to help draft agreements with equipment manufacturers, developers of process technologies, renewable power companies, and innovators in the energy transition.    An area of particular interest to me as part of my work advising on the development and financing of mining projects is anticipating what can go wrong in a partnership – and what can go right.    This means is understanding what motivates each party, where the risks are most likely to lie, and what forms an exit could take if things don’t go to plan. It also means identifying what success looks like, and how parties can remain in, exit from, and even sell a fruitful partnership.    In the nine months since our own merger, we’ve been able to provide even more detailed recommendations to an ever-larger array of global clients seeking cross-sector transactions.    Mining companies have taken our advice on how to structure these JV arrangements to protect their commercial interests and manage emerging and often unpredictable risks.     To learn more, please read our report: https://lnkd.in/ertUumpR    #AOShearman 

Are You Making This Critical Mistake in Your Agile Strategy?   Armed with sticky notes and an ambitious plan, Kunal (name changed) dived headfirst into the transformation, convinced that his lone efforts would be enough.   He ignored vendor partners, certain that his internal team had everything under control. But soon, cracks began to show. Deadlines were missed, products were delayed, and market demands went unmet. Kunal found himself constantly scrambling, questioning the strength of his strategy.   Weren't Kunal's efforts enough?   In my 3 decades of transformation experience, I have often seen that much effort goes into increasing the client's Business Agility.   What is Business Agility? Business Agility is the ability of an organization to quickly react to market developments and changing client needs, maintaining a competitive edge. This requires the capacity to swiftly generate, validate, and pivot on new ideas. Often, this means enhancing how our client can execute a strategy, reduce time-to-market, and increase collaboration between operations and development.   Our experience teaches us that such a journey attracts much of our client's attention. However, we must recognize that in the current era, organizations are rarely self-sufficient. They are often part of a supply chain or rely on multiple suppliers.   This raises an important question: Can organizations truly be agile if they don't include their suppliers in their transformation? If only one part of the chain is strengthened, does that make the whole chain stronger?   Here are some aspects you may want to consider:   👉🏻 Engage with Suppliers: Establish regular communication channels with your suppliers to ensure they are aligned with your agile transformation goals. Consider them as integral partners in your strategy rather than external entities.   👉🏻 Collaborative Workshops: Organize workshops and training sessions that include both internal teams and suppliers. Focus on shared goals, collaborative problem-solving, and aligning on agile practices and methodologies.   👉🏻 Integrate Systems: Implement integrated systems that allow for real-time data sharing and collaboration across the supply chain. This ensures transparency, faster decision-making, and a unified approach to market demands.   👉🏻 Feedback Loops: Create mechanisms for continuous feedback between your organization and your suppliers. Regularly review performance metrics, identify bottlenecks, and adapt strategies based on mutual feedback.   👉🏻 Shared KPIs: Develop and track shared Key Performance Indicators (KPIs) with your suppliers to measure the success of the agile transformation.   What steps are you taking to ensure your entire supply chain is agile and ready for market changes? DM me for support in your transformation journey and achieving business agility.   #BusinessAgility #SupplyChainManagement #Leadership #Transformation #AgileMindset

Too many businesses stop at the idea that UNS is simply a way to make data more accessible. While that’s true, the UNS is so much more than just a data tool — it’s the heart of an autonomous manufacturing industry. Here’s why: UNS doesn’t just unify data; it creates a live, shared data environment where machines, systems, and even AI can communicate and collaborate in real-time. How exactly does this work in practice? Here’s how UNS facilitates full automation in a typical manufacturing environment: 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜𝐬 The UNS allows automated triggering of updates to production schedules and inventory needs when a customer order is created in ERP. Maintenance and Quality operations are also scheduled through the UNS. As production activities take place on the factory floor, the UNS ensures that actual Operations Performance data flows back from Level 3 to Level 4, informing business planners of progress, delays, or inefficiencies, allowing them to adjust business operations on the fly. 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 Definitions of work activities and specific work requests automatically flow within Level 3 through the UNS, ensuring that the shop floor executes the right tasks at the right time. As work progresses, UNS enables continuous updates of the work schedule, adjusting to unforeseen changes or disruptions and ensuring seamless coordination between Level 4 and Level 3. Once work is completed, UNS automates the flow of performance data from Level 3 to Level 4, ensuring that business planners have a real-time view of productivity. 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 UNS ensures that real-time data on equipment capability and performance flows from Level 2 to Level 3, providing manufacturing operations management with continuous updates on machine availability, performance, and quality. Additionally, UNS enables instructions from Level 3 to be sent back to Level 2, dynamically adjusting equipment settings, production parameters, and even initiating maintenance tasks based on real-time conditions on the shop floor. 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐀𝐈-𝐃𝐫𝐢𝐯𝐞𝐧 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧𝐬 UNS enhances the utility of your predictive data by making it actionable across different organizational areas. This facilitates the transformation of predictive analytics into prescriptive analytics, and more broadly, into prescriptive operations and automated corrective actions. For example, UNS can integrate with AI-driven systems to adjust production schedules on the fly, reduce energy consumption during non-peak hours, or alert maintenance teams about impending machine failures — before downtime occurs. So, as you think about UNS, this should always be your true north star.