Inventory Replenishment Strategies

Here is an interesting career column in Nature by Gerald Schweiger on what he calls a “point of no returns” in research funding. The core idea is simple and uncomfortable. At some point, the total cost of competing for grants becomes equal to, or even higher than, the money that is actually being awarded. Time spent writing proposals, reviewing them, coordinating consortia, and running administrative processes can collectively exceed the value of the funded research itself. When that happens, the system is no longer inefficient. It is extractive. Using the concept of the Szilard point, the author illustrates this with the EU funding call “GenAI for Africa.” Out of 215 proposals, only two are expected to be funded. Depending on the assumptions, the estimated total cost of preparing and evaluating those applications ranges from about €5.3 million to more than €40 million, for a call with a total budget of €5 million. Even the most conservative estimate suggests that taxpayers and researchers may have spent more on the process than on the science. What makes this especially troubling is not just the waste of money, but the waste of attention, energy, and intellectual focus. Early-career researchers learn very quickly that publishing, networking, and even choosing research questions are often subordinated to one overarching goal: securing the next grant. Science becomes optimized for survival in funding competitions rather than for curiosity, rigor, or societal impact. This is not an argument against selectivity or quality control. Scarce resources always require difficult allocation decisions. But it is an argument against pretending that hyper-competition is automatically fair, efficient, or meritocratic. When success rates drop below one percent, we are no longer selecting the best ideas. We are mostly selecting who can afford to play the game longest. If we want to change this, we need to be willing to rethink funding as a system, not just tweak individual calls. More focused calls, staged application procedures, partial lotteries after quality thresholds, or peer-nomination models are not radical ideas. They are pragmatic attempts to reduce systemic waste and redirect effort back to where it belongs. A concrete first step would be this: funders should be required to publicly report not only success rates and awarded budgets, but also estimated application and evaluation costs. Once we routinely ask whether a call is approaching or crossing the Szilard point, it becomes much harder to justify business as usual. Here is the link: https://lnkd.in/dyDqzBNR #Academia #ResearchFunding #AcademicLife #ResearchSystem

📦 Understanding Re-Order Point (ROP) and Replenishment in Warehouse Management 📦 In supply chain and warehouse management, knowing when to reorder stock is crucial for maintaining the right balance between inventory availability and cost efficiency. One of the key concepts in inventory management is the Re-Order Point (ROP). But how do you calculate it accurately? And what are the most effective replenishment strategies? 🔹 What is the Re-Order Point (ROP)? ROP is the threshold at which stock must be replenished to prevent shortages before the next delivery arrives. In other words, it is the minimum inventory level at which a new purchase order should be placed. 🔢 Basic ROP Formula: Without Safety Stock: 📌 ROP = Lead Time (Days) × Average Daily Consumption With Safety Stock: 📌 ROP = (Lead Time × Average Daily Consumption) + Safety Stock 🛠 Example Case: A warehouse has a daily material consumption of 10 units, with a procurement lead time of 7 days. 📌 ROP = 7 × 10 = 70 So, when the stock reaches 70 units, the company should immediately reorder to avoid running out of stock while waiting for the next delivery. 🔹 Effective Replenishment Strategies Determining the ROP alone is not enough. Businesses must also adopt the right replenishment strategy to ensure a steady inventory flow without excessive overstocking. Here are three common strategies: 1️⃣ Just-In-Time (JIT) This approach ensures that stock is ordered only when it is needed. It is suitable for businesses with stable demand and reliable suppliers who can deliver quickly. ✅ Pros: Reduces storage costs and minimizes inventory obsolescence. ❌ Challenges: Highly dependent on a smooth supply chain—any disruption can cause stockouts. 2️⃣ Fixed Order Quantity With this method, orders are placed in fixed quantities whenever the stock reaches the ROP. The order quantity is often based on Minimum Order Quantity (MOQ) or Economic Order Quantity (EOQ). ✅ Pros: Helps maintain consistent stock levels. ❌ Challenges: Can lead to overstocking if demand drops unexpectedly. 3️⃣ Periodic Review System Stock levels are reviewed at fixed intervals (e.g., monthly), and orders are placed accordingly. ✅ Pros: Suitable for items with fluctuating demand. ❌ Challenges: If the review period is too long, stockouts may occur before the next replenishment cycle. 🎯 Conclusion Determining the optimal Re-Order Point (ROP) is essential to ensure stock availability without excessive inventory costs. By understanding consumption patterns, lead time, and choosing the right replenishment strategy, warehouse operations can run efficiently and seamlessly, avoiding both stockouts and overstock situations. 🔥 What ROP and replenishment strategy do you use in your warehouse? Let’s discuss in the comments! #Inventory #Warehouse #Supplychain #SCM #Logistic #Rop #Replenishment

Inflation isn't just about rising prices; it's a catalyst for changing consumer behaviors. As purchasing power shifts, businesses must adapt swiftly to meet evolving demands. Hindustan Unilever Limited (HUL), a leader in the FMCG sector, showcases how embracing AI can turn these challenges into opportunities. 📌 The Challenge #HUL observed significant fluctuations in demand across its diverse product portfolio during inflationary periods. Premium products experienced slower sales, leading to overstock situations, while budget-friendly items frequently faced stockouts. Traditional forecasting methods, relying heavily on historical sales data, struggled to keep pace with these rapid changes in consumer preferences. 📊 The Solution: AI-Driven Demand Forecasting To address this, HUL integrated AI-powered analytics into its demand forecasting processes. This advanced system enabled the company to: Analyze Real-Time Consumer Behavior: By examining current purchasing patterns and consumer sentiment, HUL could detect emerging trends and shifts in preferences. Incorporate External Economic Indicators: The AI model factored in various economic indicators, such as inflation rates and consumer confidence indices, to predict their impact on product demand. Optimize Inventory Management: With precise demand forecasts, HUL adjusted its inventory levels accordingly, ensuring optimal stock across all product categories. 🔹 Key Insight: The AI-driven approach revealed that demand for budget-friendly products was increasing at a rate three times higher than traditional models had predicted, while premium product sales were declining in specific regions. 📈 The Impact 20% Reduction in Unsold Premium Stock: By aligning inventory with actual demand, HUL minimized excess stock of premium items. 35% Improvement in Stock Availability for Budget-Friendly Products: Ensuring that high-demand, cost-effective products were readily available led to increased customer satisfaction. Enhanced Revenue and Profit Margins: Optimized inventory management reduced holding costs and prevented lost sales, positively impacting the bottom line. 💡 The Lesson In times of economic uncertainty, relying solely on historical data can be a pitfall. HUL's proactive adoption of AI-driven demand forecasting exemplifies how leveraging advanced analytics allows businesses to stay agile and responsive to market dynamics, ensuring they meet consumer needs effectively How is your organization utilizing data analytics to navigate market fluctuations? #datadrivendecisionmaking #businessstrategies #dataanalytics #demandforecasting

The RESourceEU Action Plan has just been adopted! Building on the Critical Raw Materials Act (CRMA), it provides financing and concrete tools to protect industry from geopolitical and price shocks, promote projects on critical raw materials in Europe and beyond, and partner with like-minded countries to diversify supply chains. The plan aims to fast-track relevant projects and reduce strategic dependencies. 1. Protect European industry from geopolitical and price shocks - The Commission will set up a European Critical Raw Materials Centre in early 2026. - The Raw Materials Platform will facilitate the efforts of the companies to aggregate demand, jointly purchase strategic raw materials and secure offtake agreements. - Work is underway with Member States on a coordinated EU approach to stockpiling critical raw materials. - To protect the Single Market and bolster supply chain resilience, the Action Plan foresees monitoring, crisis coordination and defence against hostile interference. - To boost Europe's recycling capacity, in early 2026, restrictions on the export of scraps and waste of permanent magnets as well as targeted measures on aluminium scrap. Similar actions will be considered for copper scrap if this proves necessary. - A targeted amendment to the CRMA expands product labelling requirements and incentivises recycling of pre-consumer waste for permanent magnets. Shares of recycled content in permanent magnets will support recycling in the EU. 2. Promote critical raw materials projects by de-risking investments and fast-tracking permitting - The Commission will accelerate EU-relevant projects by mobilising financial de-risking tools and removing regulatory bottlenecks to fast-track Strategic Projects with the potential to reduce dependencies by up to 50% by 2029. - - The EU will mobilise up to €3 billion over the next 12 months to support concrete projects that can provide alternative supplies in the short term. 3. Partner with like-minded countries for strong and diversified supply chains - The EU will deepen cooperation with like-minded partners to diversify supply and accelerate industrial cooperation, - It will build on the existing 15 Strategic Partnerships signed with resource-rich countries, with South Africa being the most recent one. - The Commission will also launch negotiations with Brazil.  - The Commission will further pursue win-win investment projects under Global Gateway with emerging markets and developing economies. - The EU supports the Canada-led G7 Critical Minerals Production Alliance and the G7 roadmap for standards-based markets and will promote strong diversification through G20 Critical Minerals Framework. https://lnkd.in/erWe-cR6

A few years ago, I interviewed a seasoned supply planner from a global FMCG giant. I asked him, "How do you ensure uninterrupted service when forecasts are often wrong?" He smiled and replied, "I don’t trust forecasts blindly. I trust buffers." That stuck with me. We often talk about safety stock like it’s just another calculation - based on service levels, variability, and lead time. But what we often miss is that safety stock is not a backup plan - it’s a confidence plan. When I worked with a food company in North India, we faced wild swings in demand during festive seasons. Despite best efforts, our forecast error remained in the 25–30% range. Initially, we adjusted demand. Then we tried pushing supply. Nothing worked consistently. Until we recalibrated safety stock - not as a static percentage, but as a dynamic lever. We used historical MAPE to segment SKUs: ↳ High forecast error items had higher safety stock, but only if they were fast-movers ↳ For low runners, we capped safety stock and focused on lead time reduction This single change lifted our service levels from 87% to 95% - without inflating inventory across the board. Here’s what I learned: Safety stock isn’t about covering up forecasting failures. It’s about strategically absorbing volatility where it matters most. It’s not "extra" inventory—it’s "essential" inventory. We often praise forecast accuracy, but sometimes, it’s the silent buffers - well-planned, SKU-specific safety stocks - that save the day. Would love to hear - how do you approach safety stock? Static formula or dynamic levers?

Forecasting is a common application of data science, and it's crucial for businesses to manage their inventory, especially those with perishable items effectively. In a recent tech blog, the data science team from Afresh shared an innovative approach to accurately predict demand, incorporating non-traditional factors such as in-store promotions. Promotions are common in grocery stores, helping customers discover and purchase discounted items. However, these promotions can significantly alter customer behavior, making traditional forecasting methods less reliable. Traditional models struggle to incorporate these factors, often leading to higher prediction errors. To address this challenge, Afresh’s data science team developed a deep learning forecasting model that integrates various features, including promotional activities tied to specific products. The model's performance was evaluated using a normalized quantile loss metric, showing an 80% reduction in loss during promotion periods. This example highlights the superior performance of this solution and showcases the power of deep learning in solving a critical issue for the grocery industry. #machinelearning #datascience #forecasting #inventory #prediction – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gWRgTJ2Q 

Mastering Supply Chain with Key Formulas & Real-Life Cues Whether you're new to supply chain or brushing up your knowledge, these core formulas can boost your decision-making, efficiency, and ROI. Here's a breakdown with simple explanations & relatable examples: 📦 Inventory Management 1. Economic Order Quantity (EOQ) Formula: √(2DS/H) Cue: "How much should I order to balance cost & storage?" E.g., Ordering 707 units avoids excess cost and space. 2. Reorder Point (ROP) Formula: Lead Time × Daily Demand Cue: "When should I reorder?" E.g., If lead time is 5 days and demand is 100/day → ROP = 500 units. 3. Safety Stock (SS) Formula: Z × σ Cue: "Buffer stock for demand surges or delays." E.g., With 95% service level and std dev 30 → SS ≈ 50 units. 4. Inventory Turnover Formula: COGS / Avg Inventory Cue: "How fast am I selling and restocking?" Higher turnover = better cash flow & efficiency. 🚚 Logistics & Transportation 1. Transportation Cost (TC) Formula: Distance × Rate × Weight Cue: "What will my shipment cost?" 2. Freight Cost per Unit Formula: Total Freight / Units Shipped Cue: "Shipping cost per product?" 3. Lead Time (LT) Cue: "How long between ordering and receiving?" 4. Cycle Time (CT) Cue: "Speed from receiving an order to shipping it out." ⛓ Supply Chain Performance 1. Fill Rate (FR) Formula: (Units Filled / Ordered) × 100 Cue: "Are we meeting demand?" E.g., 950 filled out of 1000 → FR = 95% 2. On-Time Delivery (OTD) Formula: (On-Time / Total Orders) × 100 Cue: "How reliable are we?" 3. Supply Chain Cycle Time (SCCT) Cue: "Total time from raw material to customer." 4. Total Cost of Ownership (TCO) Formula: Price + Ops + Maintenance Cue: "What’s the lifetime cost?" 📊 Forecasting 1. Moving Average (MA) Formula: Sum of Past Demand / # of Periods Cue: "What’s my average demand?" 2. Exponential Smoothing (ES) Formula: a × Actual + (1-a) × Forecast Cue: "React to recent trends." 3. Seasonal Index (SI) Formula: Seasonal Demand / Avg Demand Cue: "How much does demand vary seasonally?" 🛒 Procurement & Purchasing 1. Total Cost of Procurement (TCP) Formula: Price + Ordering + Holding Cue: "What’s the full cost of stocking?" 2. Supplier Performance Index (SPI) Formula: (Quality × Delivery × Price) / 3 Cue: "Is my supplier reliable and cost-effective?" 3. Purchase Order Lead Time (POLT) Cue: "Time between placing and receiving orders." ⚖️ Other Strategic Tools 1. Break-Even Analysis (BEA) Formula: Fixed Costs / (Price - Variable Cost) Cue: "When do we start making profit?" 2. Cost-Benefit Analysis (CBA) Formula: Benefits / Costs Cue: "Is this decision worth it?" 3. Return on Investment (ROI) Formula: (Gain - Cost) / Cost Cue: "What’s the return on this investment?" hashtag #SupplyChain hashtag #Logistics hashtag #InventoryManagement hashtag #Procurement hashtag #Transportation hashtag #Forecasting hashtag #SCM hashtag #LogisticsManagement hashtag #OperationsExcellence hashtag

Ever wonder why some e-commerce brands always seem to have the right products in stock, while others struggle with overstock or empty shelves? It all comes down to demand forecasting—and in 2025, it’s getting an AI-powered upgrade. ● From guesswork to precision Traditional forecasting relies on historical sales data. AI-driven tools now go beyond that, integrating real-time factors like weather, local events, and even social media trends. The result? Forecasts with 90%+ accuracy instead of the usual 50%. ● GenAI: the next step Generative AI takes it further by analyzing unstructured data (customer reviews, trends, emerging demand signals) and answering questions in plain language. No more complex spreadsheets—just instant insights for better inventory planning. ● AI tools leading the way: ✔ Simporter – AI-powered forecasting that integrates multiple data sources to predict sales trends. ✔ Forts – uses AI for demand and supply planning, ensuring optimized inventory. ✔ ThirdEye Data – AI-driven forecasting that factors in seasonality and customer behavior. ✔ Swap – AI-based logistics platform that enhances inventory management. ✔ Nosto – AI-driven personalization that recommends the right products at the right time. ● Why this matters for #ecommerce? ✔️ Avoid stockouts that frustrate customers ✔️ Reduce excess inventory and free up cash ✔️ Adapt quickly to market shifts How are you managing demand forecasting in your store? #shopify

Your inventory can drain your working capital if it’s not managed properly 🎯 I have met many founders who complain about their cash flow problem. Despite their business are actually growing, their cash flow is lacking behind 📉. When I made deeper conversation, I found out their inventory level are high. Many of them don’t realize they actually hold high level of stock. In Stanford Seed Transformation Program, we introduce the use of Days of Inventory (DIO) as one of the tools to our cohort participant so they can measure how efficient they managed their inventory as working capital. Essentially the DIO helps to assess inventory management efficiency by showing how quickly inventory is converted to sales, which can reveal issues like overstocking, slow sales, or potential out of stock. If you manage it properly, it could help you better manage your cash flow, reduce holding costs, and make better decisions about inventory levels 💸. Here’s several practical tips that can help you optimize your inventory level: 1️⃣ Use relevant software: Implement software to track your inventory real-time, which can automate updates and provide better visibility. 2️⃣ Leverage data to make forecast: Analyze your historical data and market trends to estimate future demand of your products. It would help to prevent out of stock or excess inventory. 3️⃣ Perform regular audits: Perform regular stock counts to ensure your records match physical inventory and to identify discrepancies or slow-moving items. 4️⃣ Perform ABC analysis: Categorize items based on their value and sales frequency to focus your management efforts on the most important products. 5️⃣ Centralize control: If you have multiple locations then you better centralize your inventory control to improve overall visibility and management. 🤔 As SME owner, what’s your biggest inventory headache right now? Is it overstock, out of stock, or slow movers? Feel free to share in the comments or DM me directly. 🙏 If you're looking to scale up your SME or early-stage business and strengthen your financial foundation, let’s connect. Together, we can explore impactful strategies for success. #ScalingUp #BusinessTransformation #Financialmanagement #FractionalCFO

The main goal of portfolio selection and construction is to create a profitable portfolio; however, this task is difficult, otherwise we would all be millionaires or billionaires. Markets are dynamic and influenced by numerous factors, while static historical data often fails to capture these dynamics. Investors seek portfolios that optimize the trade-off between risk and return, requiring robust asset allocation. Such requirement is challenging because stock returns are highly unpredictable due to the stock market's nonlinearity, noise, and chaotic nature, making asset selection difficult. To enhance portfolio selection and construction, researchers have incorporated multi-source and multi-aspect data to supplement fundamental and technical stock price data. They have also developed hybrid models involving statistics, econometrics, signal processing, and machine/deep learning (ML/DL) in recent years, which have been shown to outperform single models. DL models like LSTM and CNN excel at capturing temporal and spatial patterns in stock data, improving predictions of returns and volatility. Hybridizing CNN and LSTM (CNN-LSTM) leverages their strengths; CNN for spatial data and LSTM for time series, enabling them to handle complex market dynamics effectively. In [1] which is shared in the comments, the authors proposed a framework combining the essence of DL for stock selection through prediction and optimal portfolio formation through the mean-variance (MV) model. Their proposed framework involves a hybrid CNN-LSTM model in the first stage, which blends the benefits of the CNN and the LSTM. The framework combines feature extraction with sequential learning to analyze temporal data fluctuations. In their experiments, they used 13 input features, combining fundamental market data and technical indicators to capture the nuances of the highly volatile stock market data. The shortlisted stocks with high potential returns, identified during the selection phase, are advanced to the second stage for optimal stock allocation using the MV model. Their proposed hybrid framework is validated through comparison with four baseline strategies and relevant studies, demonstrating superior performance in terms of annual cumulative returns, Sharpe ratio, and average return-to-risk ratio, both with and without transaction costs. #QuantFinance The workflow is depicted in Fig. 3 on page 8, and its detailed description is covered on pages 7 and 8. It is straightforward to implement.