Inventory Management Tools

Running simulations: base model vs. lookahead model I see people posting on the use of “simulations” for planning inventory policies. If you are using a lookahead model (which is typical for most real-world inventory problems), there are two models where simulation can be used:   1.    The base model, which can be a simulator or the real world. 2.    The lookahead model, which is used in the policy for planning the future to make a decision now. See the figure below - I use the same notational style for both models, but the lookahead model uses tildes on each variables, which also carry two time subscripts: the point in time we are making the decision, and the time period within the lookahead model.   The base model is used to evaluate the policy, and is needed to perform any parameter tuning. The base model can be based on history or a simulation of what you think the future can be.   When simulating inventory policies, special care has to be used because we do not have historical data on market demand – we typically just have sales, which can be “censored” (a topic that has been recognized in the inventory literature for over 60 years). For example, if we run out of product (and there is no back ordering), we lose the sales, which typically means that we do not see (or record) them.   I find it is generally best to run simulations using mathematical models of uncertainty so that we can run many simulations, testing different policies. Stockouts depend on properly simulating the tails of distributions, along with market shifts, price changes and supply chain disruptions. There are, of course, settings where you have no choice but to test your ideas in the field. It is expensive, risky, and slow, but sometimes you just have no choice, especially when you have to capture human behavior.   If your policy requires planning into the future, you really need to be using a stochastic (probabilistic) model of the future which properly captures the tails of distributions. With long lead times, you should also plan for the possibility of significant disruptions, which can mean that you also have to capture the decisions you might make in the future. See chapter 19 of:   https://lnkd.in/dB99tHtM (“tinyurl.com/” with “RLandSO”)   for an in-depth treatment of direct lookahead policies. #supplychain #inventory  Nicolas Vandeput Joannes Vermorel

Machine Learning Interview Question with Solution for a Walmart Data Scientist Role Question: Walmart collects data from various sources like sales, inventory, and customer behavior. One of the main goals is to predict product demand to optimize inventory levels. Suppose you are provided with historical sales data, including features like Date, Store_ID, Product_ID, Sales_Quantity, and Promotion. How would you build a machine learning model to forecast sales for the next 30 days? Solution: To solve this problem, we can follow these steps:    ⏩ Understand the Problem: The goal is to predict Sales_Quantity for the next 30 days, which makes this a time series forecasting problem.    ⏩ Data Preprocessing:        - Handling Missing Values: If there are any missing values, we need to fill them appropriately (e.g., using median or forward fill for missing sales quantities).        - Feature Engineering: Create additional features such as:             - Lag features (previous sales quantities)             - Rolling averages (7-day, 30-day)             - Holidays (since Walmart sales may spike during holidays)             - Days of the week (sales patterns may differ between weekdays and weekends)    ⏩ Train-Test Split:        Split the data into a train set (e.g., sales before the last month) and a test set (last month of sales).    ⏩ Model Selection: Some of the models we can consider are:         - Random Forest Regressor: Can handle non-linear relationships and provide feature importance.         - XGBoost or LightGBM: These are tree-based gradient boosting models that work well for structured data like sales forecasting.         - ARIMA (AutoRegressive Integrated Moving Average): A classic time-series forecasting model.    ⏩ Training the Model:        - Use historical data to train the model on sales quantity (Sales_Quantity) as the target variable.        - Ensure to include relevant features like promotions, store IDs, and lagged sales as inputs.    ⏩ Evaluation Metrics:        Use Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE) to evaluate the model's performance on the test set.    ⏩ Hyperparameter Tuning:        Perform Grid Search or Random Search for hyperparameter tuning to optimize model performance.    ⏩ Deployment:        Once the model is trained and evaluated, deploy it to forecast future sales and adjust inventory levels accordingly. Can you think of any alternate solution? Please share in comments! #dsinterviewpreparation #ml #walmartinterview

Inflation isn’t just an economic challenge—it’s a test of agility for businesses. As costs rise and purchasing power shifts, companies that rely on gut instinct risk falling behind. The real winners? Those who use data-driven insights to navigate uncertainty. 1️⃣ Understanding Consumer Behavior: What’s Changing? Inflation reshapes spending habits. Some consumers trade down to budget-friendly options, while others delay non-essential purchases. Businesses must analyze: 🔹 Spending patterns: Are customers shifting to smaller pack sizes or private labels? 🔹 Channel preferences: Is there a surge in online shopping due to better deals? 🔹 Regional variations: Inflation doesn’t hit all demographics equally—hyperlocal data matters. 📊 Example: A retail chain used real-time sales data to spot a shift toward economy brands, allowing it to adjust promotions and retain price-sensitive customers. 2️⃣ Pricing Trends: Data-Backed Decision-Making Raising prices isn’t the only response to inflation. Smart pricing strategies, backed by AI and analytics, can help businesses optimize margins without losing customers. 🔹 Dynamic pricing models: Adjust prices based on demand, competitor moves, and seasonality. 🔹 Price elasticity analysis: Determine how much a price hike impacts sales before making a move. 🔹 Personalized discounts: Use customer data to offer targeted promotions that drive loyalty. 📈 Example: An e-commerce platform analyzed customer behavior and found that small, frequent discounts led to better retention than infrequent deep discounts. 3️⃣ Demand Forecasting & Inventory Optimization Stocking the right products at the right time is critical in an inflationary market. Predictive analytics can help businesses: 🔹 Anticipate demand surges—especially in essential goods. 🔹 Optimize supply chains to reduce excess inventory and prevent stockouts. 🔹 Reduce waste in perishable categories like F&B, where price-sensitive demand fluctuates. 📦 Example: A leading FMCG brand leveraged AI-driven demand forecasting to prevent overstocking of premium products while ensuring budget-friendly variants were always available. 💡 The Takeaway Inflation isn’t just about rising costs—it’s about shifting consumer priorities. Companies that embrace data-driven decision-making can optimize pricing, fine-tune inventory, and strengthen customer loyalty. 𝑯𝒐𝒘 𝒊𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒂𝒅𝒂𝒑𝒕𝒊𝒏𝒈 𝒕𝒐 𝒊𝒏𝒇𝒍𝒂𝒕𝒊𝒐𝒏𝒂𝒓𝒚 𝒑𝒓𝒆𝒔𝒔𝒖𝒓𝒆𝒔? 𝑨𝒓𝒆 𝒚𝒐𝒖 𝒖𝒔𝒊𝒏𝒈 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒓𝒆𝒇𝒊𝒏𝒆 𝒚𝒐𝒖𝒓 𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒚? 𝑳𝒆𝒕’𝒔 𝒅𝒊𝒔𝒄𝒖𝒔𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔! #datadrivendecisionmaking #dataanalytics #inflation #inventoryoptimization #demandforecasting #pricingtrends

 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨 : 𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐧𝐢𝐧𝐠 𝐈𝐧𝐯𝐞𝐧𝐭𝐨𝐫𝐲 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 The Challenge: Our inventory management system was struggling to keep up with the growing volume of stock and sales data. The manual tracking process led to frequent stockouts and overstock situations, causing operational inefficiencies and affecting customer satisfaction. The Solution: We leveraged SQL to automate and optimize our inventory management process. Here’s how we did it: Steps: 1.Centralized Database Creation: Consolidated inventory data from multiple sources into a single SQL database.   Example Query to Create Inventory Table: CREATE TABLE Inventory ( ProductID INT PRIMARY KEY, ProductName VARCHAR(255), StockLevel INT, ReorderLevel INT, LastUpdated DATE );   2.Automated Stock Monitoring: Developed SQL queries to automatically monitor stock levels and trigger alerts for reorder points.   Example Query for Reorder Alerts: SELECT ProductID, ProductName, StockLevel FROM Inventory WHERE StockLevel <= ReorderLevel;   3.Dynamic Reporting: Created dynamic reports to track inventory levels, reorder statuses, and historical stock trends.   Example Query for Inventory Report: SELECT ProductID, ProductName, StockLevel, LastUpdated FROM Inventory ORDER BY LastUpdated DESC;   Impact: Operational Efficiency: Reduced manual tracking efforts, saving time and minimizing errors. Optimized Stock Levels: Improved inventory turnover by maintaining optimal stock levels. Enhanced Customer Satisfaction: Reduced stockouts and overstock situations, ensuring product availability.   Visuals: Include screenshots of the SQL queries, inventory reports, and a before-and-after comparison of stock levels. How do you manage inventory in your organization? Share your strategies and experiences in the comments!  follow more for Priyanka SG #SQL #InventoryManagement #DataOptimization #OperationalEfficiency #BusinessIntelligence

Ever stocked up on a product that turned into a dust-gathering flop? Or worse, missed out on a sales surge because your shelves were empty? That's the pain of bad demand forecasting, and it's felt across the manufacturing world. Get this: businesses with accurate demand forecasts enjoy a whopping 70%-90% reduction in inventory holding costs AND a 98% service-level rate.  Those numbers aren't magic; they're the result of ditching guesswork and embracing data analytics. Why Demand Forecasting Matters? 👉 Optimized Production: Produce what you'll actually sell. No more overstocking or frustrating shortages. 👉 Smoother Operations: Match your resources to real demand. Plan staffing, material procurement, and production schedules with confidence. 👉 Happy Customers = Happy Bottom Line: Have the right products available at the right time. Boost customer satisfaction and sales. Accurate demand forecasting has a ripple effect: 👉 Reduced Waste: Overproduction leads to wastage at every level. Forecast accurately, and minimize your environmental impact. 💪 Better Pricing Strategy: Understand demand peaks and valleys to make smarter, data-backed pricing choices. 👊 Boost in Competitiveness: Stay ahead of the game by anticipating market trends before your competitors even see them coming. Demand forecasting isn't about staring into a crystal ball. It's about using data analytics to uncover hidden patterns and build smart predictive models: 👁️🗨️ Historical Sales Data: The foundation of any good forecast. 👀 Market Trends: Watch for economic indicators, competitor moves, and changes in consumer preferences. 🙌 External Factors: Seasonality, promotions, even the weather can influence demand. 💥 Advanced Analytics: Machine learning algorithms can spot patterns humans miss, leading to supercharged forecasting accuracy. Here's what to analyze to up your demand forecasting game: 👉 Product-Level Specificity: Don't forecast in broad strokes. Break it down by SKU, location, and timeframe for granular insights. 👉 Time Horizons: Need both short-term (production planning) and long-term (strategic decisions) forecasts. 👉 Forecast Accuracy Tracking: Measure how your predictions stack up against reality, and keep refining those models. Wrangling complex demand data and building those super-smart forecasts can be tough. That's where Google's magic comes in. We can help you make sense of the numbers and get the insights you need to make confident, profit-driving decisions. Ready to conquer your demand forecasting challenges? Let's chat! Follow Omkar Sawant for more information! #demandforecasting #dataanalytics #manufacturing #supplychain #AI

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

🚀 Excited to share my latest project: a fully autonomous Smart Warehouse Management System built using the Agent Communication Protocol (ACP)! This innovative system features four intelligent agents InventoryBot, OrderProcessor, LogisticsBot, and WarehouseManager working seamlessly together to manage stock, schedule deliveries, and handle reorders, all through standardized, real-time communication. 🌟 What is ACP?   ACP is a framework that enables autonomous agents to communicate effectively using structured messages with defined performatives (e.g., ASK, REQUEST_ACTION, TELL, CONFIRM). It ensures clear, reliable interactions, making it ideal for complex systems like smart warehouses where coordination is key. 🌟 How It Works:   Scenario 1: Stock Alert & Reorder - The OrderProcessor checks stock levels with InventoryBot and triggers reorders to maintain minimum availability (e.g., reordering to fill low laptop stock).  Scenario 2: Delivery Scheduling - The WarehouseManager directs LogisticsBot to schedule deliveries of goods, with LogisticsBot confirming the schedule including a tracking ID for transparency.  Scenario 3: Low Stock Management - InventoryBot alerts the WarehouseManager of low stock (e.g., 5 tablets), prompting a confirmation that 15 tablets are needed; the WarehouseManager then requests OrderProcessor to place an order for 15 tablets, with OrderProcessor confirming via a PO number.  The interactive frontend visualizes these interactions, complete with a Statistics dashboard (e.g., total messages: 6, active conversations: 3, registered agents: 4) to monitor performance, making it perfect for real-world adoption. 🏭Impact on Logistics: This solution transforms the logistics industry by reducing manual oversight, optimizing stock levels, and streamlining delivery schedules. With real-time data and automated reordering, warehouses can operate 24/7, cut costs, and improve customer satisfaction key drivers in today’s fast-paced supply chain. This showcase how AI and ACP can revolutionize warehouse management. Check out the demo video to see it in action!

Because wrong service levels and inventory targets kill the supply chain... This infographic shows how to set them up in 7 steps: ✅ 1️⃣ Understand Historical Demand Patterns & Segment the Portfolio 👉 use historical demand data and calculate demand variability. Segment SKUs based on their value and demand variability. ✅ 2️⃣ Define the Required Service Levels 👉 decide the service level targets that the business needs. The higher the service level, more is the inventory needed. ✅ 3️⃣ Determine Lead Times 👉 understand inbound, production and outbound lead times. This will impact how much safety stock the company needs to maintain service levels. ✅ 4️⃣ Apply Seasonal Indexing 👉 Use the formula to calculate safety stock: Z×σd×L ❓ Where: Z is the Z-score corresponding to the service level (e.g., Z=1.65 for 95% service level); σ_d is the standard deviation of demand; L is the lead time in periods. ✅ 5️⃣ Set Reorder Points 👉 calculate Average Lead Time X Average Daily Demand + Safety Stock Calculate reorder points (ROP) to determine when to place an order ✅ 6️⃣ Balance Inventory Targets with Working Capital 👉 use the inventory turnover ratio and days of inventory on hand (DOH) to monitor and set reasonable inventory targets without overstocking. ✅ 7️⃣ Create Feedback Mechanisms & Monitor Performance 👉 track service levels and inventory performance weekly. Identify areas where the targets are not met and safety stock levels, lead times, and demand patterns need adjustments. Any others to add?

Stock Transfer Process In SAP, stock transfer processes are used to move inventory between different locations, plants, or storage areas within an organization. These processes ensure that inventory is accurately tracked and managed throughout the supply chain. Here are the key types of stock transfer processes in SAP: 1. Stock Transfer within a Plant (Intra-Plant Transfer): • Movement Type 311: Used for transferring stock between storage locations within the same plant. • Process: • Goods Issue: The stock is issued from the source storage location. • Goods Receipt: The stock is received into the destination storage location. • No accounting document is generated since the transfer occurs within the same plant and valuation area. 2. Stock Transfer between Plants (Inter-Plant Transfer): • Movement Type 301: Used for transferring stock between different plants within the same company code. • Process: • Goods Issue at Source Plant: Stock is removed from inventory at the source plant. • Goods Receipt at Destination Plant: Stock is added to inventory at the destination plant. • Accounting document is generated to reflect the change in stock location across plants. 3. Stock Transfer Order (STO): • Stock Transport Order: A more controlled and documented method of transferring stock, similar to a purchase order. • Two Types: • With Delivery (Two-Step Process): • Goods Issue: Issued from the supplying plant. • Goods Receipt: Received into the receiving plant. • Without Delivery (One-Step Process): • Direct posting of goods receipt in the receiving plant. • Benefits: Provides better control and documentation, allowing for transportation planning and tracking. 4. Cross-Company Code Stock Transfer: • Inter-Company Stock Transfer: Used to transfer stock between plants belonging to different company codes. • Process: • Similar to STO but involves two company codes. • Billing Document: A billing document may be generated for internal billing purposes. • Financial Impact: Affects financial statements of both company codes involved. 5. Stock Transfer with Consignment: • Consignment Stock Transfer: Transferring stock owned by a vendor but stored at your plant. • Process: • Stock is moved without a change of ownership until used or consumed. • Settlement with the vendor occurs based on actual usage. Key Considerations: • Movement Types: Determine how stock movements are recorded and what kind of documents are generated. • Valuation Impact: Ensure correct valuation of stock, especially in cross-plant or cross-company transfers. • Integration: Stock transfers often integrate with SAP modules like Warehouse Management (WM) and Transportation Management (TM) for optimized logistics and handling. Stock transfers in SAP are vital for maintaining accurate inventory levels, reducing transportation costs, and ensuring the efficient operation of supply chain processes.