Resource Demand Forecasting

SAP Demand Planning SAP Demand Planning is a critical component of the SAP Integrated Business Planning (IBP) suite, designed to help organizations anticipate and meet customer demand more accurately and efficiently. Here are the key elements and features of SAP Demand Planning: Key Features: 1. Statistical Forecasting: • Utilizes advanced algorithms to analyze historical data and predict future demand. • Offers various forecasting models such as time-series, causal analysis, and regression models. 2. Demand Sensing: • Provides near-term demand visibility using real-time data. • Adjusts forecasts based on the latest market signals, such as point-of-sale data or customer orders. 3. Collaboration Tools: • Facilitates collaboration across departments and with external partners to align demand forecasts with business objectives. • Allows for consensus forecasting by integrating inputs from sales, marketing, and supply chain teams. 4. What-if Analysis: • Supports scenario planning to evaluate the impact of different business strategies or external factors on demand. • Helps in risk assessment and decision-making by visualizing potential outcomes. 5. Integration with Supply Planning: • Seamlessly integrates with supply planning processes to ensure that production and procurement plans are aligned with demand forecasts. • Helps in balancing supply and demand across the entire supply chain. 6. Machine Learning and AI: • Leverages machine learning algorithms to improve forecast accuracy by continuously learning from new data and trends. • Identifies patterns and anomalies that may affect demand. 7. User-Friendly Interface: • Provides a customizable and intuitive user interface for planners to easily access and analyze demand data. • Offers dashboards and reports for real-time visibility into demand trends and KPIs. Benefits: • Improved Forecast Accuracy: Reduces forecasting errors, leading to better inventory management and customer satisfaction. • Enhanced Responsiveness: Enables organizations to quickly adapt to changes in demand and market conditions. • Cost Reduction: Optimizes inventory levels, reducing excess stock and carrying costs. • Strategic Alignment: Ensures that demand plans are aligned with business goals and operational capacities. Implementation Considerations: • Data Quality: Accurate demand planning relies heavily on high-quality data from various sources. • Change Management: Successful implementation requires stakeholder buy-in and training to adapt to new processes and tools. • Integration: Ensuring seamless integration with existing ERP and supply chain systems is crucial for a comprehensive view of demand and supply. SAP Demand Planning is a powerful tool that helps organizations improve their demand forecasting capabilities, leading to more efficient and responsive supply chain operations.

Most demand forecasts are built on a single method chosen by habit. Simple moving average because it is familiar. Exponential smoothing because someone set it up years ago. The method stays even when the data changes. The problem is that no single forecasting method works best for every demand pattern. Stable demand with no trend behaves differently than demand with a clear upward trend. Seasonal products need a completely different approach than items with flat, irregular consumption. Using the wrong method does not just produce a less accurate forecast. It produces systematically biased safety stock levels, reorder points, and procurement timing. The Demand Forecasting Tool runs five methods simultaneously on your historical data: Simple Moving Average, Weighted Moving Average, Single Exponential Smoothing, Holt's Double Exponential Smoothing for trending data, and Holt-Winters Triple Exponential Smoothing for data with both trend and seasonality. For each method, it automatically optimizes the smoothing parameters to minimize error on your specific data rather than using defaults. It then scores all five methods against your history using three error metrics: MAPE, MAD, and MSE. The best-fit method is identified automatically and used to generate the forward forecast. The Safety Stock tab takes the forecast error directly from the best method and calculates safety stock and reorder point across four service level targets using the standard formula. Paste your data, set your lead time and service level, and get a defensible stocking recommendation in under two minutes. Link in the comments. #SupplyChain #DemandForecasting #InventoryManagement #ProcurementAnalytics #CPSM

𝗬𝗼𝘂 𝘁𝘂𝗻𝗲 𝘆𝗼𝘂𝗿 𝗺𝗼𝗱𝗲𝗹 𝗽𝗲𝗿𝗳𝗲𝗰𝘁𝗹𝘆 – 𝗯𝘂𝘁 𝗶𝘁 𝗰𝗼𝗹𝗹𝗮𝗽𝘀𝗲𝘀 𝘄𝗵𝗲𝗻 𝗕𝗹𝗮𝗰𝗸 𝗙𝗿𝗶𝗱𝗮𝘆 𝗵𝗶𝘁𝘀.🧙♂️ “Demand forecasting” sounds like one problem. But it’s at least two – and they need different solutions. For example: 1. Daily demand forecasting for the complete product range. Thousands of items, every day, across all locations. We often use algorithms like gradient boosting, deep learning – and yes, even “standard” regressions. The challenge: include everything – price, seasonality, trends, stock levels – and keep it stable without overfitting. The risk? These models tend to learn the average. Peaks often get smoothed out or missed entirely. 2. Then there’s peak event forecasting for holidays, promos, or major events. Totally different game. We need models built to target the spikes – that recognize events and adjust dynamically. They might not be the best at modeling the average though! But they’re better at capturing outliers and extremes. Sometimes lightweight time series models do better here. Or quantile regressions combined with external signals. The goal: anticipate sales behavior when it breaks the usual patterns. My word of caution? Assuming the same model can handle both. This is a great reminder to check early what your business actually needs forecasting for. #ALDITechfluencer #DataScience #DemandForecasting

Demand forecasting errors silently bleed profits and cash. This document shows 7 red flags in demand forecasting and how to fix them: 1️⃣ Over-reliance on historical data ↳ How to fix: incorporate external data like market trends, competitor activity, and consumer sentiment to enrich forecasts 2️⃣ Ignoring promotions and discounts ↳ How to fix: build a promotions-adjusted forecasting model, considering historical uplift from similar campaigns 3️⃣ Forgetting cannibalization effects ↳ How to fix: model cannibalization effects to adjust forecasts for existing products 4️⃣ One-size-fits-all forecasting method ↳ How to fix: use demand segmentation (for example, high variability vs. stable demand); do not treat all SKUs equally 5️⃣ Not monitoring forecast accuracy ↳ How to Fix: track metrics like MAPE, WMAPE, bias, to improve over time 6️⃣ High forecast error with no accountability ↳ How to fix: tie accountability to S&OP (sales and operations) meetings 7️⃣ Past sales (instead of demand) consideration ↳ How to fix: make the initial predictions based on the unconstrained demand; not on sales that are impacted by cuts and out of stock situations Any others to add?

See how easily you can project monthly volumes, predict your business's revenue patterns with precision and plan your production and budget accordingly. Understanding and calculating the seasonality of your revenue can transform how you manage your financial planning. Why Measure Average Volume Demand? Measuring the average volume demand helps you identify patterns in your demand over different periods. By recognizing these patterns, you can adjust your forecasts and budgets to reflect more accurate expectations, preventing potential issues like overcapacity or underproduction. Steps to Calculate Average Seasonality: 1. Collect Data: Gather historical revenue data for multiple years. 2. Calculate Monthly Averages: Determine the average revenue for each month across the years. 3. Compute Overall Average: Find the overall average revenue across all months and years. 4. Determine Seasonal Indices: Divide each monthly average by the overall average to get the seasonal index for each month. Benefits of Applying Seasonal Indices: • Prevent Overcapacity: By anticipating peak periods, you can manage resources better and avoid production bottlenecks. • Optimize Production: Ensure that production schedules align with demand, reducing waste and improving efficiency. • Enhanced Forecast Accuracy: More precise forecasts lead to better financial planning and decision-making. This technique is not only useful when creating monthly budgets and forecasts, but also when crafting long range plans. When we apply the monthly seasonality to the yearly projection, we are able to achieve a granularity that will show us more clearly other aspects of our plan that we are not able to see from the yearly perspective. The capacity constraint is one example. In this case, I have this insight even years ahead to either increase capacity, improve capacity distribution along the year (if possible) or even plan better the volume production. To help you get started, I've created an Excel template for calculating seasonality. You can download it from the link below and integrate it into your budgeting process. https://buff.ly/44WU3tV

Machine Learning-Powered Demand Sensing: Revolutionizing Real-Time Decision Making In the realm of demand forecasting, machine learning (ML) is reshaping the landscape by enabling real-time analysis for predicting short-term demand with exceptional precision. Unlike conventional methods that rely solely on historical data, ML-driven demand sensing incorporates a wide array of data sources, including sales figures, inventory levels, weather patterns, social media trends, and economic indicators, to swiftly identify fluctuations in demand. For instance, in the context of event management, demand sensing proves invaluable in anticipating attendance variations influenced by external factors such as weather conditions or concurrent events. Through sophisticated ML algorithms, subtle trends like a sudden spike in ticket purchases triggered by social media engagements can be detected, empowering organizers to promptly adjust their strategies related to inventory, staffing, or promotions. This innovative approach not only slashes forecast errors by as much as 50% but also streamlines resource distribution and mitigates risks associated with overbooking or inventory shortages. By translating raw data into actionable intelligence, demand sensing fosters agility and accuracy in navigating dynamic market conditions.

My previous post discussed the pitfalls of applying AI/ML models to proxy demand signals in forecasts. Today’s post discusses randomness in demand. Here’s a pop quiz - Is a demand forecast: 1) a single time series of predicted demand, OR 2) Is it a statistical distribution? If you chose 1, I believe you would be in the majority of planners in how we apply forecasts in supply chains. However, we can intuitively agree that demand is composed of predictable and random components. Enter probabilistic forecasting - the ability to produce statistical demand distributions. But the big question is what is the utility of the complexity introduced by demand distributions? Quick sidebar - there is an entire family of demand patterns where you are better off not forecasting and just using replenishment models to “pull” signals. That is not the focus of my discussion here. Having led large demand planning teams, I have observed planners across e-commerce and consumer product supply chains. The one thing I have observed is that planners are not wired to think in probabilistic terms, especially around demand. It’s far more tempting to operate with a single demand prediction and make operational plans around it. So why do analytically sharp planners struggle with demand distributions? A demand distribution models a statistical distribution with probabilities around quantiles of the distribution. For example, the 90th percentile of demand (denoted P90) is one where there is only a 10% chance that demand is above it. Planners already struggle to align demand with S&OP/IBP stakeholders. It is natural that planners have little patience to deal with complex distributions that are unwieldy. And the natural response is to run with a single prediction - typically the mean forecast. What a tragedy then to invest in a sophisticated AI/ML-forecasting solution but only use mean forecasts that ignore the randomness in demand! So what is the fix? In my opinion, it is essential that we generate demand distributions - another story on how valid the distributions themselves are. But I would keep distributions out of the S&OP/IBP domain. S&OP should continue to focus on aligning the mean forecast along with any business overrides. Instead, I would move demand distributions to the domain of inventory strategy to model the trade-off between service levels and cost-to-serve. Smart teams have figured out inventory models that ingest demand distributions, business inputs, lifecycle policies, and costs to optimize inventory investments. This gives planners the room to have a conscious inventory strategy that is codified in policies, respect randomness in demand and explicitly target service levels and costs. In Summary, build agile and robust supply chains using probabilistic demand but be thoughtful on when and where to introduce probabilistic computation in your planning process!! Drop a comment on what has worked for you.

Ever had a record-breaking Q4 turn into a disaster because you ran out of inventory at the worst possible moment? You're not alone. Stockouts cost DTC brands billions every year. Here's the secret: Q4 success isn't just about great marketing. It's about razor-sharp demand forecasting. At 1 Commerce, we've seen too many brands rely on last year's data alone. But consumer behavior moves fast, especially during the holidays. To stay ahead, you need a smarter approach: → 𝗟𝗼𝗼𝗸 𝗕𝗲𝘆𝗼𝗻𝗱 𝗟𝗮𝘀𝘁 𝗬𝗲𝗮𝗿: Factor in recent trends, market shifts, and competitor actions, not just historical sales. → 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: Advanced demand forecasting tools anticipate spikes better than traditional methods. → 𝗞𝗲𝗲𝗽 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝗻 𝗠𝗶𝗻𝗱: Have backup suppliers and agile fulfillment strategies ready in case of unexpected surges. The bottom line? Accurate forecasting is your best defense against lost sales and your best offense for capturing maximum revenue in Q4. ↳ What's your biggest challenge when forecasting for peak season demand? #DemandForecasting #Q4Planning #EcommerceStrategy #AvoidStockouts #DTCSuccess

❄️ When the Forecast Says “1–78 Inches of Snow”… Welcome to Demand Planning in 2026 Anyone who is living in Minnesota is seeing the weekend forecasts like this today. “Possible snow accumulation: 1–78 inches.” Translation: no one wants to be wrong. It is funny for weather, but in supply chains this kind of uncertainty can cost millions in inventory, service levels, and lost sales. The good news is AI tools are helping demand planners move from guessing to learning faster. 5 Tips for Demand Planning During Uncertainty Using AI 1. Use AI to create multiple forecast scenarios. Instead of one forecast, generate best case, base case, and worst case scenarios. AI models can simulate thousands of demand outcomes based on historical patterns and current signals. 2. Feed the model more real world signals. Weather, promotions, macroeconomic indicators, social trends, and regional events all influence demand. Modern AI forecasting tools ingest these signals automatically. 3. Focus on forecast ranges instead of single numbers. A range often reflects reality better than a precise number. AI tools can calculate probability bands so planners understand risk levels. 4. Continuously reforecast, not monthly AI models allow near real time updates as new data arrives. That means planners can adjust supply decisions before small changes become big problems. 5. Pair AI with planner judgment AI is powerful, but planners still understand context like new product launches, retailer behavior, and competitive shifts. The best results come from human insight plus machine learning. Sometimes demand planning feels exactly like a Minnesota weather forecast. The goal is not predicting the exact snowfall. The goal is being prepared for whatever actually hits. #DemandPlanning #SupplyChain #AI #Forecasting #SupplyChainAI #RetailStrategy

"Our salespeople are responsible for generating our forecasts, and they own the final numbers. They are crushing it." Said no one to me ever. Often, when I discuss with companies with low demand planning maturity, their process is driven by salespeople. This usually results in, ▪️ A lot of politics ▪️ Biased forecasts (either too high as they confuse demand forecasts and supply plans or under forecasts as salespeople want to beat targets) ▪️ Inaccurate forecasts, as humans aren't the best at generating baseline forecasts. ▪️ 100% manual and time-intensive process and poor utilization of salespeople. Here's how I would design a scalable demand planning process. 1️⃣ Use an ML-generated forecast as a baseline. This forecast should already leverage most of your business drivers (promotions, shortages, prices, per orders, sell-outs—if available) and generate forecasts for new products. 2️⃣ Allow demand planners to enrich forecasts if they have specific insights/information that the model isn't aware of ("I just called our client(s), they told me XXX.") Salespeople can propose insights to demand planners. 3️⃣ Track Forecast Value Added to ensure that the team is adding value. Coach people to success. If you have difficulties with step 2️⃣, focus on four essentials: new products, phased-out products, new clients, and lost clients. You will already add a lot of value. --- If you enjoy demand planning content, I forecast that you will love my mailing list. https://lnkd.in/gSWngz9u