Using Data To Forecast Supply Chain Demand

Forecasting solutions touting the use of AI/ML models are hard to avoid these days. But there is a hidden risk that most companies tend to ignore. The latest models are great but I worry these are being applied in a way that will only amplify “the bullwhip effect”. What do I mean? Bull whip effect is the distortion of the demand signal as it travels from the consumption end of the supply chain to the production end, while traversing multiple physical and informational nodes along the way. As a result, the demand signal at the production end could be orders of magnitude variable than actual consumption. This is nothing new and we have known about this effect for two decades plus. As we apply algorithms to forecasts, unless we account for the bullwhip effect, we are bound to amplify distortion despite best intentions. Now, this is not about outlier elimination which I believe algorithms do a pretty good job of eliminating. I am talking about misinterpreting noise as signal, over-interpreting variability and causing inventory gyrations that ultimately hurt customers. A classic example is using order/shipment data at Distribution Centers for forecasting or worse yet, factory shipments as a proxy for demand. Most S&OP plans only focus on order and shipment data without systematically factoring in channel inventory and demand. So what is the fix? In my opinion, if you are a consumer company (CPG, Hi-Tech, Retail, Pharma/Healthcare, and even manufacturing), build the capability to forecast a demand signal that is as close to the final consumption point. For example, a CPG brand could forecast retail/e-commerce sell-through demand, normalize it for channel inventory and then propagate that signal up into the supply chain. And the best part - those same AI/ML models will work much better for you. To be honest, B2B and industrial companies also benefit from a similar approach by getting closer to end customer demand. Better yet, this unlocks better demand intelligence which fuels better S&OP judgements, new product forecasting quality, lifecycle management, capacity planning and more. If you are looking for a 10x transformation, this is one of them. It’s bizarre to me when I see companies side-stepping this fundamental step and then complain about forecast accuracy, or data cleanliness or something else hurting their supply chain service levels and costs. Leaders who are pursuing unlocking growth from their supply chains while reducing cost-to-serve need to lead from the front in championing this capability. 

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.

Because with a bad forecast everything else will fail... This infographic contains 7 steps to create and improve a forecast: ✅ Step 1 - Start with Historical Data Collection & Cleaning 👉 gather and clean past sales data (ideally 3 years) 👉 remove outliers, fill in gaps, and ensure data accuracy before analysis ✅ Step 2 - Segment Your Demand 👉 break down your demand into segments to create more granular forecasts 👉 examples: volume, value, product categories, customer types, regions ✅ Step 3 - Generate a Baseline Statistical Forecast 👉 as starting point, generate a baseline forecast using statistical methods like time series analysis ✅ Step 4 - Apply Seasonality and Trend Adjustments 👉 use historical seasonal patterns and emerging trends to fine-tune your forecast for upcoming periods ✅ Step 5 - Collaborate & Fine-tune in S&OP Meetings 👉 collaborate with sales, marketing, finance, and operations to align on one consensus forecast ✅ Step 6 - Adjust for Market Intelligence 👉 incorporate insights from sales teams, marketing campaigns, external research, and product launches to adjust your baseline forecast ✅ Step 7 - Incorporate Forecasts into S&OE (Sales & Operations Execution) 👉 drive actionability in the short term based on this aligned forecast, helping the team respond quickly to deviations 💥 Bonus Step: Build a Continuous Feedback Loop 👉 track forecast accuracy by comparing actual sales to forecasted figures, and regularly update your model based on this feedback Any other steps to consider? #supplychain #salesandoperationsplanning #integratedbusinessplanning #procurement