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Posted 13 February 2024 by
Hendrik De Cuyper
Head of Sales Anaplan

Continuous innovation in Demand Planning with Anaplan: Driving value with AI & advanced analytics

In the fast-paced world of supply chain management, staying ahead requires more than just forecasting. Anaplan emerges as a powerful ally, offering a comprehensive suite of tools to modernize demand planning processes. This blog provides a deep dive into three key areas: Exception-based Demand Planning, Machine Learning in Demand Forecasting, and Demand Sensing with Anaplan.  

Exception-Based Demand Planning with Anaplan 

Forecasting vs. Replenishment Models: Handling Different Demand Types 

In demand planning, managing diverse demand types is critical. Anaplan provides a nuanced approach by distinguishing between forecasting and replenishment models. The reality is that, for slow-moving, erratic products, the forecast will always be wrong. To elevate planning maturity, it’s critical to leverage smart segmentation in order to understand where demand planners should spend their time and in which products should be tackled through better inventory policies.   

Enhancing Exception Identification and Management 

Anaplan goes beyond conventional forecasting methods by proactively identifying and managing exceptions. Through advanced analytics and real-time insights, the platform empowers users to spot anomalies early on, allowing for swift corrective actions and minimizing disruptions in the forecasting process. Don’t let your team be overwhelmed by the trivial many, instead help them to focus on the vital few. Some examples of typical exceptions to review include: forecast combinations with a strong bias, low forecast accuracy,; high demand volatility; and large revenue contributors.  

Closing the Feedback Loop for Improved Planning Processes 

Anaplan facilitates continuous improvement by providing a mechanism that closes the feedback loop between process and performance. This allows you to  leverage the forecast to understand where your internal stakeholders are adding value to the forecast, and or where its accuracy is decreased. Closing this loop ensures that you can critically assess the value of every process step in every cycle, as well as quantifying the contributions of the various actors in the demand planning process. 

Machine Learning in Demand Forecasting 

Embrace Machine Learning (ML) to revolutionize demand forecasting and usher in a new era of precision and efficiency. Anaplan stands at the forefront of this transformative journey, recognizing the unparalleled value ML brings to the table in bolstering prediction accuracy. By effortlessly integrating ML algorithms into its platform, Anaplan’s PlanIQ empowers businesses to harness the potential of external data sources, transcending traditional forecasting limitations. 

Democratizing Machine Learning for Demand Planning Success 

Gone are the days of heavy reliance on data engineers to navigate the complexities of machine learning. With Anaplan, democratizing machine learning is not just a vision but a reality! Businesses can now seamlessly set up and deploy ML algorithms in a matter of days, unlocking a world of actionable insights with a remarkable return on investment. This accessibility revolutionizes demand planning, empowering teams to leverage the power of ML without the need for extensive technical expertise. 

Navigating Potential Pitfalls and Ensuring Long-Term Success with Anaplan 

Although integrating ML) in demand forecasting offers significant advantages, ensuring long-term success and adoption also means addressing the full scope of potential pitfalls. Anaplan recognizes that achieving a robust baseline forecast is just the beginning; it’s equally crucial to maintain clarity around the demand planning process. This includes defining clear roles and responsibilities within the organization and establishing realistic expectations regarding what ML can and cannot provide. Anaplan promotes transparency and clarity surrounding ML-driven demand forecasting initiatives to facilitate long-term adoption and success. By clearly defining the capabilities—and the limitations—of ML models, organizations can manage expectations and foster trust among stakeholders. Providing transparent insights into the model’s performance metrics, assumptions, and uncertainties ensures that decision-makers can confidently incorporate ML-driven forecasts into their planning processes, promoting informed and agile decision-making. 

Demand Sensing with Anaplan 

Concept and Benefits of Demand Sensing 

Increasingly, organizations need to be more nimble than relying solely a monthly demand planning process—this needs to be complemented with advanced demand sensing capabilities. Demand sensing is integral to real-time demand management and, in Anaplan, this means dynamically analysing current market demand signals. The benefits of this approach include improved responsiveness to market changes, reduced stockouts, and enhanced customer satisfaction through more accurate fulfillment

How Anaplan Supports Real-Time Demand Sensing 

Anaplan’s support for real-time demand sensing is a testament to its agility and ensures that organizations can stay ahead in the fast-paced world of demand management. The platform equips users with the tools they need to monitor and analyse real-time demand patterns. By doing so, businesses can make informed decisions promptly, adapting their strategies to ever-changing market dynamics.  

In conclusion, Anaplan emerges as a powerful ally in the realm of demand planning. Whether it’s exception-based planning, harnessing the power of machine learning, or staying agile through demand sensing, Anaplan’s features empower businesses to navigate the complexities of modern demand management. 

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