How do you update and improve your demand forecasting models and methods over time?

While gauging your demand forecasting models' performance is important, relying solely on quantitative metrics may not provide a comprehensive understanding of your success. Overemphasis on numerical accuracy might lead to neglecting qualitative aspects such as customer behavior, supplier relationships, and market intelligence. Furthermore, focusing too much on accuracy could result in overfitting your models, making them less adaptable to market changes. Strive for a balance between quantitative and qualitative approaches, adopting a holistic view of your forecasting performance that considers both numerical data and business intelligence insights.

Refresh data: Clean historical sales, include real-time sales, consider weather. Use advanced tools: Machine learning for patterns, test future scenarios, handle seasonality. Continuously learn: Track errors, review forecasts, A/B test methods. Stay adaptable: Update models, allow expert input, adjust forecasting horizons.

From my experience, this is about measuring your forecasting accuracy. Because without measuring it, you don’t even know if and where to improve. I have seen businesses where they complained about poor accuracy, but when asked where they currently stand, they didn’t even know because they were not measuring it. So, start with measurement and then move on to improving it." Obviously there are many other KPI's which they will help you to measure and indicate where the issue is.

1. Continuous Analysis: Regularly collect and clean data. 2. Model Review: Evaluate and adjust models based on performance. 3. New Technologies: Use machine learning and AI. 4. External Factors: Incorporate market data and seasonality. 5. Training: Train the team and stay updated. 6. Feedback: Compare forecasts with actual data and continuously improve.

To keep demand forecasting models accurate, it’s essential to regularly update methods and incorporate feedback. Start by analyzing historical data trends and checking accuracy with metrics like MAPE. Adopting advanced methods, such as machine learning or predictive analytics, helps in capturing complex patterns and seasonality. Adding external data like economic indicators or competitor activity can improve accuracy. Regular model testing and scenario analysis highlight areas for improvement. Collaborating with cross-functional teams ensures models align with business needs, and ongoing learning keeps techniques current with market changes, refining forecasts for evolving demand dynamics.

Incorporate collaborative approaches to reduce error and uncertainty in demand forecasting. Engaging with stakeholders, such as suppliers, partners, and customers, can provide valuable insights into market trends and demand drivers. By fostering open communication channels and sharing data, you can improve the accuracy and reliability of your forecasts, while also building stronger relationships with key stakeholders. Collaborative forecasting can lead to better decision-making and improved overall business performance.

Os Desvios padr?es devem ser eliminados, ou ajustados, nas grandes médias para criar o modelo de predi??o. Damos o nome para variáveis "dummy"

While choosing the right models and methods for your demand forecasting is essential, it's important to recognize that no model or method can guarantee perfect forecasts. Relying too heavily on any single approach can lead to overconfidence in your forecasts and underestimation of the inherent uncertainties in demand. It's crucial to maintain a healthy skepticism towards your forecasting models and methods, continually questioning their assumptions and limitations. This mindset can help you stay open to new ideas and improvements and avoid becoming overly reliant on a specific model or method. Ultimately, this will allow you to adapt more readily to changes in the market and improve your overall forecasting performance.

Contrate um matemático. Fizmos para um grande grupo econ?mico, lider de mercado brasileiro, uma otimiza??o para chegar em um modelo apropriado. O que foi feito, foram ajustes e elimina??o de grandes desvios, chegando em um total de dessete rotinas matemáticas. obs: executávamos todas, até achar o padr?o ótimo para ser reconhecido pela matemática (redes neurais) Este saudoso amigo, foi um phd em matemática, ao qual ajudamos na cria??o de um padr?o otimo. Isto foi em 1998, no Brasil. Ainda n?o chamávamos de IA.

To update and enhance demand forecasting models over time, validate and test them through: Continuous Evaluation: Regularly assess model performance against actual demand data to identify inaccuracies and areas for improvement. Testing Enhancements: Experiment with new algorithms, variables, or data sources to refine forecasting accuracy and adapt to changing market conditions. Iterative Refinement: Implement feedback loops to incorporate insights from stakeholders, industry trends, and technological advancements, ensuring ongoing model optimization.

Cross-validation: Split data into training and testing sets to evaluate model performance, e.g., testing accuracy on past quarterly sales data. Backtesting: Apply models to historical data, like last year’s holiday season, to check if they accurately predict demand spikes. Simulation: Run scenarios such as sudden raw material shortages to see how models perform under various conditions, ensuring robust demand forecasting.

To update and improve demand forecasting models over time, implement and communicate through: Transparent Documentation: Clearly document model enhancements and methodologies, ensuring consistency and understanding among stakeholders. Training and Education: Provide training sessions or resources to educate team members on updated models and methods, promoting adoption and alignment. Feedback Mechanisms: Establish feedback channels for stakeholders to provide insights and suggestions, fostering collaboration and continuous improvement.

To update and enhance forecasting models over time: Data Analysis: Continuously collect and analyze new data to identify trends and patterns. Model Evaluation: Regularly assess model performance against actual demand to identify areas for improvement. Incorporate Feedback: Incorporate feedback from stakeholders and adjust models accordingly, integrating new techniques or algorithms as needed.

To continuously improve demand forecasting models: 1. Evaluate existing models. 2. Analyze historical data for patterns. 3. Select appropriate forecasting techniques. 4. Fine-tune model parameters. 5. Include external factors influencing demand. 6. Stay updated with new forecasting methods. 7. Establish a feedback loop for model refinement. 8. Collaborate with stakeholders for insights. 9. Automate processes where possible. 10. Develop scenarios for risk assessment. 11. Document methods and decisions. 12. Regularly review and iterate the forecasting process. By following these steps and maintaining a flexible and adaptive approach, you can continuously enhance the accuracy and reliability of your demand forecasting models over time.

To update and enhance demand forecasting methods: Continuous Learning: Incorporate new data and insights to refine models. Advanced Techniques: Explore and adopt cutting-edge statistical and machine learning algorithms. Feedback Loop: Gather feedback from model users and stakeholders to iteratively improve accuracy and relevance.

Por detrás dos grandes modelos preditivos, tem que haver um bom profissional matemático e um bom profissional de controladoria. Estes modelos s?o ótimos para ajudar nas previs?es, deixando que a decis?o final, ou "a arte" fique com você gestor. IA ou algoritmos inteligentes ajudam, mas o artista nas análises prescritivas, em minha opini?o ainda é do gestor. (2024) Fiz os créditos de um mestrado de IA em 2001, e ainda n?o ví análises prescritivas econ?mico-financeiras serem resolvidas por máquinas (IA)