How do you measure and improve the accuracy of your demand forecasts?

1) Collect and analyze historical sales data and external data, such as economic indicators, consumer behavior and competitor actions. 2) Choose the right forecasting method for your business and products (qualitative, quantitative, causal, time series). 3) Validate and adjust your forecasts regularly, based on the actual performance and feedback from your stakeholders. 4) Compare your forecasts with actual sales and demand, and calculate the forecast error and accuracy. 5) Communicate & collaborate with sales, marketing and operations teams to request their input. 6) Align your supply chain with your demand by ensuring you have the right inventory levels, replenishment policies, and contingency plans to meet customer expectations.

we encountered issues with inaccurate forecasts due to poor data understanding. In my experience, truly understanding your data involves analyzing historical trends, identifying patterns, and assessing data quality. One thing I’ve found helpful is using data visualization tools to uncover insights and anomalies that might not be apparent from raw data alone. This approach allows you to better interpret the data, make informed decisions, and adjust forecasts more effectively. Ensuring data accuracy and relevance is crucial for reliable and actionable forecasting.

En FORSITIS, sabemos que todo comienza con entender los datos. No se trata solo de recolectar información, sino de desglosarla, analizarla y encontrar patrones que a primera vista pueden parecer invisibles. Ayudamos a nuestros clientes a interpretar sus datos con una visión estratégica, transformando números en decisiones de negocio. La clave está en saber qué datos realmente importan para predecir la demanda con precisión. En FORSITIS, no solo ayudamos a manejar la información, sino a convertirla en el motor que impulsa decisiones más acertadas y estrategias de abastecimiento más inteligentes.

One of the best ways I have found is to bring in the Sales forecast understand their accuracy and be able to help them increase that accuracy. Which allows you to also increase your demand accuracy. Look for what motivates your Suppliers and if they want a better forecast see if they are willing to provide discounts which can be used also on the Sales side to improve wins and improve your overall forecast. It all starts with are you able to sell you and Suppliers who understand that and work with you to improve your sales stand to also gain.

Demand forecasting é um pilar para o sucesso no varejo. Concordo que entender os dados e validar previsões constantemente são essenciais. Além disso, incluir insights de equipes de vendas e marketing ajuda a refinar a precisão e alinhar a estratégia com a realidade do mercado. E claro, adaptar o supply chain de forma ágil é fundamental para atender as flutuações da demanda sem comprometer a experiência do cliente. Importante sempre, se atentar a particularidades sazonais, como por exemplo um verão mais quente que o do ano anterior, transformando todo o planejamento de demanda.

Seleccionar el método de pronóstico correcto es esencial para cualquier empresa que quiera ajustar su demanda de manera eficiente. En FORSITIS, trabajamos mano a mano con nuestros clientes para identificar qué método se adapta mejor a su contexto específico. Desde análisis históricos hasta modelos predictivos avanzados, no imponemos una única solución, sino que ayudamos a las empresas a seleccionar la que mejor se alinee con sus necesidades y capacidades. Nuestro rol como consultores es optimizar el proceso, garantizando que las previsiones no solo sean más precisas, sino también más útiles en la toma de decisiones estratégicas.

I struggled with choosing the right forecasting method for varying product lines. In my experience, selecting the best method depends on factors like data availability, demand patterns, and business needs. One thing I’ve found helpful is starting with a combination of qualitative methods (like expert opinions) and quantitative methods (like time series analysis) to identify what works best. Using a hybrid approach allows for flexibility and accuracy, as it combines historical data with market insights. Regularly evaluating the chosen method against actual performance helps in refining forecasts and improving accuracy over time

A key element is choosing the right forecasting method based on forecastability analysis and understanding the data baseline, quality and demand factors. Methods such as qualitative, quantitative, causal and time-series are all options to compare and figure out the best method for the given demand segment and situation. Statistical analysis is fundamental. AI can help greatly with demand pattern analysis as well as using new AI-based forecasting methods. I recommend to revisit the allocated methods frequently.

En FORSITIS, entendemos que ningún pronóstico es perfecto, y ahí es donde entra el verdadero valor de la validación y el ajuste continuo. Ayudamos a nuestros clientes a crear un sistema de retroalimentación constante, donde los pronósticos no solo se validan contra la realidad, sino que se ajustan a medida que surgen nuevas variables. Nuestro enfoque es ágil, adaptativo y basado en el aprendizaje continuo. Es a través de este proceso de ajuste donde los equipos de compras pueden mejorar de manera significativa la precisión de sus previsiones y evitar costosos errores en su cadena de suministro.

To measure demand forecast accuracy, use key metrics like Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and tracking signal to gauge how close your predictions are to actual demand. Improving accuracy involves using multiple forecasting models, combining qualitative and quantitative approaches, and incorporating historical data to uncover trends. External factors such as seasonality and economic conditions should also be considered to improve predictions. Collaboration with cross-functional teams like sales and marketing ensures forecasts are well-rounded and account for real-time changes.

Yes, validating forecasts with stakeholders and using a forecast accuracy KPI is a good practice. Forecast accuracy is measured with metrics like Mean Absolute Error (MAE) for average error, Root Mean Squared Error (RMSE) to penalize larger errors, and Mean Absolute Percentage Error (MAPE) for percentage-based comparisons. However, each has issues: MAE ignores error direction, RMSE is sensitive to outliers, and MAPE struggles with zero values. Mean Absolute Scaled Error (MASE) helps standardize across series but needs a baseline forecast. These metrics have limitations, such as outlier sensitivity, interpretability issues, and challenges with small or zero values, so combining metrics often provides the clearest accuracy view.

I faced inaccuracies in our sales forecasts that impacted inventory levels. In my experience, validating and adjusting forecasts involves regularly comparing forecasted data with actual sales and using historical trends to refine predictions. One thing I’ve found helpful is employing a rolling forecast approach, which updates predictions periodically based on recent data and market conditions. This flexibility helps account for unexpected changes and improves forecast accuracy. Additionally, involving cross-functional teams in the forecasting process ensures a more comprehensive view and better alignment with real-time needs.

La clave para una previsión de demanda precisa no solo está en los números, sino en cómo esos números se alinean con la cadena de suministro. En FORSITIS, ayudamos a nuestros clientes a conectar estos puntos, asegurando que su cadena de suministro esté sincronizada con sus previsiones. Esto no solo evita la acumulación innecesaria de inventarios, sino que también mejora la agilidad operativa. Nuestro objetivo como consultores es crear procesos de abastecimiento que respondan de manera dinámica a la demanda real, garantizando que la eficiencia y la precisión trabajen de la mano para maximizar los resultados.

To align your supply chain with demand, use real-time data for demand-driven planning and adjust production accordingly. Build an agile supply chain that can quickly adapt to demand changes through flexible processes and responsive suppliers. Collaborate through Sales and Operations Planning (S&OP) to ensure supply capabilities match demand forecasts. Optimize inventory levels with strategies like safety stock based on demand variability. Leverage advanced technologies like Artificial Intelligence (AI) and Robotic Process Automation (RPA) to automate demand sensing and ensure real-time adjustments across the supply chain.

we faced challenges aligning our supply chain with fluctuating demand. In my experience, achieving alignment involves using real-time data to forecast demand accurately and adjust supply chain operations accordingly. One thing I’ve found helpful is implementing integrated planning tools that link sales forecasts with inventory levels. This approach allows for proactive adjustments, reducing overstock and stockouts. Regularly reviewing and updating forecasts based on actual sales data also ensures that the supply chain remains agile and responsive to market changes, enhancing overall efficiency.