How do you use historical data to forecast demand?

Implementing forecasting tools at a regional level has shown me that choosing the right method is essential for effective demand planning. Qualitative methods, like surveys and expert opinions, are critical when historical data is scarce or unreliable, especially to capture emerging trends. On the other hand, quantitative methods, such as time series analysis or causal models, excel in precision, provided there’s robust data available. The key lies in aligning the methodology with business goals, data availability, and the required level of accuracy to make strategic decisions. When its necessary to manage a large list of skus and huge number of customers, the statistical engine tools will help you to select the best forecast with low error

Quantitative methods can be very useful in determining future demand. Even where seasonality exists, there are quantitative methods that are useful in these markets. Having completed a research paper on this subject and raised forecast accuracy levels to above 85% for large entities, I know that the value these methods can add to a business and their partners is huge in respect to reducing costs and focusing attention. However, in my experience, few executives believe this. Due to this limited perspective of demand forecasting held by many in senior management, investments in this field in regards to systems, qualified resources, and training is extremely low, which becomes a self fulfilling prophecy when forecasts fail to impress.

- Identify patterns in data behavior, such as growth or decline trends over time and recurring seasonal patterns; - Use statistical models such as moving averages, exponential smoothing, ARIMA, or machine learning models; - Split the data into training and test sets to validate the model's accuracy. The model should be trained on part of the historical data and then tested on a different set to check if it predicts correctly; - After applying the model, adjust it based on the test results. Check if it is accurate enough to capture demand variations; - Use the model to forecast future demand and continuously monitor the actual results versus the predictions to refine and improve the model.

To utilize historical data for demand forecasting: Apply the moving average method, calculating averages of past demand to predict future trends. Utilize exponential smoothing, which places more weight on recent data points for forecasting. Implement time series analysis to identify patterns and seasonality in historical demand data for accurate predictions.

Forecasting demand using historical data is essential for optimizing inventory and preventing shortages. Here's a brief process: Choose a Forecasting Method: Select methods like time series analysis, causal models, or AI-driven techniques. AI can reduce forecasting errors by 20-50%. Collect and Prepare Historical Data: Gather past sales, seasonality, and external factors (e.g., promotions). Using long-term data can improve forecast accuracy by 15%. Apply and Evaluate: Measure forecast accuracy with methods like Mean Absolute Percentage Error (MAPE) to track performance and reduce errors by 5-10%. Adjust and Update: Adapt forecasts in real-time to respond to market changes.

Implementing forecasting tools regionally has taught me the importance of collecting and preparing reliable historical data. This data spanning sales records, inventory levels, promotions, seasonality, and external factors forms the backbone of any accurate forecast. Ensuring sufficient coverage, such as 12 months for a yearly forecast, is crucial. Equally important is cleaning the data to remove outliers, errors, or gaps that could skew results. By organizing robust datasets, we enable forecasting models to provide insights that drive precise demand and supply planning, ultimately improving decision-making and operational efficiency. Since my standpoint sell-out information is better to use as a main input.

Since Covid, historical data has been difficult to trust. As we get further out, we mitigate the effects of that data. Nonetheless, awareness of the story told by historical data is important. This is where technologies available today help with the cleansing of data and making sense of the data we have. I agree that this conversation cannot be held in a vacuum. Different industries, different companies, different scenarios need to all be considered. History is a good place to start. Enrichment and understanding of that data is a must.

Although ERP systems are finally getting a foothold amongst SMEs in the UK, the appreciation around the accuracy and completeness of data is virtually non-existent. Before verifying data it is important to know what should be captured in each field. In order to do this well an understanding of what each field is used for, what fields or other data the field influences, as well as the effect the field may have on MRP output or other required information is crucial. Having been fortunate to implement systems through getting to know the set ups required has been extremely valuable. Seeing multiple people in different areas of the business having access to change parameters at will, further highlights a lack of understanding of controls needed.

To forecast demand using historical data: Gather relevant historical data on sales, market trends, and external factors influencing demand. Clean and preprocess the data, removing outliers and inconsistencies to ensure accuracy. Organize the data into a format suitable for analysis, such as a time series dataset, ready for forecasting models.

Die Qualit?t der Verlaufsdaten ist essentiell. In meiner Beratungst?tigkeit habe ich gesehen, wie wichtig es ist, nicht nur Daten zu sammeln, sondern auch deren Kontext zu verstehen. Selbst wenn ERP-Systeme zum Einsatz kommen, wird der Prozess nicht immer so gelebt, dass die notwendige Datenqualit?t erreicht wird. Das Entfernen von Ausrei?ern oder das Einbeziehen von saisonalen Mustern kann die Prognosegenauigkeit signifikant beeinflussen. Für die Genauigkeit einiger Modelle ist es jedoch besser, wenn die Daten zuvor nicht oder wenig manuell bereinigt wurden.

Implementing regional forecasting tools has reinforced the importance of applying and continuously evaluating forecasting methods. Once historical data is ready, leveraging software or algorithms to apply techniques like time series analysis is essential. Adjustments for trends, seasonality, and errors refine accuracy. However, the real value lies in evaluating reliability through metrics such as Mean Absolute Error (MAE) or Mean Absolute Percentage Error (MAPE). These indicators reveal how closely forecasts align with actual outcomes. Regular evaluation ensures forecasts remain a reliable foundation for demand and supply planning, driving better alignment with business goals and market realities.

Forecasting systems have the functionality to perform scenario planning to identify which method is more useful for you. The time it takes to perform iterations or try various methods manually is extremely time consuming. Using the standard measures such as mape or others is useful in getting a result and the tracking signal adds information around any bias that may be present. My advice to those with a high number of items is to focus resources on the top 20% items of your Pareto and perform more standard methods of B and C category items which usually have more volatility in their demand profiles. In measuring forecast accuracy levels, measure the different categories separately too. An overall forecast figure has little to no value.

To forecast demand using historical data: Apply a forecasting method like ARIMA or neural networks to the prepared dataset. Evaluate the accuracy of the forecasts using metrics like Mean Absolute Error or Root Mean Squared Error. Refine the model parameters or consider alternative methods based on the evaluation results to improve forecast accuracy.

Bei der Anwendung von Prognosemethoden ist es entscheidend, die Methodik kontinuierlich zu hinterfragen und zu verbessern. In meiner Laufbahn habe ich gelernt, dass die regelm??ige überprüfung der Prognosegenauigkeit und des Modells unverzichtbar ist. Hierbei kommt in der Praxis h?ufig die Kennzahl MAPE zum Einsatz, was nicht immer die beste Wahl ist. Früher war es notwendig und üblich, die Artikel zu segmentieren, um dann je Segment ein Modell auszuw?hlen und dem Artikel zuzuweisen. Mit der Verfügbarkeit von günstiger Rechenleistung und modernen Prognosemethoden wird das weniger notwendig und es k?nnen individuelle Modelle verwendet werden.

Tenho mais de 10 anos de experiência com planejamento de demanda e posso contribuir da seguinte forma para mercado de constru??o civil ( Parts Solutions) - Demanda histórica ( 6,12,24 meses) -Classifica??o PQR com XYZ ( de forma a termos uma matriz considerando coberturas diferentes para itens com pre?os menores / maiores) -Demanda média ponderada aplicando pesos ( 100%) de acordo com o período de escolha -Demanda via MRP considerando ponto de ressuprimento, estoque de seguran?a, etc. Mas nenhum desses pontos pode seguir sozinho. é necessário ter o alinhamento com a equipe responsável por nos gerar essas demandas.

Implementing regional forecasting tools has shown me that a forecast is never static; it’s a dynamic tool that must evolve with new information and market changes. Adjustments are essential when new demand drivers emerge, such as product launches, competitor actions, or economic shifts. Regular updates, informed by fresh sales reports, customer feedback, or market research, ensure the forecast remains relevant and accurate. This iterative approach not only refines projections but also enhances agility in decision-making, enabling the business to adapt proactively to changing conditions and maintain alignment with strategic goals. IBP and S&OP processes are the main interaction in the company to improve, adjust and update the forecast.

By analyzing past trends and patterns, we can identify seasonal fluctuations, market trends, and other relevant factors that impact demand. For instance, in my previous role in the retail industry, we utilized historical sales data to predict customer demand during holiday seasons, allowing us to adjust inventory levels and staffing accordingly. This proactive approach not only optimized customer satisfaction but also improved our financial performance. I believe that incorporating historical data into demand forecasting is a strategic imperative for businesses aiming to stay agile and responsive in dynamic markets.

Demand forecasts are required over the medium to long term and need to be revised based on new inputs. The forecast advises the business and partners to make adjustments in capacity and resource requirements to meet any increase or decrease in projected demand. The company can itself cause an increase in the forecast by procuring automation to increase output levels. This could increase the forecast of certain items that were previously limited. There are so many drivers that could affect forecasts so it is important for the head of planning/forecasting to be familiar with the industry and all competitors. It is important to ensure any forecast adjusted take into account supplier lead times so teams are able to make the changes required.

To refine demand forecasts using historical data: Continuously adjust forecasting models based on new data and changing market conditions. Implement techniques like seasonal adjustments or trend analysis to account for evolving patterns. Regularly review forecast performance and update models accordingly to enhance accuracy over time.

Die Anpassungsf?higkeit ist in der heutigen schnelllebigen Gesch?ftswelt entscheidend. In meiner Erfahrung als Berater ist es wichtig, Prognosen regelm??ig zu überprüfen und zu aktualisieren, insbesondere in Reaktion auf Marktver?nderungen oder neue Gesch?ftsstrategien. Dabei ist es vorteilhaft, die Prognoseanpassungen in verschiedenen Horizonten und Ebenen zu tracken, sodass der Auswirkung (positiv/negativ) im Nachgang analysiert werden kann.