How do you update and revise your demand forecasts based on changing conditions and data?

Explaining qualitative and quantitative methods: 1.Qualitative Methods include: The Delphi Model - Specialists inside specific market segments are approached to generate forecasts. Sales Force Opinion - Sales groups are entrusted with presenting an estimate for their particular region. Market Research - The organization utilizes customer surveys to recognize potential demand. 2.Quantitative Methods Include: Trend Projection - This strategy uses pattern detection to analyze historical data. Barometric -Barometric demand forecasting utilizes present current events to determine what's to come. Econometric - The econometric methodology utilizes complex mathematical plans to investigate data and factors that influence the data.

Staying attuned to customers' preferences and needs is crucial. Choosing the appropriate method, whether qualitative or quantitative, is essential. Asking precise questions in qualitative research and concentrating on relevant variables in quantitative analysis further enhances the process.

To update and revise demand forecasts based on changing conditions and data, it's essential to continually monitor key performance indicators, compare actual data with forecasted data, and proactively identify changes in external factors, such as shifts in market demand, fluctuations in supply chain partners' performance, or changes in economic conditions. Regularly updating forecasting models with new data inputs and incorporating feedback from sales and marketing teams can help refine the accuracy of the forecasting results. Additionally, conducting sensitivity analysis and scenario planning can help inform dynamic supply chain decision-making.

Demand forecasting is a critical aspect of supply chain management and involves predicting future demand for products or services. - I recommend calculating averages of past demand over specific time intervals to identify trends, assigning weights to recent data points to give more importance to the latest observations - Establish relationships between demand and various independent variables, such as price, advertising, or seasonality - Conduct small-scale trials or pilot projects to observe customer response and extrapolate to larger markets, gathering input from representative consumer groups to understand preferences and potential demand.

Keep your demand predictions current using the latest data analytics and flexible models that can adjust as needed. Stay in touch with your sales team to understand what's happening in the market and what customers are into. Remember the time of the year (if the business is seasonal) and watch the economy for ups/downs. Work closely with internal teams (marketing and operations) to get a wide range of information. Change your models as you get new information and see how well your past predictions did. Learn from any mistakes in your forecasts to stay quick and ready for whatever the market does next.

During my time at Flipkart, I worked on a Demand Forecasting project using Time Series Analysis(TSA). Here are the top reasons for choice of this method. 1. Handling Seasonality and Trends: E-commerce demand often exhibits strong seasonal patterns (e.g.,holidays) and trends (e.g.,market trends). TSA methods, such as seasonal decomposition and trend modeling, are effective in capturing and forecasting these patterns accurately, aiding optimized inventory management. 2. Adaptability to Product Categories: These platforms deal with a wide range of products, varying in demand patterns & seasonality. TSA techniques can be tailored to different SKU levels, allowing for customized forecasting models accounting specific demand characteristics.

Time Series Analysis is an important method of demand forecasting. 1. Time Series Analysis analyzes a grouping of data points gathered over an interval of time. 2. Data points are recorded at steady intervals over a set timeframe as opposed to simply recording the data points haphazardly or intermittently. 3. Time series analysis normally requires an enormous number of data points to guarantee consistency and unwavering quality. A substantial amount of data guarantees you have a good test size and that analysis can slice through noisy data. It likewise guarantees that any patterns or examples found are not exceptions and can represent occasional change.

Utilizing time series analysis is a fundamental method for updating demand forecasts based on historical data patterns. By examining past demand data over time, trends, seasonality, and other patterns can be identified and used to inform future projections. Time series models, such as ARIMA (AutoRegressive Integrated Moving Average), enable forecasters to capture and analyze these patterns, providing a reliable basis for adjusting forecasts in line with changing conditions.

Time series analysis in demand forecasting is crucial for optimizing inventory and resource management in the supply chain. It facilitates aligning production and distribution with anticipated demand patterns, minimizing stockouts or overstock situations. However, its primary limitation lies in its reliance on historical data, making it less effective in accounting for sudden or unexpected changes in demand. Integrating qualitative factors like market intelligence is essential for a more resilient and adaptive supply chain strategy.

Time series analysis is a powerful tool for revising demand forecasts, particularly in stable environments where historical patterns are likely to repeat. Techniques like moving averages, exponential smoothing, and ARIMA (Auto-Regressive Integrated Moving Average) can help you identify trends, seasonality, and cyclical patterns in your data. Regularly updating these models with the latest data ensures that your forecasts remain relevant and responsive to recent changes.

Yet, it's crucial to fully grasp the factors influencing causality, and causal analysis might not consider all variables or their interactions. Nevertheless, many appreciate causal analysis for its focus on factors that can be controlled and its ability to guide actionable decision-making.

Causal analysis is an important quantitative method for demand forecasting, and it assumes that demand is influenced by various explanatory variables, such as price, income, advertising, weather, or other relevant factors. The goal of causal analysis is to establish and quantify the relationships between these variables and demand using Regression Analysis, Econometric Models, and Machine Learning Algorithms.

Employing causal analysis involves identifying and analyzing the relationships between demand and various causal factors, such as economic indicators, marketing activities, and external events. By understanding the drivers of demand fluctuations, forecasters can incorporate causal variables into their models to account for changes in market conditions and adjust forecasts accordingly. Causal analysis enhances the accuracy of demand forecasts by considering the broader context and underlying factors influencing demand.

Causal analysis goes beyond time series by incorporating external factors that influence demand, such as economic indicators, marketing campaigns, and competitive actions. By identifying and quantifying these causal relationships, you can make more accurate adjustments to your demand forecasts when conditions change. For instance, if a new competitor enters the market, you can revise your forecasts to account for potential shifts in customer behavior.

As we tell our customers at Turbine (helloturbine.com), no computer is going to know your business better than you do. The forecast is the starting point and human expertise always needs to polish the final version of the forecast.

I think it’s important that the judgement introduced is not just from one person. Ideally it’s a group of outside experts that understand your business and the market conditions your company is currently facing. Even then it’s important to be OK with being wrong and regularly updating your assumptions and making regular adjustments.

To revise demand forecasts, monitor real-time data such as sales trends and market conditions regularly. Incorporate external factors like economic shifts, seasonality, and competitor actions to adjust projections accordingly. Engage cross-functional teams for insights on customer behaviour and product demand. Finally, implement a continuous review process, allowing for scenario planning and timely updates based on new data.

Judgmental adjustment is a qualitative method for demand forecasting that involves modifying quantitative forecasts based on human expertise, intuition, or feedback, this method acknowledges that there are aspects of demand that might not be fully captured by quantitative models alone. Organizations often seek a balance between quantitative precision and qualitative insights to achieve more accurate and robust demand forecasts.

Judgmental adjustment allows forecasters to incorporate subjective insights and expertise into demand forecasts, particularly when data or models alone may not capture all relevant factors. By leveraging the knowledge and experience of stakeholders, forecasters can adjust quantitative forecasts based on qualitative considerations, such as market intelligence, emerging trends, or unique circumstances. Judgmental adjustments enhance forecast accuracy by integrating human judgment with statistical techniques, improving the relevance and reliability of forecasts.

Collaborative planning in demand forecasting is a strategic approach that involves the sharing of information and coordination of forecasts among various stakeholders in the supply chain. - Collaborative planning emphasizes transparent communication by sharing relevant information across the supply chain, this includes data on inventory levels, production schedules, and market insights. - Real-time sharing of data allows stakeholders to access up-to-date information, enabling better decision-making and alignment of plans. - Collaborative planning often involves reaching a consensus forecast, where stakeholders work together to agree on a shared prediction of future demand, this helps in reducing forecasting errors.

Implementing collaborative planning processes facilitates the integration of diverse perspectives and expertise in demand forecasting. By engaging cross-functional teams and stakeholders from sales, marketing, operations, and supply chain management, companies can leverage collective insights and knowledge to refine demand forecasts. Collaborative planning fosters alignment, transparency, and accountability, enabling organizations to adapt forecasts more effectively to changing conditions and data.

La planificación colaborativa en la previsión de la demanda es un enfoque esencial en la logística moderna. Al permitir que todos los actores de la cadena de suministro compartan información en tiempo real, se logra una sincronización más precisa de la oferta con la demanda. Por ejemplo, una colaboración efectiva entre un cliente minorista y su proveedor puede evitar situaciones de desabastecimiento al prever picos de demanda durante promociones o eventos especiales. Sin embargo, el éxito de este método depende en gran medida de la confianza mutua y la transparencia en el intercambio de datos. Las plataformas tecnológicas que facilitan esta colaboración son fundamentales para garantizar la precisión y la agilidad en la toma de decisiones.

Collaborative planning, forecasting, and replenishment (CPFR) involves working closely with partners across the supply chain to update demand forecasts. This collaborative approach ensures that all parties have a shared understanding of demand expectations and can make coordinated adjustments based on new data or changing conditions. By fostering communication and cooperation, CPFR helps align forecasts with real-time market dynamics.

Collaborative planning, while beneficial for optimizing supply chain efficiency, can also pose various challenges. Collaborative planning relies heavily on accurate and high-quality data. Organizations may be reluctant to share sensitive information. Inconsistent processes and standards among supply chain partners can hinder collaboration. Partners located in different time zones and geographic regions. Different regions may have varying regulatory requirements that affect supply chain operations.

I recommend using advanced technologies like machine learning to enable more accurate demand forecasts by analyzing complex patterns and adapting to changing conditions. AI-powered systems can provide real-time insights, allowing organizations to adjust inventory levels quickly in response to dynamic market conditions. Automation of certain processes, driven by AI, enables faster and more informed decision-making in inventory management.

There are many approach for this. However, based on my experience for this kind of case, error analysis can be helpful to identify "hidden pattern" Causing forecast error in the statistical or ML/DL approach. It might necessary to make separate forecast based on certain special condition.

Measuring forecast accuracy is essential for evaluating the performance of demand forecasts and identifying areas for improvement. By comparing forecasted values with actual demand data over time, forecast accuracy metrics such as MAPE (Mean Absolute Percentage Error) or MASE (Mean Absolute Scaled Error) provide quantitative assessments of forecast quality. Analyzing forecast accuracy helps forecasters identify biases, errors, and sources of uncertainty, informing adjustments and revisions to improve future forecasts.

Regularly measuring the accuracy of your demand forecasts is essential for continuous improvement. Key performance indicators (KPIs) like mean absolute percentage error (MAPE) and forecast bias help you assess the reliability of your forecasts and identify areas for refinement. By tracking accuracy over time and analyzing the reasons for discrepancies, you can make more informed revisions to your forecasting models and processes.

You should check your forecasts by comparing actual demand with what you thought would be. You can use the following measures: The mean Absolute Error (MAE) shows the average mistake, making it a straightforward way to see accuracy. The Mean Absolute Percentage Error (MAPE) tells you the error rate in percentage, which is excellent for comparing various products or sizes. The Root Mean Squared Error (RMSE) highlights more significant mistakes because it squares the errors. Use and review each one of these metrics to compare how your forecasting is doing. Doing so lets you notice which method works best for your business.