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Winter has traditionally been associated with cold temperatures, snow, and ice, but this year's winter in many parts of the world has been hot. According to the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Information (NCEI), the global land and ocean surface temperature for 2022 was the sixth-warmest ever since 1880, with record-breaking temperatures in certain regions. According to NOAA, the United States experienced its warmest January on record, with an average temperature of 6.2 degrees Celsius above the average of the 20th century.
This unseasonably warm weather is problematic for global supply chains for numerous reasons. Warmer temperatures can hinder crop growth and development, significantly threatening agriculture and dependent industries. In some parts of California, for instance, the warmer temperatures can cause fruit crops to bloom prematurely and suffer damage. In addition, warmer winters can increase pest populations and increase the possibility of floods and untimely rains. Such natural events can impact manufacturing, procurement, warehousing & logistics, and inventory management.
Hence, demand forecasting needs to evolve with accurate weather forecasting inputs to mitigate the adverse effects of warmer winters. Companies should upgrade their existing conventional forecasting techniques to improve forecasting that considers atypical weather patterns.
Analyzing the potential outcomes of various possible scenarios is the objective of scenario analysis in demand planning. Demand planning software can produce more accurate and reliable forecasts by incorporating weather data into scenario analysis, enabling planners to understand potential outcomes and plan accordingly.
Weather data is essential to scenario analysis in demand planning because it provides valuable information about past and current weather conditions. This data can be used to develop scenarios and assess their potential impact on future demand. This move can significantly improve forecasting for various industries, including agriculture, logistics, FMCG, and energy production.
There are a variety of weather data sources that can be utilized in scenario analysis. Satellites, which provide a wide range of weather data, including temperature, humidity, and precipitation, are among the most important sources. Additionally, ground sensors, weather balloons, and aircraft data are the next level of data sources. These data sources can be combined with the existing demand planning inputs, such as demand history, seasonality, etc., to provide a comprehensive demand forecast.
These models incorporate a variety of variables, including temperature, humidity, wind speed, and atmospheric pressure, when generating forecasts. The precision of the forecasts depends on the data's quality and the forecasting models' elaboration.
In recent years, significant improvements have been made in applying machine learning and artificial intelligence to weather forecasting. These technologies can enhance the accuracy of supply chain forecasts by incorporating weather as a variable. For instance, major global ice cream manufacturing companies have started including weather variables like temperature and humidity to improve their ice-cream forecasting.
In the following section, we will discuss the various scenario analyses that can be applied to weather forecasting and how weather data can be incorporated into supply chain planning and demand forecasting.
Several types of scenario analysis can be used in forecasting, taking weather as a variable, each with advantages and disadvantages. Below, we will discuss three commonly used types of scenario analysis: sensitivity analysis, probabilistic forecasting, and ensemble forecasting.
Sensitivity analysis is a scenario analysis method that examines the impact of changes in one or more variables on the outcome of a forecast. This type of analysis is useful when there is uncertainty about the value of certain variables and can help forecasters better understand how changes in those variables might affect the forecast. For instance, a restaurant may experience higher demand for cold beverages and salads during hot weather, while a clothing retailer may see increased demand for summer clothing during a heatwave. In such cases, sensitivity analysis can be used to evaluate the impact of changes in temperature, precipitation, or other weather variables on future demand.
Sensitivity analysis is relatively straightforward and can provide valuable insights into the impact of different variables on a forecast.
Sensitivity analysis assumes that variable changes are independent, which may not always be accurate. Additionally, sensitivity analysis does not provide a full range of possible outcomes but focuses on specific variables.
Probabilistic forecasting is a scenario analysis method that involves creating a range of possible outcomes based on the probability of different events. This type of analysis considers the likelihood of varying weather conditions occurring and provides a range of possible outcomes based on those probabilities. For instance, a clothing retailer may use probabilistic forecasting to estimate the range of possible effects for sales of winter clothing during the upcoming season. This could involve considering the probability of different weather patterns, such as mild or severe winter conditions, and the likelihood of changes in consumer preferences, such as a shift towards more sustainable or ethical clothing options. By estimating these events' probability and potential impact on demand, the retailer can develop more accurate demand forecasts and make better-informed decisions about inventory levels, production schedules, and marketing strategies.
Advantages: Probabilistic forecasting provides a range of possible outcomes, which can help forecasters better understand the likelihood of different scenarios occurring. This analysis type can also help identify high-risk events requiring special preparation or response.
Disadvantages: Probabilistic forecasting relies on statistical models and assumptions about the distribution of weather events, which may not always be accurate. Additionally, probabilistic forecasting can be challenging to explain to non-experts, as it involves presenting a range of possible outcomes rather than a single forecast.
Ensemble forecasting is a scenario analysis method involving creating multiple forecasts using different models or data sets. This type of analysis considers the uncertainty by creating a range of possible outcomes based on different models or data sources. For example, a retailer selling seasonal clothing may use ensemble forecasting to create multiple demand forecasts based on different weather forecasts as variables. By comparing the final forecasts generated by each model, the retailer can identify which weather variables have the most significant impact on demand and adjust its production and inventory strategies accordingly.
Ensemble forecasting provides a range of possible outcomes based on different models or data sets, which can help planners understand the range of possible outcomes. This type of analysis can also help to identify areas of agreement or disagreement between different models and cherry-pick the best results.
Ensemble forecasting can be computationally intensive and require significant resources. Additionally, the results of ensemble forecasting can be challenging to interpret, as they involve multiple forecasts rather than a single prediction.
There are many benefits to incorporating weather data into scenario analysis. Here are some of the most significant benefits:
Incorporating weather data into scenario analysis can help us create more accurate forecasts, make better decisions, reduce risk, increase efficiency, and communicate more effectively. By using these methods, we can create more resilient communities and prepare for the impacts of climate change.
While incorporating weather data into forecasting and scenario analysis has many benefits, there are also several challenges and limitations. Here are some of the most significant challenges:
Overall, while there are many benefits to using scenario analysis with weather data, it's essential to be aware of the challenges and limitations. By understanding these challenges, we can work to overcome them, create more accurate forecasts, and better prepare for the impact of weather on supply chains.
In conclusion, incorporating weather data into supply chain planning can result in more accurate forecasts and improved decision-making.
Utilizing weather data in scenario analysis has numerous advantages, including improved predictions, decision-making, risk reduction, increased efficiency, and communication. However, knowing the challenges and limitations of utilizing weather data, including data availability, accuracy, interpretation, modeling complexity, uncertainty, and cost, is crucial. Companies can better understand the range of potential outcomes and prepare in advance by employing various types of scenario analysis, such as sensitivity analysis, probabilistic forecasting, and ensemble forecasting.