Decomposition

What is Decomposition?

Decomposition is a valuable forecasting practice in the field of logistics that involves breaking down time series data into different components. By separating the data into two or more component series, each component can be forecasted individually and then combined to create a final forecast.

The reason why decomposition is important is because it allows us to account for varying trends within the individual components. In many cases, time series data can exhibit different patterns and trends over time. For example, a product's demand might have a seasonal pattern, where sales increase during certain times of the year. However, there may also be an underlying trend of increasing or decreasing demand over the long term.

By decomposing the time series data, we can identify and forecast these different components separately. This enables us to capture the unique characteristics and trends of each component more accurately. For instance, we can forecast the seasonal component based on historical patterns and adjust our inventory or production plans accordingly. Similarly, we can forecast the underlying trend component to understand the overall direction of demand and make informed decisions about future investments or capacity planning.

Once the individual components are forecasted, they are re-composited to produce a final forecast. This final forecast takes into account the different trends and patterns identified in the decomposition process. By combining the individual forecasts, we can obtain a more comprehensive and accurate prediction of future demand or other relevant factors.

Decomposition is particularly useful when the individual components exhibit varying trends. For example, if the seasonal component of a product's demand is increasing over time, while the underlying trend is decreasing, it would be challenging to capture these dynamics without decomposition. By separating and forecasting each component separately, we can better understand and respond to these changing trends.

In conclusion, decomposition is a powerful forecasting practice in logistics that involves breaking down time series data into different components. By forecasting each component individually and then re-compositing them, we can obtain a more accurate and comprehensive forecast. This method is especially valuable when the individual components exhibit varying trends, allowing us to capture and respond to these dynamics effectively.