Part Level Demand Forecasting at Scale
The challenges of demand forecasting include ensuring the right granularity, timeliness, and fidelity of forecasts. Due to limitations in computing capability and the lack of know-how, forecasting is often performed at an aggregated level, reducing fidelity.
In this blog, we demonstrate how our Solution Accelerator for Part Level Demand Forecasting helps your organization to forecast at the part level, rather than at the aggregate level using the Databricks Lakehouse Platform. Part-level demand forecasting is especially important in discrete manufacturing where manufacturers are at the mercy of their supply chain. This is due to the fact that constituent parts of a discrete manufactured product (e.g. cars) are dependent on components provided by third-party original equipment manufacturers (OEMs). The goal is to map the forecasted demand values for each SKU to quantities of the raw materials (the input of the production line) that are needed to produce the associated finished product (the output of the production line).
Lufthansa increases on-time flights by wind forecasting with Google Cloud ML
The magnitude and direction of wind significantly impacts airport operations, and Lufthansa Group Airlines are no exception. A particularly troublesome kind is called BISE: it is a cold, dry wind that blows from the northeast to southwest in Switzerland, through the Swiss Plateau. Its effects on flight schedules can be severe, such as forcing planes to change runways, which can create a chain reaction of flight delays and possible cancellations. In Zurich Airport, in particular, BISE can potentially reduce capacity by up to 30%, leading to further flight delays and cancellations, and to millions in lost revenue for Lufthansa (as well as dissatisfaction among their passengers).
Machine learning (ML) can help airports and airlines to better anticipate and manage these types of disruptive weather events. In this blog post, we’ll explore an experiment Lufthansa did together with Google Cloud and its Vertex AI Forecast service, accurately predicting BISE hours in advance, with more than 40% relative improvement in accuracy over internal heuristics, all within days instead of the months it often takes to do ML projects of this magnitude and performance.
Forecasting Algorithms: A Tool to Optimize Energy Consumption
For example, a client connected to the main grid on a variable energy contract, with a controllable battery and solar panels, must satisfy an electricity demand. The two sources of uncertainty in the future are the electricity demand (load) and the renewable energy production. In order to avoid a black out while minimizing the total electricity cost over the time horizon, we need to forecast them.
We usually forecast both the mean value and a probability distribution. This is so that we can evaluate the level of uncertainty and assess the spectrum of all possible scenarios in the future. For example, rather than saying that the electricity production of solar panels will be 150 kWh tomorrow, it is better to make a prediction of the probability. If we say that there is a probability of 95% that the electricity production will be between 120 kWh and 180 kWh, we can be aware of the extreme values, such as in the case of high or low production.