Which algorithm type is appropriate for planning supply?

The appropriate algorithm type for planning supply is a time-series based optimizer. This algorithm is particularly effective because it takes historical data into account to predict future demands and optimize supply levels accordingly. It uses statistical methods to analyze time-related data patterns, enabling businesses to anticipate fluctuations in demand due to seasonal trends, market shifts, or other influencing factors.

In supply planning, accuracy is crucial since it directly impacts inventory levels, procurement processes, and production scheduling. A time-series based optimizer helps ensure that supply aligns closely with forecasted demand, thereby reducing the risk of stockouts or excess inventory.

While statistical forecasting can also play a role in supply planning by providing demand forecasts, it does not directly optimize supply levels in the same way that a time-series based optimizer does. Compute quotas and local updates focus on different facets of supply chain management, such as managing allocations or executing adjustments at local levels, which may not specifically address the overall optimization of supply in relation to demand trends. Therefore, the time-series based optimizer stands out as the best choice for aligning supply planning with forecasted demand effectively.