Implementing an auto-ordering system based on forecasting algorithms and machine learning is a pivotal step in optimizing supply chains and managing inventory. In today's fast-paced market, where demand can fluctuate rapidly, accurate forecasting is crucial for maintaining competitiveness and satisfying customer needs. However, demand forecasts alone are not sufficient; they must be seamlessly integrated into business processes to minimize manual effort.

A classic formula for ordering necessary goods to a retail location is:

Order = Forecast for the supply period + Safety stock - Inventory

Naturally, the longer the supply period, the more goods need to be ordered to ensure stock lasts until the next delivery. The figure below illustrates the forecasting horizon needed to avoid empty shelves:

Safety stock minimizes the risk of empty shelves due to demand volatility. There are various methodologies for calculating safety stocks based on the desired service level and the statistics of forecasting algorithm errors. Here, we aim to showcase the methodology of converting forecasts into orders.
Inventory includes the stock in the store at the time of order formation, which can be both physical and virtual (goods in transit). Both types of inventory are considered when ordering.

The supply schedule is a crucial factor that, alongside the forecast, determines the volume of goods ordered to a retail location. It helps identify the forecasting horizon needed to ensure stores are stocked adequately.

When reviewing submission and delivery schedules, it's important to understand that when placing an order on Monday for delivery on Wednesday, the Tuesday delivery order is already approved; thus, the goods in the approved order should be accounted for in the virtual inventory of the store or as "goods in transit." Similarly, when forming an order on Friday: the order for delivery on Saturday was formed on Thursday, so the approved amount of this order must also be considered.
The supply schedule imposes requirements on the forecasting algorithm in terms of the necessary planning horizon for volumes. This requirement may change on different order submission dates: on Tuesday, a two-day forecast suffices, while on Thursday, a four-day forecast is necessary to ensure enough goods until Sunday evening if the delivery is on Monday morning. The variation in the forecasting horizon also affects the cumulative forecasting error; therefore, the safety stock should account for this factor.

The calculation of auto-ordering is an arithmetic task, making it easy to automate and eliminating the need for manual management. The supply schedule is a key factor in determining the quantity of goods in the order and, consequently, the inventory in the store. Increasing the lead time between ordering and delivery inevitably leads to higher inventory levels, although it reduces logistical costs. Therefore, it is essential to find a balance between delivery frequency and warehouse stocks. Applying machine learning methods in auto-ordering helps automate routine processes, minimize human errors, and respond swiftly to changes in market conditions.