## 2 Literature Review  
The use of machine learning in supply chains is known in the literature. The main focus of this paper is the forecasting of lead time. Customers usually require an accurate estimation of lead time to ensure their production and delivery due date [10]. Usually, companies make forecasting based on full knowledge of their production line conditions, and this type of forecasting is called manufacturing lead-time forecasting (MLTF). However, sometimes, the forecasting of supplier companies might not be available for customers or 3 PLs, at least not for each order. Therefore, the customers or 3 PLs might need to make their own forecasting of lead time based on historical data about their suppliers. We call this type customer lead-time forecasting (CLTF). Based on that, the definition of lead time is different in both cases. The first one is the time that elapses between the release of an order and its completion (arrival in the finished goods inventory) [11]. In the second definition, transportation time until reaching the customer or the 3 PL is added. Although MLTF is well known in the literature, little was published on CLTF. In this study, we assume that the exact situation in the supplier factory is a black box. For example, the production and lead time for other customers are unknown, so historical data are only available for the customers of the furniture design company under consideration. The problem in this study is related to other decisions. In this study, forecasting performed by the 3 PL is for the purpose of shipment consolidation. One of the studies that investigated this problem is the one by [PERSON] et al. [12], who focused on transshipping multiple products from multiple suppliers to a single end customer. The 3 PL consolidates the inbound shipments so as to reduce costs. Consolidation sometimes leads to aggregating less-than-pallet-size shipments from different suppliers. Therefore, a pallet loading problem is needed. Such a problem was investigated in the study by [PERSON] et al. [13] using machine learning. This means that different decision problems in the supply chain are interconnected. The disadvantage of shipment consolidation is that it increases procurement interdependencies. Therefore, [PERSON] and [PERSON] [14] used Graph Neural Networks (GNN) to predict hidden links in the supply chain and gain visibility to prepare contingency plans. Another direction is to increase cooperation between different supply chain parties. Such cooperation can enhance innovation activities and therefore competitiveness [15]. Most of the research was performed on make-to-stock environments to select the best inventory control policy. However, there are some studies that investigated engineer-to-order or make-to-order environments. Most of the studies used machine learning and data mining with its different techniques for forecasting [16; 17]. However, there are other studies, which used simulation or operations research [18; 19; 20; 21]. Moreover, Mean Absolute Percentage Error (MAPE) is usually used to estimate the performance of forecasting methods [6]. [PERSON] et al. [7] performed a comprehensive procedure for comparing several machine-learning techniques for manufacturing lead-time estimation in the case of failure of the machines. The best model is selected in a study by [PERSON] et al. [6] based on MAPE. They based their study on weekly lead times for a large complex electric-motor assembly line, where data are taken for 70 weeks. One of the make-to-stock studies is performed by [PERSON] et al. [22], who used artificial intelligence (AI)-based simulation to predict the lead time of supply chain members. They used an Artificial Neural Network (ANN) together with a Genetic Algorithm (GA). The objective was to design inventory and routing in the best way to assure adequate inventory level and also provide an efficient route. This is part of using the correct inventory control policy.