## 2 Literature Review
The prediction of lead time was necessary for planned order releases and shop scheduling in a study by [PERSON] and [PERSON] [23]. Therefore, they investigated the prediction of lead times under dynamic conditions. They assumed variable product mixes and demands with equipment or staffing changes. On the other hand,Indrianti et al. [20] determined the delivery time of a set of customer orders in a repetitive make-to-order manufacturing company that utilizes recycled waste products as raw materials. A simulation approach was applied to find the minimum total flow time or makespan to determine the delivery time of each order. Moreover, [PERSON] et al. [24] proposed a methodology, which has been developed into a software tool, for complex engineered-to-order products. This is achieved through the examination of the characteristics of the product. [PERSON] et al. [16] used regression trees for lead-time forecasting in make-to-order manufacturing. Furthermore, [PERSON] et al. [18] reviewed studies for the prediction of lead times in engineer-to-order environments with a typically large number of individual parts and complex production processes. In such studies, operations research and machine learning are usually used. In a study by [PERSON] et al. [19] in a produce-to-order environment, researchers quote a reliable lead time for a new job (or order) upon its arrival to a manufacturing system. Usually, MLTF forecasting is performed by the company itself or at least after knowing enough details of the company production processes. For example, [PERSON] et al. [25] investigated manufacturing lead time in the optics industry using analytical and machine learning techniques for customized products. [PERSON] et al. [26] investigated manufacturing lead time using supervised machine learning approaches depending on historical production data from the manufacturing execution system (MES). Moreover, lead-time forecasting was performed based on ANN, fuzzy regression (FR), and conventional regression (CR) in a study by [PERSON] et al. [6]. Their study estimates the weekly lead times of an actual assembly shop. Another study that depends on MES data is the one by [PERSON] et al. [27], which combines simulation and statistical learning methods. On the other hand, few studies focused on CLTF, which is the focus of this paper. Forecasting of purchasing lead time was investigated in a study by [PERSON] et al. [5] in the context of the pharmaceutical supply chain by machine learning regression algorithms. The support vector machines approach was found to have the best performance. Their work was motivated by the activity of a logistic company that receives the pharmaceutical products from the suppliers and then organizes the shipping to healthcare facilities. Moreover, [PERSON] and [PERSON] [28] investigated material management and the effect of lead-time estimation based on the identification of the factors that influence it, such as the price of materials. They collected a large sample of procurement data from 16 building construction projects, and cluster analysis and regression analysis were used. Furthermore, [PERSON] and [PERSON] [29] considered determining a realistic delivery time in the supply chain by reducing bias. Lead-time estimation was expressed in the literature in different ways such as the study by [PERSON] [30], who used ANN to estimate due date assignment in a small batch and multi-assortment make-to-order production company. The study made a comparison between different due date assignment methods. Moreover, [PERSON] et al. [31] investigated the lead time prediction in a customer-individualized products' environment, where planning complexity grew significantly in such a case. They investigated the prediction of transition times (TT). As up to 99% of lead time consists of TT, which is the time between two processing steps, they used a data mining methodology for order-specific TTs. Some studies concentrated on special conditions such as [PERSON] et al.