Data Preprocessing in Supply Chain Management Analytics - A Review of Methods, the Operations They Fulfill, and the Tasks They Accomplish. Data Preprocessing in Supply Chain Management Analytics.

Data preprocessing is thought of as one of the most important steps in data analytics. This is especially true for the field of Supply Chain Management (SCM), in which the handling of huge data sets is the norm. Data preprocessing consists of multiple tasks, operations, and methods. Thus, this research focusses on identifying the specific data preprocessing tasks in SCM analytics, the operations used to solve them, and the methods used to meet the goals of the respective operations. To this end, a literature review, covering literature from 2011 to 2022, was conducted to analyze documented approaches to data preprocessing in SCM. The resulting overview presents the interrelationship between data preprocessing tasks, data preprocessing operations, and data preprocessing methods in SCM analytics. Results indicate that data transformation seems to be a commonly investigated task in SCM related data preprocessing, while data integration represents an area requiring further research. Furthermore, Principal Component Analysis (PCA), was found to be the most common method across the single tasks of data preprocessing, further highlighting the importance of transforming data by manipulating the features into a form such that when analytics algorithms are applied, they will give optimal results. This research hence presents researchers and practitioners a point of reference to identify the specific data preprocessing method used for specific data preprocessing operations in order to fulfill a specific data preprocessing task.