Neural Networks: an Exploratory Data Analysis of Logistics Performance

Neural networks are a data processing technique that provides us a powerful tool to handle non-linear data and model complex relationships between data. Self-organising maps, a type of neural networks, has been used successfully as an exploratory data analysis method in applications like presenting the welfare states of the countries or analysing and representing financial data. Logistics includes many activities through the chain of purchasing the material and processing it to products that finally are delivered to customers. The firm’s turbulent environment and the integrated approach of the logistics have made the analysis of the logistics performance even more important and challenging task. Today, firms have collected most of the needed data for logistics performance measurement in their normal reporting process and their databases include much logistics data. However, we need to pay more attention to the data structuring than is usually done today. In this study, we use neural networks, especially self-organising maps, for an exploratory data analysis of logistics performance. As a result, we found that self-organising maps provide us a new type of logistics performance monitoring capability.