Opportunities of on-demand logistics operations

Abstract

As a result of information technology and digitalization, it is possible to get to know the previous customer demands and more details. This allows companies to operate flexible and adaptive, more efficient logistics processes using the known demands. This is the basis of on-demand activities. However, in addition to understanding and using the tools, it is important to know the timeliness of the processes, because only in this way can the necessary tools be determined and applied effectively. In our article first we will examine the typical logistical demand processing processes. Based on these studies, we define a general demand fulfillment case study, which includes the characteristics of typical processes and formulates the risks associated with changes in demands and helps to develop adaptivity.  The circumstances related to logistics are constantly changing due to the impact of digitalization. Practical solutions that have been thought to be rock-solid need to be reinterpreted in such a way that they can be implemented for the technical solutions of the future. Current production planning solutions operate on-demand planning. This includes (old) stocking concepts that want to ensure the number of raw materials needed for this type of design, but in many cases, this can result redundant and unnecessary high stock. The problem is the constantly changing on-demand supply of production, which requires a high level of IT support. On the other hand, only a very limited percentage of companies have systems or processes for providing highly automated care. As a consequence, production planning is forced to adapt its production programmed to the available stocks and processes. Thus, the role of information and the extent to which related analytics solutions are applied are appreciated in these processes, as they are the foundations of the entire organizational performance.

 In our research, therefore, we focus on the processes by which we can help bridge the gaps between current production planning and supply. The goal of this paper to highlight the necessity of the on-demand operation optimization and the possible on-demand solutions.