Steffen Sowade

Modelling the Costs of Autonomous Logistics

Autonomous control strategies are a suitable concept in order to manage the dynamics of complex logistic systems in a decentralized manner. Previous research results show the advantage of autonomous control for scenarios in production logistics and in supply chain management. In each case, autonomous control was able to increase the flexibility, the robustness and the performance of the logistic processes if they were struck by unforeseen events, e.g. machine breakdowns. Researchers have already investigated the logistic performance behaviour of several specific autonomous control methods. Supplementary, they developed the Autonomous Logistics Engineering Methodology (ALEM) which enables logistics process experts to model autonomous logistic processes. Nevertheless, an additional economic analysis of such systems would be helpful in order to determine if and which deployment of an autonomous control system should be implemented. However, a method for such an economic evaluation is still missing. It should quantify the costs and benefits of a specific logistic process.

Evaluating the Effects of Embedded Control Devices in Autonomous Logistic Processes

Embedded control devices enhance logistic objects with the ability of decision making and execution and can increase the flexibility and robustness of logistic processes. While the influence of different autonomous control strategies on logistic performance has been investigated, an evaluation of a control devices’ influence is missing. This work analyses the behaviour of these control devices by means of discrete event simulations for an autonomously controlled production logistics process and a centralised controlled process. The investigation follows the idea that the impact of such devices can be described as additional decision making time. The larger the decision making time is and the smaller the actual processing time is, the higher is the loss of manufacturing time for control purposes. The investigated autonomously controlled process compensates the additional decision making time for specific levels of uncertainties in terms of machine failures.

Scalability Effects in Modeling Autonomously Controlled Logistic Processes: Challenges and Solutions in Business Process Modeling

Modeling of autonomous logistic processes requires detailed knowledge about logistic systems and about the design principles of autonomous control. The Autonomous Logistic Engineering Methodology incorporates both types of knowledge in varying degree in order to guide logistic process experts through the modeling process. Although the methodology enables modeling of any autonomous logistic process, two important challenges affect the methodology’s scalability in case of large logistic scenarios that consist of several organizational independent companies. This article analyzes both consequential challenges, namely the increase of a system’s complexity and the lack of obtainable information. It presents two logistic scenarios of different scale and discusses the selected challenges in detail. In addition, the article suggests a new type of model visualization and a set of collaboration mechanisms allowing to overcome these challenges.