Katja Windt

Changing Paradigms in Logistics — Understanding the Shift from Conventional Control to Autonomous Cooperation and Control

The understanding of logistics as the integrated planning, control, realization and monitoring of all internal and network-wide material-, part- and product flows including the necessary information flow along the complete value-added chain is still valid: but the logistic performance is becoming more and more dependent on technological innovations. One reason for this is increasing complexity in combination with a high incidence of potentially disruptive factors. The increasing number of part variants and their combination during the production process of automobiles, for instance, leads to a tremendous number of possible combinations. The resultant complexity can no longer be managed feasibly by means of centralized planning and control systems. In addition, today’s customers expect a better accomplishment of the logistical targets, especially a higher due date reliability, and shorter delivery times. In order to cope with these requirements the integration of new technologies and control methods has become necessary. This is what characterizes the ongoing paradigm shift from a centralised control of “non-intelligent” items in hierarchical structures towards a decentralised control of “intelligent” items in heterarchical structures in logistic processes. Such intelligent items could include both raw materials, components or products, as well as transit equipment (e.g. pallets, packages) and transportation systems (e.g. conveyors, trucks).

Catalogue of Criteria for Autonomous Control in Logistics

Over the past years an increase in complexity of production and logistics systems regarding organisational, time-related and systemic aspects could be observed (Philipp et al. 2006). As a result, it is often impossible to make all necessary information available to a central entity in real time and to perform appropriate measures of control in terms of a defined target system. This development is caused by diverse changes, for example, short product life cycles as well as a decreasing number of lots with a simultaneously rising number of product variants and higher product complexity (Scherer 1998). Hence, new demands were placed on competitive companies, which cannot be fulfilled with conventional control methods. Conventional production systems are characterized by central planning and control processes, which do not allow fast and flexible adaptation to changing environmental influences. Establishing autonomous control seems to be an appropriate method to meet these requirements. The major aim of establishing autonomous logistics processes is to improve the logistics system’s performance. The basis for achievement of this objective is a comprehensive understanding of the term autonomy in the context of logistics processes. The idea of autonomous control is to develop decentralised and heterarchical planning and controlling methods in contrast to existing central and hierarchical planning and controlling approaches (Scholz-Reiter et al. 2006).

A classification pattern for autonomous control methods in logistics

Autonomous control in logistics enables single logistics objects to control the production and transportation process. This shift from central planning to decentralized control in real-time offers many possibilities to cope with highly dynamic and complex systems. The algorithms that define the decision behavior of each logistics object, autonomous control methods, play a key role in the successful implementation of autonomous control in logistics systems. A transparent classification is needed in order to identify the basic elements these methods consist of. This classification supports the evaluation of autonomous control methods in terms of gaining knowledge about which method characteristics are responsible for a method’s performance. This paper defines what autonomous control methods are, works out their fundamental characteristics, presents multiple methods developed so far, and compares these methods regarding characteristics and performance.

Complexity cube for the characterization of complex production systems

Owing to the increasing complexity of todays logistic systems, new planning and control methods are necessary. Autonomously controlled processes are a possible solution to cope with these new requirements. In order to verify this thesis, the development of an evaluation system is necessary which measures the logistic objective achievement, the level of autonomous control and the level of complexity. The current paper presents adequate operationalization of the complexity in production systems. For this purpose a complexity cube is derived in order to characterize production systems regarding their level of complexity. The different types of complexity in this cube are represented by vectors which allow measurement and comparison of different types of complexity for different production systems. The application of the complexity cube is illustrated using an exemplary job shop manufacturing scenario.

Autonomous Cooperation and Control in Logistics

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Evaluation of Autonomous Logistic Processes — Analysis of the Influence of Structural Complexity

The concept of autonomous control requires on one hand logistic objects that are able to receive local information, process these information, and make a decision about their next action. On the other hand, the logistic structure has to provide distributed information about local states and different alternatives to enable decisions generally. These features will be made possible through the development of Ubiquitous Computing technologies (Fleisch et al. 2003).

A manufacturing systems network model for the evaluation of complex manufacturing systems

Purpose - – The topology of manufacturing systems is specified during the design phase and can afterwards only be adjusted at high expense. The purpose of this paper is to exploit the availability of large-scale data sets in manufacturing by applying measures from complex network theory and from classical performance evaluation to investigate the relation between structure and performance. Design/methodology/approach - – The paper develops a manufacturing system network model that is composed of measures from complex network theory. The analysis is based on six company data sets containing up to half a million operation records. The paper uses the network model as a straightforward approach to assess the manufacturing systems and to evaluate the impact of topological measures on fundamental performance figures, e.g., work in process or lateness. Findings - – The paper able to show that the manufacturing systems network model is a low-effort approach to quickly assess a manufacturing system. Additionally, the paper demonstrates that manufacturing networks display distinct, non-random network characteristics on a network-wide scale and that the relations between topological and performance key figures are non-linear. Research limitations/implications - – The sample consists of six data sets from Germany-based manufacturing companies. As the model is universal, it can easily be applied to further data sets from any industry. Practical implications - – The model can be utilized to quickly analyze large data sets without employing classical methods (e.g. simulation studies) which require time-intensive modeling and execution. Originality/value - – This paper explores for the first time the application of network figures in manufacturing systems in relation to performance figures by using real data from manufacturing companies.

Flow control by periodic devices: a unifying language for the description of traffic, production, and metabolic systems

Metabolic systems need to show high performance under typical environmental conditions and, at the same time, maintain certain functions under a broad range of perturbations and varying conditions. It is precisely this robustness with respect to large environmental changes that makes metabolic networks a potentially very interesting role model for technical production and distribution systems. Here we develop a formalism to compare these systems and show that optimization strategies from one domain can also be successfully applied to the other domains.

Ermittlung des angemessenen Selbststeuerungsgrades in der Logistik — Grenzen der Selbststeuerung

Die Frage des angemessenen Grades der Dezentralisierung wird in verschiedenen Wissenschaftsdisziplinen betrachtet. Zudem ist in der industriellen Praxis, beispielsweise in der Automobilindustrie, zu beobachten, dass in regelmasig wiederkehrenden Zyklen Organisations- und Fuhrungsstrukturen starker zentralisiert werden und in einem darauf folgenden Zyklus wiederum uberwiegend dezentralisiert werden. Letzteres hat den Vorteil schnellere und den jeweiligen Standort betreffende Entscheidungen durchfuhren zu konnen. Die Frage nach einer situationsgemasen Ausrichtung von zentralen zu dezentralen Strukturen und Ablaufen stellt sich demnach immer wieder neu. Eine angemessene bzw. sinnvolle Grenze zwischen zentraler und dezentraler Ausrichtung ist von einer Vielzahl Parametern abhangig und bedarf einer theoretischen Betrachtung.

Modelling dynamic bottlenecks in production networks

Bottlenecks, as the key ingredients for improving the performances of the production networks, have been profoundly studied. However, the major definitions of bottlenecks are derived in terms of the throughput and based on the theory of constraints (TOC). Moreover, before the specific measures can be applied on them, it is not straightforward to localise dynamic bottlenecks due to their complex dynamic characteristics. Distinguishing from the traditional view at the bottlenecks, this article therefore develops a systematic and comprehensive definition of dynamic bottlenecks of the production networks based on both the TOC and the bottleneck-oriented logistic analysis. Afterwards, the defined dynamic bottlenecks are modelled by means of discrete simulation using practical data, aiming at visualising them in the production network. By applying the logistic operating curves, the practical application of the proposed research and its procedures is discussed as well.