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are handed over to the objects and system components,the wider the scope of delegated decision-power andresponsibility for the entire process and the bigger thepart of self-determined activities are. In the end, selfcontrollingprocess solutions can occur.Self-control of logistics systems as process runningparadigmThe term self-control describes decentralizeddecision-making processes in heterarchic structures. Forthis, interacting elements of unpredictable systems needto have the ability and possibility to autonomously makedecisions. Following Scholz-Reiter et al. (2005) selfcontrollingin logistics aims at coping with the dynamicand structural complexity of horizontal and vertical cooperationsbetween companies without providing allinformation relevant for making decisions to a centralinstance. This would be the case if the logistics objectitself carries out information processing, decisionmaking,and execution.Here, the term decision-making covers the entireprocess from triggering the decision-making process viaidentifying and evaluating options for deciding toimplementing and controlling the chosen decisionalternative. Information processing includes all activitiesfor recording, storing, transforming, and transmittinginformation. Finally, execution of a decision is directedtowards implementing the decision made at the materialflow layer. In comparison to the typical heteronomouscontrol of today with decisions finally made by highercontrol and management layers implementation ofapproaches for self-control is expected to reduce thenumber of instances involved in a decision process and atthe same time also the volume of information flowsrequired for communication between these differentlayers. In the end, this should help to make certainprocess-related decisions faster, more flexible and thoughmore efficient.As previously already discussed, logistics objects canonly decide out of their local point of view and withfocus on their individual objectives. This is appropriate,for example, when searching for the best route at acrossing taking the process, its current situation, and theobject’s target into consideration. However, to achieve aglobal optimum for the process as a whole controldecisions are to be taken from a foresighted globalperspective repeatedly that affects both individual or agroup of resources and their influence on the process aswell as several objects and their future lives. Here, thequestion is to be asked for limitations of such a localview and from when a global perspective would be betteror even necessary instead. Additional, it is worth toinvestigate if self-control is really the better option thanheteronomous control with regard to the achievableprocess performance and the guaranteed process securityin general or for particular types of logistics processes.Perhaps, similar effects could eventually be achievedwithin traditional control and management architecturesas well through the improvement of material flowtracking and tracing by use of RFID?When talking about the paradigm of self-control, acontrol solution’s capability for self-learning has to bediscussed as well. An intelligent object decidesautonomously. From observing the effects of those cleveror not so clever decisions, the latter ones eventually evenbeing revised or modified, it could gain experience asinput for an individual learning process to improve theown decision-making competence. Pre-condition wouldbe a suitable design of the implemented controlfunctionality, e.g. by use of neuronal networks, to be ableto make use if those experience in future decisionmakingprocesses.Certainly, adaptive control algorithms resulting fromsuch learning curves are exclusively linked to the objectitself and would get lost as soon as this object leaves thesystem. To let further objects benefit from thoseexperience-based adaptations of the control algorithm aswell, they need to be either shared between the objects orforwarded to a (central) coordinating instance. Due to theobviously existing analogies to the problems inknowledge explication and sharing with human beings, itwould seem to be useful to investigate respectiveapproaches and methods from knowledge managementfor their applicability to keep object “knowledge” aswell.Process autonomy and process complexityIf intelligent objects are able to act self-determined,new chances open up for autonomous processes, i.e.those processes in which object transformations largelyare realized without any interaction with other processesor higher control and management layers. Scholz-Reiteret al. (2005), for example, describe a robot system fordistribution logistics, which is equipped with intelligentidentification, sensor and location technologies to form abasic component for self-controlling logistics processes:A package with transponder does not know just its ownidentification, but further logistics data, such as itsdestination, too. By use of a reader an unloading robot,for example, recognizes these data and transform theminto respective actions, such as local movement ordistribution. With this, the intelligent package controls itsown distribution leading to an autonomous process witha clearly separated mode of action, i.e. with clearlydefined interfaces to other processes and as few aspossible information exchange relations with them. Here,the object’s level of intelligence determines its autonomyand the process’ level of self-control. The level of selfcontroldecisively depends on the studied logisticssystem, even if any logistics process will always be in thesame lines as given by the central circumstances.Consequently, it can be expected that the frequentlyquoted paradigm shift from heteronomous control to selfcontrolin logistics practice will more likely appear as aAnnual Proceedings of Vidzeme University College “ICTE in Regional Development”, 200657