Owing to its inherent modeling flexibility, simulation is often regarded as the proper means for supporting decision making on supply chain design. The ultimate success of supply chain simulation, however, is determined by a combination of the analyst's skills, the chain members' involvement, and the modeling capabilities of the simulation tool. This combination should provide the basis for a realistic simulation model, which is both transparent and complete. The need for transparency is especially strong for supply chains as they involve (semi)autonomous parties each having their own objectives. Mutual trust and model effectiveness are strongly influenced by the degree of completeness of each party's insight into the key decision variables. Ideally, visual interactive simulation models present an important communicative means for realizing the required overview and insight. Unfortunately, most models strongly focus on physical transactions, leaving key decision variables implicit for some or all of the parties involved. This especially applies to control structures, that is, the managers or systems responsible for control, their activities and their mutual attuning of these activities. Control elements are, for example, dispersed over the model, are not visualized, or form part of the time-indexed scheduling of events. In this article, we propose an alternative approach that explicitly addresses the modeling of control structures. First, we will conduct a literature survey with the aim of listing simulation model qualities essential for supporting successful decision making on supply chain design. Next, we use this insight to define an objectoriented modeling framework that facilitates supply chain simulation in a more realistic manner. This framework is meant to contribute to improved decision making in terms of recognizing and understanding opportunities for improved supply chain design. Finally, the use of the framework is illustrated by a case example concerning a supply chain for chilled salads.

Group Technology exploits similarities in product and process design to meet the diversity of customer demand in an economic way. In this paper we consider one of the implementations of this concept -family-based dispatching. Intrinsic to family-based dispatching is the grouping of similar types of products for joint processing. In this way the number of set-ups may be reduced. Consequently, lead-time performance of the shop can be improved. We extend existing rules for family-based dispatching by including data on upcoming job arrivals. Typically, this type of data resides in the minds of the operators, or is stored in a shop-floor control system. Its availability allows for (1) better estimates of the composition of a process batch for a family, (2) the consideration of families for which no products are available at the decision moment, and (3) the possibility to start set-ups in anticipation of future job arrivals. The potential of including forecast data in decision-making is demonstrated by an extensive simulation study of a single-machine shop. Results indicate the possibility of significant improvements of flow time performance.

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