Invariant conditions in value system simulation models

a r t i c l e i n f o Keywords: Value system Supply chain Value chain Business process Resource-event-agent ontology Integration Virtual organization This paper presents a framework for the integration of supply chain (or logistics/distribution), value chain (or financial), and business process (or operational/manufacturing) simulation models, which should facilitate assessing the impact of supply chain and operational changes on an enterprise's financial performance. A Design Science approach is taken to demonstrate that the REA ontology, which provides a shared conceptual ground for these three model types, and its axioms, which describe invariant conditions for value systems, can help to build conceptually sound simulation models and identify the integration points between these models. It is further shown how these three types of simulation models can be integrated into one value system model for discrete event simulation, making use of the ExSpecT simulation tool. With this ontology-based framework , simulation model builders should be able to scope their models better and define integration points with other models, which is expected to promote the (re)use of simulation models for different purposes (e.g., simulating logistical, operational and financial performance). Information technology is important for acquiring competitive advantage in dynamic business environments [1]. When the cost of error is high, information technology provides practitioners with the information that is needed to develop conceptual models that provide a true and fair view of a future reality. These conceptual models are then used to simulate and analyze the predicted behavior of the future reality. For example, before an airplane prototype makes its maiden trip many simulation models have been made to study the predicted behavior of individual airplane parts and the plane as a whole. These simulation models support technology advances, while saving money and lives. Church and Smith [2] advocate and demonstrate the use of simulation models for managerial decisions, potentially saving money and jobs. Where most current approaches limit themselves to the simulation of logistical and manufacturing processes , considering only logistical and operational parameters such as production cost, service time, product quality and process flexibility [3–5], Church and Smith stress that business performance is mainly evaluated in terms of financial parameters (e.g., profit, net present value). Consequently, not only logistical and operational parameters such as operational cost but also financial parameters such as cost of capital should be taken into account when building simulation models for evaluating the future performance of …