A Validation Framework for Bulk Distribution Logistics Simulation Models

Background : Simulation of business processes allows decision-makers to explore the implications and trade-offs of alternative approaches, policies and configurations. Trust in the simulation as a stand-in proxy of the real system depends on the validation of the computer model as well as on that of the data used to run it and judge its behaviour. Though validation frameworks exist, they provide little guidance for validation in the context of data-poor endeavours, such as those where observations as sourced from historical records were acquired for purposes other than the simulation itself. As simulation of complex business systems as logistic distribution networks can only rely on this type of data, there is a need to address this void and provide guidance for practitioners and fostering the conversation among academics. This paper presents a high-level development and validation framework applicable to simulation in data-poor environments for modelling the process of bulk distribution of commodities. Method : Traditionally accepted approaches were synthesised so as to develop an into a flexible three-stage modelling and validation approach to guide the process and improve the transparency of adapting available data sources for the simulation itself. The framework suggests the development of parallel paths for the development of computer and data models which, in the last stage, are merged into a phenomenological model resulting from the combination of both. The framework was applied to a case study involving the distribution of bulk commodities over a country-wide network to show its feasibility. Results : The method was flexible, inclusive of other frameworks, and suggested considerations to be made during the acquisition and preparation of data to be used for the modelling and exploration of uncharted scenarios. Conclusions : This work provides an integrative, transparent, and straightforward method for validating exploratory-type simulation models for endeavours in which observations cannot be acquired through direct experimentation on the target system.