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This paper studies the role of infrastructure factors in implementing knowledge management in the National Iranian Copper Industries Company (NICICo). We evaluate the readiness level of the company for the implementation of knowledge management processes. Knowledge management readiness presents a measure of the degree to which an organization may be ready, prepared, or willing to obtain the benefits, which arise from implementation of knowledge management processes. We investigate the influence of three basic factors (information technology system, knowledge process, and organizational culture) in implementing knowledge management processes. Data were collected via a survey instrument. The results show that current conditions regarding the three factors are not appropriate for implementing knowledge management in NICICo. Alongside utilizing knowledge management tools and techniques and prior to implementing a new technology, it is necessary to realize these tools and merge them with human resources and with the organizational culture; this is because culture plays a key role in knowledge management implementation. The requirements for successful implementation of knowledge management include gaining a complete understanding of the relevant business culture through the different levels of the organizational hierarchy, as well as identifying vital factors for success. This has not yet been achieved in NICICo.

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In order to deal with the various kind of risks in a supply chain, we need to have different approaches. In this study, we propose a mathematical programming model to manage the supply risk considering multi layer feature of the supply chain. The aim of this model is managing the supply chain risk by controlling the selection of suppliers. By having this approach, we aim to lower the risk of supplier disruption. We examine various datasets to observe the behaviour of the proposed model in different data sizes through the several steps. Analysing the results of different datasets, we show the trend of objective value by increasing data sizes. Besides, we analyse the increasing ratio of cost within different steps of the model. Finally, we discuss the effect of our proposed approach on the total cost.