Integration of incremental filter-wrapper selection strategy with artificial intelligence for enterprise risk management

Identification of the Critical Factors for Global Supply Chain Management under the COVID-19 Outbreak via a Fusion Intelligent Decision Support System

Under the ravages of COVID-19, global supply chains have encountered unprecedented disruptions. Past experiences cannot fully explain the situations nor provide any suitable responses to these fatal shocks on supply chain management (SCM), especially in todays’ highly intertwined/globalized business environment. This research thus revisits and rechecks the crucial components for global SCM during such special periods, and the basic essence of such management covers numerous perspectives that can be categorized into a multiple criteria decision making (MCDM) approach. To handle this complex issue appropriately, one can introduce a fusion intelligent system that involves data envelopment analysis (DEA), rough set theory (RST), and MCDM to understand the reality of the analyzed problem in a faster and better manner. Based on the empirical results, we rank the priorities in order as cash management and information (D), raw material supply (B), global management strategy (C), and productivity and logistics (A) for improvement in SCM. This finding is confirmed by companies now undergoing a downsizing strategy in order to survive in this harsh business environment.

Identifying the key factors of subsidiary supervision and management using an innovative hybrid architecture in a big data environment

In a highly intertwined and connected business environment, globalized layout planning can be an effective way for enterprises to expand their market. Nevertheless, conflicts and contradictions always exist between parent and subsidiary enterprises; if they are in different countries, these conflicts can become especially problematic. Internal control systems for subsidiary supervision and management seem to be particularly important when aiming to align subsidiaries' decisions with parent enterprises' strategic intentions, and such systems undoubtedly involve numerous criteria/dimensions. An effective tool is urgently needed to clarify the relevant issues and discern the cause-and-effect relationships among them in these conflicts. Traditional statistical approaches cannot fully explain these situations due to the complexity and invisibility of the criteria/dimensions; thus, the fuzzy rough set theory (FRST), with its superior data exploration ability and impreciseness tolerance, can be considered to adequately address the complexities. Motivated by efficient integrated systems, aggregating multiple dissimilar systems' outputs and converting them into a consensus result can be useful for realizing outstanding performances. Based on this concept, we insert selected criteria/dimensions via FRST into DEMATEL to identify and analyze the dependency and feedback relations among variables of parent/subsidiary gaps and conflicts. The results present the improvement priorities based on their magnitude of impact, in the following order: organizational control structure, business and financial information system management, major financial management, business strategy management, construction of a management system, and integrated audit management. Managers can consider the potential implications herein when formulating future targeted policies to improve subsidiary supervision and strengthen overall corporate governance.

Corporate Social Responsibility and Corporate Performance: A Hybrid Text Mining Algorithm

Until now, the works regarding the relationships between corporate operating performance and corporate social responsibility (CSR) could not reach a conclusive result (positive, natural, and negative). This circumstance can be attributed to two main reasons: (1) inadequate performance measurement and (2) ignoring the multi-dimensional nature of CSR. To combat this, we provided a hybrid decision framework that consisted of two main procedures: (1) performance measurement via linear programming algorithm and (2) CSR’s multi-dimensional nature extraction via text mining. By joint utilization of a linear programming algorithm and text mining, we could gain more insights from the outcome. The proposed decision framework, tested by real cases, is a promising alternative method for performance prediction. Managers can take this model as a roadmap and allocate resources to suitable places, as well as reach the goal of sustainable development.