Most decision-supports methods are dedicated to the identification and characterization of risks and opportunities. The concrete exploitation of these risks and opportunities is generally depending on the ability of users to analyze multi-dimensional situations, to mobilize their experience and to foresee consequences. In this article, a new and original data science-based vision of risk and opportunity management for decision-making purpose is introduced. The main expected benefit of this vision is to enable decision makers to manage the performance trajectory of a considered system by visualizing and combining the impact of risks and opportunity.

Purpose As the dynamics of the external environment of the enterprise continue to increase, the support of information systems for organizational agility becomes increasingly important. Collaborative Management System (CMS) is a new type of information system that can cope with the dynamic changes of the organization. Effective knowledge transfer is the core of the system implementation. The purpose of this study is to explore the knowledge transfer barriers faced by CMS in its implementation process. Design/methodology/approach Through field interviews with a representative CMS provider, this paper summarizes the barriers of knowledge transfer during CMS implementation into three aspects. Findings Based on the innovative measures taken by the company and relevant literature, the corresponding mitigating strategies are proposed. Originality/value The findings enrich the implementation methodology of agile information systems by exploring the knowledge transfer problem from a novel context. The study also provides a reference for practical implementation to overcome the dilemma of knowledge transfer.

With the application of risk management and accident response in the railway domain, risk detection and prevention have become key research topics. Many dangers and associated risk sources must be considered in collaborative scenarios of heavy‐haul railways. In these scenarios, (1) various risk sources are involved in different data sources, and context affects their occurrence, (2) the relationships between contexts and risk sources in the accident cause mechanism need to be explicitly defined, and (3) risk knowledge reasoning needs to integrate knowledge from multiple data sources to achieve comprehensive results. To express the association rules among core concepts, this article constructs two ontologies: The accident‐risk ontology and the context ontology. Concept analysis is based on railway domain knowledge and accident analysis reports. To sustainably integrate knowledge, an integrated evolutionary model called scenario‐risk‐accident chain ontology (SRAC) is constructed by introducing new data sources. The SRAC is integrated through expert rules between the two ontologies, and its evolution process involves new knowledge through a new risk source database. After three versions of the upgrade process, potential risk sources can be mined and evaluated in specific contexts. To evaluate the risk source level, a long short‐term memory (LSTM) neural network model is used to capture context and risk text features. A model comparison for different neural network structures is performed to find the optimal evaluation results. Finally, new concepts, such as risk source level, and new instances are updated in the context‐aware risk knowledge reasoning framework.

This article deals with the question of risk and opportunity identification based on data management as one main step of the convergence of artificial intelligence and industrial engineering. Two main subjects are addressed in this article: (i) the data management framework that could be the backbone for the whole approach, and (ii) the modeling theoretical background that could be used as a basement for the definition of a formal system for risk and opportunity modeling. The general principles presented in the article are used to define outlooks and to organize them as milestone of a roadmap.

The paper proposes a research framework for risk identification approach in collaborative networks dedicated to develop a formalizing, structured reference for risk identification and risk mitigation and explore an effective mechanism that can motivate diverse partners to manage risks collaboratively. The approach is based on a formalized vision of Danger/Risk/Consequence chain that is defined as the primary schema of the proposed methodology. The DRC chain indicates five risk-related concepts and their interrelationships, which is able to well describe risk-related collaborative contexts. Cascading effect in the concept chain are presented for further interpreting. Furthermore, a supply chain scenario of three use cases is given to illustrate the proposed framework.

Traditional recommendation algorithms measure users’ online ratings of goods and services but ignore the information contained in written reviews, resulting in lowered personalized recommendation accuracy. Users’ reviews express opinions and reflect implicit preferences and emotions towards the features of products or services. This paper proposes a model for the fine-grained analysis of emotions expressed in users’ online written reviews, using film reviews on the Chinese social networking site Douban.com as an example. The model extracts feature-sentiment word pairs in user reviews according to four syntactic dependencies, examines film features, and scores the sentiment values of film features according to user preferences. User group personalized recommendations are realized through user clustering and user similarity calculation. Experiments show that the extraction of user feature-sentiment word pairs based on four syntactic dependencies can better identify the implicit preferences of users, apply them to recommendations and thereby increase recommendation accuracy.

The concept of collaborative networks has been encountered very frequently these days as the reply when trying to adapt and enhance enterprises in this tremendously competitive commercial environments. A lot of knowledge has been gathered for collaborative networks so far, from defining network kinds to levelizing partnerships and also proposing models for partnership developments. But most of these efforts didn't tackle a very vital obstacle which is detecting and predicting collaboration possibilities between enterprises. In this paper, a new enterprise characteristics classification is proposed, which will be used as a profile for characterizing enterprises susceptible to take part in a collaborative network. The proposed detection approach is based on the enterprise characteristics concept as well as collaboration network types. Also a hypothesis to rank the potential partners using KPIs is shown along with the big picture of this approach accompanied by the future work that has to be done.

This article presents an original theory for system management, based on physics principles. That theory considers that risks and opportunity can be seen as forces pushing or pulling a system with regards to its objective and its KPIs. Based on that proposal, this article presents the theory, based on (i) identification of susceptibility of systems to internal and external characteristics (danger, favorable conditions), thus creating forces (risks and opportunities), and (ii) evaluation of the sensibility of systems to these forces, thus creating consequences (damages or benefits). This article also presents the practical vision of that theory by detailing the way to observe the force-inducted trajectory of the considered system with regards to its KPIs. An illustrative example and discussions about the perspectives conclude the article.