Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR

Enterprise architecture (EA) is an approach for managing all components of enterprise and relationships among them. By implementing EA, the organization will be threatened from different aspects. We used failure mode and effect analysis (FMEA) which is a powerful tool for evaluating EA risks. In traditional FMEA, risk priority number (RPN), has been calculated by multiplication of three criteria, severity, occurrence and detection. Because of some drawbacks of the traditional FMEA, this paper—instead of calculating RPN—prioritizes EA risk factors with fuzzy VIKOR. VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian, means Multi-criteria Optimization and Compromise Solution) is a multiple attribute decision making technique which aims to rank EA risk factors with respect to the criteria. As regards using linguistic variables, fuzzy approach is used to allow experts to use linguistic variables. The proposed method is used for evaluating twenty EA risk factors, which integrates knowledge and experience acquired from professional experts.     

Automatic data collection for enterprise architecture models

Enterprise Architecture (EA) is an approach used to provide decision support based on organization-wide models. The creation of such models is, however, cumbersome as multiple aspects of an organization need to be considered, making manual efforts time-consuming, and error prone. Thus, the EA approach would be significantly more promising if the data used when creating the models could be collected automatically—a topic not yet properly addressed by either academia or industry. This paper proposes network scanning for automatic data collection and uses an existing software tool for generating EA models (ArchiMate is employed as an example) based on the IT infrastructure of enterprises. While some manual effort is required to make the models fully useful to many practical scenarios (e.g., to detail the actual services provided by IT components), empirical results show that the methodology is accurate and (in its default state) require little effort to carry out.     

CIMOSA: enterprise engineering and integration

Enterprises are rather complex systems which have to be managed for their internal affairs, but more importantly for the many relations to the different environments in which they are operating. Today, these environments are changing much more rapidly and the need for relevant information becomes of paramount importance in the decision making processes at all levels of enterprise management. Fluctuations in market demands, technology evolution and changing regulations require very flexible enterprise operations, capable of reacting to those changes. These reactions must be based on relevant and up-to-date information which must be supported by new decision support technology. The challenges in decision support concern the identification of relevant information, easy access and intelligent use of this information. Building and maintaining the enterprise knowledge base and enabling its efficient use for decision support are major tasks of enterprise engineering. Enterprise integration and its subsequent operation in the global environment of customers, suppliers and regulatory bodies will heavily depend on the availability and the continuous extension of this knowledge base. Enterprise modelling will play an important role in creating the knowledge base and in using it for enterprise integration and operational decision support. The paper discusses enterprise engineering as an enterprise life-cycle oriented discipline for identification, design, and implementation of enterprises and their continuous evolution. Current problems in the field are identified and initiatives are presented.     

A method of determining attribute weights in evidential reasoning approach based on incompatibility among attributes

Decisions on attribute weights are important problems in multiple attribute decision making. Many methods have been proposed to create attribute weights which are used to aggregate attributes in a simple additive weighting way. In this paper, a method of deriving attribute weights from incompatibility among attributes and possible constraints on the weights is developed based on the evidential reasoning approach in which attribute aggregation is nonlinear rather than linear. The incompatibility is a flexible combination of deviation incompatibility and decision incompatibility with a relaxation coefficient. The deviation incompatibility measures differences between assessments of alternatives on each attribute and the decision incompatibility quantifies differences between assessments of alternatives on one attribute and the aggregated assessments of the alternatives. For a specific alternative, two pairs of optimization problems with a constraint on the difference between potential weights and the combination of deviation incompatibility and decision incompatibility are designed to generate the favorable intervals of attribute weights and those of utilities of assessment grades. A problem of car performance assessment is investigated to demonstrate the applicability of the proposed method. The method is validated by comparison with other methods of producing attribute weights using the problem.     

Towards the development of the framework for inter sensing enterprise architecture

Inter-enterprise architecture (IEA) is a new concept that seeks to apply the tools and methodologies of enterprise architecture (EA) in a collaborative context, in order to model collaborative organizations in an inclusive manner. According to the main enterprise architectures proposed to this point, an EA should be conformed at least for a framework, a methodology and a modelling language. Sensing enterprise (SE) is an attribute of an enterprise or a network that allows it to react to business stimuli originating on the Internet. These fields have come into focus recently, and there is not evidence of the use of IEA for modelling a SE, while finding an interesting gap to work on. Thus, this paper proposes an initial framework for inter sensing enterprise architecture (FISEA), which seeks to classify, organize, store and communicate, at the conceptual level, all the elements for inter-sensing enterprise architectures and their relationships, ensuring their consistency and integrity. This FISEA provides a clear idea about the elements and views that create collaborative network and their inter-relationships, based on the support of Future Internet.     

An optimum design selection approach for product customization development

Consumer preferences and information on product choice behavior can be of significant value in the development processes of innovative products. In this paper, product customization evaluation and selection model is introduced to support imprecision inherent of qualitative inputs from customers and designers in the decision making process. Focusing on customer utility generation, an optimum design selection approach based on fuzzy set decision-making is proposed, where design attributes priority is identified from customer preferences using an analytical hierarchy process. A multi-attribute analysis diagram is developed to visualize the preference of each attribute from the expert’s group decision. Conjoint analysis is used in the product customization to focus on customer utility generation in terms of multiple criteria. The use of the decision-making method is illustrated with a case example that highlights the utility of the proposed method.     

基于D-U空间的混合型多属性决策方法

针对混合型决策中不同类型属性值无法有效保留其不确定信息的问题,根据不确定原理,将联系数的D-U空间理论引入到混合型多属性决策问题中,提出一种基于D-U空间的混合型多属性决策方法。将确定性与不确定视为一个整体,提出不同类型属性值在D-U空间中的映射转换法则,使得不同类型属性值在空间中得以统一量化,并明确属性中的不确定信息,避免不确定信息丢失而造成的决策偏差。在决策过程中,通过计算空间中属性向量的模和幅角的值进行方案选择,以描述各方案的稳定性,使排序准则具有直观的意义。通过2个算例验证了该方法的适用性和实用性。     

需求驱动的虚拟企业合作伙伴选择

针对虚拟企业建立过程中的合作伙伴选择问题,以合作需求为驱动,将粗糙数理论与可拓评价相结合,建立合作伙伴选择的粗糙可拓评价方法.首先,利用质量功能展开法分析企业的合作需求及其重要度,并将其转化为选择决策属性及其重要度,其中采用粗糙数处理分析过程中的不确定语义信息,基于极大熵准则对粗糙质量功能展开进行计算;然后,基于企业的合作需求构建合作伙伴选择粗糙可拓评价方法,从中推荐出最合适的候选企业;最后,通过实例分析与比较验证所提出方法的可行性和有效性.     

A novel credibility-based group decision making method for Enterprise Architecture scenario analysis using Data Envelopment Analysis

Analysis and selection of Enterprise Architecture (EA) scenarios is a difficult and complex decision making process directly effecting the long-term business strategies realization. This complexity is associated with contradictory objectives and significant uncertainties involved in analysis process. Although a large body of intuitive and analytical models for EA analysis has evolved over the last few years, none of them leads to an efficient and optimized ranking in fuzzy environments. Moreover, it is necessary to simultaneously employ some complementary methods to reflect the ambiguity and vagueness as the main sources of uncertainty. This paper incorporates the concept of Data Envelopment Analysis (DEA) model into EA scenario analysis through a group analysis under uncertain conditions. To resolve the vagueness and ambiguity of the EA analysis, fuzzy credibility constrained programming and p-robustness technique are applied, respectively. Not only is the proposed DEA model linear, robust, and flexible in aggregating experts’ opinion in a group decision making process, but it also is successful in discrimination power improvement – a major shortcoming associated with classic DEA model. The proposed model provides useful solutions to support decision making process for large-scale Information Technology (IT) development planning.     

Data transformation in the evidential reasoning-based decision making process

This paper describes the application of an evidential reasoning (ER)‐based decision making process to multiple‐criteria decision making (MCDM) problems having both quantitative and qualitative criteria. The ER approach is based on the decision theory and the theory of evidence and it uses the concept of ‘degree of belief’ to assess decision alternatives on each attribute. When faced with MCDM problems, evaluation and selection or ranking of alternatives appear to be both challenging and vital to arrive at a rational and robust decision. In the presence of both qualitative and quantitative evaluations in an MCDM problem, it is necessary, when using the ER‐based decision making process, to transform or convert quantitative data into a belief structure using a number of grades so that the converted belief structure and the original quantitative data are equivalent in values or utilities. This paper suggests three scenarios for data transformation and examines how the ranking of decision alternatives is changed when different scenarios of data transformation are used. Ranking of UK universities using the ER approach is illustrated as an example.     

Possibilistic programming approach for fuzzy multidimensional analysis of preference in group decision making

Multiple attribute decision making (MADM) problems are the most encountered problems in decision making. Fuzziness is inherent in decision making process and linguistic variables are well suited to assessing an alternative on qualitative attributes using fuzzy rating. A few techniques in MADM assess the weights of attributes based on preference information on alternatives. But they are not practical any more when the set of all paired comparison judgments from decision makers (DMs) on attributes are not crisp and also we have to deal with fuzzy decision matrix. This paper investigates the generation of a possibilistic model for multidimensional analysis of preference (LINMAP). The model assesses the fuzzy weights as well as locating the ideal solution with fuzzy decision making preference on attributes and fuzzy decision matrix. All of the information is assumed as triangular fuzzy numbers (TFNs). This method is developed in group decision making environments and formulates the problem as a possibilistic programming with multiple objectives.     

基于权重集结和相对优势关系的多属性决策方法

分析多属性决策方法中决策矩阵规范化和属性权重计算等步骤可能对决策方法合理性造成的不良影响,为克服这些不良影响,提出一种新的多属性决策方法.该方法采用群决策模式进行赋权,在对专家意见进行一致性分析的基础上,集结各位专家给出的属性权重,通过定义备选方案在属性值为实数、区间数和语言值等不同类型属性上的相对优势关系构造判断矩阵,并以此建立方案效用值计算的线性目标规划模型,从而实现备选方案的评价和排序.实例研究表明了所提出方法的可行性和有效性.     

A fuzzy multi-criteria decision support procedure for enhancing information delivery in extended enterprise networks

Extended enterprise networks, which are information networks comprising enterprises that are mostly geographically dispersed but functionally integrated, are favorable to the creation of a collaborative industrial environment to support supply chain activities and smoothen data exchanges for the production of customized parts as well as products. However, there is a lack of research literature related to the generic approach which can enhance the automatic delivery of the right information to the right enterprise within an entire network. This paper introduces a fuzzy multi-criteria decision support (FMCDS) procedure for enhancing information delivery within extended enterprise networks complemented with a case study in mold and die industry. The FMCDS procedure, based on an analytic hierarchy process, breaks down the complex comparative problem of information receivers into hierarchical multi-criteria decision-making processes. The incorporation with fuzzy set is used to suggest the relative strengths of the corresponding criteria, thereby enabling the construction of a more useful fuzzy judgment procedure to facilitate decision-making. Case study findings indicate that the procedure is helpful to provide key decision support in terms of automatic delivery of desired information to the needed enterprise. By using the suggested novel procedure for push delivery of the needed information to relevant enterprises within an extended enterprise network, the proposed FMCDS procedure provides more alternatives and ideas for supporting effective decision-making in information delivery.     

Multiple attribute decision‐making method for dealing with heterogeneous relationship among attributes and unknown attribute weight information under q‐rung orthopair fuzzy environment

A Q‐rung orthopair fuzzy set (q‐ROFS) originally proposed by Yager (2017) is a new generalization of orthopair fuzzy sets, which has a larger representation space of acceptable membership grades and gives decision makers more flexibility to express their real preferences. In this paper, for multiple attribute decision‐making problems with q‐rung orthopair fuzzy information, we propose a new method for dealing with heterogeneous relationship among attributes and unknown attribute weight information. First, we present two novel q‐rung orthopair fuzzy extended Bonferroni mean (q‐ROFEBM) operator and its weighted form (q‐ROFEWEBM). A comparative example is provided to illustrate the advantages of the new operators, that is, they can effectively model the heterogeneous relationship among attributes. We prove that some existing known intuitionistic fuzzy aggregation operators and Pythagorean fuzzy aggregation operators are special cases of the proposed q‐ROFEBM and q‐ROFEWEBM operators. Meanwhile, several desirable properties are also investigated. Then, a new knowledge‐based entropy measure for q‐ROFSs is also proposed to obtain the attribute weights. Based on the proposed q‐ROFWEBM and the new entropy measure, a new method is developed to solve multiple attribute decision making problems with q‐ROFSs. Finally, an illustrative example is given to demonstrate the application process of the proposed method, and a comparison analysis with other existing representative methods is also conducted to show its validity and superiority.     

粗糙集在融合图像质量评价中的应用

提出了一种基于粗糙集理论的融合图像质量评价方法。采用粗糙集属性约简方法把冗余的属性和冲突的对象从决策表中删去,从而对决策信息系统进行了相对简化,使模型具有一定的抗信息丢失能力。该方法运用到融合图像质量评价中去,实验结果表明其有效性。     

Exploring the future of enterprise architecture: A Zachman perspective

Today, and for the foreseeable future, organizations will face ever-increasing levels of complexity and uncertainty. Many believe that enterprise architecture (EA) will help organizations address such difficult terrain by guiding the design of adaptive and resilient enterprises and their information systems. This paper presents the “Grand Challenges” that we believe will challenge organizations in the future and need to be addressed by enterprise architecture. As a first step in using enterprise architecture as a solution for overcoming identified challenges, the Zachman Enterprise Architecture Framework is used to guide and structure the discussion. The paper presents the “Grand Challenges” and discusses promising theories and models for addressing them. In addition, current advances in the field of enterprise architecture that have begun to address the challenges will be presented. In conclusion, final thoughts on the future of enterprise architecture as a research field and a profession are offered.     

云制造环境中信息不完全的合作伙伴选择

针对云制造环境中合作伙伴选择信息不完全、动态多属性问题,提出一种基于广义优序法的合作伙伴选择模型。首先,针对不同的数据采用不同的方式将其量化为广义优序数,把伙伴企业选择问题转化为广义优序数矩阵问题,并给出了基于灰色关联法的属性变权计算方法和具有惩罚机制的时序权重确定方法。随后,通过综合广义优序数对候选企业进行排序选择。最后,通过实验仿真验证该模型是可行的和有效的。     

Proposing a context-aware enterprise mashup readiness assessment framework

Web mashups have received a lot of attention recently as more enterprises are engaged in enterprise 2.0 initiatives. So far, there is insufficient research on the adoption and diffusion of Web mashups in an enterprise context. In this paper, we discussed the benefits and issues associated with enterprise mashups. We proposed a context-aware enterprise mashup readiness assessment framework to help enterprise managers and decision makers determine their needs and readiness for enterprise mashups. A case study was conducted to verify the usefulness of the proposed framework. The results show that our work can be used to guide enterprises with their decision making for the adoption of enterprise mashups and thus contribute to the development of a rigorous strategy for enterprise mashup adoption, development and diffusion.     

基于决策分类的决策表分解方法研究*

针对当前基于属性重要性的决策表属性集分解方法存在的不足,提出了一种新型的基于决策分类的决策表属性集分解方法。分析了近似分类质量和属性重要性与决策分类之间的关系,利用粗糙集理论,从提高子决策表中决策分类正确性的角度出发考虑条件属性与决策属性之间的关系,提出了决策表分解的条件属性选择量度并对决策表实施属性集分解。     

Determination of the effectiveness of the realization of enterprise business objectives and improvement strategies in an uncertain environment

This study determines the effectiveness of business objectives and key performance indicators (KPIs) of the identified business objectives for different types of enterprise. The evaluations of the relative importance of KPIs are called a fuzzy group‐decision‐making problem. The KPI weights are determined using the fuzzy analytic hierarchical process. The effectiveness of business objectives and KPIs is determined using the adopted Balanced Scorecard Designer Pro. Determination of the KPI rank with respect to each enterprise type is on the basis of Pareto analysis. The priority of management initiatives of the improvement strategy corresponds to the calculated rank, and it has a critical effect on the competitive advantage of any enterprise. The results discuss manufacturing, public and service enterprises, which exist in central Serbia. The proposed approach can also be adapted to different businesses.