Search

Although collaborative decision-making with multiple stakeholders in supply chain has become an important research issue in recent years, current research on the mechanism/process, methods, and performance assessment of collaborative decision-making still lags behind the practical needs in supply chain management. This paper reviews the existing literature in the past decade on collaborative supply chain from the perspective of decision-making in four stages with a causal framework. This paper reveals a general retrospect of the main subjects developed and investigated with related applications of cases, which includes both theoretical assumptions and practical data. The findings indicate that most articles in this field are based on a direct relationship between collaboration and performance improvement but lack of a mechanism between them. Additionally, dynamic collaboration with members in the whole supply chain both horizontally and vertically has not been conducted by previous studies. This paper provides a new conceptual framework and future research directions for further study in supply chain collaboration. [ABSTRACT FROM AUTHOR]

One of the key aspects of digitalization brought by Industry 4.0 is driven by the Internet of Things. Perhaps, the most interesting component of digital transformation is Augmented Reality. Augmented reality is merging the digital and physical worlds in the same experience. This new technology is accepted and applications are initiated in various areas including manufacturing. The main usage area in manufacturing is planning, execution, and verification of the assembly and maintenance operations. Likewise, augmented reality is used in the training of inexperienced people and to guide them by remote expert support. However, the augmented reality application selection, especially for non-technology savvy manufacturing organizations is challenging. Detailed and time-consuming analyses are required to understand the key features to compare and select the most suitable augmented reality application. In this paper, the most critical nine augmented reality software selection criteria are identified. To help organizations to apply such criteria and make a selection that is most suitable for the enterprise, a multi criteria decision-making approach is suggested. The suggested method is based on a fuzzy spherical number. The complex proportional assessment method is used to calculate the rankings. Therefore, the suggested method is called SF-COPRAS. Moreover, the detailed definition of augmented reality features and terminologies is explained. Several augmented reality use-case scenarios are discussed for manufacturing organizations on their Industry 4.0 initiatives. This paper aims to guide decision makers on their augmented reality software selection journey. The offered framework aims to save time for investigating augmented reality solution features systematically and objectively. [ABSTRACT FROM AUTHOR]

The oil industry in Thailand primarily relies on truck transportation due to the versatility of trucks. Different truck sizes can easily access many customer locations better than any other transportation mode. However, although trucks have many advantages, the transportation cost per unit is very expensive compared to railway, air, waterway, and pipe transportation modes. Previous data indicated that shipping costs were approximately 703.995 USD/month/fleet and the transportation utilization was 83.145%. Therefore, efficiencies should be identified to improve business operations. This paper proposes an oil transportation plan with transshipment points by adopting Network Flow Models (NFM) to develop a Generalized Network Flow Model (GNFM). The developed GNFM was applied to solve three different problems and compared to the existing method. The objectives of the proposed model were to minimize total cost and maximize truck efficiency. Multi-Criteria Decision Analysis (MCDA) was considered to set the optimal decision criteria for managing the oil transportation network with the transshipment sites. The proposed model with the Cluster Model (CT) provided optimal results, with a total cost of 663.539 USD/month/fleet and a truck efficiency of 88.029%. [ABSTRACT FROM AUTHOR]

The study presents analytical data-based multi-criteria approach of critical success factors of infrastructure construction projects analyzed in the Ethiopian con- struction industry. This multi-criteria technique helps to improve the decision cap- abilities and ultimate performance of construction processes in various low-income countries of the East African region. The aim of this paper is to establish a logical relationship and interdependencies of success-related factors for enhancing decision making for various project teams and identify priorities while taking into account all known construction organizational constraints. A structured hierarchical matrix was developed based on a pre-identified success-related factors, and initially evaluated by experienced professionals as part of a content validation of the survey. Different professionals working in various construction organizations in Ethiopia were invited to participate in the questionnaire survey. All the required data analysis, including sen- sitivity performance, was conducted through Expert Choice© 11. Further, Kendall’s coefficient of concordance was conducted to examine and compare multiple expert responses. Based on the findings, the top success-related factors that affect decision making in construction projects are Adequate Goals/Objectives, Consultant’s Competency, Prior Experience of Consulting Firms, Consulting Firm’s Willingness and Cooperation, and Financial Standing of Contractor. The results are based on their global priority weights in the hierarchical model. The findings highlighted that there is disagreement between the major stakeholders involved in the construction process. The contribution of the study is introducing a bench-marking multi-criteria decision analysis technique to enhance decision making in the Ethiopian infrastructure sector. [ABSTRACT FROM AUTHOR]

Decision-making based on expert's opinion by data envelopment analysis (DEA) models requires crisp number. However, the assumption of expert might not always be accurate. This paper introduces fuzzy DEA credibility constrained and relative closeness (RC) index to cope with uncertainty data. The proposed approach transforms the traditional DEA models to be fuzzy events by using credibility measure. Furthermore, RC index was used to increase the discrimination power of traditional DEA. Two numerical examples with different credibility level are presented for demonstrate the proposed fuzzy DEA credibility constrained and RC index. The findings indicate that the credibility level impact on the RC index on both numerical examples. The increasing level of credibility value would drop the value of RC index. [ABSTRACT FROM AUTHOR]

Decision-making in complex systems such as nuclear power plants (NPPs) is a difficult task at best. The safety and integrity of many such high-capital cost-intensive installations depend on the operator's capability to correctly diagnose and take appropriate measures to avoid any abnormal operations of an NPP. Therefore, the role of the expert systems in the offline training programs for the operators is ever increasing. In this paper, we describe the development of an expert system, "QNP_SHELL," to assist, offline QNPP operators and plant personnel in a better familiarization to infer the anticipated and foreseen malfunctions from the observed symptoms. QNP_SHELL's inferencing mechanism is of the "Rule-based" type and to search the knowledge base it adopts the "Depth First" technique. The diagnostic performance of the trainee operators using QNP_SHELL on various accidents at QNPP has been found, through both the qualitative and quantitative evaluations, satisfactory. [ABSTRACT FROM AUTHOR]

This paper proposes an improvement in the form of modeling the demand in models of stochastic inventories. For these purposes, it is proposed to occupy the Kernel estimation method to the demand per unit of time and demand during the lead time to generate input scenarios for a stochastic programming in two-stage, in a model of continuous review with shortage. An application of this methodology considers the obtaining of this scenarios from the modeling of actual data of demand of inputs of food service. The possibility of working directly with the densities and accumulated probabilities of demand in an empirical and more realistic way with regard to the data available, improve the task for the decision makers of the operations management. [ABSTRACT FROM AUTHOR]

Material handling equipment (MHE) is important for every industry because it has an effect on the productivity of manufacturing. Conveyor systems are presently one popular type of MHE. This paper presents an integration of the analytic network process (ANP) with the benefits, opportunities, costs and risk (BOCR) model in order to select the best conveyor system. The proposed model established a network with four merits, six strategies criteria, and twenty six sub-criteria with four alternatives (present, roller conveyor, chain conveyor, and monorail). The ANP is to determine the relative weights of an evaluative criteria and decision alternatives. Therefore, the final ranking of the alternatives are calculated by synthesizing the score of each alternative under BOCR. The results showed that the best alternative under all five methods is the chain conveyor. These research results can be easily applied, adapted and used to improve performance of selecting the conveyer system in small and medium enterprises through large industries. [ABSTRACT FROM PUBLISHER]