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This study aims to explore the influence of social interaction processes on transactive memory system (TMS) practice, the mediation of knowledge integration to the relationship between TMS and team performance and the moderation of team psychological safety to the relationship among TMS, knowledge intentions and team performance.

The authors collected data from a sample of 366 team members from 55 research and development (R&D) teams in Taiwan and conduct the analysis using the partial least squares method.

The results of this study indicate that social interaction processes have a positive effect on a TMS; a TMS can foster team performance, but knowledge integration mediates the relationship between the TMS and team performance; and team psychological safety can moderate the relationship between the TMS, knowledge integration and team performance.

Existing studies not only fail to explore the influence of social interaction processes on a TMS practice but also lack empirical analyses to explore knowledge integration as a mediator and team psychological safety as a moderator. This study fills that gap by developing a model that includes these types of relationships and suggests the importance of the TMS in the context of R&D.

Studies have not investigated how the International Standards Organization (ISO) 9000 quality management system (QMS) guides manufacturers’ and their partners’ creative processes in devising an innovative supply chain structure. Accordingly, this study aims to explore this subject.

A systematic literature analysis was conducted to explore how the ISO 9000 QMS might guide manufacturers’ and supply chain partners’ creative processes and the further development of an innovative supply chain organizational structure. The systematic literature analysis is divided into the following two parts: the first part collects related literature to analyze and explore based on the research questions and purpose. The second part is an association analysis. The results of the association analysis show that these related literatures are suitable for the analysis and exploration of the research questions and purpose.

This study developed an analytic framework to compare the elements of four components of the ISO 9000 QMS and the ingredients that drive creativity and innovation; the study also reviewed related publications to deeply analyze the relationship between the elements of the four components and the ingredients of creativity and innovation. The authors observed that when manufacturers and partners implement the ISO 9000 QMS, their use of the elements of the four components engages all ingredients and thereby facilitates the manufacturers’ and partners’ creative process in the development of an innovative organizational structure.

According to the results of the analysis, practitioners can understand how to improve the innovation of an organization’s supply chain from ISO 9000 QMS components. In addition, the research has resulted in developing additional research propositions concerning the relationship between the four components of the ISO 9000 QMS and ingredients that drive creativity and innovation. Based on these propositions, more research hypotheses can be further developed.

The research results prove that the ISO 9000 QMS has a positive effect on facilitating the creative process and innovation, with regard to the organizational structure, and it also provides the underlying reasons for this effect.

This paper aims to provide an innovative solution on new service development (NSD) implementation by the project management competences and processes of IPMA ICB 3.0 and PMI PMBOK Guide 5th E.

By breaking NSD work breakdown structure into five control accounts, each account has its own suggested work packages concluded from reviewed literatures.

This research sequentially presents proper PM competences and processes as solutions for these control accounts and their work packages.

This study was to provide an innovative solution on NSD implementation by the project management competences and processes of IPMA ICB 3.0 and PMI PMBOK Guide 5th E. By breaking NSD work breakdown structure into five control accounts, each account has its own suggested work packages concluded from reviewed literatures. This research sequentially presents proper PM competences and processes as solutions for these control accounts and their work packages. Consequently, we concluded that the PM competences and processes furnish a favorable base where any NSD project is suitable to deploy without compromising its original features.

Consequently, it is concluded that the PM competences and processes furnish a favorable base where any NSD project is suitable to deploy without compromising its original features.

The purpose of this paper is to establish mechanisms for process improvement so that production efficiency and product quality can be expected, and create a sustainable development in terms of circular economy.

The authors obtain a critical value from statistical hypothesis testing, and thereby construct a process capability indices chart, which both lowers the chance of quality level misjudgment caused by sampling error and provides reference for the processes improvement in poor quality levels. The authors used the bottom bracket of bicycles as an example to demonstrate the model and methods proposed in this study.

This approach enables us to plot multiple quality characteristics, despite varying attributes and specifications, onto the same process capability analysis chart. And it therefore increases accuracy and precision to reduce rework and scrap rates (reduce), increase product availability, reduce maintenance frequency and increase reuse (reuse), increase the recycle rates of components (recycle) and lengthen service life, which will delay recovery time (recovery).

Parts manufacturers in the industry chain can upload their production data to the cloud platform. The quality control center of the bicycle manufacturer can utilized the production data analysis model to identify critical-to-quality characteristics. The platform also offers reference for improvement and adds the improvement achievements and experience to its knowledge management to provide the entire industry chain. Feedback is also given to the R&D department of the bicycle manufacturer as reference for more robust product designs, more reasonable tolerance designs, and selection criteria for better parts suppliers, thereby forming an intelligent manufacturing loop system.

This study is aimed to develop a novel intuitionistic fuzzy P-graph with Gaussian membership function to help decision-makers deal with complex process network systems.

Two fuzzy P-graph case studies of the cogeneration system were selected, and relevant data were collected, including the structure and flow sequence of the system, and the rate of material and product transitions between the operating units. Gaussian function membership was set according to the restriction of fuzzy upper and lower bounds. Then the α-cut was used to obtain different upper and lower bound restrictions of each membership degree. After finding the optimal and suboptimal solutions for different membership degrees, the results of non-membership and hesitation were calculated.

The proposed method will help the decision maker consider the risk and provide more feasible solutions to choose the optimal and suboptimal solutions based on their own or through experience. The proposed model in this study has more flexibility in operation and decision making.

This study is the first to propose a novel intuitive fuzzy P-graph and demonstrates the effectiveness and flexibility of the method by two case studies of the cogeneration system. However, the addition of hesitation can increase the error tolerance of the system. Even for the solutions with a high degree of membership, optimal and suboptimal solutions still exist for the decision maker to select. Since decision makers expect the higher achievement of the target requirements; thus, it is important to have more feasible solutions with a high degree of membership.

The purpose of this paper is to develop a novel intuitionistic fuzzy seasonality regression (IFSR) with particle swarm optimization (PSO) algorithms to accurately forecast air pollutions, which are typical seasonal time series data. Seasonal time series prediction is a critical topic, and some time series data contain uncertain or unpredictable factors. To handle such seasonal factors and uncertain forecasting seasonal time series data, the proposed IFSR with the PSO method effectively extends the intuitionistic fuzzy linear regression (IFLR).

The prediction model sets up IFLR with spreads unrestricted so as to correctly approach the trend of seasonal time series data when the decomposition method is used. PSO algorithms were simultaneously employed to select the parameters of the IFSR model. In this study, IFSR with the PSO method was first compared with fuzzy seasonality regression, providing evidence that the concept of the intuitionistic fuzzy set can improve performance in forecasting the daily concentration of carbon monoxide (CO). Furthermore, the risk management system also implemented is based on the forecasting results for decision-maker.

Seasonal autoregressive integrated moving average and deep belief network were then employed as comparative models for forecasting the daily concentration of CO. The empirical results of the proposed IFSR with PSO model revealed improved performance regarding forecasting accuracy, compared with the other methods.

This study presents IFSR with PSO to accurately forecast air pollutions. The proposed IFSR with PSO model can efficiently provide credible values of prediction for seasonal time series data in uncertain environments.

The purpose of this paper is to develop a decision support system to consider geographic information, logistics information and greenhouse gas (GHG) emission information to solve the proposed green inventory routing problem (GIRP) for a specific Taiwan publishing logistics firm.

A GIRP mathematical model is first constructed to help this specific publishing logistics firm to approximate to the optimal distribution system design. Next, two modified Heuristic-Tabu combination methods that combine savings approach, 2-opt and 1-1 λ-interchange heuristic approach with two modified Tabu search methods are developed to determine the optimum solution.

Several examples are given to illustrate the optimum total inventory routing cost, the optimum delivery routes, the economic order quantities, the optimum service levels, the reorder points, the optimum common review interval and the optimum maximum inventory levels of all convenience stores in these designed routes. Sensitivity analyses are conducted based on the parameters including truck loading capacity, inventory carrying cost percentages, unit shortage costs, unit ordering costs and unit transport costs to support optimal distribution system design regarding the total inventory routing cost and GHG emission level.

The most important finding is that GIRP model with reordering point inventory control policy should be applied for the first replenishment and delivery run and GIRP model with periodic review inventory control policy should be conducted for the remaining replenishment and delivery runs based on overall simulation results. The other very important finding concerning the global warming issue can help decision makers of GIRP distribution system to select the appropriate type of truck to deliver products to all retail stores located in the planned optimal delivery routes depending on GHG emission consumptions.