We present here a perspective detailing the current state-of-the-art technologies for the characterisation of nanoparticles (NPs) in liquid suspension. We detail the technologies involved and assess ...their applications in the determination of NP size and concentration. We also investigate the parameters that can influence the results and put forward a cause and effect analysis of the principle factors influencing the measurement of NP size and concentration by NP tracking analysis and dynamic light scattering, to identify areas where uncertainties in the measurement can arise. Also included are technologies capable of characterising NPs in solution, whose measurements are not based on light scattering. It is hoped that the manuscript, with its detailed description of the methodologies involved, will assist scientists in selecting the appropriate technology for characterising their materials and enabling them to comply with regulatory agencies' demands for accurate and reliable NP size and concentration data.
Sustainability-related risk and vulnerability management have attained significant attention from academia and industry. Manufacturing industries in developing countries such as Pakistan are under ...severe economic pressure and striving to boost sustainable supply chain practices for achieving business excellence. In this context, the objectives of the present research are to examine the critical supply chain risks associated with sustainable development goals, namely social, economic, and environmental factors. The failure mode and effect analysis (FMEA) technique is employed for categorizing the risk factors and Pareto analysis for highlighting the more crucial and risky factors. For this purpose, a large-scale survey was carried out in the textile industries of Pakistan to develop a risk mitigation model for sustainability-related risks and vulnerability in a textile supply chain (TSC). It captures the input expressions of experts for risk factors, namely severity (s), occurrence (o), and detection (d) for calculating the risk priority numbers (RPNs) of identified alternatives. The results depict that endogenous environmental risks categorize as the most significant for the textile manufacturing industries, and the interfaces between the various risks associated with sustainability-related are also found very high. This study would be a toolkit for the industrial managers and policy-makers for creating sustainable manufacturing culture on organizational premises.
Improving overall performance and increasing operational reliability are currently among the leading research topics in the field of hydraulic systems. In recent years, the use of artificial ...intelligence-based modeling and design techniques has developed rapidly to account for the nonlinear properties of Gaussian systems and to predict fault reasoning in hydraulic systems. In this study, feature acquisition and selection are proposed to prepare input data for a simulation-based learning approach. In addition, a cause-and-effect analysis is performed by considering various what-if scenarios as external disturbances that affect the response of the hydraulic press. While the objective of the sheet metal bending cycle and a pulley system is to initiate a load on the hydraulic press, an intelligent sensing system is used to observe the behavior of the hydraulic press during the phases of sheet metal bending cycle, i.e., the forming, leveling, and movement. In addition, the Gaussian process regression method is used to build data-driven prediction models with different predictors that contribute significantly to improving predictive accuracy. The condition diagnosis indicates the accurate performance of predictive models observing the coefficient of determination R2 at 0.998 for the bending phase, 0.962 for the leveling phase, and 0.999 for the movement phase. Although the approximation of the simulation model is efficient, it is found that certain features are reasonably well approximated with regard to the forming phases.
Purpose This study aims to investigate the combined use of System Dynamics (SD) applications in Enterprise Engineering (EE) research and practice. SD application in EE is becoming widely accepted as ...a tool to support decision-making processes and for capturing relationships within enterprises. Design/methodology/approach A systematic literature review (SLR) is conducted using a standard SLR method to provide a comprehensive review of existing literature. The search was conducted on ten platforms identifying 30 publications which were analysed through the use and development of a codebook. Findings The SLR showed that 90% of the result set consisted of peer-reviewed academic conferences and journal papers. The SLR identified a highly dispersed author set of 83 authors. Amongst these authors, Vinay Kulkarni was an active author who has co-authored up to four publications in this research area. The analysis further revealed that the combined use of SD applications and EE is an emerging research area that still needs to develop in maturity. While all phases of EE have received attention, the current research work is more focused on the design phase. The important gap between model development and implementation is identified. Originality/value The study elucidates the existing status of interdisciplinary research combining techniques from the SD and EE disciplines, suggesting future research topics that combine the strengths of these existing disciplines.
Abstract
Supply chain practitioners are striving to improve the performance of agri‐food supply chains (AFSC) due to the lack of understanding of the mutual impacts of lean, agile, resilience, and ...green (LARG) practices in improving the performance of AFSC. There is a lack of specific methods to assess the power of their subsequent attributes. This paper identifies 12 unique challenges related to the implementation of LARG practices under the context of AFSC. The identification of challenges and the interdependency relationships among the LARG challenges are developed using a multistage approach. The multistage approach is composed of the generalized interval‐valued trapezoidal fuzzy numbers (GIVTFNs), the degree of similarity method, and the decision‐making trial and evaluation laboratory (DEMATEL) method. The finding of the study indicates that “Lack of understanding between the customer and other stakeholder requirements” and “Lack of transparency and trust” are the most significant challenges in the cause group and are the driving elements for implementing LARG practices. Furthermore, “Lack of competitive advantages” and “Lack of monitoring and auditing the ongoing supply chain activities” fall under the effect category, which are influenced by the cause groups' challenges. The identified challenges can be controlled and handled strategically on a priority basis for successfully implementing LARG practices in the agri‐food industry. The finding of study will help practitioners to overcome the LARG challenges and to improve the overall performance of AFSC.
In today’s dynamic and interconnected business landscape, organizational performance optimization is imperative for sustained success. To achieve this, it is essential to discern and address the ...complex cause-and-effect relationships within an organization. This article introduces the Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) method as a powerful tool for evaluating and enhancing organizational performance. The Fuzzy DEMATEL method, an extension of the traditional DEMATEL approach, accommodates the inherent ambiguity and uncertainty in real-world data, offering a more robust and accurate analysis. This method enables decision-makers to identify causal relationships among various factors impacting organizational performance, classify these factors as cause or effect, and quantify the degree of their influence using fuzzy logic.
Business intelligence (BI) is a process in which data collected from disparate sources inside and outside the organisation are combined in order to provide meaningful information for making better ...and quicker decisions. Many businesses have adopted BI technologies and have obtained significant value from its application, contributing to increased organisational productivity and competitiveness. The aim of this paper is to identify the cause and effect relationship for BI benefits. Fuzzy decision-making trial and evaluation laboratory (fuzzy DEMATEL) technique is applied to diagnose the interrelationships among BI benefits. For this purpose, 10 expert professionals participated in this research. Eighteen BI benefits in four dimensions of organisational benefits, business supplier/partner relation benefits, internal processes efficiency benefits, and customer intelligence benefits are investigated. Findings show that "improved coordination with business partners/suppliers" and "increased revenue" are the highest cause and effect benefits, respectively. This reveals that, better partnership and collaboration with upstream suppliers and downstream customers lead to revenue growth and improved competitiveness. This paper presents insights about the interrelationships among BI value in industrial organisations. The findings will help managers to focus on what causes the benefits related to BI implementation that lead to greater competitive advantage. In addition, it demonstrates that fuzzy DEMATEL is a useful managerial technique when applied to software system evaluation for identifying the critical relationships between benefits.
As part of the Naming the Pain in Requirements Engineering (NaPiRE) initiative, researchers compared problems that companies in Brazil and Germany encountered during requirements engineering (RE). ...The key takeaway was that in RE, human interaction is necessary for eliciting and specifying high-quality requirements, regardless of country, project type, or company size.
Bioprocess development generates extensive datasets from different unit operations and sources (e.g. time series, quality measurements). The development of such processes can be accelerated by ...evaluating all data generated during the experimental design. This can only be achieved by having a clearly defined data logging and analysis strategy. The latter is described in this manuscript. It consists in a combination of a feature based approach along with principal component analysis and partial least square regression.
Application of this combined strategy is illustrated by applying it in an upstream processing (USP) case study. Data from the development and optimization of an animal component free USP of Sabin inactivated poliovirus vaccine (sIPV) was evaluated. During process development, 26 bioreactor runs at scales ranging from 2.3 to 16 L were performed. Several operational parameters were varied, and data was routinely analyzed following a design of experiments (DoE) methodology.
With the strategy described here, it became possible to scrutinize all data from the 26 runs in a single data study. This included the DoE response parameters, all data generated by the bioreactor control systems, all offline data, and its derived calculations. This resulted in a more detailed, reliable and exact view on the most important parameters affecting bioreactor performance.
In this case study, the strategy was applied for the analysis of previously produced data. Further development will use this data analysis methodology for continuous enhancing and accelerating process development, intensified DoE and integrated process modelling.
Many open source software (OSS) products today are market leaders, 1 which suggests that the development of OSS is key to the growth of the software industry. OSS projects increasingly tend to be ...incorporated in large-scale projects or "software ecosystems" to reduce effort and accelerate innovation.
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