This paper constructs a heterogeneous agent model for the foreign exchange market that is based on the law of supply and demand and includes carry trade, central bank intervention, and macroeconomic fundamentals. With the artificial intelligence method of the unscented Kalman filter, this paper investigates carry traders’ expectation formation and risk aversion and the impact of their activities on the movement of the Chinese yuan exchange rate and on the efficiency of central bank intervention. The findings demonstrate that carry traders’ activities are partially responsible for fluctuations in the Chinese yuan exchange rate; carry traders behave with obvious risk aversion; their activities tend to weaken the ability of the central bank to intervene in China’s foreign exchange market; and the volatility of the Chinese yuan exchange rate and the weight of carry traders are negatively related. Based on these empirical results, specific suggestions for exploring sustainable financial resources are provided.
Purpose The purpose of this paper is to suggest how firms could use big data to facilitate product innovation processes, by shortening the time to market, improving customers’ product adoption and reducing costs. Design/methodology/approach The research is based on a two-step approach. First, this research identifies four potential key success factors for organisations to integrate big data in accelerating their product innovation processes. The proposed factors are further examined and developed by conducting interviews with different organisation experts and academic researchers. Then a framework is developed based on the interview outputs. The framework sets out the key success factors involved in leveraging big data to reduce lead times and costs in product innovation processes. Findings The three determined key success factors are: accelerated innovation process; customer connection; and an ecosystem of innovation. The authors believe that the developed framework based on big data represents a paradigm shift. It can help firms to make new product development dramatically faster and less costly. Research limitations/implications The proposed accelerated innovation processes demand a shift in traditional organisational culture and practices. It is, though, meaningful only for products and services with short life cycles. Moreover, the framework has not yet been widely tested. Practical implications This paper points to the vital role of big data in helping firms to accelerate product innovation processes. First of all, it allows organisations to launch new products to market as quickly as possible. Second, it helps organisations to determine the weaknesses of the product earlier in the development cycle. Third, it allows functionalities to be added to a product that customers are willing to pay a premium for, while eliminating features they do not want. Last, but not least, it identifies and then prioritises customer needs for specific markets. Originality/value The research shows that firms could harvest external knowledge and import ideas across organisational boundaries. An accelerated innovation process based on big data is characterised by a multidimensional process involving intelligence efforts, relentless data collection and flexible working relationships with team members.
This paper presents a detailed analysis of four different powder recycling strategies for the metal additive manufacturing process focused on examining the efficacy of recycling Ti6Al4V alloy powder for the Laser-Based Powder Bed Fusion (LBPF) process. The study evaluates four distinct recycling strategies (A, B, C, D) and their impact on powder-saving mechanisms. The experimental methodology involves careful leftover powder sampling, implementation of recycling strategies and print cycle analysis from each recycling strategy. The results show that Strategies C and D exhibit the lowest powder waste, allowing 6 times the reuse of leftover powder and producing 384 parts with minimal powder waste at the end of the process. Strategy B also performs well, enabling 8 cycles and building 512 parts with only a small amount of powder wasted. Conversely, Strategy A yields the highest powder waste despite achieving the same number of cycles and prints. This proposed approach requires a total volume of 224,000 mm3 of powder for each build. This entails using 500g of gas-atomized Ti-6Al-4V powder, with an average particle size D90 of approximately 50 μm. This features a continuous mixing technique, wherein each build combines 50% virgin powder with 50% of the previously collected and sieved powder.
Prior studies presented the sustainable supply chain management practices, but an approach from stakeholders is still untapped. The interaction between forward and reverse flows also needs to be involved in investment recovery. Sustainable supply chain management is an increasing concern in the environmental, social and economic performance. This study uses fuzzy Delphi method to validate a set of criteria and uses exploratory factor analysis to confirm the aspects. This study applies stakeholder theory in combination with fuzzy set theory and decision-making trial and evaluation method to explore the interrelationships among attributes. The results show that sustainable supply management and process management are the major cause aspects. Investment recovery has not been noticed in the healthcare industry, reflected in the weak interaction. The top five criteria are supplier assessment, environmental management systems, green certification of supplier, supplier collaboration and health and safety certifications. This study provides theoretical and managerial implications.
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