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The purpose of this paper is to develop a novel research model to examine the relationship among information sharing (IS), supply chain integration (SCI), operational performance (OP) and business performance (BP) in the fashion supply chains.

A survey of 247 executives from Chinese fashion brand firms was conducted and the data were analyzed to investigate how IS affects the organizational BP. Structural equation modeling (SEM) was applied to study the relationship among IS, SCI, OP and BP.

The empirical research results indicate that IS is critical to enhance the SCI and OP, and both SCI and OP exert mediating effects on BP of fashion brands. This result also reveals constructive suggestions that allow fashion brands to strengthen their SCI and OP, as well as BP.

Multiple data sources were applied to develop the sampling frame, and respondents were selected (according to their experience and position) to ensure they had the knowledge and expertise to provide valid response. However, this could not guarantee the adequacy of the sample. This limitation is compounded by the reliance on a simple respondent per firm, which precludes testing for inter-rater reliability.

The empirical findings provide an enhanced understanding of the relationship among IS, SCI, OP and BP in Chinese fashion brand settings. The research results will help fashion brands to improve supply chain efficiency and enhance company performance.

Although previous studies have realized that the value of IS varies in different industries, few have specifically explored the impact on the fashion industry characterized by short life cycles, high volatility, low predictability and high impulse purchasing. To fill this knowledge gap, the present study employed a questionnaire survey and SEM techniques to explore the relationship among IS, SCI, OP and BP in the fashion supply chain. Comprehending the impact mechanism of IS on organizational performance can provide useful management insights into the development of effective strategies that allow enterprise to improve BP.

To find the system with minimum investment and best quality performance that is capable of producing all of the product variants, assessing the complexity of designing assembly system at the early concept stage is an essential step, which helps and instructs a designer to create a product- and system-oriented assembly solution with the least complexity. The purpose of this paper is to propose a quantifying measurement of complexity in the design of a modular automated assembly system.

The configurable assembly system is becoming a trend, which enables companies to quickly respond to changes caused by different product variants but without a large investment. One of the enabling factors is the availability of modular solutions of assembly modules that can be configured according to different technical requirements. This paper develops a methodology using fuzzy evaluation to calculate the design complexity in the design phase for a modular automatic assembly system. Fuzzy linguistic variables are used to measure the interaction among the influence factors, to deal with the uncertainty of the judgement. The proposed method investigates three matrices to present how the function-based assembly modules, design complexity factors, part attributes and product components, which are regarded as the main influence factors, complicate the construction of a modular assembly system. The design complexity is derived and quantified based on these assessments.

The proposed approach presents a formal quantification to evaluate the design complexity with regard to a modular assembly system from beginning, which can be identified and used as criteria to indicate the quality of performance and investment cost in advance. A mathematical model based on the fuzzy logic is established to provide both theoretical and practical guidance for the paper. To validate the predictive model, the statistic relationships between the assessed system design complexity, real assembly defect rate and investment cost are estimated based on regression analysis. The application of the presented methodology is demonstrated with regard to a traditional rear drive unit in the automotive industry.

This paper presents a developed method, which addresses the measures of complexity found in the design of a modular assembly system. It would help to run the design process with better resource allocation and cost estimation in a quantitative approach.

It was verified that the micro-texture in the front and back of the tool at the same time had a positive effect on improving the milling behavior and surface quality of the tool. The purpose of this study is to explore the rationality of simultaneous placement of micro-textures on the front and rear surfaces of ball-end milling cutters, analyze the influence of micro-texture parameters on tool milling behavior and workpiece surface quality, reveal its internal mechanism, and obtain the best micro-texture parameters by optimization.

First, the mechanism of micro-texture is studied based on the energy loss model. Second, the orthogonal experiment is designed to analyze the influence of micro-texture parameters on tool milling behavior and reveal its mechanism by combining simulation technology and cutting experiment. Finally, the parameters are optimized based on the artificial bee colony algorithm.

The results show that the simultaneous placement of micro-texture on the rake face and flank face of the tool has a positive effect on improving the milling behavior and surface quality of the tool. Taking milling force, tool wear and surface roughness as the evaluation criteria, the optimal parameter combination is obtained: the rake face micro-texture diameter is 50 µm, the distance from the micro-texture is 200 µm and the distance from the cutting edge is 110 µm; the diameter of the micro-textured flank is 40 µm, the distance from the micro-texture is 170 µm and the distance from the cutting edge is 130 µm.

Taking milling force, tool wear and surface roughness as the evaluation criteria, the optimal parameter combination is obtained: the rake face micro-texture diameter is 50 µm, the distance from the micro-texture is 200 µm and the distance from the cutting edge is 110 µm; the diameter of the micro-textured flank is 40 µm, the distance from the micro-texture is 170 µm and the distance from the cutting edge is 130 µm, which provides theoretical support for the further study of the micro-textured tool.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2023-0022/

The purpose of this paper is to optimize the welding parameters: rotating speed and plunging depth of carbon steel and pure copper joints using friction stir spot welding (FSSW) with the aid of the design of experiments (DOE) method.

Carbon steel and pure copper sheets were welded using the FSSW technique with a cylindrical tool and without a probe. The welding parameters were: rotating speed: 1,120, 1,400 and 1,800 RPM and plunging depth: 0.2 and 0.4 mm. The welding process was carried out both with and without pre-heating. The welded specimens were analyzed using a shear tensile test. A microstructural investigation at the optimum conditions was carried out. The results were analyzed and optimized using the statistical software Minitab and following the DOE method.

Pre-heating the sample and increasing the rotating speed and plunging depth increased the tensile shear force of the joint. The plunging depth has the biggest effect on the joint efficiency compared with the rotating speed. The optimum shear force (4,560 N) was found at 1,800 RPM, 0.4 mm plunge depth and with pre-heating. The welding parameters were modified so that the samples were welded at 1,800 RPM and at plunging depths of 0.45–1 mm in 0.05 mm steps. The optimized shear force was 5,400 N. The fractured samples exhibited two types of failure mode: interfacial and nugget pull-out.

For the first time, pure copper and carbon steel sheets were welded using FSSW and a tool without a probe with ideal joint efficiency (95 percent).