Takaya Kato, Breno Nunes and Prasanta Kumar Dey
The purpose of this paper is to examine how firms create and sustain competitive advantage in the inter-firm business relationships from a supplier’s perspective. It also…
Abstract
Purpose
The purpose of this paper is to examine how firms create and sustain competitive advantage in the inter-firm business relationships from a supplier’s perspective. It also investigates what factors affect their competitiveness and relationship between buyers and suppliers.
Design/methodology/approach
This is an exploratory study on keiretsu partnerships composed of four main phases: analysis of theoretical perspectives, construction of a conceptual framework, interview of a CEO, and finally, a survey questionnaire with Japanese automotive suppliers.
Findings
As a result, this paper classified these 11 companies into four supplier groups (affiliated or independent Tier 1 suppliers; affiliated or independent Tier 2 suppliers) and analysed their competitiveness developing the research propositions further. The benefits of affiliation under a keiretsu partnership are discussed, showing that there may be little benefit in being an affiliated Tier 1 supplier. Even more critical, the results show that independent Tier 2 supplier may be more competitive than affiliated tier ones.
Originality/value
These intriguing results reveal an urgent need of investigating Japanese automotive supply chains from the suppliers’ perspectives in the future research. This paper extended the literatures on competitive advantage and business relationships at both theory and managerial practice.
Details
Keywords
Phongsatorn Saisutjarit and Takaya Inamori
The purpose of this paper is to investigate the time optimal trajectory of the multi-tethered robot (MTR) on a large spinning net structures in microgravity environment.
Abstract
Purpose
The purpose of this paper is to investigate the time optimal trajectory of the multi-tethered robot (MTR) on a large spinning net structures in microgravity environment.
Design/methodology/approach
The MTR is a small space robot that uses several tethers attached to the corner-fixed satellites of a spinning net platform. The transition of the MTR from a start point to any arbitrary designated points on the platform surface can be achieved by controlling the tethers’ length and tension simultaneously. Numerical analysis of trajectory optimization problem for the MTR is implemented using the pseudospectral (PS) method.
Findings
The globally time optimal trajectory for MTR on a free-end spinning net platform can be obtained through the PS method.
Research limitations/implications
The analysis in this paper is limited to a planar trajectory and the effects caused by attitude of the MTR will be neglected. To make the problem simple and to see the feasibility in the general case, in this paper, it is assumed there are no any limitations of mechanical hardware constraints such as the velocity limitation of the robot and tether length changing constraint, while only geometrical constraints are considered.
Practical implications
The optimal solution derived from numerical analysis can be used for a path planning, guidance and navigation control. This method can be used for more efficient on-orbit autonomous self-assembly system or extravehicular activities supports which using a tether-controlled robot.
Originality/value
This approach for a locomotion mechanism has the capability to solve problems of conventional crawling type robots on a loose net in microgravity.
Details
Keywords
Pengpeng Cheng, Daoling Chen and Jianping Wang
For comfort evaluation of underwear pressure, this paper proposes an improved GA algorithm to optimize the weight and threshold of BP neural network, namely PSO-GA-BP neural…
Abstract
Purpose
For comfort evaluation of underwear pressure, this paper proposes an improved GA algorithm to optimize the weight and threshold of BP neural network, namely PSO-GA-BP neural network prediction model.
Design/methodology/approach
The objective parameters of underwear, body shape data, skin deformation and other data are selected for simulation experiments to predict the objective pressure and subjective evaluation in dynamic and static state. Compared with the prediction results of BP neural network prediction model, GA-BP neural network prediction model and PSO-BP neural network prediction model, the performance of each prediction model is verified.
Findings
The results show that the BP neural network model optimized by PSO-GA algorithm can accelerate the convergence speed of the neural network and improve the prediction accuracy of underwear pressure.
Originality/value
PSO-GA-BP model provides data support for underwear design, production and processing and has guiding significance for consumers to choose underwear.