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The development of artificial intelligence has created new opportunities and challenges in industries. The competition between robots and humans has elicited extensive attention among legal researchers. In this exploratory study, we addressed issues regarding the introduction of robots to the practice of legal service through a semistructured interviews with lawyers, judges, artificial intelligence experts, and potential clients. An extended robot lawyer technology acceptance model with five facets and 11 elements is proposed in this study. This model highlights two dimensions: 'legal use' and 'perception of trust.' In summary, this study provides new specific implications and exhibits three characteristics, namely, derivative, macroscopic, and instructive, in the legal services with artificial intelligence. In addition, artificial intelligence robot lawyers are being developed with some of the abilities necessary to substitute for human beings. Nevertheless, working with human lawyers is imperative to produce benefits from this type of reciprocity. [ABSTRACT FROM AUTHOR]

In 2013, the German government implemented the strategic initiative Industry 4.0 to establish itself as a leader in advanced manufacturing and the transformation to digitalisation. Numerous manufacturers continue to explore ways to evaluate and enhance their capabilities to tackle market competition. This empirical study adopts a maturity model that is based on the Economic Development Board's Singapore Smart Industry Readiness Index. The model will allow companies to conduct self-assessments so that they can systematically and comprehensively align themselves with Industry 4.0. This study conducts a clustering analysis to explore the maturity of Industry 4.0 in Taiwan, the clustering's index factors, and the impact of key factors on companies' self-assessment. Classifying 80 Taiwan enterprises into four categories, it shows a significantly positive correlation among process, technology, and organisation. Further, a majority of the 80 companies tested under the maturity model still appear to be immature or partially mature, and needing much improvement and the re-evaluation of their transformation strategies related to Industry 4.0. Finally, the Singapore Smart Industry Readiness Index is suitable to conduct self-assessments in Taiwan-based enterprises. These findings could serve as useful maturity and grouping guidelines for practitioners and researchers. [ABSTRACT FROM AUTHOR]

Future challenges posed to semiconductor manufacturing services firms demands a system framework which could not only introduce, development, and design new products into mass production with customer-acceptable quality level and profitable approaches, but also re-use best practice in the shop floor to continuously optimise the existing order fulfilment process with effectiveness and efficiency. Advanced product quality planning (APQP) tool integrated with agent-based knowledge management (KM) intelligence develops a systematic framework that responds to such challenges. We propose an APQP tool working with an agent-based knowledge management system (ABKMS) which builds on organisational knowledge resources and works with the APQP concept as well as KM agents. The proposed framework reflects how an APQP tool with KM agents seeks significant inroads into process knowledge repository to recommend a solution for decision making in delivering semiconductor manufacturing services in customer selection, parts and material selection, bill of material, equipments, and tools selection, with a modified APQP concept to work out the approach to be used to develop the streamlined and common process. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of resource portfolio planning of firms in high-tech, capital-intensive manufacturing industries. In light of the strategic importance of resource portfolio planning in these industries, we offer an alternative approach to modelling capacity planning and allocation problems that improves the deficiencies of prior models in dealing with three salient features of these industries, i.e. fast technological obsolescence, volatile market demand, and high capital expenditure. This paper first discusses the characteristics of resource portfolio planning problems including capacity adjustment and allocation. Next, we propose a new mathematical programming formulation that simultaneously optimises capacity planning and task assignment. For solution efficiency, a constraint-satisfied genetic algorithm (CSGA) is developed to solve the proposed mathematical programming problem on a real-time basis. The proposed modelling scheme is employed in the context of a semiconductor testing facility. Experimental results show that our approach can solve the resource portfolio planning problem more efficiently than a conventional optimisation solver. The overall contribution is an analytical tool that can be employed by decision makers responding to the dynamic technological progress and new product introduction at the strategic resource planning level. [ABSTRACT FROM AUTHOR]

Distributors in a supply chain usually limit their own warehouse in finite capacity for cost reduction and excess stock is held in a rent warehouse. In this study, we examine inventory control for deteriorating items in a two-warehouse setting. Assuming that there is an incentive offered by a rent warehouse that allows the rental fee to decrease over time, the objective of this study is to maximise the joint profit of the manufacturer and the distributor. An optimisation procedure is developed to derive the optimal joint economic lot size policy. Several criteria are identified to select the most appropriate warehouse configuration and inventory policy on the basis of storage duration of materials in a rent warehouse. Sensitivity analysis is done to examine the results of model robustness. The proposed model enables a manufacturer with a channel distributor to coordinate the use of alternative warehouses, and to maximise the joint profit of the manufacturer and the distributor. [ABSTRACT FROM PUBLISHER]