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Strategic management

Undergraduate/postgraduate modules in strategic management.

The case portrays a Chinese surrogate manufacturer – Cool-Comfort Shoes International Co. Ltd. (CCS) – which attempted to build its own brand, Ace-of-Biz (AoB). The surrogate manufacturing business had accumulated the funds needed to develop its AoB brand for sale in the domestic market. The 2007 world financial crisis and subsequent world recession caused exports and, thus, surrogate manufacturing to plummet. CCS was hoping that their loss in export of surrogate products would be more than compensated for by the gain in the domestic sales of AoB. However, despite 10 years of commitment, AoB's sales still had not grown sufficiently to counter the slowdown in exports, and the leaders at CCS were wondering what the future would hold for the company and its AoB brand.

This case study provides students with an ideal context to develop an appreciation of how changes in the domestic and international business environment affect the corporate and business strategies of a small- to medium-sized enterprise and the differences between corporate and business strategies, and to demonstrate their ability to apply a number of strategic management tools and techniques for the critical appraisal of a strategic situation and justify their recommended course of action.

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Inventory carrying costs represent one of the highest costs of distribution. Although they are a necessary input to the design of logistical systems, such costs are ignored by many companies and when they are used usually represent estimates or industry benchmarks. The authors present a methodology designed to provide managers with a practical framework for determining the costs of carrying inventory.

The COVID-19 pandemic emphasises the need for antiviral materials that can reduce airborne and surface-based virus transmission. This study aims to propose the use of additive manufacturing (AM) and surrogate modelling for the rapid development and deployment of novel copper-tungsten-silver (Cu-W-Ag) microporous architecture that shows strong antiviral behaviour against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

The research combines selective laser melting (SLM), in-situ alloying and surrogate modelling to conceive the antiviral Cu-W-Ag architecture. The approach is shown to be suitable for redistributed manufacturing by representing the pore morphology through a surrogate model that parametrically manipulates the SLM process parameters: hatch distance (h_d), scan speed (S_s) and laser power (L_p). The method drastically simplifies the three-dimensional (3D) printing of microporous materials by requiring only global geometrical dimensions solving current bottlenecks associated with high computed aided design data transfer required for the AM of porous materials.

The surrogate model developed in this study achieved an optimum parametric combination that resulted in microporous Cu-W-Ag with average pore sizes of 80 µm. Subsequent antiviral evaluation of the optimum architecture showed 100% viral inactivation within 5 h against a biosafe enveloped ribonucleic acid viral model of SARS-CoV-2.

The Cu-W-Ag architecture is suitable for redistributed manufacturing and can help reduce surface contamination of SARS-CoV-2. Nevertheless, further optimisation may improve the virus inactivation time.

The study was extended to demonstrate an open-source 3D printed Cu-W-Ag antiviral mask filter prototype.

The evolving nature of the COVID-19 pandemic brings new and unpredictable challenges where redistributed manufacturing of 3D printed antiviral materials can achieve rapid solutions.

The papers present for the first time a methodology to digitally conceive and print-on-demand a novel Cu-W-Ag alloy that shows high antiviral behaviour against SARS-CoV-2.

Direct energy deposition (DED) is an additive manufacturing process that allows to produce metal parts with complex shapes. DED process depends on several parameters, including laser power, deposition rate and powder feeding rate. It is important to control the manufacturing process to study the influence of the operating parameters on the final characteristics of these parts and to optimize them. Computational modeling helps engineers to address these challenges. This paper aims to establish a framework for the development, verification and application of meshless methods and surrogate models to the DED process.

Finite pointset method (FPM) is used to solve conservation equations involved in the DED process. A surrogate model is then established for the DED process using design of experiments with powder feeding rate, laser power and scanning speed as input parameters. The surrogate model is constructed using neutral networks (NN) approximations for the prediction of maximum temperature, clad angle and dilution.

The simulations of thin wall built of Ti-6Al-4V titanium alloy clearly demonstrated that FPM simulation is successful in predicting temperature distribution for different process conditions and compare favorably with experimental results from the literature. A methodology has been developed for obtaining a surrogate model for DED process.

This methodology shows how to achieve realistic simulations of DED process and how to construct a surrogate model for further use in optimization loop.

For high crack-susceptibility materials such as Inconel 713LC (IN713LC) nickel alloy, fabricating crack-free components using the laser powder bed fusion (LPBF) technique represents a significant challenge because of the complex interactions between the effects of the main processing parameters, namely, the laser power and scanning speed. Accordingly, this study aims to build up a methodology which combines simulation model and experimental approach to fabricate high-density (>99.9%) IN713LC components using LPBF process.

The present study commences by performing three-dimensional (3D) heat transfer finite element simulations to predict the LPBF outcome (e.g. melt pool depth, temperature and mushy zone extent) for 33 representative sample points chosen within the laser power and scanning speed design space. The simulation results are used to train a surrogate model to predict the LPBF result for any combination of the processing conditions within the design space. Then, experimental trials were performed to choose the proper hatching space and also to define the high crack susceptibility criterion. The process map is then filtered in accordance with five quality criteria, namely, avoiding the keyhole phenomenon, improving the adhesion between the melt pool and the substrate, ensuring single-scan-track stability, avoiding excessive melt pool evaporation and suppressing the formation of micro-cracks, to determine the region of the process map which improves the relative density of the IN713LC component and minimizes the micro-cracks. The optimal processing conditions are used to fabricate IN713LC specimens for tensile testing purposes.

The optimal processing conditions predicted by simulation model are used to fabricate IN713LC specimens for tensile testing purposes. Experimental results show that the tensile strength and elongation of 3D-printed IN713LC tensile bar is higher than those of tensile bar made by casting. The yield strength of 791 MPa, ultimate strength of 995 MPa, elongation of 12%, and relative density of 99.94% are achieved.

The present study proposed a systematic methodology to find the processing conditions that are able to minimize the formation of micro-crack and improve the density of the high crack susceptivity metal material in LPBF process.

Laser powder bed fusion (LPBF) provides the means to produce unique components with almost no restriction on geometry in an extremely short time. However, the high-temperature gradient and high cooling rate produced during the fabrication process result in residual stress, which may prompt part warpage, cracks or even baseplate separation. Accordingly, an appropriate selection of the LPBF processing parameters is essential to ensure the quality of the built part. This study, thus, aims to develop an integrated simulation framework consisting of a single-track heat transfer model and a modified inherent shrinkage method model for predicting the curvature of an Inconel 718 cantilever beam produced using the LPBF process.

The simulation results for the curvature of the cantilever beam are calibrated via a comparison with the experimental observations. It is shown that the calibration factor required to drive the simulation results toward the experimental measurements has the same value for all settings of the laser power and scanning speed. Representative combinations of the laser power and scanning speed are, thus, chosen using the circle packing design method and supplied as inputs to the validated simulation framework to predict the corresponding cantilever beam curvature and density. The simulation results are then used to train artificial neural network models to predict the curvature and solid cooling rate of the cantilever beam for any combination of the laser power and scanning speed within the input design space. The resulting processing maps are screened in accordance with three quality criteria, namely, the part density, the radius of curvature and the solid cooling rate, to determine the optimal processing parameters for the LPBF process.

It is shown that the parameters lying within the optimal region of the processing map reduce the curvature of the cantilever beam by 17.9% and improve the density by as much as 99.97%.

The present study proposes a computational framework, which could find the parameters that not only yield the lowest distortion but also produce fully dense components in the LPBF process.

To investigate, analyse and identify the reasons for contradictory conclusions in past studies of country of origin (COO) influences on buyers' beliefs and purchase intentions.

A review of 96 published studies, discussions and commentaries, with separate sections relating to methodological an contextual issues, the latter in separate sections relating to national cultures and stereotypes, cross‐cultural differences, product‐brand‐market‐segment variations, hybridisation, and price and communications strategies.

Study contexts and methodologies varied significantly, often without an explicit rationale, and were judged inappropriate in some cases. Conflicting findings seem to be largely the result of this variation.

The observed variations and contradictions hinder generalisation and theory building. COO studies should be pursued from a target customer perspective and should adopt a comprehensive approach that incorporates the influences, interactions and potential interconnectedness of factors such as brand names, hybridization of offerings, communication and promotional activities, customer characteristics and market dynamics.

Marketing practitioners cannot treat COO as a self‐contained marketing and marketing communications strategy, but need to consider the effects, interactions and interconnectedness of other influences on customer beliefs and buying intentions. A more integrated approach is urgently required. The Norwegian Seafood Export Council's success in exporting to Taiwan offers a case example of effective implementation of COO strategy.

A wide‐ranging evaluation of the frequently flawed research studies available as the basis for developing theory and practice with respect to the role and effect of COO in marketing.

With an increasingly fierce competition of the shipbuilding industry, advanced technologies and excellent management philosophies in the manufacturing industry are gradually introduced to domestic shipyards. The purpose of this study is to promote the lean management of Chinese ship outfitting plants by lean production strategy.

To promote the lean implementation of Chinese shipyards, the lean practice of ship-pipe part production is highlighted by lot-sizing optimization and strategic CONWIP (constant work-in-process) control. A nonlinear programming model is formulated to minimize the total cost of ship-pipe part manufacturing and the particle swarm optimization (PSO)-based algorithm is designed to resolve the established model. Besides, the pull-from-the-bottleneck (PFB) strategy is used to control ship-pipe part production, verified by Simulink simulation.

Results show that the proposed lean strategy of the programming model and strategic PFB control could assist Chinese ship outfitting plants to leverage competitive advantage by waste reduction and lean achievement. Specifically, the PFB double-loop control strategy shows better performance when there is high productivity and the PFB single-loop control outperforms at lower productivity scenarios.

To verify the effectiveness of the proposed lean strategy, a case study is performed to validate the formulated model. Also, simulation experiments realized by FlexSim software are conducted to testify results obtained by the constructed programming model.

Lean production management practice of the shipyard building industry is performed by the proposed lean production strategy through lot-sizing optimization and strategic PFB control in terms of ship-pipe part manufacturing.

In multidisciplinary design optimization (MDO), if the relationships between design variables and some output parameters, which are important performance constraints, are complex implicit problems, plenty of time should be spent on computationally expensive simulations to identify whether the implicit constraints are satisfied with the given design variables during the optimization iteration process. The purpose of this paper is to propose an ensemble of surrogates-based analytical target cascading (ESATC) method to tackle such MDO engineering design problems with reduced computational cost and high optimization accuracy.

Different surrogate models are constructed based on the sample point sets obtained by Latin hypercube sampling (LHS) method. Then, according to the error metric of each surrogate model, the repeated ensemble of surrogates is constructed to approximate the implicit objective functions and constraints. Under the framework of analytical target cascading (ATC), the MDO problem is decomposed into several optimization subproblems and the function of analysis module of each subproblem is simulated by repeated ensemble of surrogates, working together to find the optimum solution.

The proposed method shows better modeling accuracy and robustness than other individual surrogate model-based ATC method. A numerical benchmark problem and an industrial case study of the structural design of a super heavy vertical lathe machine tool are utilized to demonstrate the accuracy and efficiency of the proposed method.

This paper integrates a repeated ensemble method with ATC strategy to construct the ESATC framework which is an effective method to solve MDO problems with implicit constraints and black-box objectives.

Surrogate-assisted evolutionary algorithms (SAEAs) are the most popular algorithms used to solve design optimization problems of expensive and complex engineering systems. However, it is difficult for fixed surrogate models to maintain their accuracy and efficiency in the face of different issues. Therefore, the selection of an appropriate surrogate model remains a significant challenge. This paper aims to propose a dynamic adaptive hybrid surrogate-assisted particle swarm optimization algorithm (AHSM-PSO) to address this issue.

A dynamic adaptive hybrid selection method (AHSM) is proposed. This method can identify multiple ensemble models formed by integrating different numbers of excellent individual surrogate models. Then, according to the minimum root-mean-square error, the best suitable surrogate model is dynamically selected in each generation and is used to assist PSO.

Experimental studies on commonly used benchmark problems, and two real-world design optimization problems demonstrate that, compared with existing algorithms, the proposed algorithm achieves better performance.

The main contribution of this work is the proposal of a dynamic adaptive hybrid selection method (AHSM). This method uses the advantages of different surrogate models and eliminates the shortcomings of experience selection. Furthermore, the empirical results of the comparison of the proposed algorithm (AHSM-PSO) with existing algorithms on commonly used benchmark problems, and two real-world design optimization problems demonstrate its competitiveness.