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Much has been written about manufacturing strategy and its role in supporting firms in achieving competitive advantage in the marketplace. However, little is available on the process of manufacturing objective deployment, that is, on how to translate a given objective into the choice of action plans. There is a lack of methods to assist managers in identifying the range of alternative actions prior to arriving at a final decision. To address this gap, this paper revisits Burbidge’s connectance model and explains how the concept could be utilised as a tool for manufacturing improvement action plan selection. A software tool, tool for action plan selection was developed based on the concept and tested in a number of industrial case studies. This paper concludes by discussing the implication of this work for managers and academics.

Some researchers argue that consumers’ lack of knowledge is an important factor increasing risk for a new product derived from emerging agricultural technology. The purpose of this paper is to discuss the potential impacts and the differential effects of subjective and objective perceptions on Chinese consumers’ preferences for the application of a novel biotechnology.

Taking transgenic technology as an example and employing data from a survey of 1,000 consumers in Jiangsu Province, the authors develop a mixed-process regression model based on Fishbein’s multiple attributes attitude model.

The results suggest that there are apparent differences between Chinese consumers’ subjective perceptions and objective perceptions concerning transgenic technology and genetically modified (GM) food, and there exists certain selective perceptions of the emerging biotechnology. Having a subjective perception concerning transgenic technology has a positive effect on consumers’ overall attitudes, whereas subjective and objective perceptions concerning GM foods have a negative effect on consumers’ overall attitudes. Self-identification generated from subjective perception occupies a dominant position in determining consumers’ attitudes.

Consumers’ attitudes regarding an agricultural product depend on their perception of the attributes of the technology used to produce such a product. This study attempts to distinguish and empirically test urban consumers’ subjective perceptions (self-assessed or perceived) and objective perceptions (obtained from a test) about transgenic technology and GM foods and the impact of these four types of perception on the consumers’ attitudes regarding the application of transgenic technology. In this paper, the authors construct a mixed-process regression model to address the possible endogeneity of the perception variables.

The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms. The proposed application is for engineering design problems.

This study proposes two novel approaches which focus on faster convergence to the Pareto front (PF) while adopting the advantages of Strength Pareto Evolutionary Algorithm-2 (SPEA2) for better spread. In first method, decision variables corresponding to the optima of individual objective functions (Utopia Point) are strategically used to guide the search toward PF. In second method, boundary points of the PF are calculated and their decision variables are seeded to the initial population.

The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance metrics. Performance evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms (such as NSGA-II and SPEA2) and recent ones such as NSLS and E-NSGA-II in most of the benchmark functions. It is also tested on an engineering design problem and compared with a currently used algorithm.

The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives. A complex example of Welded Beam has been shown at the end of the paper.

The algorithm would be useful for many design problems and social/industrial problems with conflicting objectives.

This paper presents two novel hybrid algorithms involving SPEA2 based on: local search; and Utopia point directed search principles. This concept has not been investigated before.

Data envelopment analysis (DEA) is a multicriteria technique which can take into account multiple inputs and outputs to produce a single aggregate measure of relative efficiency for a set of comparable units. DEA takes into consideration other objectives by including the appropriate variables as part of the DEA model. However, as we will demonstrate, collapsing all the inputs and outputs of several objectives into one aggregate performance measure weakens DEA's ability to discriminate the individual impact of each of these objectives. In this chapter, we apply a multiple objective extension to DEA, called multiple objective DEA (MODEA), which simultaneously controls the weights assigned to the variables found in more than one objective. This MODEA approach more fully measures the impact of each objective and allows the decision-maker to address trade offs among these objectives. The usefulness of the MODEA approach is demonstrated by applying it to the hypothetical example.

The paper seeks to determine whether different aspects of migrant pre‐migration characteristics (human capital and motivation to migrate) and post‐migration behaviour (social integration and career self‐management) predict migrants' post‐migration career success.

The research employed a survey questionnaire applied to a sample of 210 migrants who had migrated from Sri Lanka to New Zealand. Twenty‐three independent and three dependent (career success – objective and subjective) variables were measured. Sequential multiple regression analysis was applied, mirroring the time‐sequenced theory of career development.

Overall, migrants' occupational status had declined markedly following migration. Variables representing human capital, social integration and career self‐management perspectives all contributed substantially to explaining variances in career success, especially objective career success, but motivation to migrate did not. Human capital variables were especially influential in determining pre‐migration success, acculturation in the host country and education in the host country in post‐migration success. Effects of career self‐management behaviours on success were relatively small.

A limitation is the cross‐sectional design, and possible non‐generalisability beyond a single migrant group and host country.

The paper discusses implications for migrants, policy makers and future research.

Migration, and interest in research on migrants' careers, is growing. This paper applies a wide range of predictor variables and a logical causal model to predicting migrant career success, indicates significant effects, and points to positive actions that may be taken by government, organisations and migrants.

Uncertainty is inevitable in real-world engineering optimization. With an outer-inner optimization structure, most previous robust optimization (RO) approaches under interval uncertainty can become computationally intractable because the inner level must perform robust evaluation for each design alternative delivered from the outer level. This paper aims to propose an on-line Kriging metamodel-assisted variable adjustment robust optimization (OLK-VARO) to ease the computational burden of previous VARO approach.

In OLK-VARO, Kriging metamodels are constructed for replacing robust evaluations of the design alternative delivered from the outer level, reducing the nested optimization structure of previous VARO approach into a single loop optimization structure. An on-line updating mechanism is introduced in OLK-VARO to exploit the obtained data from previous iterations.

One nonlinear numerical example and two engineering cases have been used to demonstrate the applicability and efficiency of the proposed OLK-VARO approach. Results illustrate that OLK-VARO is able to obtain comparable robust optimums as to that obtained by previous VARO, while at the same time significantly reducing computational cost.

The proposed approach exhibits great capability for practical engineering design optimization problems under interval uncertainty.

The main contribution of this paper lies in the following: an OLK-VARO approach under interval uncertainty is proposed, which can significantly ease the computational burden of previous VARO approach.

This paper aims to describe an interactive action planning software tool (TAPS) for manufacturing objective deployment, and to report the results of its application in five companies.

A process approach based on action research was adopted in this research. Under this approach, a series of four to five workshops were conducted in each company. Each workshop took half a day and involved a multi‐disciplinary team of four to six managers. Besides direct observation in the workshops, a structured questionnaire was also used to elicit managers' feedback at the end of each workshop.

The TAPS approach (integration of Burbidge's connectance concept and the analytic hierarchy process) helps managers to visualise and represent their perceptions of the relationships between variables and objectives through a sequential, analytical process. Results from the case studies indicate that TAPS provides the following benefits to managers: it improves collective understanding, reduces the complexity of strategy deployment into manageable steps, facilitates discussion, and manages organizational knowledge.

The research is so far limited to application in five case studies. Future research will involve further cases and improvements to the usability of the software tool.

For practitioners, the paper provides them with an interactive software tool (TAPS) for effective strategic action planning. For academics, this paper provides an approach for researching strategy deployment, and a potential classroom teaching tool for operations management.

This study explores how balanced scorecard format and reputation from environmental performances interact to influence performance evaluations.

Two general options exist for inserting environmental measures into a scorecard: embedded among the four traditional perspectives or grouped in a fifth perspective. Prior balanced scorecard research also assumes negative past environmental performances. In such settings, and when low management communication levels exist on the importance of environmental strategic objectives (a common practitioner scenario), environmental measures receive less decision weight when they are grouped in a fifth scorecard perspective. However, a positive environmental reputation would generate loss aversion concerns with reputation, leading to more decision weight given to environmental measures. Participants (N=138) evaluated performances with scorecards in an experimental design that manipulates scorecard format (four, five-perspectives) and past environmental performance operationalizing reputation (positive, negative).

The environmental reputation valence’s impact is more (less) pronounced when environmental measures are grouped (embedded) in a fifth perspective (among the four traditional perspectives), when the environmental feature of the measures is more (less) salient.

Findings provide the literature with original empirical results that support the popular, but often anecdotal, position of advocating a fifth perspective for environmental measures to help emphasize and promote environmental stewardship within an entity when common low management communication levels exist. Specifically, when positive past environmental performances exist, entities may choose to group environmental performance measures together in a fifth scorecard perspective without risking those measures receiving the discounted decision weight indicated in prior studies.

Two-dimensional warranty policies exist for certain consumer products, such as automobiles. Here, warranty is specified in terms of the time since the sale of the product as well as mileage incurred during that period. Thus, at the time of purchasing the product, the manufacturer may offer a warranty of three years or 30,000 miles, whichever occurs first. Failures in the product within this specified period of time or mileage will be covered by the manufacturer.

In this chapter, we consider the scenario of enterprise warranty programs, where customers are given the option of extending the original warranty. Thus, the buyer could be given an option to purchase a five year—50,000 mile warranty, whichever occurs first. Of course, the buyer will be expected to pay a premium to purchase this extended warranty. Such enterprise warranty programs are also found in other consumer durables, such as refrigerators, washers, dryers, and cooking ranges.

This chapter explores determination of the decision variables, such as product price, warranty time, and usage limit under the original conditions and further, for the enterprise warranty, that is, the extended warranty time and extended usage limit, as well as the premium to be charged to the buyer who selects the extended warranty. Mathematical models are developed based on maximizing the expected unit profit by selecting an enterprise warranty program. Additionally, some other objectives are also considered based on the proportional increase in the expected unit profit due to the increased market share attained through the offering of an enterprise warranty program. Some results are obtained through consideration of various goal values of the chosen objectives.

Reports that over the past several years, the concept of manufacturing strategy has been at the forefront of both managerial and academic thought, and that studies in the field lack a cohesive foundation that can guide researchers’ efforts in building a testing theory ‐ the research gap. States that an inability to relate specific programmes to long‐term objectives demonstrates the difficulty in bringing manufacturing strategy to the factory floor ‐ the practice gap. Believes that the unsatisfactory progress observed in research and practice is because of the failure to study rigorously the process of operationalizing manufacturing strategy. Builds a process model of manufacturing strategy and presents the results. Describes the model which focuses on three constructs of manufacturing strategy: competitive priorities, manufacturing objectives and action programmes for investment. Using data from a large‐scale survey, explores how manufacturing managers attempt to link their decisions in those three components of manufacturing strategy.