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The aim of this paper is to investigate how the distinction between contact and non‐contact activities influences the design of service delivery systems and to identify key design decisions for structuring front office and back office work.

Building on current literature, the paper identifies three design decisions and associated performance trade‐offs. The design decisions regard the degree of customer contact in the process, the decoupling of activities and the grouping of employees. The design decisions and the trade‐offs are empirically validated in five case studies of 15 service delivery systems in the financial services sector.

Distinguishing between the three design decisions is more suitable for describing today's practices than traditional front office – back office thinking. For each design decision a trade‐off was observed consisting of several design considerations. However, the trade‐offs do not involve the weighing of one set of performance objectives against another, as the design choices contribute to the same objectives, yet in different ways.

This study concentrated on a limited number of cases in the financial services sector. The contents of the trade‐offs should be tested on a larger scale and in different industries. In order to develop design guidelines, future research should also examine the impact of contingency factors, such as the service being delivered and strategic priorities.

The three design decisions and the trade‐offs improve understanding of the impact of customer contact on a service organisation and provide support for designing service delivery systems in practice.

This paper aims to investigate the current state of research on sustainability-related manufacturing trade-offs (i.e. giving preference and priority to one dimension over others) that affect suppliers in developed and less developed countries (LDCs). There is growing pressure on suppliers to adopt sustainable practices into their global supply chains. Successfully implementing all three dimensions of the triple bottom line (TBL) imperative can provide organizations with an added capability and potentially result in competitive advantage and a focus on sustainable development goals (SDGs) in the long run. However, designing supply chains for achieving the TBL requires suppliers to recognize and overcome numerous trade-offs.

A systematic literature review comprising 71 papers published between 2004 and 2020 was undertaken using a content analysis approach to identify trade-offs affecting suppliers.

This study firstly identified eight sustainability-related trade-offs affecting suppliers from a TBL perspective in both developed and LDCs, consequently, allowing for a detailed discussion on trade-off factors and conditions unique to both developed and LDCs. Together, these findings enable this study to present initiatives and investment-related decisions for supply chains from a TBL perspective.

In spite of the plethora of studies on sustainable supply chains, there is little research on trade-offs, specifically those affecting sustainability of suppliers operating in LDCs. This paper addresses this critical gap and advances the literature on sustainability-related supply chain trade-offs.

The purpose of this paper is to explore how functional and appropriateness arguments influence the adoption of modularity principles during the design of a professional service architecture.

Action design research was conducted to examine the design process of a modular service architecture for specialised elderly care by a multi-professional group. Data collection methods included, partly participatory, observations of the interactions between professionals during the design process, interviews and document analysis. Data analysis focussed on the emerging design choices and the arguments underlying them.

A wide range of both functional and appropriateness considerations were enlisted during the design process. The three core modularity principles were adapted to varying degrees. In terms of the design outcome, the interdependencies between the modularity principles necessitated two trade-offs in the modular design. A third trade-off occurred between modularity and the need for professional inference where services were characterised by uncertainty. Appropriateness was achieved through the professionals reframing and translating the abstract modularity concept to reconcile the concept’s functionality with their professional norms, values and established practices.

The study adds to service modularity theory by formulating three trade-offs that are required in translating the core modularity principles into a functional set of design choices for a multi-professional service environment. Moreover, the inherent intertwinedness of the core modularity principles in professional services requires an iterative design process. Finally, the authors saw that the ambiguity present in the service modularity concept can be used to develop a design that is deemed appropriate by professionals.

Geographical Indications (GI) are complex and multi-purpose institutions. Their objectives include encouraging diversification of agricultural production, improving farmers’ income, countering depopulation of rural areas, satisfying consumer demand for high-quality good, and protecting consumers from food fraud. The authors argue that such objectives are not necessarily aligned as divergence may arise among stakeholders (such as farmers, consumers or rural communities) about the optimal design of the GI. The paper aims to discuss these issues.

The authors developed a simple, static game-theory model describing the basic choices that a planner faces in designing a GI.

The authors conclude that the optimal organization requires finding trade-offs among conflicting but equally desirable objectives. Perfect monitoring is not a sufficient condition to resolve such conflicts. Sub-consortia and flexible production agreements may increase the efficiency of a GI.

The authors described basic trade-offs in GI design using the simplest possible model. To this purpose the authors introduced limiting assumptions that may be relaxed in future research. The representation of the GI agreement focussed on the quality level only, abstracting from all other consideration. Using a static model prevented us from explicit modeling of (loss of) reputation effects. The simplifying assumptions about consumer behavior and cost functions have reduced the generality of the results. Extensions of the model may consider introducing additional elements in the GI agreement such as production areas or governance models, dynamic games and general functional forms.

The authors found that in designing a GI: first, promotion approaches revolving around small groups of local leaders (i.e. efficient, high-quality producers) might overshoot quality, resulting in unsustainable production agreements; second, introducing degrees of flexibility in the production agreement may help achieving a sustainable GI; and finally, sub-consortia/optional labels may help dealing with producers’ heterogeneity.

The authors found that setting a high standard in the production agreement is not sufficient condition for delivering quality food to consumers, as producers might have incentive to commit frauds. A simple command and control approach to quality in GI’s is not always the most efficient strategy, because it may reduce participation. In designing the GI, the goals of identity preservation and food quality must be balanced with consideration of producers’ incentives. The involvement of producers in the design of the GI is a critical success driver. Yet, this practice can be problematic because of producers’ heterogeneity.

The paper provides theoretical foundation for best practices in forming a GI, including: multi-stakeholder involvement, management of farmer heterogeneity and monitoring.

The purpose of this paper is to introduce a new design optimization technique for a surface mounted permanent magnet (SMPM) machine to increase sensorless performance at high loadings by compromising with torque capability.

An SMPM parametric machine model was created and analysed by finite element analysis (FEA) software by means of the Matlab environment. Eight geometric parameters of the machine were optimized using genetic algorithms (GAs). The outer volume of the machine, namely copper loss per volume, was kept constant. In order to prevent sensorless performance loss at high loading, an optimization process was realized using two loading stages: maximum torque with minimum ripple at nominal load and maximum self-sensing capability at twice load. In order to show the effectiveness of the proposed technique, the obtained results were compared with the classical one-stage optimization realized for each loading condition separately.

With the proposed technique, fairly good performance results of the optimization were obtained when compared with the one-stage optimizations. Using the proposed technique, sensorless performance of the motor was highly increased by compromising torque capability for high loading. Additionally, this paper shows that the self-sensing properties of a SMPM machine should be considered at the design stage of the machine.

In related literature, design optimization studies for the sensorless capability of SMPM motor are very few. By increasing optimization performance, new proposed technique provides to achieve good result at high load for sensorless performance compromising torque capability.

Rather than organize as traditional firms, many of today’s companies organize as platforms that sit at the nexus of multiple exchange and production relationships. This chapter considers a most basic question of organization in platform contexts: the choice of boundaries. Herein, I investigate how classical economic theories of firm boundaries apply to platform-based organization and empirically study how executives made boundary choices in response to changing market and technical challenges in the early mobile computing industry (the predecessor to today’s smartphones). Rather than a strict or unavoidable tradeoff between “openness-versus-control,” most successful platform owners chose their boundaries in a way to simultaneously open-up to outside developers while maintaining coordination across the entire system.

Strategies for accelerated multi-objective optimization of aerodynamic surfaces are investigated, including the possibility of exploiting surrogate modeling techniques for computational fluid dynamic (CFD)-driven design speedup of such surfaces. The purpose of this paper is to reduce the overall optimization time.

An algorithmic framework is described that is composed of: a search space reduction, fast surrogate models constructed using variable-fidelity CFD models and co-Kriging, and Pareto front refinement. Numerical case studies are provided demonstrating the feasibility of solving real-world problems involving multi-objective optimization of transonic airfoil shapes and accurate CFD simulation models of such surfaces.

It is possible, through appropriate combination of surrogate modeling techniques and variable-fidelity models, to identify a set of alternative designs representing the best possible trade-offs between conflicting design objectives in a realistic time frame corresponding to a few dozen of high-fidelity CFD simulations of the respective surfaces.

The proposed aerodynamic design optimization algorithmic framework is novel and holistic. It proved useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search space, which is extremely challenging when using conventional methods due to the excessive computational cost.

This study aims to propose a computationally efficient framework for multi-objective optimization (MO) of antennas involving nested kriging modeling technology. The technique is demonstrated through a two-objective optimization of a planar Yagi antenna and three-objective design of a compact wideband antenna.

The keystone of the proposed approach is the usage of recently introduced nested kriging modeling for identifying the design space region containing the Pareto front and constructing fast surrogate model for the MO algorithm. Surrogate-assisted design refinement is applied to improve the accuracy of Pareto set determination. Consequently, the Pareto set is obtained cost-efficiently, even though the optimization process uses solely high-fidelity electromagnetic (EM) analysis.

The optimization cost is dramatically reduced for the proposed framework as compared to other state-of-the-art frameworks. The initial Pareto set is identified more precisely (its span is wider and of better quality), which is a result of a considerably smaller domain of the nested kriging model and better predictive power of the surrogate.

The proposed technique can be generalized to accommodate low- and high-fidelity EM simulations in a straightforward manner. The future work will incorporate variable-fidelity simulations to further reduce the cost of the training data acquisition.

The fast MO optimization procedure with the use of the nested kriging modeling technology for approximation of the Pareto set has been proposed and its superiority over state-of-the-art surrogate-assisted procedures has been proved. To the best of the authors’ knowledge, this approach to multi-objective antenna optimization is novel and enables obtaining optimal designs cost-effectively even in relatively high-dimensional spaces (considering typical antenna design setups) within wide parameter ranges.

Increasing speeds combined with the level of integration that can be obtained with advanced IC technology has dramatically changed the interconnection requirements for high performance electronic systems. With much of today's circuitry being implemented in custom silicon, IC technology has allowed both a dramatic reduction in size and a tremendous increase in performance. However, in terms of the interconnection problem, the by‐product of advanced IC technology is a new generation of IC s that often require several hundred I/OS, exhibit rise times of 150 ps to 300 ps, and dissipate several watts per device. As demanding requirements are placed upon circuit boards, the complexity of the design task increases dramatically, since a working solution must simultaneously address interconnection density, signal integrity and thermal performance. This paper examines embedded discrete wiring technology as a high density solution that meets the requirements necessary for transporting high speed digital signals.

The purpose of this paper is to comparatively study the conventional, i.e. single magnet, and novel hybrid-magnet switched-flux permanent-magnet (HMSFPM) machines.

The HMSFPM machines utilize two magnet types, i.e. low-cost ferrites and NdFeB. Thus, a set of magnet ratios (?), defined as the quotient of the NdFeB volume to the total PM volume, is introduced. This allows any desired performance and cost trade-off to be designed. Series- and parallel-excited magnet configurations are investigated using 2-dimensional finite element analysis.

The torque of the HMSFPM machines is lower than the NdFeB SFPM machine but the flux-weakening performance is improved for similar machine efficiency. If the machine dimensions are unconstrained, the HMSFPM machines can have the same torque for reduced material costs and a moderate increase in machine dimensions. Ferrite SFPM machines have the lowest cost for the same torque but a significant increase in machine dimensions is required. Finally, the series-excited HMSFPM machine is the preferred over the parallel-excited HMSFPM machine because it has superior demagnetization withstand capability.

Mechanical and winding eddy current losses are not considered in the efficiency map calculations.

The NdFeB SFPM, ferrite SFPM, series-excited HMSFPM, and the parallel-excited HMSFPM machines are compared for their electromagnetic performance, flux-weakening, PM demagnetization, efficiency, and material costs.