This paper reviews "socially interactive robots": robots for which social human-robot interaction is important. We begin by discussing the context for socially interactive robots, emphasizing the relationship to other research fields and the di#erent forms of "social robots". We then present a taxonomy of design methods and system components used to build socially interactive robots. Finally, we describe the impact of these these robots on humans and discuss open issues. An expanded version of this paper, which contains a survey and taxonomy of current applications, is available as a technical report[61].

This paper is a review of research in product development, which we define as the transformation of a market opportunity into a product available for sale. Our review is broad, encompassing work in the academic fields of marketing, operations management, and engineering design. The value of this breadth is in conveying the shape of the entire research landscape. We focus on product development projects within a single firm. We also devote our attention to the development of physical goods, although much of the work we describe applies to products of all kinds. We look inside the “black box ” of product development at the fundamental decisions that are made by intention or default. In doing so, we adopt the perspective of product development as a deliberate business process involving hundreds of decisions, many of which can be usefully supported by knowledge and tools. We contrast this approach to prior reviews of the literature, which tend to examine the importance of environmental and contextual variables, such as market growth rate, the competitive environment, or the level of top-management support.

An important managerial problem in product design in the extent to which testing activities are carried out in parallel or in series. Parallel testing has the advantage of proceeding more rapidly than serial testing but does not take advantage of the potential for learning between tests, thus resulting in a larger number of tests. We model this trade-off in the form of a dynamic program and derive the optimal testing strategy (or mix of parallel and serial testing) that minimizes both the total cost and time of testing. We derive the optimal testing strategy as a function of testing cost, prior knowledge, and testing lead time. Using information theory to measure the test efficiency, we further show that in the case of imperfect testing (due to noise or simulated test conditions), the attractiveness of parallel strategies decreases. Finally, we analyze the relationship between testing strategies and the structure of design hierarchy. We show that a key benefit of modular product architecture lies in the reduction of testing cost.

Increasingly shorter product life cycles impel rms to design, develop, and market more products in less time than ever before. Overlapping of design and development stages is commonly regarded as the most promising strategy to reduce product development times. However, overlapping typically requires additional resources and can be costly. Our research addresses the trade-o between product development time and costs and introduces an algorithm to determine an appropriate overlapping strategy under di erent scenarios. The methodology developed was successfully employed at Rocketdyne Division of Rockwell International. 1.

this paper we describe the development of an analytical approach for evaluating sequential design process completion time and for determining the sensitivities of design time with respect to individual task durations and transition probabilities. Techniques are also detailed for collecting process metadata and calibrating a design process model. Example applications illustrate the use of the methodology in analyzing and improving software and hardware design processes. Categories and Subject Descriptors: J.6 [Computer Applications]: Computer-Aided Engineering - computer-aided design; K.3.2 [Computers and Education]: Computer and Information Science Education - self-assessment

Abstract—This paper presents a process modeling and analysis technique for managing complex design projects using advanced simulation. The model computes the probability distribution of lead time in a stochastic, resource-constrained project network where iterations take place among sequential, parallel, and overlapped tasks. The model uses the design structure matrix representation to capture the information flows between tasks. We use a simulation-based analysis to account for many realistic aspects of design process behavior which were not possible in previous analytical models. We propose a heuristic for the stochastic, resource-constrained project scheduling problem in an iterative project network. The model can be used for better project planning and control by identifying leverage points for process improvements, and for evaluating alternative planning and execution strategies. An industrial example is provided to illustrate the utility of the model. Index Terms—Design iteration, design structure matrix, process modeling, project management, project simulation.

This paper presents the use of a Markov-based model for analyzing iterative design processes. Techniques are developed for collecting process metadata and calibrating the model. An experiment is described that demonstrates the utility and accuracy of the model for simulating design processes and identifying design process bottlenecks. 1.0 Introduction In today's highly-competitive market, it is imperative not only to deliver products that work, but also to deliver them on time. While great effort and expense has been invested in developing---and purchasing---more powerful point CAD tools, surprisingly little work has been done in creating techniques for analyzing and improving the design process itself. To address this need, we have developed a system for measuring, diagnosing, and simulating iterative, sequential design processes that is based on the use of a Markov model. To demonstrate the efficacy of the system, an experiment was performed with a group of designers, each working o...

Schedule risk is an important category of risk in complex system product development. This paper presents a framework that facilitates understanding schedule risk from a systems perspective. Research findings from literature and a Delphi-type survey of experienced product development managers and system engineers at a major aerospace company are synthesized into a framework characterizing sources of schedule uncertainty. The framework includes not only key uncertainty drivers but also the hypothesized or theorized relationships between them. Since risk is more than just uncertainty, consequences of schedule overruns and of schedule uncertainty itself are also discussed. This research contributes a more comprehensive, systems view to the studies of product development and risk management and to the practice of both in industry. The paper also examines potential paths for future

Abstract: This paper deals with the quantitative modeling and planning of the product development process in concurrent engineering (CE). CE requires that product design and its related process design be carried out concurrently. Many existing CE models highlight the impact of the upstream design on the downstream design. While in our research,a quantitative model of the product development process in CE based on the network of product–process design activity pairs is presented to describe both the impact of the upstream product design on the downstream process design and the process design’s ability of discovering the faults in the product design. Based on this model,a method is presented to determine the appropriate concurrency degree between the product and process design activities and estimate the mean duration of product–process design activity pair. Then an approach is proposed to solve the integrated optimization problem of both the allocation of resources and the planning of the product development process in CE. Its objective is to allocate the resources for the product development project reasonably to complete it before the due date,minimize its lead-time and keep the average resource utilization rate above a given level.

As design processes continue to increase in complexity it is important to base process improvement decisions on quantitative analysis. In this paper we develop an analytical approach to determining sensitivities of overall process time with respect to individual task durations and transitions. Two applications are used to illustrate this approach. First, sensitivities are ranked in a Pareto Analysis involving an ASIC design process to determine which process features have the greatest impact on the overall process time. Second, sensitivities are utilized as gradients in an optimization problem to determine the lower bound of the process completion time for a software design process. 1.0 Introduction Accompanying the explosive growth in complexity of hardware/software system designs is a corresponding growth in complexity of design processes. Along with the need to ensure that products are delivered at acceptable quality levels, there is also tremendous pressure to reduce the duration...