Developing a conceptual model of lead performance measurement and benchmarking: A multiple case analysis

Purpose: Environmental turbulence including rapid changes in technology and markets has resulted in the need for new approaches to performance measurement and benchmarking. There is a need for studies that attempt to measure and benchmark upstream, leading or developmental aspects of organizations. Therefore, the aim of this paper is twofold. The first is to conduct an in-depth case analysis of lead performance measurement and benchmarking leading to the further development of a conceptual model derived from the extant literature and initial survey data. The second is to outline future research agendas that could further develop the framework and the subject area.

Design/methodology/approach: A multiple case analysis involving repeated in-depth interviews with managers in organisational areas of upstream influence in the case organisations.

Findings: It was found that the effect of external drivers for lead performance measurement and benchmarking was mediated by organisational context factors such as level of progression in business improvement methods. Moreover, the legitimation of the business improvement methods used for this purpose, although typical, had been extended beyond their original purpose with the development of bespoke sets of lead measures.

Practical implications: Examples of methods and lead measures are given that can be used by organizations in developing a programme of lead performance measurement and benchmarking.

Originality/value: There is a paucity of in-depth studies relating to the theory and practice of lead performance measurement and benchmarking in organisations.

Introduction to Domain-Driven Data Mining Special Section

In the last decade, data mining has emerged as one of the most dynamic and lively areas in information technology. Although many algorithms and techniques for data mining have been proposed, they either focus on domain independent techniques or on very specific domain problems. A general requirement in bridging the gap between academia and business is to cater to general domain-related issues surrounding real-life applications, such as constraints, organizational factors, domain expert knowledge, domain adaption, and operational knowledge. Unfortunately, these either have not been addressed, or have not been sufficiently addressed, in current data mining research and development.Domain-Driven Data Mining (D3M) aims to develop general principles, methodologies, and techniques for modeling and merging comprehensive domain-related factors and synthesized ubiquitous intelligence surrounding problem domains with the data mining process, and discovering knowledge to support business decision-making. This paper aims to report original, cutting-edge, and state-of-the-art progress in D3M. It covers theoretical and applied contributions aiming to: 1) propose next-generation data mining frameworks and processes for actionable knowledge discovery, 2) investigate effective (automated, human and machine-centered and/or human-machined-co-operated) principles and approaches for acquiring, representing, modelling, and engaging ubiquitous intelligence in real-world data mining, and 3) develop workable and operational systems balancing technical significance and applications concerns, and converting and delivering actionable knowledge into operational applications rules to seamlessly engage application processes and systems.

Understanding New Product Project Performance

Purpose – The purpose of this paper is to summarize the accumulated body of knowledge on the performance of new product projects and provide directions for further research. Design/methodology/approach – Using a refined classification of antecedents of new product project performance the research results are meta-analyzed in the literature in order to identify the strength and stability of predictor-performance relationships. Findings – The results reveal that 22 variables have a significant relationship with new product project performance, of which only 12 variables have a sizable relationship. In order of importance these factors are the degree of organizational interaction, R&D and marketing interface, general product development proficiency, product advantage, financial/business analysis, technical proficiency, management skill, marketing proficiency, market orientation, technology synergy, project manager competency and launch activities. Of the 34 variables 16 predictors show potential for moderator effects. Research limitations/implications – The validity of the results is constrained by publication bias and heterogeneity of performance measures, and directions for the presentation of data in future empirical publications are provided. Practical implications – This study helps new product project managers in understanding and managing the performance of new product development projects. Originality/value – This paper provides unique insights into the importance of predictors of new product performance at the project level. Furthermore, it identifies which predictor-performance relations are contingent on other factors.

Incremental learning from stream data

Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries. Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process. In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance. Detailed system level architecture and design strategies are presented in this paper. Simulation results over several real-world data sets are used to validate the effectiveness of this method.

A Capabilities-Aware Framework for Using Computational Accelerators in Data-Intensive Computing

Multicore computational accelerators such as GPUs are now commodity components for highperformance computing at scale. While such accelerators have been studied in some detail as stand-alone computational engines, their integration in large-scale distributed systems raises new challenges and trade-offs. In this paper, we present an exploration of resource management alternatives for building asymmetric accelerator-based distributed systems. We present these alternatives in the context of a capabilities-aware framework for data-intensive computing, which uses an enhanced implementation of the MapReduce programming model for accelerator-based clusters, compared to the state of the art. The framework can transparently utilize heterogeneous accelerators for deriving high performance with low programming effort. Our work is the first to compare heterogeneous types of accelerators, GPUs and a Cell processors, in the same environment and the first to explore the trade-offs between compute-efficient and control-efficient accelerators on data-intensive systems. Our investigation shows that our framework scales well with the number of different compute nodes. Furthermore, it runs simultaneously on two different types of accelerators, successfully adapts to the resource capabilities, and performs 26.9% better on average than a static execution approach.