Many real-world networks have a nested structure. Examples range from biological ecosystems (e.g. mutualistic networks), industry systems (e.g. New York garment industry) to inter-bank networks (e.g. Fedwire bank network). A nested network has a graph topology such that a vertex’s neighborhood contains the neighborhood of vertices of lower degree. Thus, the adjacency matrix is stepwise, which can be found in both bipartite and non-bipartite networks. Despite the strict mathe- matical characterization and their common occurrence, it is not easy to detect nested graphs unequivocally. Among others, there exist three methods for detection and quantification of nestedness that are being widely used: BINMATNEST, NODF, and FCM. However, those methods fail in detecting nestedness for graphs with low (NODF) and high (NODF, BINMATNEST) density or were developed for bipartite networks (FCM). The common shortcoming of these approaches is the underlying asumption that all vertices belong to the nested component. However, many real- world networks have solely a sub-component (i.e. not all elements of the graph) that is nested. Thus, unveiling which vertices pertain to the nested component, is an important research question, unaddressed by the methods available so far. In this contribution, we study in detail the algorithm Nestedness detection based on Local Neighborhood (NESTLON). This algorithm detects nestedness on a broad range of nested graphs independently of their density and resorts solely on local information. By means of a benchmarking model we are able to tune the degree of nestedness in a controlled manner. Our results show that NESTLON outperforms both BIN- MATNEST and NODF.
The valuation of Flexible Manufacturing Systems is one of the most frequently undertaken productivity improvement activities. In practice, the introduction of an FMS into industry must be done on the basis of cost justification. Recently developed techniques for the evaluation of the value of flexibility typically include the computation of stochastic dynamic programs. However, the computational effort of stochastic dynamic programs grows combinatorially and limits application to real world problems. In this contribution, we derive fast approximations to the stochastic dynamic program and compare their results to the exact solution. The proposed methods show an excellent worst case behavior (1%) for a wide range of volatility of the underlying stochastic profit margins and costs for switching the production mode. The computational effort is reduced by a factor of more than 200.
This paper proposes a new model for studying the new product development process in an artificial environment. We show how connectionist models can be used to simulate the adaptive nature of agents' learning exhibiting similar behavior as practically experienced learning curves. We study the impact of incentive schemes (local, hybrid and global) on the new product development process for different types of organizations. Sequential organizational structures are compared to two different types of team-based organizations, incorporating methods of Quality Function Deployment such as the House of Quality. A key finding of this analysis is that the firms' organizational structure and agents' incentive system significantly interact. We show that the House of Quality is less affected by the incentive scheme than firms using a Trial & Error approach. This becomes an important factor for new product success when the agents' performance measures are conflicting.
This paper proposes a new model for studying the new product development process in an artificial environment. We show how connectionist models can be used to simulate the adaptive nature of agents' learning exhibiting similar behavior as practically experienced learning curves. We study the impact of incentive schemes (local, hybrid and global) on the new product development process for different types of organizations. Sequential organizational structures are compared to two different types of team-based organizations, incorporating methods of Quality Function Deployment such as the House of Quality. A key finding of this analysis is that the firms' organizational structure and agents' incentive system significantly interact. We show that the House of Quality is less affected by the incentive scheme than firms using a Trial & Error approach. This becomes an important factor for new product success when the agents' performance measures are conflicting.
Conjoint analysis is one of the most important tools to support product development, pricing and positioning decisions in management practice. For this purpose, various models have been developed. It is widely accepted that models that take consumer heterogeneity into account, outperform aggregate models in terms of hold-out tasks. The aim of our study is to investigate empirically whether predictions of choice-based conjoint models which incorporate heterogeneity can successfully be generalized to a whole market. To date no studies exist that examine the real world performance of choice-based conjoint models by use of aggregate scanner panel data. Our analysis is based on four commercial choice-based conjoint pricing studies including a total of 43 stock keeping units (SKU) and the corresponding weekly scanning data for approximately two years. An aggregate model serves as a benchmark for the performance of two models that take heterogeneity into account, hierarchical Bayes (HB) and latent class (LC). Our empirical analysis demonstrates that, in contrast to the performance using hold-out tasks, the real world performance of HB and LC is similar to the performance of the aggregate model. Our results indicate that heterogeneity cannot be generalized to a whole market and suggest that aggregate models are sufficient to predict market shares (MSs).
The literature on recommendation systems indicates that the choice of the methodology significantly influences the quality of recommendations. The impact of the amount of available data on the performance of recommendation systems has not been systematically investigated. The authors study different approaches to recommendation systems using the publicly available EachMovie data set containing ratings for movies and videos. In contrast to previous work on this data set, here a significantly larger subset is used. The effects caused by the available number of customers and movies as well as their interaction with different methods are investigated. Two commonly used collaborative filtering approaches are compared with several regression models using an experimental full factorial design. According to the findings, the number of customers significantly influences the performance of all approaches under study. For a large number of customers and movies, it is shown that simple linear regression with model selection can provide significantly better recommendations than collaborative filtering. From a managerial perspective, this gives suggestions about the selection of the model to be used depending on the amount of data available. Furthermore, the impact of an enlargement of the customer database on the quality of recommendations is shown.
This paper presents DELI, a new interactive tool for supporting new product development decisions. DELI addresses the `chicken and egg` problem in new product development: a product's features shape the way that the market is segmented and targeted, but that very segmentation/targeting itself determines which features the product needs to incorporate. It is very useful, therefore, to be able to look at attributes, product positions and segments in a `single hit' to measure the key trade-offs available. DELI integrates segmentation, visualisation of competitive structures and the segment-specific identification of new product functionality. Several interactive features support the search for new products. Furthermore, the authors introduce a novel conditional segmentation, mapping and positioning approach for an improved representation of products and customers within one map, supporting interpretation and segment-specific new product development.
