Modelling information usage and decision processes in new product introductions : an information-processing perspective.

The objective of this study is to understand the problem solving process used in new product introductions, and other unstructured business problems. I hope this understanding will contribute to improved decision support systems. Based on Cognitive psychology theories (in particular, Anderson, 1983, 1987), a set of propositions were outlined and investigated by using a computer model. One application of the expert system shell, used here, is to try to model the expert\u27s knowledge. The shell is used to develop a system that simulates the expert\u27s approach to problem solving. The differences between this application and expert system development, are: (i) the focus is on trying to understand the mind of the expert, instead of trying to replace him; and (ii) the problem area is ill-structured, instead of narrow and well-defined. The introduction of new products into markets is an example of an ill-structured problem, in a business setting. In particular, identifying opportunities is to create new products--their future growth and competitiveness often depends on this. The method adopted, computer simulation, has both advantages and limitations. The advantages include: (i) in-depth analysis of the problem-solving process; (ii) operationalizing the theory; and (iii) producing a program that can act as a research vehicle for future projects. The limitations are: (i) small sample size; (ii) lack of clear-cut validation procedures; and (iii) dependence on shell features. The findings, for the most part, supported the propositions (i) The expert model clearly had more procedural knowledge than the textbook model. This supports the proceduralization theory of skill acquisition. (ii) Reasoning by analogy was used by both expert and novices. The use of weak methods by the expert does not support the theory. (iii) The expert adopted a forward reasoning strategy within a task agenda. This supports the hierarchical goal structure theory of Anderson. (iv) The use of soft information was also observed

Knowledge data discovery and data mining in a design environment

Designers, in the process of satisfying design requirements, generally encounter difficulties in, firstly, understanding the problem and secondly, finding a solution [Cross 1998]. Often the process of understanding the problem and developing a feasible solution are developed simultaneously by proposing a solution to gauge the extent to which the solution satisfies the specific requirements. Support for future design activities has long been recognised to exist in the form of past design cases, however the varying degrees of similarity and dissimilarity found between previous and current design requirements and solutions has restrained the effectiveness of utilising past design solutions. The knowledge embedded within past designs provides a source of experience with the potential to be utilised in future developments provided that the ability to structure and manipulate that knowledgecan be made a reality. The importance of providing the ability to manipulate past design knowledge, allows the ranging viewpoints experienced by a designer, during a design process, to be reflected and supported. Data Mining systems are gaining acceptance in several domains but to date remain largely unrecognised in terms of the potential to support design activities. It is the focus of this paper to introduce the functionality possessed within the realm of Data Mining tools, and to evaluate the level of support that may be achieved in manipulating and utilising experiential knowledge to satisfy designers' ranging perspectives throughout a product's development

Evaluating strategies for implementing industry 4.0: a hybrid expert oriented approach of B.W.M. and interval valued intuitionistic fuzzy T.O.D.I.M.

open access articleDeveloping and accepting industry 4.0 influences the industry structure and customer willingness. To a successful transition to industry 4.0, implementation strategies should be selected with a systematic and comprehensive view to responding to the changes flexibly. This research aims to identify and prioritise the strategies for implementing industry 4.0. For this purpose, at first, evaluation attributes of strategies and also strategies to put industry 4.0 in practice are recognised. Then, the attributes are weighted to the experts’ opinion by using the Best Worst Method (BWM). Subsequently, the strategies for implementing industry 4.0 in Fara-Sanat Company, as a case study, have been ranked based on the Interval Valued Intuitionistic Fuzzy (IVIF) of the TODIM method. The results indicated that the attributes of ‘Technology’, ‘Quality’, and ‘Operation’ have respectively the highest importance. Furthermore, the strategies for “new business models development’, ‘Improving information systems’ and ‘Human resource management’ received a higher rank. Eventually, some research and executive recommendations are provided. Having strategies for implementing industry 4.0 is a very important solution. Accordingly, multi-criteria decision-making (MCDM) methods are a useful tool for adopting and selecting appropriate strategies. In this research, a novel and hybrid combination of BWM-TODIM is presented under IVIF information

An approach for selecting cost estimation techniques for innovative high value manufacturing products

This paper presents an approach for determining the most appropriate technique for cost estimation of innovative high value manufacturing products depending on the amount of prior data available. Case study data from the United States Scheduled Annual Summary Reports for the Joint Strike Fighter (1997-2010) is used to exemplify how, depending on the attributes of a priori data certain techniques for cost estimation are more suitable than others. The data attribute focused on is the computational complexity involved in identifying whether or not there are patterns suited for propagation. Computational complexity is calculated based upon established mathematical principles for pattern recognition which argue that at least 42 data sets are required for the application of standard regression analysis techniques. The paper proposes that below this threshold a generic dependency model and starting conditions should be used and iteratively adapted to the context. In the special case of having less than four datasets available it is suggested that no contemporary cost estimating techniques other than analogy or expert opinion are currently applicable and alternate techniques must be explored if more quantitative results are desired. By applying the mathematical principles of complexity groups the paper argues that when less than four consecutive datasets are available the principles of topological data analysis should be applied. The preconditions being that the cost variance of at least three cost variance types for one to three time discrete continuous intervals is available so that it can be quantified based upon its geometrical attributes, visualised as an n-dimensional point cloud and then evaluated based upon the symmetrical properties of the evolving shape. Further work is suggested to validate the provided decision-trees in cost estimation practice

Introduction: food relocalisation and knowledge dynamics for sustainability in rural areas

The chapter presents the literature on local food and local knowledge and introduces the case studies analysed in the volum

Knowledge, Food and Place: a way of producing a way of knowing

The article examines the dynamics of knowledge in the valorisation of local food, drawing on the results from the CORASON project (A cognitive approach to rural sustainable development: the dynamics of expert and lay knowledge), funded by the EU under its Framework Programme 6. It is based on the analysis of several in-depth case studies on food relocalisation carried out in 10 European countries