Enhancing SWOT Analysis with TRIZ-based Tools to Integrate Systematic Innovation in Early Task Design

AbstractSWOT analysis is the classical tool for framing the key elements towards problem design/development in various fields of activity and at various levels of interest (e.g. leadership, strategy, production process, marketing, product development, distribution, business model, operational management, etc.). Revealing the major strengths, weaknesses, threads and opportunities does not necessarily lead to an effective project formulation. Key pieces of information are usually missing in the classical SWOT analysis, like the relevance of each strength, weakness, thread and opportunity in meeting the intended vision and targets, as well as compatibility of the elements. A structured framework for setting up a comprehensive SWOT analysis is introduced in this paper. TRIZ-based tools are part of this framework for defining reliable solutions to various barriers and conflicting problems emerging from SWOT elements. This framework brings innovation in the early phase of the planning process of the envisaged system, thus minimizing the risk to define low effective areas of intervention. A case study on process improvement demonstrates the relevance of the proposed approach

Reliability Improvement On Feasibility Study For Selection Of Infrastructure Projects Using Data Mining And Machine Learning

With the progressive development of infrastructure construction, conventional analytical methods such as correlation index, quantifying factors, and peer review are no longer satisfactory in support for decision-making of implementing an infrastructure project in the age of big data. This study proposes using a mathematical model named Fuzzy-Neural Comprehensive Evaluation Model (FNCEM) to improve the reliability of the feasibility study of infrastructure projects by using data mining and machine learning. Specifically, the data collection on time-series data, including traffic videos (278 Gigabytes) and historical weather data, uses transportation cameras and online searching, respectively. Meanwhile, the researcher sent out a questionnaire for the collection of the public opinions upon the influencing factors that an infrastructure project may have. Then, this model implements the backpropagation Artificial Neural Network (BP-ANN) algorithm to simulate traffic flows and generate outputs as partial quantitative references for evaluation. The traffic simulation outputs used as partial inputs to the Analytic Hierarchy Process (AHP) based Fuzzy logic module of the system for the determination of the minimum traffic flows that a construction scheme in corresponding feasibility study should meet. This study bases on a real scenario of constructing a railway-crossing facility in a college town. The research results indicated that BP-ANN was well applied to simulate 15-minute small-scale pedestrian and vehicle flow with minimum overall logarithmic mean squared errors (Log-MSE) of 3.80 and 5.09, respectively. Also, AHP-based Fuzzy evaluation significantly decreased the evaluation subjectivity of selecting construction schemes by 62.5%. It concluded that the FNCEM model has strong potentials of enriching the methodology of conducting a feasibility study of the infrastructure project

North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2

This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies

Applying the Analytic Hierarchy Process to Oil Sands Environmental Compliance Risk Management

Oil companies in Alberta, Canada, invested $32 billion on new oil sands projects in 2013. Despite the size of this investment, there is a demonstrable deficiency in the uniformity and understanding of environmental legislation requirements that manifest into increased project compliance risks. This descriptive study developed 2 prioritized lists of environmental regulatory compliance risks and mitigation strategies and used multi-criteria decision theory for its theoretical framework. Information from compiled lists of environmental compliance risks and mitigation strategies was used to generate a specialized pairwise survey, which was piloted by 5 subject matter experts (SMEs). The survey was validated by a sample of 16 SMEs, after which the Analytic Hierarchy Process (AHP) was used to rank a total of 33 compliance risks and 12 mitigation strategy criteria. A key finding was that the AHP is a suitable tool for ranking of compliance risks and mitigation strategies. Several working hypotheses were also tested regarding how SMEs prioritized 1 compliance risk or mitigation strategy compared to another. The AHP showed that regulatory compliance, company reputation, environmental compliance, and economics ranked the highest and that a multi criteria mitigation strategy for environmental compliance ranked the highest. The study results will inform Alberta oil sands industry leaders about the ranking and utility of specific compliance risks and mitigations strategies, enabling them to focus on actions that will generate legislative and public trust. Oil sands leaders implementing a risk management program using the risks and mitigation strategies identified in this study will contribute to environmental conservation, economic growth, and positive social change

An investigation into the impact of decision support systems on strategic marketing planning practice

Relatively few companies gain the benefits from marketing planning claimed by prescriptive literature. This results from cognitive, procedural, resource, organisational, cultural and data availability barriers to effective planning. Research in other domains suggests that decision support systems (DSS) could assist in reducing some of these barriers. The research aim was therefore to examine whether and how DSS could be used to improve strategic marketing planning practice. The research method incorporated: iterative development of a DSS named EXN4AR a formative evaluation of the prototype system using a survey and a multiple-case study; and a further multiple-case study of users of other, related systems to explore the extent to which the results from the EXMAR evaluation could be generalised. The study confirms that software can play a valuable role in reducing some of the barriers to effective planning. Systems can assist with the effective application of analytical marketing tools through automated calculations, graphical display and on-line guidance, thus reducing the technical marketing knowledge required. Support for fast iteration allows these tools to be used to facilitate group strategy debates. Endeavours to move planning out of the hands of specialists and into cross-functional teams can be further aided by cross-functional analyses and by automated assistance with managing the complexity of multiple-level plans. The electronic format can support moves towards continuous planning based on a live marketing model of the business, helping the organisation to respond to internal or external changes without the constraints of the annual planning cycle. Other barriers such as cultural problems must, however, be reduced by other means. Various factors contributing to success in system implementation are identified, including top management support, sufficiently wide planning team definition, appropriate definition of planning units, sufficiently flexible planning procedures, ease of use, and a system that is seen as empowering rather than controlling

Proceedings of the 9th MIT/ONR workshop on C3 Systems, held at Naval Postgraduate School and Hilton Inn Resort Hotel, Monterey, California June 2 through June 5, 1986

GRSN 627729"December 1986."Includes bibliographical references and index.Sponsored by Massachusetts Institute of Technology, Laboratory for Information and Decision Systems, Cambridge, Mass., with support from the Office of Naval Research. ONR/N00014-77-C-0532(NR041-519) Sponsored in cooperation with IEEE Control Systems Society, Technical Committee on C.edited by Michael Athans, Alexander H. Levis

Rule-based system architecting of Earth observation satellite systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 399-412).System architecting is concerned with exploring the tradespace of early, high-level, system design decisions with a holistic, value-centric view. In the last few years, several tools and methods have been developed to support the system architecting process, focusing on the representation of an architecture as a set of interrelated decisions. These tools are best suited for applications that focus on breadth - i.e., enumerating a large and representative part of the architectural tradespace -as opposed to depth - modeling fidelity. However, some problems in system architecting require good modeling depth in order to provide useful results. In some cases, a very large body of expert knowledge is required. Current tools are not designed to handle such large bodies of knowledge because they lack scalability and traceability. As the size of the knowledge base increases, it becomes harder: a) to modify existing knowledge or add new knowledge; b) to trace the results of the tool to the model assumptions or knowledge base. This thesis proposes a holistic framework for architecture tradespace exploration of large complex systems that require a large body of expert knowledge. It physically separates the different bodies of knowledge required to solve a system architecting problem (i.e., knowledge about the domain, knowledge about the class of optimization or search problem, knowledge about the particular instance of problem) by using a rule-based expert system. It provides a generic population-based heuristic algorithm for search, which can be augmented with rules that encode knowledge about the domain, or about the optimization problem or class of problems. It identifies five major classes of system architecting problems from the perspective of optimization and search, and provides rules to enumerate architectures and search through the architectural tradespace of each class. A methodology is also defined to assess the value of an architecture using a rule-based approach. This methodology is based on a decomposition of stakeholder needs into requirements and a systematic comparison between system requirements and system capabilities using the rules engine. The framework is applied to the domain of Earth observing satellite systems (EOSS). Three EOSS are studied in depth: the NASA Earth Observing System, the NRC Earth Science Decadal Survey, and the Iridium GEOscan program. The ability of the framework to produce useful results is shown, and specific insights and recommendations are drawn.by Daniel Selva Valero.Ph.D