Modelling to Learn — A Case Study in International Minerals Investment

This paper presents the system dynamics modelling methodology as a means of capturing the nature of complex systems. A case study is used to expose the fine points of the methodology. The theoretical underpinning for this case study is based on a highly detailed simulation model designed to examine the factors impacting the flow of international minerals investment funds. Emphasis here is on the usefulness of the modelling methodology as an aid to decision-making, the reader is referred elsewhere for a discussion of model development.     

A proposed methodology for the prediction of mental workload, based on engineering system parameters

In the design of engineering systems, mental workload is one of the most important factors in the allocation of cognitive tasks. Current methods of task allocation have criteria that are defined in only general terms and are thus not very useful in aiding detailed decision-making in system design. Whilst there are many quantitative criteria available to determine the physical space in human-machine interaction, system designers really require an explicit model and specific criteria for the following identification of the mental workload imposed by the system; prediction of both human and system performance; evaluation of the alternatives of system design; and the design of system components. It is argued that the available methods of workload or performance are either too domain-dependent to apply to the design of other systems, or subject-dependent and thus do not reflect the objective workload imposed by the system. The presented research adopts a new approach to cognitive task analysis in dynamic decision-making systems. Based on the characteristics derived from task analysis, a general conceptual model of the prediction of mental workload in system design is proposed. In the new model, workload is represented by a set of system parameters—task arrival rate, task complexity, task uncertainty, and performance requirements—which are considered to be the main sources of workload. In this context, workload becomes an objective demand of engineering systems, independent of any subjective factors. Whether an individual or population is overloaded depends upon their workload threshold with respect to the specified task and environment. It is hoped that this new model, after both laboratory and industrial validation, could be used by system designers to predict the workload imposed on people by systems.     

Development of Economic Consequence Methodology for Process Risk Analysis

A comprehensive methodology for economic consequence analysis with appropriate models for risk analysis of process systems is proposed. This methodology uses loss functions to relate process deviations in a given scenario to economic losses. It consists of four steps: definition of a scenario, identification of losses, quantification of losses, and integration of losses. In this methodology, the process deviations that contribute to a given accident scenario are identified and mapped to assess potential consequences. Losses are assessed with an appropriate loss function (revised Taguchi, modified inverted normal) for each type of loss. The total loss is quantified by integrating different loss functions. The proposed methodology has been examined on two industrial case studies. Implementation of this new economic consequence methodology in quantitative risk assessment will provide better understanding and quantification of risk. This will improve design, decision making, and risk management strategies.     

A Simplified Approach to Risk Assessment Based on System Dynamics: An Industrial Case Study

Seveso plants are complex sociotechnical systems, which makes it appropriate to support any risk assessment with a model of the system. However, more often than not, this step is only partially addressed, simplified, or avoided in safety reports. At the same time, investigations have shown that the complexity of industrial systems is frequently a factor in accidents, due to interactions between their technical, human, and organizational dimensions. In order to handle both this complexity and changes in the system over time, this article proposes an original and simplified qualitative risk evaluation method based on the system dynamics theory developed by Forrester in the early 1960s. The methodology supports the development of a dynamic risk assessment framework dedicated to industrial activities. It consists of 10 complementary steps grouped into two main activities: system dynamics modeling of the sociotechnical system and risk analysis. This system dynamics risk analysis is applied to a case study of a chemical plant and provides a way to assess the technological and organizational components of safety.     

A Methodology for Risk Analysis Based on Hybrid Bayesian Networks: Application to the Regasification System of Liquefied Natural Gas Onboard a Floating Storage and Regasification Unit

This article presents an iterative six‐step risk analysis methodology based on hybrid Bayesian networks (BNs). In typical risk analysis, systems are usually modeled as discrete and Boolean variables with constant failure rates via fault trees. Nevertheless, in many cases, it is not possible to perform an efficient analysis using only discrete and Boolean variables. The approach put forward by the proposed methodology makes use of BNs and incorporates recent developments that facilitate the use of continuous variables whose values may have any probability distributions. Thus, this approach makes the methodology particularly useful in cases where the available data for quantification of hazardous events probabilities are scarce or nonexistent, there is dependence among events, or when nonbinary events are involved. The methodology is applied to the risk analysis of a regasification system of liquefied natural gas (LNG) on board an FSRU (floating, storage, and regasification unit). LNG is becoming an important energy source option and the world's capacity to produce LNG is surging. Large reserves of natural gas exist worldwide, particularly in areas where the resources exceed the demand. Thus, this natural gas is liquefied for shipping and the storage and regasification process usually occurs at onshore plants. However, a new option for LNG storage and regasification has been proposed: the FSRU. As very few FSRUs have been put into operation, relevant failure data on FSRU systems are scarce. The results show the usefulness of the proposed methodology for cases where the risk analysis must be performed under considerable uncertainty.     

Systems innovation manager

Each year organizations spend a significant amount of money developing new products and processes in an effort to satisfy customer demands and manufacture high-quality products efficiently. Both development processes - product and process, are complex, resource intensive and thrive on innovation. They demand a variety of skills and resources, but in particular, participation among all staff in generating ideas, managing projects and implementing change. There are currently a number of software tools and methods that facilitate change in a systems environment. These range from complex modelling tools to information management tools. The tools have been developed around paradigms, e.g. world class manufacturing, total quality management and business process reengineering. They are often complex, requiring the efforts of skilled designers and managers. Current thinking within a systems environment reflects a more participative and less specialist approach to managing innovation and change. There is a need to create compromise between detailed project engineering and good management practice. This paper introduces a new paradigm centred on good management practice, and identifies the critical issues in systems innovation and change. The paradigm is articulated through a series of change levers and a methodology that guides managers and designers. It is supported by a series of software tools that together bring innovation management to life within the industrial organization.     

Critical Asset and Portfolio Risk Analysis: An All-Hazards Framework

This article develops a quantitative all-hazards framework for critical asset and portfolio risk analysis (CAPRA) that considers both natural and human-caused hazards. Following a discussion on the nature of security threats, the need for actionable risk assessments, and the distinction between asset and portfolio-level analysis, a general formula for all-hazards risk analysis is obtained that resembles the traditional model based on the notional product of consequence, vulnerability, and threat, though with clear meanings assigned to each parameter. Furthermore, a simple portfolio consequence model is presented that yields first-order estimates of interdependency effects following a successful attack on an asset. Moreover, depending on the needs of the decisions being made and available analytical resources, values for the parameters in this model can be obtained at a high level or through detailed systems analysis. Several illustrative examples of the CAPRA methodology are provided.     

Decision support tool for lean product and process development

Lean and concurrent engineering (CE) are widely acknowledged business process improvement strategies. These strategies can improve processes, reduce costs, and cut waste enabling organisations to remain competitive. Lean manufacturing offers an enterprise-wide methodology that improves reliability and flexibility while reducing lead-times and inventory carrying costs. Companies in manufacturing and service sectors are focusing on integrating lean manufacturing methodology with other applications, so that, all their systems and processes are aligned. This article proposes a CE framework based on application of information technology and object-oriented methodology for lean manufacturing. This approach expected to give manufacturing companies an extra edge in today's competitive market. A case study is presented in this article to demonstrate the effectiveness of CE framework in a machine tool manufacturing company. CE practice was adopted for new products, to align the systems and processes of the company. Product development lead-time was found to decrease by more than 50% compared to similar development projects carried out by the company. The need for rework was found to be negligible and the development cost was reduced considerably.     

A reference model for collaborative design in mould industry

The mould industry is an indispensable foundation for mass production by manufacturer and influences the time to market of new product. It is difficult to integrate the new product development processes and information systems among the mould companies which are mainly small and medium enterprises. The design chain operations reference-model (DCOR) provides a well-recognised methodology to standardise the management process for a product development project and enables the integration for mould industry. The DCOR focuses on a primary and preliminary product development process and is necessary to extend the detail templates for the industrial requirements. Hence, the purpose of this research is to develop an approach that extends the DCOR to the mould industry. The framework of the developed model, which is called the design chain operations reference-model for the mould industry, is adopted for a case mould company for a feasibility discussion.     

Expert Judgment-Based Risk Assessment Using Statistical Scenario Analysis: A Case Study—Running the Bulls in Pamplona (Spain)

In this article, we present a methodology to assess the risk incurred by a participant in an activity involving danger of injury. The lack of high-quality historical data for the case considered prevented us from constructing a sufficiently detailed statistical model. It was therefore decided to generate a risk assessment model based on expert judgment. The methodology is illustrated in a real case context: the assessment of risk to participants in a San Fermin bull-run in Pamplona (Spain). The members of the panel of "experts on the bull-run" represented very different perspectives on the phenomenon: runners, surgeons and other health care personnel, journalists, civil defense workers, security staff, organizers, herdsmen, authors of books on the bull-run, etc. We consulted 55 experts. Our methodology includes the design of a survey instrument to elicit the experts' views and the statistical and mathematical procedures used to aggregate their subjective opinions.     

STOCK MARKET RESEARCH METHODOLOGY: A CASE FOR THE SYSTEMS APPROACH

Few stock market “truths” are known despite extensive research by academicians, investment advisors, and investors. This state is hypothesized to be a result of an emphasis on external or “black box” methods of inquiry. The use of internal methods or systems analysis is suggested as the way to bring about new insights into the stock market. In this article, the stock market is described as a system. Methods to study this system, ranging on a continuum with external and internal poles, are presented. The danger of heavy reliance on external methodologies and the need for a more balanced methodology is illustrated with reference to the “random-walk hypothesis.” Finally, the advantages of internal methodologies are detailed.     

Design and Assessment Methodology for System Resilience Metrics

By providing objective measures, resilience metrics (RMs) help planners, designers, and decisionmakers to have a grasp of the resilience status of a system. Conceptual frameworks establish a sound basis for RM development. However, a significant challenge that has yet to be addressed is the assessment of the validity of RMs, whether they reflect all abilities of a resilient system, and whether or not they overrate/underrate these abilities. This article covers this gap by introducing a methodology that can show the validity of an RM against its conceptual framework. This methodology combines experimental design methods and statistical analysis techniques that provide an insight into the RM's quality. We also propose a new metric that can be used for general systems. The analysis of the proposed metric using the presented methodology shows that this metric is a better indicator of the system's abilities compared to the existing metrics.     

Intersystem Common Cause Analysis of a Diesel Generator Failure

The methodology and results reported in this paper are based on an analysis of a hypothetical accident occurring in a two unit power plant with shared systems (i.e., the diesel generator, the emergency service water, and the residual heat removal service water systems). The accident postulated is a loss of coolant accident (LOCA) in one out of two nuclear units in conjunction with a loss of offsite power (LOOP) and a failure of one out of four diesel generators to start. To analyze the intersystem effects, we needed to develop and apply a new methodology, intersystem common cause analysis (ICCA). The ICCA methodology revealed problems which were not identified by the traditional intrasystem failure modes and effects analysis (FMEA) performed earlier by the design teams. The first potential problem arises if one unit experiences a LOCA and diesel generator failure while one loop of its residual heat removal system is in the suppression pool cooling mode (SPCM); in this event, it is likely that minimum emergency core cooling system (ECCS) requirements will not be met. The second potential problem arises if a diesel generator fails while both units are simultaneously subjected to a controlled forced shutdown (a LOCA need not be postulated for either unit); in this event, it is likely that one unit will be required to use a heat removal path identified as off-normal in the final safety analysis report (FSAR) for the two unit plant. These and other potential concerns identified through application of the ICCA presented here were resolved early in the design phase.     

A Multivariate Methodology for Simultaneously Capturing and Clustering Judgment Policies

This paper presents a new linear model methodology for clustering judges with homogeneous decision policies and differentiating dimensions which distinguish judgment policies. This linear policy capturing model based on canonical correlation analysis is compared to the standard model based on regression analysis and hierarchical agglomerative clustering. Potential advantages of the new methodology include simultaneous instead of sequential consideration of information in the dependent and independent variable sets, decreased interpretational difficulty in the presence of multicollinearity and/or suppressor/moderator variables, and a more clearly defined solution structure allowing assessment of a judge's relationship to all of the derived, ideal policy types. An application to capturing policies of information systems recruiters responsible for hiring entry-level personnel is used to compare and contrast the two techniques.     

基于能值分析的产业集聚区生态经济绩效评价研究

运用能值分析理论及方法,将产业集聚系统的产业、人口、资源和环境生态等各个子系统统一起来,建立基于系统结构、功能和生态效率和可持续发展指数的综合评价体系,对产业集聚系统的发展现状、动态与动因整合分析,以此来指导和优化产业集聚的可持续发展模式,并以大连七贤岭产业化基地为案例开展了生态经济绩效评价。     

The multicriteria selection of products in technological diversification strategies: an application to the Spanish automotive industry based on AHP

The economic recession, the dynamism and complexity of the markets and the generalised globalisation of the world economy have forced many companies to rethink and reorganise their industrial strategies. Through identifying and taking advantage of the key technologies of a company, this work presents a new and original methodology for dealing with a general technological diversification process. The methodology, which includes an innovative cognitive perspective and its corresponding decisional tool (decision support system) has been used to select the best product in a process of technological diversification that, along with the key technologies, identifies the sub-levels consonant to the sub-technologies and applications to sectors and products. The multicriteria selection tool employed in the proposed methodology, based on the analytic hierarchy process, allows the integration of multiple scenarios, actors and criteria, both tangible and intangible. The methodology has been applied to a case study in the Spanish automotive auxiliary sector.     

Conceptualizing Structurable Tasks in the Development of Knowledge-Based Systems*

Conventional approaches to knowledge-based system (KBS) development are not appropriate for building KBSs when the application task is structurable (i.e., exhibits a certain degree of ill structure). Building a KBS for structurable tasks requires an understanding of the problem-solving strategies used by an expert to manage the ill structure, while at the same time relying on domain theories to understand the structured parts of the task. This paper presents a methodology for developing a knowledge model for structurable tasks during the conceptualization stage of KBS development. This is equivalent to building a logical model for design during the development of conventional information systems. The methodology relies on prior research on the decomposition and characterization of a task based on its various attributes. The paper also illustrates the use of the methodology in the case of KBS development for financial hedging. The paper concludes with some observations about the potential impact of this methodology on other stages in the KBS development process.     

Prioritising indicators in improving supply chain performance using fuzzy AHP: insights from the case example of four Indian manufacturing companies

Abstract

In this contribution, we aim to prioritise the indicators to enhance the organisational supply chain’s (SC) effectiveness from an industrial perspective. It will help industries to develop strategies for managing the SC effectively and ensuring improvement in performance continuously. To achieve this, this work proposes to use a two-phase research methodology. First, 36 SC performance improvement indicators are recognised from a literature survey and from field and industrial expert’s inputs. Secondly, a structural model is proposed using the fuzzy analytical hierarchy process (AHP) to prioritise the indicators strategically to improve the SC performance. The fuzzy AHP method helps determine the priority of concerns of the identified indicators under fuzzy surroundings. Inputs in this research are taken from four ancillary Indian plastic manufacturing firms. Research findings indicate that collaborations and information exchange dimension indicators obtained the highest priority in improving SC performance. The model proposed is considered very useful for the SC managers/practitioners/decision-makers to understand better and distinguish the essential SC performance improvement indicators and to take systematic decisions specifically to improve the performance of business in a SC context. Sensitivity analysis was conducted to examine the priority ranking of the indicators.     

Application of TOC/OPT rules in a medium sized shop

Abstract

Abstract. Theory of constraints (TOC) and optimized production technology (OPT) are frequently presented as the better options for production control systems. Despite this enthusiasm, there is very little literature on how these principles can be implemented in practice without having to resort to the expensive proprietary software. In this paper we present a case study in which the OPT/TOC ideas were implemented without using the software. Management's major concern was reduction of work in process and increased delivery performance. It was decided to test whether the ideas of OPT/TOC were applicable in this environment and more importantly to which extent these two performance measures could be improved. The paper reports on the methodology used to test the OPT/TOC ideas in this shop and presents some early results of improvement, as well as some new problems which arose due to the new approach.     

Definition and aggregation of a performance measurement system in three aeronautical workshops using the ECOGRAI method

The requirement for a performance measurement system is essential in order to know the status of the production system and then to improve its control. Focused on research on the control of production systems for 20 years with the development of GRAI methodology, the LAPS/GRAI of University Bordeaux 1 worked for more than 10 years on the definition and the implementation of performance indicator systems through the development of ECOGRAI method and on the aggregation of performance. This paper presents an application of the ECOGRAI method to a workshop inside an aeronautical subcontracting company close to Bordeaux. The first part of the paper will be dedicated to a short presentation of the ECOGRAI method. In particular, we will present six phases of the structured approach and the main tools which are used in the method: the GRAI grid, the coherence panel and the specification sheet and the identity card of the indicators. In the second part, we will present a test case. First, we will present the context of the test case and the objectives that we had to meet. Then we will present the models of the control system and of the physical system through the GRAI grid and actigrams. We will show how we proposed to model the global control of the company and the detailed control of each workshop. Then, we will focus particularly on the way we have identified the performance indicators, based on the objectives and decision variables of the GRAI grid. We will also show how we solved the problem of aggregation of performance in relation to the coordination of decision-making. In the third part, we will detail how we ensured that these indicators were adapted to the control of the workshop and how we ensured their consistency. Finally, we will show to what extent the implementation of the coherent performance indicator system had an impact on the organization and the running of the workshop.