Evidencing and Sharing Value with Decision Making Models in an Industrial Maintenance Network

The objective of this thesis is to concretize the potential benefits that the industrial maintenance case network could achieve through using the value-based life-cycle model and the flexible asset management model. It is also inspected what factors prevent value creation and sharing in the maintenance contract practices of the case network. This thesis is a case study which utilizes modelling. Four scenarios were developed to demonstrate value creation in the future. The data was partly provided by the collaborating company, partly gathered from public financial statement information. The results indicate that value has been created through the past maintenance of the collaborating company’s rod mill and that profitability of the collaborating company has been mostly on satisfactory level during the past few years. Potential value might be created by increasing the share of proactive maintenance of the rod mill in the future. Profitability of the network could be improved in the future through flexible asset management operations. The main obstacle for value creation and sharing seems to be the lack of sufficient trust between the network members.

Decision making models embedded into a web-based tool for assessing pest infestation risk

Current practices in agricultural management involve the application of rules and techniques to ensure high quality and environmentally friendly production. Based on their experience, agricultural technicians and farmers make critical decisions affecting crop growth while considering several interwoven agricultural, technological, environmental, legal and economic factors. In this context, decision support systems and the knowledge models that support them, enable the incorporation of valuable experience into software systems providing support to agricultural technicians to make rapid and effective decisions for efficient crop growth. Pest control is an important issue in agricultural management due to crop yield reductions caused by pests and it involves expert knowledge. This paper presents a formalisation of the pest control problem and the workflow followed by agricultural technicians and farmers in integrated pest management, the crop production strategy that combines different practices for growing healthy crops whilst minimising pesticide use. A generic decision schema for estimating infestation risk of a given pest on a given crop is defined and it acts as a metamodel for the maintenance and extension of the knowledge embedded in a pest management decision support system which is also presented. This software tool has been implemented by integrating a rule-based tool into web-based architecture. Evaluation from validity and usability perspectives concluded that both agricultural technicians and farmers considered it a useful tool in pest control, particularly for training new technicians and inexperienced farmers.

Deconstruction plan assessment using multi-criteria decision-making methods

The purpose of this paper is to present two multi-criteria decision-making models, including an Analytic Hierarchy Process (AHP) model and an Analytic Network Process (ANP) model for the assessment of deconstruction plans and to make a comparison between the two models with an experimental case study. Deconstruction planning is under pressure to reduce operation costs, adverse environmental impacts and duration, in the meanwhile to improve productivity and safety in accordance with structure characteristics, site conditions and past experiences. To achieve these targets in deconstruction projects, there is an impending need to develop a formal procedure for contractors to select a most appropriate deconstruction plan. Because numbers of factors influence the selection of deconstruction techniques, engineers definitely need effective tools to conduct the selection process. In this regard, multi-criteria decision-making methods such as AHP have been adopted to effectively support deconstruction technique selection in previous researches. in which it has been proved that AHP method can help decision-makers to make informed decisions on deconstruction technique selection based on a sound technical framework. In this paper, the authors present the application and comparison of two decision-making models including the AHP model and the ANP model for deconstruction plan assessment. The paper concludes that both AHP and ANP are viable and capable tools for deconstruction plan assessment under the same set of evaluation criteria. However, although the ANP can measure relationship among selection criteria and their sub-criteria, which is normally ignored in the AHP, the authors also indicate that whether the ANP model can provide a more accurate result should be examined in further research.

Improving the economic decision making capability and viability of Chinese wool textile mills

The successful restructuring of Chinese industries is of immense importance not only for the continued development of China but also to the stability of the world economy. The transformation of the Chinese wool textile industry illustrates well the many problems and pressures currently facing most Chinese industries. The Chinese wool textile industry has undergone major upheaval and restructuring in its drive to modernize and take advantage of developments in world textile markets. Macro level ownership and administrative reforms are well advanced as is the uptake of new technology and equipment. However, the changing market and institutional environment also demands an increasing level of sophistication in mill management decisions including product selection, input procurement, product pricing, investment appraisal, cost analysis and proactive identification of new market and growth opportunities. This paper outlines a series of analyses that have been integrated into a decision-making model designed to assist mill managers with these decisions. Features of the model include a whole-of-mill approach, a design based on existing mill structures and information systems, and the capacity for the model to be tailored to individual mills. All of these features facilitate the adoption of the model by time and resource constrained managers seeking to maintain the viability of their enterprises in the face of extremely dynamic market conditions.

Menedzsmentképességek és döntéshozatali közelítésmódok longitudinális elemzése a versenyképesség-kutatások alapján (Longitudinal analysis of management capabilities and decision making approaches based on the competitiveness researches)

Számos korábbi kutatás – köztük a szerzők korábbi vizsgálatai is – azt mutatja, hogy a menedzsmentképességek és a vállalatok versenyképessége között pozitív kapcsolat áll fenn, a jobban teljesítő és a proaktívabb vállalatok rendre felkészültebb, jobb vezetői képességekkel bíró, kockázatvállalóbb vezetőkkel rendelkeznek. Az is megfigyelhető, hogy az ebből a nézőpontból sikeresebben működő vállalatok döntéseiben az átlagosnál is erősebben érvényesül a racionális közelítésmód, melynek alkalmazásával a menedzserek az optimális cselekvési alternatíva kiválasztására törekszenek. A cikkben a szerzők az elmúlt 15 év versenyképességi kutatásainak tapasztalatait összegzik, kiemelt hangsúlyt helyezve a legfrissebb felmérés eredményeire. ________________ The article summarizes the main findings of the Competitiveness Research Program with respect to the skills and capabilities of the Hungarian managers and the decision making approaches they use during their work. The results of the four surveys conducted in 1996, 1999, 2004 and 2009 are fairly stable over time: practice minded behavior, professional expertise, and problem solving skills are on the top of the list of the most developed skills of the Hungarian executives. The rational approach is the most popular among the most widespread decision making models in the authors’ sample which is rather alarming since the present turbulent economic environment may demand more adaptive and intuitive approaches.

A Feedback Learning and Mental Models Perspective on Strategic Decision Making

This study aims to be a contribution to a theoretical model that explains the effectiveness of the learning and decision-making processes by means of a feedback and mental models perspective. With appropriate mental models, managers should be able to improve their capacity to deal with dynamically complex contexts, in order to achieve long-term success. We present a set of hypotheses about the influence of feedback information and systems thinking facilitation on mental models and management performance. We explore, under controlled conditions, the role of mental models in terms of structure and behaviour. A test based on a simulation experiment with a system dynamics model was performed. Three out of the four hypotheses were confirmed. Causal diagramming positively influences mental model structure similarity, mental model structure similarity positively influences mental model behaviour similarity, and mental model behaviour similarity positively influences the quality of the decision.

A Feedback Learning and Mental Models Perspective on Strategic Decision Making

This study aims to be a contribution to a theoretical model that explains the effectiveness of the learning and decision-making processes by means of a feedback and mental models perspective. With appropriate mental models, managers should be able to improve their capacity to deal with dynamically complex contexts, in order to achieve long-term success. We present a set of hypotheses about the influence of feedback information and systems thinking facilitation on mental models and management performance. We explore, under controlled conditions, the role of mental models in terms of structure and behaviour. A test based on a simulation experiment with a system dynamics model was performed. Three out of the four hypotheses were confirmed. Causal diagramming positively influences mental model structure similarity, mental model structure similarity positively influences mental model behaviour similarity, and mental model behaviour similarity positively influences the quality of the decision

A theoretical and experimental dissociation of two models of decision‐making

La prise de décision est un processus computationnel fondamental dans de nombreux aspects du comportement animal. Le modèle le plus souvent rencontré dans les études portant sur la prise de décision est appelé modèle de diffusion. Depuis longtemps, il explique une grande variété de données comportementales et neurophysiologiques dans ce domaine. Cependant, un autre modèle, le modèle d’urgence, explique tout aussi bien ces mêmes données et ce de façon parcimonieuse et davantage encrée sur la théorie. Dans ce travail, nous aborderons tout d’abord les origines et le développement du modèle de diffusion et nous verrons comment il a été établi en tant que cadre de travail pour l’interprétation de la plupart des données expérimentales liées à la prise de décision. Ce faisant, nous relèveront ses points forts afin de le comparer ensuite de manière objective et rigoureuse à des modèles alternatifs. Nous réexaminerons un nombre d’assomptions implicites et explicites faites par ce modèle et nous mettrons alors l’accent sur certains de ses défauts. Cette analyse servira de cadre à notre introduction et notre discussion du modèle d’urgence. Enfin, nous présenterons une expérience dont la méthodologie permet de dissocier les deux modèles, et dont les résultats illustrent les limites empiriques et théoriques du modèle de diffusion et démontrent en revanche clairement la validité du modèle d'urgence. Nous terminerons en discutant l'apport potentiel du modèle d'urgence pour l'étude de certaines pathologies cérébrales, en mettant l'accent sur de nouvelles perspectives de recherche.

The Learning of the Mathematical Models to Aid Decision Making in the High Level Engineering Schools?

At present, in the University curricula in most countries, the decision theory and the mathematical models to aid decision making is not included, as in the graduate program like in Doctored and Master´s programs. In the Technical School of High Level Agronomic Engineers of the Technical University of Madrid (ETSIA-UPM), the need to offer to the future engineers training in a subject that could help them to take decisions in their profession was felt. Along the life, they will have to take a lot of decisions. Ones, will be important and others no. In the personal level, they will have to take several very important decisions, like the election of a career, professional work, or a couple, but in the professional field, the decision making is the main role of the Managers, Politicians and Leaders. They should be decision makers and will be paid for it. Therefore, nobody can understand that such a professional that is called to practice management responsibilities in the companies, does not take training in such an important matter. For it, in the year 2000, it was requested to the University Board to introduce in the curricula an optional qualified subject of the second cycle with 4,5 credits titled " Mathematical Methods for Making Decisions ". A program was elaborated, the didactic material prepared and programs as Maple, Lingo, Math Cad, etc. installed in several IT classrooms, where the course will be taught. In the course 2000-2001 this subject was offered with a great acceptance that exceeded the forecasts of capacity and had to be prepared more classrooms. This course in graduate program took place in the Department of Applied Mathematics to the Agronomic Engineering, as an extension of the credits dedicated to Mathematics in the career of Engineering.

Selecting crop models for decision making in wheat insurance

In crop insurance, the accuracy with which the insurer quantifies the actual risk is highly dependent on the availability on actual yield data. Crop models might be valuable tools to generate data on expected yields for risk assessment when no historical records are available. However, selecting a crop model for a specific objective, location and implementation scale is a difficult task. A look inside the different crop and soil modules to understand how outputs are obtained might facilitate model choice. The objectives of this paper were (i) to assess the usefulness of crop models to be used within a crop insurance analysis and design and (ii) to select the most suitable crop model for drought risk assessment in semi-arid regions in Spain. For that purpose first, a pre-selection of crop models simulating wheat yield under rainfed growing conditions at the field scale was made, and second, four selected models (Aquacrop, CERES- Wheat, CropSyst and WOFOST) were compared in terms of modelling approaches, process descriptions and model outputs. Outputs of the four models for the simulation of winter wheat growth are comparable when water is not limiting, but differences are larger when simulating yields under rainfed conditions. These differences in rainfed yields are mainly related to the dissimilar simulated soil water availability and the assumed linkages with dry matter formation. We concluded that for the simulation of winter wheat growth at field scale in such semi-arid conditions, CERES-Wheat and CropSyst are preferred. WOFOST is a satisfactory compromise between data availability and complexity when detail data on soil is limited. Aquacrop integrates physiological processes in some representative parameters, thus diminishing the number of input parameters, what is seen as an advantage when observed data is scarce. However, the high sensitivity of this model to low water availability limits its use in the region considered. Contrary to the use of ensembles of crop models, we endorse that efforts be concentrated on selecting or rebuilding a model that includes approaches that better describe the agronomic conditions of the regions in which they will be applied. The use of such complex methodologies as crop models is associated with numerous sources of uncertainty, although these models are the best tools available to get insight in these complex agronomic systems.

Supporting decision-making for self-adaptive systems:from goal models to dynamic decision networks

Context/Motivation - Different modeling techniques have been used to model requirements and decision-making of self-adaptive systems (SASs). Specifically, goal models have been prolific in supporting decision-making depending on partial and total fulfilment of functional (goals) and non-functional requirements (softgoals). Different goalrealization strategies can have different effects on softgoals which are specified with weighted contribution-links. The final decision about what strategy to use is based, among other reasons, on a utility function that takes into account the weighted sum of the different effects on softgoals. Questions/Problems - One of the main challenges about decisionmaking in self-adaptive systems is to deal with uncertainty during runtime. New techniques are needed to systematically revise the current model when empirical evidence becomes available from the deployment. Principal ideas/results - In this paper we enrich the decision-making supported by goal models by using Dynamic Decision Networks (DDNs). Goal realization strategies and their impact on softgoals have a correspondence with decision alternatives and conditional probabilities and expected utilities in the DDNs respectively. Our novel approach allows the specification of preferences over the softgoals and supports reasoning about partial satisfaction of softgoals using probabilities. We report results of the application of the approach on two different cases. Our early results suggest the decision-making process of SASs can be improved by using DDNs. © 2013 Springer-Verlag.

Multicriteria analysis in decision making under information uncertainty

This paper presents results of research related to multicriteria decision making under information uncertainty. The Bell-man-Zadeh approach to decision making in a fuzzy environment is utilized for analyzing multicriteria optimization models (< X, M > models) under deterministic information. Its application conforms to the principle of guaranteed result and provides constructive lines in obtaining harmonious solutions on the basis of analyzing associated maxmin problems. This circumstance permits one to generalize the classic approach to considering the uncertainty of quantitative information (based on constructing and analyzing payoff matrices reflecting effects which can be obtained for different combinations of solution alternatives and the so-called states of nature) in monocriteria decision making to multicriteria problems. Considering that the uncertainty of information can produce considerable decision uncertainty regions, the resolving capacity of this generalization does not always permit one to obtain unique solutions. Taking this into account, a proposed general scheme of multicriteria decision making under information uncertainty also includes the construction and analysis of the so-called < X, R > models (which contain fuzzy preference relations as criteria of optimality) as a means for the subsequent contraction of the decision uncertainty regions. The paper results are of a universal character and are illustrated by a simple example. (c) 2007 Elsevier Inc. All rights reserved.

Sequential decision making with partially ordered preferences

This paper presents new insights and novel algorithms for strategy selection in sequential decision making with partially ordered preferences; that is, where some strategies may be incomparable with respect to expected utility. We assume that incomparability amongst strategies is caused by indeterminacy/imprecision in probability values. We investigate six criteria for consequentialist strategy selection: Gamma-Maximin, Gamma-Maximax, Gamma-Maximix, Interval Dominance, Maximality and E-admissibility. We focus on the popular decision tree and influence diagram representations. Algorithms resort to linear/multilinear programming; we describe implementation and experiments. (C) 2010 Elsevier B.V. All rights reserved.