Strategic decision-making for technology implementation in developing countries

Researchers and managers stress the importance of long-term technology strategies to develop technological capabilities for global competitive advantage. This paper explores the relationship between technology decision-making and strategy in technology transfer (TT) in developing countries, with special reference to South Africa. Earlier research by the authors considered technology and operations integration in developing countries and identified factors that were important to managers in the management of technology. The paper proposes five decision-making levels as the basis of a framework for TT, and investigates the strategic issues pertaining to TT at these levels. Four South African cases studies are used to propose a framework that combines important items in technology transfer and levels of decision-making. The research suggests that technology plays a limited role in strategic decisions in developing countries, and that expectations from new technology are largely operational. Broader implications for managers are identified.

Modelling and improving human decision making with simulation

Modelling human interaction and decision-making within a simulation presents a particular challenge. This paper describes a methodology that is being developed known as 'knowledge based improvement'. The purpose of this methodology is to elicit decision-making strategies via a simulation model and to represent them using artificial intelligence techniques. Further to this, having identified an individual's decision-making strategy, the methodology aims to look for improvements in decision-making. The methodology is being tested on unplanned maintenance operations at a Ford engine assembly plant

Knowledge based improvement:simulation and artificial intelligence for identifying and improving human decision-making in an operations systems

The performance of most operations systems is significantly affected by the interaction of human decision-makers. A methodology, based on the use of visual interactive simulation (VIS) and artificial intelligence (AI), is described that aims to identify and improve human decision-making in operations systems. The methodology, known as 'knowledge-based improvement' (KBI), elicits knowledge from a decision-maker via a VIS and then uses AI methods to represent decision-making. By linking the VIS and AI representation, it is possible to predict the performance of the operations system under different decision-making strategies and to search for improved strategies. The KBI methodology is applied to the decision-making surrounding unplanned maintenance operations at a Ford Motor Company engine assembly plant.

Shareholder or stakeholder management:strategies for performance optimisation

This paper is based upon the findings of a CIMA research project into the way which corporate performance is affected by the performance measurement system adopted. It compares and contrasts the techniques in use in a sample of large companies that use a variety of techniques. We have classified these techniques into 3 types: • Value based management techniques • Stakeholder management techniques • Traditional accounting techniques. The analysis traces the interactions between corporate objectives, decision making criteria, performance measurement systems, and executive incentive schemes in order to develop an understanding of the effects of such techniques upon corporate performance. This paper seeks to provide some answers to the following two questions: • What approach leads to superior performance for a firm? • What is different between these approaches when they are used in practice, as distinct from theory? In doing so we have drawn upon both contingency theory and sociobiology theory to develop a framework for understanding the relationship between the choke of performance measurement system and the resulting performance.

Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

Planning and decision-making for energy recovery from waste at the sub-regional level

This research project has developed a novel decision support system using Geographical Information Systems and Multi Criteria Decision Analysis and used it to develop and evaluate energy-from-waste policy options. The system was validated by applying it to the UK administrative areas of Cornwall and Warwickshire. Different strategies have been defined by the size and number of the facilities, as well as the technology chosen. Using sensitivity on the results from the decision support system, it was found that key decision criteria included those affected by cost, energy efficiency, transport impacts and air/dioxin emissions. The conclusions of this work are that distributed small-scale energy-from-waste facilities score most highly overall and that scale is more important than technology design in determining overall policy impact. This project makes its primary contribution to energy-from-waste planning by its development of a Decision Support System that can be used to assist waste disposal authorities to identify preferred energy-from-waste options that have been tailored specifically to the socio-geographic characteristics of their jurisdictional areas. The project also highlights the potential of energy-from-waste policies that are seldom given enough attention to in the UK, namely those of a smaller-scale and distributed nature that often have technology designed specifically to cater for this market.

Supply chain strategies for competitive manufacture

Few people would argue that there is one single solution to the design of a manufacturing supply chain. Relatively recent concepts such as strategic outsourcing and partnership sourcing are extremely valuable to some manufacturing organisations. This paper presents the findings of a comprehensive survey of manufacturing sourcing practices in the UK. This study has set out to understand current and intended practices that manufacturing companies have with their key suppliers, and also the decision processes and circumstances that have caused these relationships to occur.

Interviewing strategies in the face of beauty:a psychophysiological investigation into the job negotiation process

After the application form is submitted, the interview is the most important method of human resource allocation. Previous research has shown that the attractiveness of interviewees can significantly bias interview outcome. We have previously shown that female interviewers give attractive male interviewees higher status job packages compared their average looking counterparts. However, it is not known whether male interviewers exhibit such biases. In the present study, participants were asked to take part in a mock job negotiation scenario where they had to allocate either a high- or low-status job package to attractive or average looking ``interviewees.'' Before each decision was made, the participant's anticipatory electrodermal response (EDR) was recorded. The results supported our previous finding in that female participants allocated a greater number of high-status job packages to attractive men. Additionally, male participants uniformly allocated a greater number of low-status job packages to both attractive men and attractive women. Overall, the average looking interviewees incurred a penalty and received a significantly greater number of low-status job packages. In general, the EDR profile for both male and female participants was significantly greater when allocating the low-status packages to the average looking interviewees. However, the male anticipatory EDR profile showed the greatest change when allocating attractive women with low-status job packages. We discuss these findings in terms of the potential biases that may occur at the job interview and place them within an evolutionary psychology framework.

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.

Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector

Guest editorial Ali Emrouznejad is a Senior Lecturer at the Aston Business School in Birmingham, UK. His areas of research interest include performance measurement and management, efficiency and productivity analysis as well as data mining. He has published widely in various international journals. He is an Associate Editor of IMA Journal of Management Mathematics and Guest Editor to several special issues of journals including Journal of Operational Research Society, Annals of Operations Research, Journal of Medical Systems, and International Journal of Energy Management Sector. He is in the editorial board of several international journals and co-founder of Performance Improvement Management Software. William Ho is a Senior Lecturer at the Aston University Business School. Before joining Aston in 2005, he had worked as a Research Associate in the Department of Industrial and Systems Engineering at the Hong Kong Polytechnic University. His research interests include supply chain management, production and operations management, and operations research. He has published extensively in various international journals like Computers & Operations Research, Engineering Applications of Artificial Intelligence, European Journal of Operational Research, Expert Systems with Applications, International Journal of Production Economics, International Journal of Production Research, Supply Chain Management: An International Journal, and so on. His first authored book was published in 2006. He is an Editorial Board member of the International Journal of Advanced Manufacturing Technology and an Associate Editor of the OR Insight Journal. Currently, he is a Scholar of the Advanced Institute of Management Research. Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector This special issue aims to focus on holistic, applied research on performance measurement in energy sector management and for publication of relevant applied research to bridge the gap between industry and academia. After a rigorous refereeing process, seven papers were included in this special issue. The volume opens with five data envelopment analysis (DEA)-based papers. Wu et al. apply the DEA-based Malmquist index to evaluate the changes in relative efficiency and the total factor productivity of coal-fired electricity generation of 30 Chinese administrative regions from 1999 to 2007. Factors considered in the model include fuel consumption, labor, capital, sulphur dioxide emissions, and electricity generated. The authors reveal that the east provinces were relatively and technically more efficient, whereas the west provinces had the highest growth rate in the period studied. Ioannis E. Tsolas applies the DEA approach to assess the performance of Greek fossil fuel-fired power stations taking undesirable outputs into consideration, such as carbon dioxide and sulphur dioxide emissions. In addition, the bootstrapping approach is deployed to address the uncertainty surrounding DEA point estimates, and provide bias-corrected estimations and confidence intervals for the point estimates. The author revealed from the sample that the non-lignite-fired stations are on an average more efficient than the lignite-fired stations. Maethee Mekaroonreung and Andrew L. Johnson compare the relative performance of three DEA-based measures, which estimate production frontiers and evaluate the relative efficiency of 113 US petroleum refineries while considering undesirable outputs. Three inputs (capital, energy consumption, and crude oil consumption), two desirable outputs (gasoline and distillate generation), and an undesirable output (toxic release) are considered in the DEA models. The authors discover that refineries in the Rocky Mountain region performed the best, and about 60 percent of oil refineries in the sample could improve their efficiencies further. H. Omrani, A. Azadeh, S. F. Ghaderi, and S. Abdollahzadeh presented an integrated approach, combining DEA, corrected ordinary least squares (COLS), and principal component analysis (PCA) methods, to calculate the relative efficiency scores of 26 Iranian electricity distribution units from 2003 to 2006. Specifically, both DEA and COLS are used to check three internal consistency conditions, whereas PCA is used to verify and validate the final ranking results of either DEA (consistency) or DEA-COLS (non-consistency). Three inputs (network length, transformer capacity, and number of employees) and two outputs (number of customers and total electricity sales) are considered in the model. Virendra Ajodhia applied three DEA-based models to evaluate the relative performance of 20 electricity distribution firms from the UK and the Netherlands. The first model is a traditional DEA model for analyzing cost-only efficiency. The second model includes (inverse) quality by modelling total customer minutes lost as an input data. The third model is based on the idea of using total social costs, including the firm’s private costs and the interruption costs incurred by consumers, as an input. Both energy-delivered and number of consumers are treated as the outputs in the models. After five DEA papers, Stelios Grafakos, Alexandros Flamos, Vlasis Oikonomou, and D. Zevgolis presented a multiple criteria analysis weighting approach to evaluate the energy and climate policy. The proposed approach is akin to the analytic hierarchy process, which consists of pairwise comparisons, consistency verification, and criteria prioritization. In the approach, stakeholders and experts in the energy policy field are incorporated in the evaluation process by providing an interactive mean with verbal, numerical, and visual representation of their preferences. A total of 14 evaluation criteria were considered and classified into four objectives, such as climate change mitigation, energy effectiveness, socioeconomic, and competitiveness and technology. Finally, Borge Hess applied the stochastic frontier analysis approach to analyze the impact of various business strategies, including acquisition, holding structures, and joint ventures, on a firm’s efficiency within a sample of 47 natural gas transmission pipelines in the USA from 1996 to 2005. The author finds that there were no significant changes in the firm’s efficiency by an acquisition, and there is a weak evidence for efficiency improvements caused by the new shareholder. Besides, the author discovers that parent companies appear not to influence a subsidiary’s efficiency positively. In addition, the analysis shows a negative impact of a joint venture on technical efficiency of the pipeline company. To conclude, we are grateful to all the authors for their contribution, and all the reviewers for their constructive comments, which made this special issue possible. We hope that this issue would contribute significantly to performance improvement of the energy sector.

European Union development:challenges and strategies

The publication represents a multi-dimensional and multi-faced, in depth assessment of the most significant determinants of the EU development as a political, economic and legal entity, in its format emerging from the Lisbon Treaty. The book represents an important contribution to our understanding of the most profound issues in the recent process of EU integration, including the issue of maintaining its cohesion and coherence under the stress of global challanges faced also by the European Union. Autohors formulated worthwhile conclusions of high value not only for academics but also for political decision-makers, which gives the book same competitive edge over its more theoretical and, hence, less practice-oriented, knack. The arumentation presented in the book would not be left without a reaction of the academic and/or professional circles. I take it almost for granted that the overall setting of the argumentation presented in it, as well as specific points made in its various chapters would find their adequate resonance in a high profile discussion likely to emerge after the book would have been published.

Risk-based model aids selection of pipeline inspection, maintenance strategies

The existing method of pipeline monitoring, which requires an entire pipeline to be inspected periodically, wastes time and is expensive. A risk-based model that reduces the amount of time spent on inspection has been developed. This model not only reduces the cost of maintaining petroleum pipelines, but also suggests an efficient design and operation philosophy, construction method and logical insurance plans.The risk-based model uses analytic hierarchy process, a multiple attribute decision-making technique, to identify factors that influence failure on specific segments and analyze their effects by determining the probabilities of risk factors. The severity of failure is determined through consequence analysis, which establishes the effect of a failure in terms of cost caused by each risk factor and determines the cumulative effect of failure through probability analysis.

An efficient knowledge transfer solution to a novel SMDP formalization of a broker's decision problem

This paper introduces a new technique for optimizing the trading strategy of brokers that autonomously trade in re- tail and wholesale markets. Simultaneous optimization of re- tail and wholesale strategies has been considered by existing studies as intractable. Therefore, each of these strategies is optimized separately and their interdependence is generally ignored, with resulting broker agents not aiming for a glob- ally optimal retail and wholesale strategy. In this paper, we propose a novel formalization, based on a semi-Markov deci- sion process (SMDP), which globally and simultaneously op- timizes retail and wholesale strategies. The SMDP is solved using hierarchical reinforcement learning (HRL) in multi- agent environments. To address the curse of dimensionality, which arises when applying SMDP and HRL to complex de- cision problems, we propose an ecient knowledge transfer approach. This enables the reuse of learned trading skills in order to speed up the learning in new markets, at the same time as making the broker transportable across market envi- ronments. The proposed SMDP-broker has been thoroughly evaluated in two well-established multi-agent simulation en- vironments within the Trading Agent Competition (TAC) community. Analysis of controlled experiments shows that this broker can outperform the top TAC-brokers. More- over, our broker is able to perform well in a wide range of environments by re-using knowledge acquired in previously experienced settings.

Research on rework strategies for reconfigurable manufacturing system considering mission reliability

Rework strategies that involve different checking points as well as rework times can be applied into reconfigurable manufacturing system (RMS) with certain constraints, and effective rework strategy can significantly improve the mission reliability of manufacturing process. The mission reliability of process is a measurement of production ability of RMS, which serves as an integrated performance indicator of the production process under specified technical constraints, including time, cost and quality. To quantitatively characterize the mission reliability and basic reliability of RMS under different rework strategies, rework model of RMS was established based on the method of Logistic regression. Firstly, the functional relationship between capability and work load of manufacturing process was studied through statistically analyzing a large number of historical data obtained in actual machining processes. Secondly, the output, mission reliability and unit cost in different rework paths were calculated and taken as the decision variables based on different input quantities and the rework model mentioned above. Thirdly, optimal rework strategies for different input quantities were determined by calculating the weighted decision values and analyzing advantages and disadvantages of each rework strategy. At last, case application were demonstrated to prove the efficiency of the proposed method.

Effective decision making and problem solving in projects

Processing information and forming opinions pose special challenges when attempting to effectively manage the new or complex tasks that typically arise in projects. Based on research in organizational and social psychology, we introduce mechanisms and strategies for collective information processing which are important for forming opinions and handling information in projects.