Integrated analytic hierarchy process and its applications:a literature review

Due to its wide applicability and ease of use, the analytic hierarchy process (AHP) has been studied extensively for the last 20 years. Recently, it is observed that the focus has been confined to the applications of the integrated AHPs rather than the stand-alone AHP. The five tools that commonly combined with the AHP include mathematical programming, quality function deployment (QFD), meta-heuristics, SWOT analysis, and data envelopment analysis (DEA). This paper reviews the literature of the applications of the integrated AHPs. Related articles appearing in the international journals from 1997 to 2006 are gathered and analyzed so that the following three questions can be answered: (i) which type of the integrated AHPs was paid most attention to? (ii) which area the integrated AHPs were prevalently applied to? (iii) is there any inadequacy of the approaches? Based on the inadequacy, if any, some improvements and possible future work are recommended. This research not only provides evidence that the integrated AHPs are better than the stand-alone AHP, but also aids the researchers and decision makers in applying the integrated AHPs effectively.

Combining analytic hierarchy process and goal programming for logistics distribution network design

Logistics distribution network design is one of the major decision problems arising in contemporary supply chain management. The decision involves many quantitative and qualitative factors that may be conflicting in nature. This paper applies an integrated multiple criteria decision making approach to design an optimal distribution network. In the approach, the analytic hierarchy process (AHP) is used first to determine the relative importance weightings or priorities of alternative warehouses with respect to both deliverer oriented and customer oriented criteria. Then, the goal programming (GP) model incorporating the constraints of system, resource, and AHP priority is formulated to select the best set of warehouses without exceeding the limited available resources. In this paper, two commercial packages are used: Expert Choice for determining the AHP priorities of the warehouses, and LINDO for solving the GP model. © 2007 IEEE.

Development of a co-operative distributed process mining system for quality assurance

In this paper, a co-operative distributed process mining system (CDPMS) is developed to streamline the workflow along the supply chain in order to offer shorter delivery times, more flexibility and higher customer satisfaction with learning ability. The proposed system is equipped with the ‘distributed process mining’ feature which is used to discover the hidden relationships among each working decision in distributed manner. This method incorporates the concept of data mining and knowledge refinement into decision making process for ensuring ‘doing the right things’ within the workflow. An example of implementation is given, based on the case of slider manufacturer.

Design and development of an agent-based information system for process improvement

To meet changing needs of customers and to survive in the increasingly globalised and competitive environment, it is necessary for companies to equip themselves with intelligent tools, thereby enabling managerial levels to use the tactical decision in a better way. However, the implementation of an intelligent system is always a challenge in Small- and Medium-sized Enterprises (SMEs). Therefore, a new and simple approach with 'process rethinking' ability is proposed to generate ongoing process improvements over time. In this paper, a roadmap of the development of an agent-based information system is described. A case example has also been provided to show how the system can assist non-specialists, for example, managers and engineers to make right decisions for a continual process improvement. Copyright © 2006 Inderscience Enterprises Ltd.

An integrated multiple criteria decision making approach for resource allocation in higher education

Resource allocation is one of the major decision problems arising in higher education. Resources must be allocated optimally in such a way that the performance of universities can be improved. This paper applies an integrated multiple criteria decision making approach to the resource allocation problem. In the approach, the Analytic Hierarchy Process (AHP) is first used to determine the priority or relative importance of proposed projects with respect to the goals of the universities. Then, the Goal Programming (GP) model incorporating the constraints of AHP priority, system, and resource is formulated for selecting the best set of projects without exceeding the limited available resources. The projects include 'hardware' (tangible university's infrastructures), and 'software' (intangible effects that can be beneficial to the university, its members, and its students). In this paper, two commercial packages are used: Expert Choice for determining the AHP priority ranking of the projects, and LINDO for solving the GP model. Copyright © 2007 Inderscience Enterprises Ltd.

Purchase decision-making in fair trade and the ethical 'gap':'is there a fair trade twix?'

Children are increasingly being recognised as a significant force in the retail market place, as primary consumers, influencers of others, and as future customers. This paper adds to the literature on children as consumers by exploring their attitudinal responses to a specific group of products: Fair Trade lines. There has been no research to date that has specifically addressed children as consumers of Fair Trade or the ethical purchase decision-making process in this area. The methodological approach taken here is an essentially interpretive and naturalistic analysis of two focus groups of school children. The analysis found that there is an urgent need to develop meaningful Fair Trade brands that combine strong brand knowledge and positive brand images to bridge the ethical purchase gap between the formation of clear ethical attitudes and actual ethical purchase behaviour. Such an approach would both capture more of the children’s primary market and influence future purchase behaviour. It is argued that Fair Trade actors should coordinate new marketing communications campaigns that build brand knowledge structures holistically around the Fair Trade process and that extend beyond merely raising consumer awareness.

A comparison of variational and Markov chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a Hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while conditional variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother. © 2008 Springer Science + Business Media LLC.

Gaussian process approximations of stochastic differential equations

Stochastic differential equations arise naturally in a range of contexts, from financial to environmental modeling. Current solution methods are limited in their representation of the posterior process in the presence of data. In this work, we present a novel Gaussian process approximation to the posterior measure over paths for a general class of stochastic differential equations in the presence of observations. The method is applied to two simple problems: the Ornstein-Uhlenbeck process, of which the exact solution is known and can be compared to, and the double-well system, for which standard approaches such as the ensemble Kalman smoother fail to provide a satisfactory result. Experiments show that our variational approximation is viable and that the results are very promising as the variational approximate solution outperforms standard Gaussian process regression for non-Gaussian Markov processes.

Examining the strategy development process through the lens of complex adaptive systems theory

The development of strategy remains a debate for academics and a concern for practitioners. Published research has focused on producing models for strategy development and on studying how strategy is developed in organisations. The Operational Research literature has highlighted the importance of considering complexity within strategic decision making; but little has been done to link strategy development with complexity theories, despite organisations and organisational environments becoming increasingly more complex. We review the dominant streams of strategy development and complexity theories. Our theoretical investigation results in the first conceptual framework which links an established Strategic Operational Research model, the Strategy Development Process model, with complexity via Complex Adaptive Systems theory. We present preliminary findings from the use of this conceptual framework applied to a longitudinal, in-depth case study, to demonstrate the advantages of using this integrated conceptual model. Our research shows that the conceptual model proposed provides rich data and allows for a more holistic examination of the strategy development process. © 2012 Operational Research Society Ltd. All rights reserved.

Does the balanced scorecard make a difference to the strategy development process?

A great number of strategy tools are being taught in strategic management modules. These tools are available to managers for use in facilitating strategic decision-making and enhancing the strategy development process in their organisations. A number of studies have been published examining which are the most popular tools; however there is little empirical evidence on how their utilisation influences the strategy process. This paper is based on a large scale international survey on the strategy development process, and seeks to examine the impact of a particular strategy tool, the Balanced Scorecard, upon the strategy process. The Balanced Scorecard is one of the most popular strategy tools whose use has evolved since its introduction in the 1990’s. Recently, it has been suggested that as a strategy tool, Balanced Scorecard can influence all elements of the strategy process. The results of this study indicate that although there are significant differences in some elements of the strategy process between the organisations that have implemented the Balanced Scorecard and those that have not, the impact is not comprehensive.

The innovation decision:an economic analysis

Studies of the determinants and effects of innovation commonly make an assumption about the way in which firms make the decision to innovate, but rarely test this assumption. Using a panel of Irish manufacturing firms we test the performance of two alternative models of the innovation decision, and find that a two-stage model (the firm decides whether to innovate, then whether to perform product only, process only or both) outperforms a one-stage, simultaneous model. We also find that external knowledge sourcing affects the innovation decision and the type of innovation undertaken in a way not previously recognised in the literature. © 2007 Elsevier Ltd. All rights reserved.

Variational Markov Chain Monte Carlo for Bayesian smoothing of non-linear niffusions

In this paper we develop set of novel Markov chain Monte Carlo algorithms for Bayesian smoothing of partially observed non-linear diffusion processes. The sampling algorithms developed herein use a deterministic approximation to the posterior distribution over paths as the proposal distribution for a mixture of an independence and a random walk sampler. The approximating distribution is sampled by simulating an optimized time-dependent linear diffusion process derived from the recently developed variational Gaussian process approximation method. Flexible blocking strategies are introduced to further improve mixing, and thus the efficiency, of the sampling algorithms. The algorithms are tested on two diffusion processes: one with double-well potential drift and another with SINE drift. The new algorithm's accuracy and efficiency is compared with state-of-the-art hybrid Monte Carlo based path sampling. It is shown that in practical, finite sample, applications the algorithm is accurate except in the presence of large observation errors and low observation densities, which lead to a multi-modal structure in the posterior distribution over paths. More importantly, the variational approximation assisted sampling algorithm outperforms hybrid Monte Carlo in terms of computational efficiency, except when the diffusion process is densely observed with small errors in which case both algorithms are equally efficient.

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.

Strategy making process, its content and context in small professional football clubs

This thesis examines the phenomenon of strategy. Making as practised by small professional football clubs. The study was undertaken because football clubs were perceived to have problems with strategy-making and because it was believed that the specific circumstances of football clubs could be outside the range of views covered by conventional views of strategy-making. The characteristics of the club environment are its uncertainty and unpredictability, simultaneous competition and co--operation, strong regulations, and a not-for-profit orientation. Small clubs in particular face a constant struggle for financial viability and survival, due in part to split business and playing objectives. The study was designed to establish the extent and nature of the difficulties clubs experience with a view to preparing the way for creating practical guidance on ways to overcome them. Clearly, in order to survive in the long term, small professional football clubs require very effective strategic decisions. This study has addressed this issue by inquiring into the nature of strategy making for these organisations with the objective to establish the general direction in which the football clubs in question should be moving. As a result, the main research question to guide this investigation was determined as: Why do small professional football clubs have difficulties making strategies. The investigation was based on an analysis the concept of strategy and its elements, the strategic vision and objectives, the process by which strategic action comes about, the strategic action itself, and the context within which this action occurs. Data has been collected, analysed and interpreted in relation to each of these elements. Together with a wide variety of published material, 20 small football clubs have been sampled and personal interviews were conducted with board members of those clubs. The findings indicate that small football clubs do indeed experience considerable difficulties in making strategies, the reasons for which lie both in the characteristics of their competitive environment and their approaches to strategy-making. The competitive environment is characterised by a cartel-like structure with a high degree of regulation, high levels of uncertainty, little control over the core product or the production process, short-term business cycles and a close geographical link between a club with its local market. The management of clubs is characterised by the need to balance conflicting sporting and business objectives. Formal planning techniques are of little use in the small football club context as decision-making processes have a strong political character and the development of novel strategies is hindered by a strong conservative, industry paradigm and a lack of financial and managerial resources. It is concluded that there is no simple advice to be given to clubs, as they must re-examine the relationship between their playing and business objectives to create a unified and workable approach.

Process orientation and information technology:customer engagement at Xerox (UK) Ltd

Xerox Customer Engagement activity is informed by the "Go To Market" strategy, and "Intelligent Coverage" sales philosophy. The realisation of this philosophy necessitates a sophisticated level of Market Understanding, and the effective integration of the direct channels of Customer Engagement. Sophisticated Market Understanding requires the mapping and coding of the entire UK market at the DMU (Decision Making Unit) level, which in turn enables the creation of tailored coverage prescriptions. Effective Channel Integration is made possible by the organisation of Customer Engagement work according to a single, process defined structure: the Selling Process. Organising by process facilitates the discipline of Task Substitution, which leads logically to creation of Hybrid Selling models. Productive Customer Engagement requires Selling Process specialisation by industry sector, customer segment and product group. The research shows that Xerox's Market Database (MDB) plays a central role in delivering the Go To Market strategic aims. It is a tool for knowledge based selling, enables productive SFA (Sales Force Automation) and, in sum, is critical to the efficient and effective deployment of Customer Engagement resources. Intelligent Coverage is not possible without the MDB. Analysis of the case evidence has resulted in the definition of 60 idiographic statements. These statements are about how Xerox organise and manage three direct channels of Customer Engagement: Face to Face, Telebusiness and Ebusiness. Xerox is shown to employ a process-oriented, IT-enabled, holistic approach to Customer Engagement productivity. The significance of the research is that it represents a detailed (perhaps unequalled) level of rich description of the interplay between IT and a holistic, process-oriented management philosophy.