Chain-store pricing and the structure of retail markets

This paper investigates competition between chain-stores and independents in the UK opticians' industry, using the relationship between the number of outlets present in a local market and the market size. Chain-stores are shown to have a significant effect on local market competition. In addition, the empirical approach is extended to allow inferences on the nature and extent of product differentiation. The results are broadly consistent with a model of vertical product differentiation in which chain-stores adopt national pricing strategies. The evidence suggests that the nature of competition between independent retailers depends on whether a chain-store is present.

Modelling data and voice traffic over IP networks using continuous-time Markov models

Common approaches to IP-traffic modelling have featured the use of stochastic models, based on the Markov property, which can be classified into black box and white box models based on the approach used for modelling traffic. White box models, are simple to understand, transparent and have a physical meaning attributed to each of the associated parameters. To exploit this key advantage, this thesis explores the use of simple classic continuous-time Markov models based on a white box approach, to model, not only the network traffic statistics but also the source behaviour with respect to the network and application. The thesis is divided into two parts: The first part focuses on the use of simple Markov and Semi-Markov traffic models, starting from the simplest two-state model moving upwards to n-state models with Poisson and non-Poisson statistics. The thesis then introduces the convenient to use, mathematically derived, Gaussian Markov models which are used to model the measured network IP traffic statistics. As one of the most significant contributions, the thesis establishes the significance of the second-order density statistics as it reveals that, in contrast to first-order density, they carry much more unique information on traffic sources and behaviour. The thesis then exploits the use of Gaussian Markov models to model these unique features and finally shows how the use of simple classic Markov models coupled with use of second-order density statistics provides an excellent tool for capturing maximum traffic detail, which in itself is the essence of good traffic modelling. The second part of the thesis, studies the ON-OFF characteristics of VoIP traffic with reference to accurate measurements of the ON and OFF periods, made from a large multi-lingual database of over 100 hours worth of VoIP call recordings. The impact of the language, prosodic structure and speech rate of the speaker on the statistics of the ON-OFF periods is analysed and relevant conclusions are presented. Finally, an ON-OFF VoIP source model with log-normal transitions is contributed as an ideal candidate to model VoIP traffic and the results of this model are compared with those of previously published work.

A probabilistic model for trust and reputation

This paper concerns the problem of agent trust in an electronic market place. We maintain that agent trust involves making decisions under uncertainty and therefore the phenomenon should be modelled probabilistically. We therefore propose a probabilistic framework that models agent interactions as a Hidden Markov Model (HMM). The observations of the HMM are the interaction outcomes and the hidden state is the underlying probability of a good outcome. The task of deciding whether to interact with another agent reduces to probabilistic inference of the current state of that agent given all previous interaction outcomes. The model is extended to include a probabilistic reputation system which involves agents gathering opinions about other agents and fusing them with their own beliefs. Our system is fully probabilistic and hence delivers the following improvements with respect to previous work: (a) the model assumptions are faithfully translated into algorithms; our system is optimal under those assumptions, (b) It can account for agents whose behaviour is not static with time (c) it can estimate the rate with which an agent's behaviour changes. The system is shown to significantly outperform previous state-of-the-art methods in several numerical experiments. Copyright © 2010, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

Using min-sum loopy belief propagation for decentralised supply chain formation

Modern business trends such as agile manufacturing and virtual corporations require high levels of flexibility and responsiveness to consumer demand, and require the ability to quickly and efficiently select trading partners. Automated computational techniques for supply chain formation have the potential to provide significant advantages in terms of speed and efficiency over the traditional manual approach to partner selection. Automated supply chain formation is the process of determining the participants within a supply chain and the terms of the exchanges made between these participants. In this thesis we present an automated technique for supply chain formation based upon the min-sum loopy belief propagation algorithm (LBP). LBP is a decentralised and distributed message-passing algorithm which allows participants to share their beliefs about the optimal structure of the supply chain based upon their costs, capabilities and requirements. We propose a novel framework for the application of LBP to the existing state-of-the-art case of the decentralised supply chain formation problem, and extend this framework to allow for application to further novel and established problem cases. Specifically, the contributions made by this thesis are: • A novel framework to allow for the application of LBP to the decentralised supply chain formation scenario investigated using the current state-of-the-art approach. Our experimental analysis indicates that LBP is able to match or outperform this approach for the vast majority of problem instances tested. • A new solution goal for supply chain formation in which economically motivated producers aim to maximise their profits by intelligently altering their profit margins. We propose a rational pricing strategy that allows producers to earn significantly greater profits than a comparable LBP-based profitmaking approach. • An LBP-based framework which allows the algorithm to be used to solve supply chain formation problems in which goods are exchanged in multiple units, a first for a fully decentralised technique. As well as multiple-unit exchanges, we also model in this scenario realistic constraints such as factory capacities and input-to-output ratios. LBP continues to be able to match or outperform an extended version of the existing state-of-the-art approach in this scenario. • Introduction of a dynamic supply chain formation scenario in which participants are able to alter their properties or to enter or leave the process at any time. Our results suggest that LBP is able to deal easily with individual occurences of these alterations and that performance degrades gracefully when they occur in larger numbers.

Discriminative training of the hidden vector state model for semantic parsing

In this paper, we discuss how discriminative training can be applied to the hidden vector state (HVS) model in different task domains. The HVS model is a discrete hidden Markov model (HMM) in which each HMM state represents the state of a push-down automaton with a finite stack size. In previous applications, maximum-likelihood estimation (MLE) is used to derive the parameters of the HVS model. However, MLE makes a number of assumptions and unfortunately some of these assumptions do not hold. Discriminative training, without making such assumptions, can improve the performance of the HVS model by discriminating the correct hypothesis from the competing hypotheses. Experiments have been conducted in two domains: the travel domain for the semantic parsing task using the DARPA Communicator data and the Air Travel Information Services (ATIS) data and the bioinformatics domain for the information extraction task using the GENIA corpus. The results demonstrate modest improvements of the performance of the HVS model using discriminative training. In the travel domain, discriminative training of the HVS model gives a relative error reduction rate of 31 percent in F-measure when compared with MLE on the DARPA Communicator data and 9 percent on the ATIS data. In the bioinformatics domain, a relative error reduction rate of 4 percent in F-measure is achieved on the GENIA corpus.

A novel framework of training hidden Markov support vector machines from lightly-annotated data

Natural language understanding (NLU) aims to map sentences to their semantic mean representations. Statistical approaches to NLU normally require fully-annotated training data where each sentence is paired with its word-level semantic annotations. In this paper, we propose a novel learning framework which trains the Hidden Markov Support Vector Machines (HM-SVMs) without the use of expensive fully-annotated data. In particular, our learning approach takes as input a training set of sentences labeled with abstract semantic annotations encoding underlying embedded structural relations and automatically induces derivation rules that map sentences to their semantic meaning representations. The proposed approach has been tested on the DARPA Communicator Data and achieved 93.18% in F-measure, which outperforms the previously proposed approaches of training the hidden vector state model or conditional random fields from unaligned data, with a relative error reduction rate of 43.3% and 10.6% being achieved.

A firm's capability to calibrate supply chain knowledge-antecedents and consequences

This paper proposes a conceptual model for a firm's capability to calibrate supply chain knowledge (CCK). Knowledge calibration is achieved when there is a match between managers' ex ante confidence in the accuracy of held knowledge and the ex post accuracy of that knowledge. Knowledge calibration is closely related to knowledge utility or willingness to use the available ex ante knowledge: a manager uses the ex ante knowledge if he/she is confident in the accuracy of that knowledge, and does not use it or uses it with reservation, when the confidence is low. Thus, knowledge calibration attained through the firm's CCK enables managers to deal with incomplete and uncertain information and enhances quality of decisions. In the supply chain context, although demand- and supply-related knowledge is available, supply chain inefficiencies, such as the bullwhip effect, remain. These issues may be caused not by a lack of knowledge but by a firm's lack of capability to sense potential disagreement between knowledge accuracy and confidence. Therefore, this paper contributes to the understanding of supply chain knowledge utilization by defining CCK and identifying a set of antecedents and consequences of CCK in the supply chain context.

Theoretical perspectives in purchasing and supply chain management:an analysis of the literature

Purpose – This paper attempts to seek answers to four questions. Two of these questions have been borrowed (but adapted) from the work of Defee et al.: RQ1. To what extent is theory used in purchasing and supply chain management (P&SCM) research? RQ2. What are the prevalent theories to be found in P&SCM research? Following on from these questions an additional question is posed: RQ3. Are theory-based papers more highly cited than papers with no theoretical foundation? Finally, drawing on the work of Harland et al., the authors have added a fourth question: RQ4. To what extent does P&SCM meet the tests of coherence, breadth and depth, and quality necessary to make it a scientific discipline? Design/methodology/approach – A systematic literature review was conducted in accordance with the model outlined by Tranfield et al. for three journals within the field of “purchasing and supply chain management”. In total 1,113 articles were reviewed. In addition a citation analysis was completed covering 806 articles in total. Findings – The headline features from the results suggest that nearly a decade-and-a-half on from its development, the field still lacks coherence. There is the absence of theory in much of the work and although theory-based articles achieved on average a higher number of citations than non-theoretical papers, there is no obvious contender as an emergent paradigm for the discipline. Furthermore, it is evident that P&SCM does not meet Fabian's test necessary to make it a scientific discipline and is still some way from being a normal science. Research limitations/implications – This study would have benefited from the analysis of further journals, however the analysis of 1,113 articles from three leading journals in the field of P&SCM was deemed sufficient in scope. In addition, a further significant line of enquiry to follow is the rigour vs relevance debate. Practical implications – This article is of interest to both an academic and practitioner audience as it highlights the use theories in P&SCM. Furthermore, this article raises a number of important questions. Should research in this area draw more heavily on theory and if so which theories are appropriate? Social implications – The broader social implications relate to the discussion of how a scientific discipline develops and builds on the work of Fabian and Amundson. Originality/value – The data set for this study is significant and builds on a number of previous literature reviews. This review is both greater in scope than previous reviews and is broader in its subject focus. In addition, the citation analysis (not previously conducted in any of the reviews) and statistical test highlights that theory-based articles are more highly cited than non-theoretically based papers. This could indicate that researchers are attempting to build on one another's work.

Selection of simulation tools for improving supply chain performance

Simulation is an effective method for improving supply chain performance. However, there is limited advice available to assist practitioners in selecting the most appropriate method for a given problem. Much of the advice that does exist relies on custom and practice rather than a rigorous conceptual or empirical analysis. An analysis of the different modelling techniques applied in the supply chain domain was conducted, and the three main approaches to simulation used were identified; these are System Dynamics (SD), Discrete Event Simulation (DES) and Agent Based Modelling (ABM). This research has examined these approaches in two stages. Firstly, a first principles analysis was carried out in order to challenge the received wisdom about their strengths and weaknesses and a series of propositions were developed from this initial analysis. The second stage was to use the case study approach to test these propositions and to provide further empirical evidence to support their comparison. The contributions of this research are both in terms of knowledge and practice. In terms of knowledge, this research is the first holistic cross paradigm comparison of the three main approaches in the supply chain domain. Case studies have involved building ‘back to back’ models of the same supply chain problem using SD and a discrete approach (either DES or ABM). This has led to contributions concerning the limitations of applying SD to operational problem types. SD has also been found to have risks when applied to strategic and policy problems. Discrete methods have been found to have potential for exploring strategic problem types. It has been found that discrete simulation methods can model material and information feedback successfully. Further insights have been gained into the relationship between modelling purpose and modelling approach. In terms of practice, the findings have been summarised in the form of a framework linking modelling purpose, problem characteristics and simulation approach.

Language independent on-off voice over IP source model with lognormal transitions

The recent explosive growth of voice over IP (VoIP) solutions calls for accurate modelling of VoIP traffic. This study presents measurements of ON and OFF periods of VoIP activity from a significantly large database of VoIP call recordings consisting of native speakers speaking in some of the world's most widely spoken languages. The impact of the languages and the varying dynamics of caller interaction on the ON and OFF period statistics are assessed. It is observed that speaker interactions dominate over language dependence which makes monologue-based data unreliable for traffic modelling. The authors derive a semi-Markov model which accurately reproduces the statistics of composite dialogue measurements. © The Institution of Engineering and Technology 2013.

Supply chain optimisation of pyrolysis plant deployment using goal programming

This paper presents a goal programming model to optimise the deployment of pyrolysis plants in Punjab, India. Punjab has an abundance of waste straw and pyrolysis can convert this waste into alternative bio-fuels, which will facilitate the provision of valuable energy services and reduce open field burning. A goal programming model is outlined and demonstrated in two case study applications: small scale operations in villages and large scale deployment across Punjab's districts. To design the supply chain, optimal decisions for location, size and number of plants, downstream energy applications and feedstocks processed are simultaneously made based on stakeholder requirements for capital cost, payback period and production cost of bio-oil and electricity. The model comprises quantitative data obtained from primary research and qualitative data gathered from farmers and potential investors. The Punjab district of Fatehgarh Sahib is found to be the ideal location to initially utilise pyrolysis technology. We conclude that goal programming is an improved method over more conventional methods used in the literature for project planning in the field of bio-energy. The model and findings developed from this study will be particularly valuable to investors, plant developers and municipalities interested in waste to energy in India and elsewhere. © 2014 Elsevier Ltd. All rights reserved.

Reconfiguration of supply chain network:an ISM-based roadmap to performance

Purpose: Short product life cycle and/or mass customization necessitate reconfiguration of operational enablers of supply chain (SC) from time to time in order to harness high levels of performance. The purpose of this paper is to identify the key operational enablers under stochastic environment on which practitioner should focus while reconfiguring a SC network. Design/methodology/approach: The paper used interpretive structural modeling (ISM) approach that presents a hierarchy-based model and the mutual relationships among the enablers. The contextual relationship needed for developing structural self-interaction matrix (SSIM) among various enablers is realized by conducting experiments through simulation of a hypothetical SC network. Findings: The research identifies various operational enablers having a high driving power towards assumed performance measures. In this regard, these enablers require maximum attention and of strategic importance while reconfiguring SC. Practical implications: ISM provides a useful tool to the SC managers to strategically adopt and focus on the key enablers which have comparatively greater potential in enhancing the SC performance under given operational settings. Originality/value: The present research realizes the importance of SC flexibility under the premise of reconfiguration of the operational units in order to harness high value of SC performance. Given the resulting digraph through ISM, the decision maker can focus the key enablers for effective reconfiguration. The study is one of the first efforts that develop contextual relations among operational enablers for SSIM matrix through integration of discrete event simulation to ISM. © Emerald Group Publishing Limited.

A fuzzy AHP based integer linear programming model for the multi-criteria transshipment problem

Purpose – The purpose of this research is to develop a holistic approach to maximize the customer service level while minimizing the logistics cost by using an integrated multiple criteria decision making (MCDM) method for the contemporary transshipment problem. Unlike the prevalent optimization techniques, this paper proposes an integrated approach which considers both quantitative and qualitative factors in order to maximize the benefits of service deliverers and customers under uncertain environments. Design/methodology/approach – This paper proposes a fuzzy-based integer linear programming model, based on the existing literature and validated with an example case. The model integrates the developed fuzzy modification of the analytic hierarchy process (FAHP), and solves the multi-criteria transshipment problem. Findings – This paper provides several novel insights about how to transform a company from a cost-based model to a service-dominated model by using an integrated MCDM method. It suggests that the contemporary customer-driven supply chain remains and increases its competitiveness from two aspects: optimizing the cost and providing the best service simultaneously. Research limitations/implications – This research used one illustrative industry case to exemplify the developed method. Considering the generalization of the research findings and the complexity of the transshipment service network, more cases across multiple industries are necessary to further enhance the validity of the research output. Practical implications – The paper includes implications for the evaluation and selection of transshipment service suppliers, the construction of optimal transshipment network as well as managing the network. Originality/value – The major advantages of this generic approach are that both quantitative and qualitative factors under fuzzy environment are considered simultaneously and also the viewpoints of service deliverers and customers are focused. Therefore, it is believed that it is useful and applicable for the transshipment service network design.

Supply chain learning needs:towards a port community perspective

The traditional role of ports in the wider supply chain context is currently being subject to a process of radical review. In broad terms, the traditional model is being replaced by a model which focuses on higher value and more knowledge intensive activities. This trend requires a change in the way in which new knowledge and skills are developed by staff in companies of all kinds within port communities. Traditional models need to be re-evaluated to reflect the increasing importance of knowledge and skills acquisition, particularly in relation to the supply chain management (SCM) concept and the evolving role of information and communications technology (ICT) in improving supply chain capability. This paper describes the case of NITL’s Foundation Certificate Programme (FCP) learning programme with specific reference to its use in addressing some of current shortcomings related to supply chain knowledge and skills in port communities. The FCP rationale is based on the need to move from traditional approaches of supply chain organisation where the various links in the chain were measured and managed in isolation from each other and thus tended to operate at cross purposes, towards more cooperative and integrated approaches.

A conceptual framework for supply chain collaboration:empirical evidence from the agri-food industry

Purpose - The purpose of this paper is to analyse the concept of supply chain collaboration and to provide an overall framework that can be used as a conceptual landmark for further empirical research. In addition, the concept is explored in the context of agri-food industry and particularities are identified. Finally, the paper submits empirical evidence from an exploratory case study in the agri-food industry, at the grower-processor interface, and information regarding the way the concept is actually applied in small medium-sized enterprises (SMEs) is presented. Design/methodology/approach - The paper employed case study research by conducting in-depth interviews in the two companies. Findings - Supply chain collaboration concept is of significant importance for the agri-food industry however, some constraints arise due to the nature of industry's products, and the specific structure of the sector. Subsequently, collaboration in the supply chain is often limited to operational issues and to logistics-related activities. Research limitations/implications - Research is limited to a single case study and further qualitative testing of the conceptual model is needed in order to adjust the model before large scale testing. Practical implications - Case study findings may be transferable to other similar dual relationships at the grower-processor interface. Weaker parts in asymmetric relationships have opportunities to improve their position, altering the dependence balance, by achieving product/process excellence. Originality/value - The paper provides evidence regarding the applicability of the supply chain collaboration concept in the agri-food industry. It takes into consideration not relationships between big multinational companies, but SMEs. © Emerald Group Publishing Limited.