52 research outputs found

    Sustainable supply chain management towards disruption and organizational ambidexterity:A data driven analysis

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    Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts’ evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation

    An Integrated Retail Supply Chain Risk Management Framework: A System Thinking Approach

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    It is often taken for granted that the right products will be available to buy in retail outlets seven days a week, 52 weeks a year. Consumer perception is that of a simple service requirement, but the reality is a complex, time sensitive system - the retail supply chain (RSC). Due to short product life-cycles with uncertain supply and demand behaviour, the RSC faces many challenges and is very vulnerable to disruptions. In addition, external risk events such as BREXIT, extreme weather, the financial crisis, and terror attacks mean there is a need for effective RSC risk management (RSCRM) processes within organisations. Literature shows that although there is an increasing amount of research in RSCRM, it is highly theoretical with limited empirical evidence or applied methodologies. With an active enthusiasm coming from industry practitioners for RSCRM methodologies and support solutions, the RSCRM research community have acknowledged that the main issue for future research is not tools and techniques, but collaborative RSC system wide implementation. The implementation of a cross-organisational initiative such as RSCRM is a very complex task that requires real-world frameworks for real-world practitioners. Therefore, this research study attempts to explore the business requirements for developing a three-stage integrated RSCRM framework that will encourage extended RSC collaboration. While focusing on the practitioner requirements of RSCRM projects and inspired by the laws of Thermodynamics and the philosophy of System Thinking, in stage one a conceptual reference model, The �6 Coefficient, was developed building on the formative work of supply chain excellence and business process management. The �6 Coefficient reference model has been intricately designed to bridge the theoretical gap between practitioner and researcher with the aim of ensuring practitioner confidence in partaking in a complex business process project. Stage two focused on a need for a standardised vocabulary, and through the SCOR11 reference guide, acts as a calibration point for the integrated framework, ensuring easy transfer and application within supply chain industries. In their design, stages one and two are perfect complements to the final stage of the integrated framework, a risk assessment toolbox based on a Hybrid Simulation Study capable of monitoring the disruptive behaviour of a multi-echelon RSC from both a macro and micro level using the techniques of System Dynamics (SD) and Discrete Event Simulation (DES) modelling respectively. Empirically validated through an embedded mixed methods case study, results of the integrated framework application are very encouraging. The first phase, the secondary exploratory study, gained valuable empirical evidence of the barriers to successfully implementing a complex business project and also validated using simulation as an effective risk assessment tool. Results showed certain high-risk order policy decisions could potentially reduce total costs (TC) by over 55% and reduce delivery times by 3 days. The use of the �6 Coefficient as the communication/consultation phase of the primary RSCRM case study was hugely influential on the success of the overall hybrid simulation study development and application, with significant increase in both practitioner and researcher confidence in running an RSCRM project. This was evident in the results of the hybrid model’s macro and micro assessment of the RSC. SD results effectively monitored the behaviour of the RSC under important disruptive risks, showing delayed effects to promotions and knowledge loss resulted in a bullwhip effect pattern upstream with the FMCG manufacturer’s TC increasing by as much as €50m. The DES analysis, focusing on the NDC function of the RSC also showed results of TC sensitivity to order behaviour from retailers, although an optimisation based risk treatment has reduced TC by 30%. Future research includes a global empirical validation of the �6 Coefficient and enhancement of the application of thermodynamic laws in business process management. The industry calibration capabilities of the integrated framework application of the integrated framework will also be extensively tested

    Investigating the direct application of chaos theory to detect, analyse and anticipate high-level variability in the logistics demand of third party logistics

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    Third party logistics providers operate in an environment where the customer demand shows high-levels of variability. Existing methods of analysis and prediction cannot capture these types of fluctuation. Newer methods of analysis, and the identification of potential chaotic conditions to explain these intense oscillations, have not been tested for applicability in these situations. The purpose of this thesis is to investigate whether the direct application of chaos theory can efficiently detect, analyse and anticipate high-level variability in the logistics demand of third party logistics (TPL). The research involves a single case study analysis. The variable investigated is the logistics extracted from the EDI files of the company in a time-series format. The time scale of the data is over two years. A framework of data analysis, called CASTS (Chaotic Analysis of Short Time Series), is constructed in order to analyse the data. It is an amalgamation of linear, non-linear and chaos theory based techniques selected to allow the detection, analysis and possible anticipation of the underlying data set. The CASTS method is composed of the application of the autocorrelation function, power spectrum, BDS statistics, mutual information, phase space plots, correlation dimension, Lyapunov exponent and finally, Hurst exponent tests. In addition a surrogate data test is performed in order to achieve a 95% level of confidence in the results. The importance of this research is fourfold. First, it proposes a solution for third party logistics to improve their operational efficiency through an enhancement of their forecasting, planning and control abilities. Secondly, it adds new knowledge to logistics management in two ways; it brings together two different sciences and provides insights that have not been explored before and; it succeeds to identify, for the first time, the presence of chaotic behaviour in real logistics data and thus give a new direction to logistics research. Thirdly, it provides CASTS as a new framework of analysis for the detection of chaotic behaviour in short time series that was not previously applied in social sciences. Finally, it has tremendous implications for industry; it concerns the logistics anticipation, planning and control. It assists companies to focus their efforts in understanding the structure and restraining the behaviour of their demand patterns rather than focusing in reactive actions

    Applications of Contemporary Management Approaches in Supply Chains

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    In today's rapidly changing business environment, strong influence of globalization and information technologies drives practitioners and researchers of modern supply chain management, who are interested in applying different contemporary management paradigms and approaches, to supply chain process. This book intends to provide a guide to researchers, graduate students and practitioners by incorporating every aspect of management paradigms into overall supply chain functions such as procurement, warehousing, manufacturing, transportation and disposal. More specifically, this book aims to present recent approaches and ideas including experiences and applications in the field of supply chains, which may give a reference point and useful information for new research and to those allied, affiliated with and peripheral to the field of supply chains and its management

    Rogue seasonality detection in supply chains

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    Supply chains face disturbances in the provision of goods and services to customers. A key disturbance which is endogenously generated from the nature of the ordering process used is rogue seasonality, which is characterised by orders and other supply chain variables showing cyclicality in their profiles and this cyclicality not present in exogenous demand. It is observed in many supply chains and is a cause of significant economic loss for entities in these chains. A useful way to manage rogue seasonality could be by detecting its presence and intensity in a system and then taking appropriate and timely action for its mitigation. Called "sense and respond", this approach has been used in various domains extensively, but its application in supply chain management has been limited. This thesis explores the application of this approach for managing rogue seasonality, with the findings from it particularly relevant for a context where many multiple echelon supply chains are being managed by a focal company. Multiple methods are used to analyse each of the rogue seasonality generation and detection components of the thesis. For understanding rogue seasonality generation, system dynamics simulations of single and three echelon linear and four echelon non linear (Beer game) systems are used. The impact of different demand processes and parameters, delays, order of delays, ordering processes, backlogs and batching on rogue seasonality is assessed. The simulation analysis is supported with empirical contexts from the steel and grocery sectors. The understanding gained on rogue seasonality together with the related contextual data is used in the sense or detection part of the thesis. The signature based approach, with the signature derived from clustering of time series data of variables is explored for detection, with the data represented in alternative domains such as amplitudes of Fourier transforms, autocorrelation function, coefficients of autoregressive model, cross correlation function and coefficients of discrete wavelet transform. The thesis determined the signature and index for detecting rogue seasonality. While the signature, which is based on the cluster profiles of the system variables indicates the presence of rogue seasonality, the intensity of rogue seasonality is indicated by the index. In a multi supply chain context, the index could be used to identify problematic supply chains from a rogue seasonality perspective and initiate appropriate management action. At present there is no measure for rogue seasonality and defining an index for the same constitutes a major contribution of this thesis. Among alternative time series representations, the frequency domain representation based on Fourier transform was found to be the most appropriate for deriving the signature and index. This is also a major contribution of the diesis, as the comprehensive assessment of time series representations carried out in this study has not been done in many studies across domains, and those that have done so, have not used any supply chain related data in the assessment. Finally, the framework for exploiting the index for managing rogue seasonality is proposed

    Classification of the Existing Knowledge Base of OR/MS Research and Practice (1990-2019) using a Proposed Classification Scheme

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    This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordOperations Research/Management Science (OR/MS) has traditionally been defined as the discipline that applies advanced analytical methods to help make better and more informed decisions. The purpose of this paper is to present an analysis of the existing knowledge base of OR/MS research and practice using a proposed keywords-based approach. A conceptual structure is necessary in order to place in context the findings of our keyword analysis. Towards this we first present a classification scheme that relies on keywords that appeared in articles published in important OR/MS journals from 1990-2019 (over 82,000 articles). Our classification scheme applies a methodological approach towards keyword selection and its systematic classification, wherein approximately 1300 most frequently used keywords (in terms of cumulative percentage, these keywords and their derivations account for more than 45% of the approx. 290,000 keyword occurrences used by the authors to represent the content of their articles) were selected and organised in a classification scheme with seven top-level categories and multiple levels of sub-categories. The scheme identified the most commonly used keywords relating to OR/MS problems, modeling techniques and applications. Next, we use this proposed scheme to present an analysis of the last 30 years, in three distinct time periods, to show the changes in OR/MS literature. The contribution of the paper is thus twofold, (a) the development of a proposed discipline-based classification of keywords (like the ACM Computer Classification System and the AMS Mathematics Subject Classification), and (b) an analysis of OR/MS research and practice using the proposed classification

    Markets and Supply Chains: An Investigation of the Institutions Influencing the Farm-Supply Chain Interface

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    Farm-level operations have lasting and amplified impacts that promulgate the entire supply chain, and the farm is increasingly in the forefront of today’s headlines on topics such as social responsibility, environmental sustainability, traceability, and food safety. Despite its significance, however, the farm remains a ‘black box’ and has traditionally operated independently with little information-sharing, trust, or collaboration with buyers downstream. This dissertation begins to unpack this ‘black box’ by employing different methodologies to identify the factors influencing exchange in the farm-supply chain interface. In Essay 1, I examine why the farm continues to be a challenge for ‘traditional’ collaborative approaches to buyer-supplier exchange. I use an interpretive approach to identify the individual and institutional factors influencing farmers’ operations decision-making. Field interviews reveal that farmers approach buyer-supplier exchange differently and tend to rely more heavily on market mechanisms to coordinate activities with buyers and inform their decision-making. In Essay 2, I build on this finding to examine the institutional factors influencing exchange in the spot market, which accounts for a majority of the total value of agricultural commodity production. I use a proprietary data set and time series econometrics to investigate how spot market exchanges between farmers and buyers are influenced by the futures market—an institution serving critical informational and risk management functions in the industry. In line with the predictions of Austrian economics, the findings indicate that farmers and buyers use the information conveyed by the futures market as they negotiate prices in the spot market. In Essay 3, I build on this finding and further explore how the futures market influences spot market exchanges by examining how information asymmetry affects the price adjustment process. I draw on economic theory to develop hypotheses that are tested using a proprietary data set and nonlinear time series econometrics. The findings suggest that buyers exploit their informational advantage by adjusting spot market prices asymmetrically. Taken together, the three essays demonstrate how institutions influence decision-making and exchange in the agricultural supply chain and offer important insights for theory, practice, and public policy
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