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

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

Fuzzy Sets, Fuzzy Logic and Their Applications

The present book contains 20 articles collected from amongst the 53 total submitted manuscripts for the Special Issue “Fuzzy Sets, Fuzzy Loigic and Their Applications” of the MDPI journal Mathematics. The articles, which appear in the book in the series in which they were accepted, published in Volumes 7 (2019) and 8 (2020) of the journal, cover a wide range of topics connected to the theory and applications of fuzzy systems and their extensions and generalizations. This range includes, among others, management of the uncertainty in a fuzzy environment; fuzzy assessment methods of human-machine performance; fuzzy graphs; fuzzy topological and convergence spaces; bipolar fuzzy relations; type-2 fuzzy; and intuitionistic, interval-valued, complex, picture, and Pythagorean fuzzy sets, soft sets and algebras, etc. The applications presented are oriented to finance, fuzzy analytic hierarchy, green supply chain industries, smart health practice, and hotel selection. This wide range of topics makes the book interesting for all those working in the wider area of Fuzzy sets and systems and of fuzzy logic and for those who have the proper mathematical background who wish to become familiar with recent advances in fuzzy mathematics, which has entered to almost all sectors of human life and activity

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Essays on proactive detection of collusion, ex post analysis of competition policy actions, and estimating overcharge

This work is an empirical analysis of collusion by using a private consumer level data set in a setting where no a priori knowledge of collusion exists. This study benefits from spatial variation in the data. For identification, the relation between price and local market power under different assumptions of conduct is central. Accordingly, first, using a simple theoretical background I provide some theoretical intuition for the measure of local market power used in this work. Second, while controlling for demand and cost shifters and using OLS and GMM, I estimate pricing equations to explore if market patterns are more consistent with collusion or competition. Results suggest that consistent with a regime switch from collusion to competition, stable relations in the market are disrupted after month seven. Third, I estimate the hypothetical overcharge associated with this finding. To this aim, first, I employ the techniques frequently used in collusion retrospectives; second, I propose importing empirical strategies from merger retrospectives. Adopting the techniques that are used widely in merger retrospectives to collusion involves either using i) basic difference-in-difference framework where locations characterised by monopolistic pricing even in competition are set as the control group for the counterfactual of regime switch; or, ii) difference-in-difference framework with treatment intensity, where the regime switch is treated as a treatment, which, at each location, produces heterogeneous effects that is inversely proportional to the level of local market power the provider enjoys at that location. I find that overcharge estimates using alternative methodologies range in 7.48-13.98%. Furthermore, results suggest that if the spatial dynamics are ignored, estimation leads to under-compensation in regions where the market powers of dominant competitor and potential competitor converge; overcompensation in regions where the market powers diverge. Finally, to address the inference problems associated with spatial dependency across observations and difference-in-difference methodology, I apply various remedies proposed in the literature. The findings are robust to alternative methods of inference."This work is funded by University of St Andrews (School of Economics and Finance). This work is funded by the Economic and Social Research Council (reference number: 1506433)." -- Acknowledgement

Managing the Paradox of Growth in Brand Communities Through Social Media

The commercial benefits of online brand communities are an important focus for marketers seeking deeper engagement with increasingly elusive consumers. Managing participation in these socially bound brand conversations challenges practitioners to balance authenticity towards the community against corporate goals. This is important as social media proliferation affords communities the capacity to reach a scale well beyond their offline equivalents and to operate independently of brands. While research has identified the important elements of engagement in brand communities, less is known about how strategies required to maximise relationships in these circumstances must change with growth. Using a case study approach, we examine how a rapidly growing firm and its community have managed the challenges of a maturing relationship. We find that, in time, the community becomes self-sustaining, and a new set of marketing management strategies is required to move engagement to the next level

Managing the Paradox of Growth in Brand Communities Through Social Media

The commercial benefits of online brand communities are an important focus for marketers seeking deeper engagement with increasingly elusive consumers. Managing participation in these socially bound brand conversations challenges practitioners to balance authenticity towards the community against corporate goals. This is important as social media proliferation affords communities the capacity to reach a scale well beyond their offline equivalents and to operate independently of brands. While research has identified the important elements of engagement in brand communities, less is known about how strategies required to maximise relationships in these circumstances must change with growth. Using a case study approach, we examine how a rapidly growing firm and its community have managed the challenges of a maturing relationship. We find that, in time, the community becomes self-sustaining, and a new set of marketing management strategies is required to move engagement to the next level