Delivery Time Uncertainty in Dynamic Supply Networks

Today, business models are invariably part of complex networks of suppliers, manufacturers and distributors. Uncertainty is recognized as an inevitable characteristic of supply networks and managers need to be aware of its specifications and consequences of that. Therefore, understanding, acknowledgement, and moderation of the causes and effect of uncertainty is crucial. Under-controlled uncertainty leads to the improvement of networks performances and reliable networks. This paper complies with uncertain complex supply networks with their fundamental types. By defining critical routes in PERT networks, a combination of stochastic and mathematical models calculates the delivery time uncertainty in supply networks. This approach can be used as a tool for managers to control and monitor uncertainty in complex networks

The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems

Strategies to Minimize the Bullwhip Effect in the Electronic Component Supply Chain

Supply chain leaders in the information technology industry face challenges regarding their ability to mitigate amplified demand and supply variability in a supply chain network--the bullwhip effect--and reduce adverse implications on their component supply chain networks. The purpose of this multiple case study was to explore the strategies supply chain leaders in the United States used to reduce the bullwhip effect. Bullwhip effect theory served as the conceptual framework. Participants in the study were 5 purposefully selected supply chain leaders in the state of Texas who successfully implemented strategies to reduce the bullwhip effect on their networks. Data were collected from semistructured interviews and analysis of documents from the participants\u27 websites. The data were analyzed using the 5 data analysis steps consistent with Yin\u27s approach: collection, stratification, reassembly, interpretation, and conclusion. Four themes emerged from data analysis: (a) collaboration strategy, (b) communication strategy, (c) component shortage reduction strategy, and (d) resource management strategy. Supply chain leaders might use the findings of this study to reduce the bullwhip effect within their networks and improve their profitability. The implications for positive social change include the potential for leaders to improve environmental sustainability by using effective supply chain strategies to reduce the accumulation of excess inventories, reduce transportation fuel usage, and lessen the consumption of natural resources

Generic modelling and simulation of operations and stock levels in supply chains

Imperial Users onl

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

Modelling an End-to-End Supply Chain System Using Simulation

Supply chains (SCs) are an important part of today’s world. Many businesses operate in the global marketplace where individual companies are no longer treated as separate entities, but as a vital part of an end-to-end supply chain (E2E-SC) system. Key challenges and issues in managing E2E-SCs are duly attributed to their extended, complex and systemic nature. In the era of uncertainty, risks and market volatility, decision makers are searching for modelling techniques to be able to understand, to control, design or evaluate their E2E-SC. This research aims to support academics and decision makers by defining a generic simulation modelling approach that can be used for any E2E-SC. This study considers the challenges and issues associated with modelling complex E2E-SC systems using simulation and underlines the key requirements for modelling an E2E-SC. The systematic literature review approach is applied to provide a twofold theoretical contribution [a] an insightful review of various contributions to knowledge surrounding simulation methods within the literature on end-to-end supply chains and [b] to propose a conceptual framework that suggests generic elements required for modelling such systems using simulation. The research adopts a simulation methodology and develops a generic guide to an E2E-SC simulation model creation process. It is a mindful inquiry into the implications relative to a simulation model development process in presence of generic elements from the proposed conceptual framework. The conceptual framework is validated with industry experts and insightful remarks are drawn. In conclusion, it is acknowledged that modelling an E2E-SC system using simulation is a challenge, and this area is not fully exploited by the business. A guide to an E2E-SC simulation model development is a theoretical and practical contribution of this research, immensely sought by businesses, which are continuously tackling day to day issues and challenges, hence often lacking resources and time to focus on modelling. The conceptual framework captures generic elements of the E2E-SC system; however, it also highlights multiple challenges around simulation model development process such as technical constraints and almost impracticability of a true reflection of an E2E-SC system simulation model. The significant contribution of this thesis is the evaluation of the proposed generic guide to E2E-SC simulate model development, which provides the architecture for better strategic supply and demand balancing as new products, price fluctuations, and options for physical network changes can be dynamically incorporated into the model. The research provides an insightful journey through key challenges and issues when modelling E2E-SC systems and contributes with key recommendations for mindful inquiries into E2E-SC simulation models

Discrete Event Simulations

Considered by many authors as a technique for modelling stochastic, dynamic and discretely evolving systems, this technique has gained widespread acceptance among the practitioners who want to represent and improve complex systems. Since DES is a technique applied in incredibly different areas, this book reflects many different points of view about DES, thus, all authors describe how it is understood and applied within their context of work, providing an extensive understanding of what DES is. It can be said that the name of the book itself reflects the plurality that these points of view represent. The book embraces a number of topics covering theory, methods and applications to a wide range of sectors and problem areas that have been categorised into five groups. As well as the previously explained variety of points of view concerning DES, there is one additional thing to remark about this book: its richness when talking about actual data or actual data based analysis. When most academic areas are lacking application cases, roughly the half part of the chapters included in this book deal with actual problems or at least are based on actual data. Thus, the editor firmly believes that this book will be interesting for both beginners and practitioners in the area of DES

Deploying hybrid modelling to support the development of a digital twin for supply chain master planning under disruptions

Supply chains operate in a highly distuptive environment where a SC master plan should be updated in line with disruptions to ensure that a high service level is provided to customers while total cost is minimised. There is an absence of knowledge of how a SC master plan should be updated to cope with disruptions using hybrid modelling. To fill this gap, we present a hybrid modelling framework to update a SC master plan in presence of disruptions. The proposed framework, which is a precursor to a SC digital twin, integrates simulation, machine learning, and optimisation to identify the production, storage, and distribution values that maximise SC service level while minimising total cost under disruptions. This approach proves effective in a SC disrupted by demand increase and lead time extension. Results show that employing hybrid modelling leads to a noticeable improvement in service level and total cost. The outcome of the new knowledge on using hybrid modelling for managing disruptions provides essential learning for the extension of modelling through a digital twin for SC master planning. We observe that in the presence of disruptions it is more economical to keep higher inventory at downstream SC members than the upstream SC members

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes