Design and Simulation of RFID-Enabled Aircraft Reverse Logistics Network via Agent-Based Modeling

Reverse Logistics (RL) has become increasingly popular in different industries especially aerospace industry over the past decade due to the fact that RL can be a profitable and sustainable business strategy for many organizations. However, executing and fulfilling an efficient recovery network needs constructing appropriate logistics system for flows of new, used, and recovered products. On the other hand, successful RL network requires a reliable monitoring and control system. A key factor for the success and effectiveness of RL system is to conduct real-time monitoring system such as radio frequency identification (RFID) technology. The RFID system can evaluate and analyze RL performance timely so that in the case of deviation in any areas of RL, the appropriate corrective actions can be taken in a quick manner. An automated data capturing system like RFID and computer simulation techniques such as agent-based (AB), system dynamic (SD) and discrete event (DE) provide a reliable platform for effective RL tracking and control, as they can respectively decrease the time needed to obtain data and simulate various scenarios for suitable best corrective actions. The functionality of the RL system can be noticeably elevated by integrating these two systems and techniques. Besides, each computer simulation approach has its own benefits for understanding the RL network from different aspects. Therefore, in this study, after designing and constructing the RL system through the real case study from Bell Helicopter Company with the aid of unified modeling language (UML), three simulation techniques were proposed for the model. Afterwards the results of all three simulation approaches (AB, SD and DE) were compared with considering two scenarios of RL RFID-enabled and RL without RFID. The computer simulation models were developed using “AnyLogic 7.1” software. The results of the research present that with exploiting RFID technology, the total disassembly time of a single helicopter was decreased. The comparison of all three simulation methods was performed as well. Keywords: Reverse logistics (RL), RFID, aerospace industry, agent-based simulation, system dynamic simulation, discrete event simulation, AnyLogi

Optimising Supply Chain Performance via Information Sharing and Coordinated Management

Supply chain management has attracted much attention in the last decade. There has been a noticeable shift from a traditional individual organisation-based management to an integrated management across the supply chain network since the end of the last century. The shift contributes to better decision making in the supply chain context, as it is necessary for a company to cooperate with other supply chain members by utilising relevant information such as inventory, demand and resource capacity. In other words, information sharing and coordinated management are essential mechanisms to improve supply chain performance. Supply chains may differ significantly in terms of industry sectors, geographic locations, and firm sizes. This study was based on case studies from small and medium sized manufacturing supply chains in People Republic of China. The study was motivated by the following facts. Firstly, small and medium enterprises have made a big contribution to China’s economic growth. Several studies revealed that most of the Chinese manufacturing enterprises became aware of the importance of supply chain management, but compared to western firms, the supply chain management level of Chinese firms had been lagging behind. Research on supply chain management and performance optimisation in Chinese small and medium sized enterprises (SMEs) was very scarce. Secondly, there had been plenty of studies in the literature that focused on two or three level supply chains whilst considering a number of uncertain factors (e.g. customer demand) or a single supply chain performance indicator (e.g. cost). However, the research on multiple stage supply chain systems with multiple uncertainties and multiple objectives based on real industrial cases had been spared and deserved more attention. One reason was due to the lack of reliable industrial data that required an enormous effort to collect the primary data and there was a serious concern about data confidentiality from the industry aspect. This study employed two SME manufacturing companies as case studies. The first one was in the Aluminium industry and another was in the Chemical industry. The aim was to better understand the characteristics of the supply chains in Chinese SMEs through performing in-depth case studies, and built models and tools to evaluate different strategies for improving their supply chain performance. The main contributions of this study included the following aspects. Firstly, this study generalised a supply chain model including a domestic supply chain part and an international supply chain part based on deep case studies with the emphasis on identifying key characteristics in the case supply chains, such as uncertainties, constraints and cost elements in association with flows and activities in the domestic supply chain and the international supply chain. Secondly, two important SCM issues, i.e. the integrated raw material procurement and finished goods production planning, and the international sales planning, were identified. Thirdly, mathematical models were formulated to represent the supply chain model taking into account multiple uncertainties. Fourthly, several operational strategies utilising the concepts of just-in-time, safety-stock/capacity, Kanban, and vendor managed inventory, were evaluated and compared with the case company's original strategy in various scenarios through simulation methods, which enabled quantification of the impact of information sharing on supply chain performance. Fifthly, a single objective genetic algorithm was developed to optimise the integrated raw material ordering and finished goods production decisions under (s, S) policy (a dynamic inventory control policy), which enabled the impact of coordinated management on supply chain performance to be quantified. Finally, a multiple objectives genetic algorithm considering both total supply chain cost and customer service level was developed to optimise the integrated raw material ordering and finished goods production with the international sales plan decisions under (s, S) policy in various scenarios. This also enabled the quantification of the impact of coordinated management on supply chain performances

Internet-of-things enabled supply chain planning and coordination with big data services: certain theoretic implications

Recent advances in information technology have led to profound changes in global manufacturing. This study focuses on the theoretical and practical challenges and opportunities arising from the Internet of Things (IoT) as it enables new ways of supply-chain operations partially based on big-data analytics and changes in the nature of industries. We intend to reveal the acting principle of the IoT and its implications for big-data analytics on the supply chain operational performance, particularly with regard to dynamics of operational coordination and optimization for supply chains by leveraging big data obtained from smart connected products (SCPs), and the governance mechanism of big-data sharing. Building on literature closely related to our focal topic, we analyze and deduce the substantial influence of disruptive technologies and emerging business models including the IoT, big data analytics and SCPs on many aspects of supply chains, such as consumers value judgment, products development, resources allocation, operations optimization, revenue management and network governance. Furthermore, we propose several research directions and corresponding research schemes in the new situations. This study aims to promote future researches in the field of big data-driven supply chain management with the IoT, help firms improve data-driven operational decisions, and provide government a reference to advance and regulate the development of the IoT and big data industry.Published versio

Internet of Things-Enabled Dynamic Performance Measurement for Real-Time Supply Chain Management - Toward Smarter Supply Chain -

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 2. Park, Jinwoo.Supply chain performance measurement has become one of the most important and critical management strategies in the pursuit of perfection and in strengthening the competitive edges of supply chains to face the challenges in todays global markets. To constantly cope with the resulting rapid changes and adopt new process designs while reviving supply chain initiatives and keeping them alive, an effective real-time performance-based IT system should be developed. And there are many researches on supply chain performance measurement system based on the real-time information system. This thesis proposes a standard framework of a digitalized smart real-time performance-based system. The framework represents a new type of smart real-time monitoring and controlling performance-based IT mechanism for the next-generation of supply chain management practices with dynamic and intelligent aspects concerning strategic performance targets. The idea of this mechanism has been derived from the main concepts of traditional supply chain workflow and performance measurement systemswhere the time-based flow is greatly emphasized and considered as the most critical success factor. The proposed mechanism is called Dynamic Supply Chain Performance Mapping (DSCPM), a computerized event-driven performance-based IT system that runs in real-time according to supply chain management principles that cover all supply chain aspects through a diversity of powerful practices to effectively capture violations, and enable timely decision-making to reduce wastes and maximize value. The DSCPM is proposed to contain different types of engines of which the most important one is the Performance Practices and Applications Engine (PPAE) due to its involvement with several modules to guarantee the comprehensiveness of the real-time monitoring system. Each of these modules is specified to control a specific supply chain application that is equipped with suitable real-time monitoring and controlling rules called Real-Time Performance Control Rules (RT-PCRs), which are expressed using Complex Event Processing (CEP) method. The RT-PCRs enable DSCPM to detect any interruptions or violation smartly and accordingly trigger real-time decision-making warnings or re-(actions) to control the performance and achieve a smart real-time working environment. The contributions of this dissertation are as follows: (1) building a conceptual framework to digitalize the supply chain, based on their strategic performance targets, deploying IoT technologies to convert its resources to smart-objects and therefore enable a dynamic and real-time supply chain performance measurement and management. (2) Demonstrating the feasibility of the DSCPM concerning performance targets by developing some practices and tool modules that are supplied with RT-PCRs (e.g., Real-time Demand Lead-time Analysis, Real-time Smart Decision-making Analysis (RT-SDA), Real-time Supply Chain Cost Tracking System (RT-SCCT), etc.). (3) Verifying the effectiveness of RT-PCRs in RT-SDA and RT-SCCT modules by building simulation models using AnyLogic simulation software.Chapter 1. Introduction 1 1.1 OVERVIEW 1 1.2 PROBLEM STATEMENT AND MOTIVATION 4 1.3 RESEARCH OBJECTIVES 7 1.4 THESIS OUTLINE 11 Chapter 2. Background and Literature Review 12 2.1 INTRODUCTION 12 2.2 SUPPLY CHAIN PERFORMANCE MEASUREMENT 13 2.3 PROCESS-ORIENTED SCPM AND SCOR MODEL 25 2.4 IOT AND SCM 31 Chapter 3. Performance-based IoT Deployment for Digital Supply Chain Transformation 40 3.1 INTRODUCTION 40 3.2 DIGITAL SC TRANSFORMATION FRAMEWORK 42 3.3 FRAMEWORK DEMONSTRATION USING A THEORETICAL CASE STUDY 65 3.4 CONCLUSION 71 Chapter 4. IoT-enabled Dynamic Supply Chain Performance Mapping based on Complex Event Processing 73 4.1 INTRODUCTION 73 4.2 REAL-TIME ENTERPRISE INTEGRATION 74 4.3 INTEGRATION OF DSCPM IN REAL-TIME SUPPLY CHAIN INFRASTRUCTURE 76 4.4 DYNAMIC SUPPLY CHAIN PERFORMANCE MAPPING FRAMEWORK (DSCPM) 77 4.5 CONCLUSION 107 Chapter 5. DSCPM-enabled Smart Real-time Performance Measurement Environment 109 5.1 DSCPM-ENABLED REAL-TIME TIME AND PERFORMANCE-BASED ANALYSIS FRAMEWORK 109 5.2 DSCPM-ENABLED REAL-TIME SC COSTS TRACKING SYSTEM 132 Chapter 6. Managing Perishability in Dairy Supply Chain using DSCPM Framework (a case study scenario) 152 6.1 INTRODUCTION 152 6.2 ASSUMPTIONS AND NOTATION 153 6.3 SIMULATION EXPERIMENTS 158 6.4 RESULTS AND DISCUSSION 161 6.5 A NEW APPROACH, FOR DESIGNING AND MANAGING PERISHABLE PRODUCTS INVENTORY SYSTEM 168 6.6 DECISIONS SENSITIVITY ANALYSIS 172 6.7 IOT COSTS-BENEFITS ANALYSIS 173 6.8 CONCLUSIONS 176 Chapter 7. Conclusions 179 7.1 CONCLUSION 179 7.2 FUTURE RESEARCH 182 Bibliography 184Docto

Contribution to the Design of Digital Supply Chain Governance Concepts for Sustainable Development of Biodiesel

Biodiesel sowie Biokraftstoffe wurden in der letzten Zeit zunehmend in mehreren Vorträgen und Konferenzen diskutiert. Während viele der wissenschaftlichen Untersuchungen die Produktionsprobleme, wie Effizienz, Diversifizierung und Prozesstechnologie (1, 2, 3 generation) behandelten, wurde jedoch nur in einigen davon Biodiesel aus der logistischen Perspektive betrachtet. Trotzdem stellt Biodiesel ein Problem für die logistischen Prozesse dar, so wie Anlagenplanung, Transport und Tourenplanung verbunden mit der Qualitätskontrolle entlang der Kontinuität der Rohmaterialversorgung. In einigen Entwicklungsländern ist das Management der Biodiesel-Industrie in eine Zwangslage geraten. Obwohl Entwicklungsländer gemeinsam eine nationale Behörde für das Biodiesel-Geschäft eingeführt haben, ist die Institution nach wie vor in den frühen Stadien der Standardisierung sowie Qualitätskontrolle. Derzeit gibt es keine Agentur für die Supply Chain Führung, die für die Integration des Biodiesel-Geschäftes zwischen vorgelagerten und nachgelagerten Bereiche fähig ist. In dieser Arbeit stellt der Autor eine Prozesstechnik vor, um die die Biodiesel-Industrie zu bewerten. Diese Prozedur kombiniert Geschäftsmodell/-analyse (unter Nutzung der General Electric/Mc. Kinsey Matrix), Simulation, Konzeptentwurf und ein Prototyping-System. Die Studie leistet einen wissenschaftlichen Beitrag für die Planung einer digitalen Biodiesel Supply Chain und bildet einen Rahmen für die Führung eines solchen Systems vom vorgelagertem zum nachgelagerten Bereich. Der Forscher verwendet eine ganzheitliche Betrachtung, wo Biodiesel nicht als eine gesonderte Einheit gesehen wird, aufgrund der Tatsache, dass es den Konsumenten, abhängig von unterschiedlichen Aspekten, von einer langen Kette ankommt. Um ihre Geschäfte zu managen, haben viele Unternehmen Enterprise-Resource-Planning eingeführt, aber leider waren sie nicht in der Lage die gesamte Wertschöpfungskette damit zu erreichen. Digitalisierung ist bei der Integration des Informationssystems von allen Supply Chain Mitgliedern wünschenswert. Um solch eine Idee anzupassen sowie den gesamten Prozess zu überwachen, muss ein Kontrollturm gebaut werden. In Folge dessen könnte die beste standardisierte Qualität und Nachhaltigkeit erreicht werden. Der Autor schlägt ebenso ein Übergangskonzept in der Implementierungsebene vor, aufgrund der Tatsache, dass die Supply Chain Mitglieder in der Realität keine ähnlichen Informationssystem-Standards zur Verfügung haben. Das Ergebnis der Literatur-Studien, Simulationen, Prototypenentwicklungen, theoretischen Argumente und Konzeptentwürfen präsentiert eine Digitalisierungsmuster in der Supply Chain von Biodiesel für die nachhaltige Entwicklung.Recently, biodiesel and biofuel have increasingly been discussed in several papers and conferences. However, only a few have examined biodiesel from the logistics perspective, while most of the scientific investigations have addressed the production issues, e.g. efficiency, diversification and processing technology (1st, 2nd or 3rd generation). In spite of this, biodiesel poses a problem for logistic processes, such as facility planning, transport, and routing-scheduling associated with quality control along with continuity of feedstock supply. In some developing countries, the management of the biodiesel industry has also become a predicament. Even though developed countries have commonly established a national agency in charge of the biodiesel business, the institution is still in the early stages of standardisation and quality control. Currently, there is no agency concerned with supply chain governance that is capable of integrating the biodiesel business from upstream to downstream. In this thesis, the author presents a procedural technique to assess the biodiesel industry. This procedure combines business modelling/analysis (using General Electric/Mc. Kinsey Matrix), simulation, conceptual design and a prototyping system. The study provides scientific insight for planning a digital biodiesel supply chain and proposes a framework for governing such a system from upstream to downstream. The researcher employs a holistic approach, where biodiesel is not seen as a separate entity because it comes to the consumers through a long chain dependent on various aspects. Currently, a number of companies have implemented Enterprise-Resource-Planning to manage their businesses, but unfortunately, they have not been able to reach the entire value chain. Digitalisation is desirable when integrating the Information Systems of all supply chain members. A control tower must be built to accommodate such an idea and monitor the entire process. Then, the best standardised quality and sustainability can be achieved. The author also offers a transition concept in the implementation level, because, in reality, the members in the supply chain have no similar Informastion-System standard. The results from literature studies, simulations, prototyping, theoretical arguments, and conceptual design present a digitalisation pattern in the biodiesel supply chain for sustainable development

Supply Chain Modeling and Simulation Using Agents

Ph.DDOCTOR OF PHILOSOPH

Supply Chain Risk Assessment for Perishable Products Applying System Dynamics Methodology - A Case of Fast Fashion Apparel Industry

With the fast progress of science and technology and with the continuously growing customer expectations, share of merchandise exhibiting characteristics of perishability is on the rise. Perishable products, through their own nature, are subject to decay, deterioration or obsolescence. As a result, their usefulness, value or functionality is gradually reduced or even lost in a short window of time and cannot be regained if it is not used or sold within a specific time window. When producing perishable products, all stages of the supply chain are exposed to much higher uncertainty than in the case of durable products, which directly means higher risk. The phases of inventory planning, lead time control, and demand forecasting for perishable products play a critical role in the overall effectiveness of the supply chain. For this reason, the system dynamics methodology, a simulation and modeling technique developed specifically to address the long term and dynamic management issues, is adopted in this study. The focus of the proposed model is on the interaction between physical processes, information flows and managerial policies of a three-level supply chain for perishable products, in general, and fast fashion apparel supply chain, in particular, so as to create the dynamics of the variables of interest. The values of supply chain key factors such as, for example, inventory, backlogs, stock-outs, forecast error, cost, and profit for each time period are some of the outputs of the proposed model. Moreover, the Conditional Value at Risk (CVaR) measure is applied to quantify and analyze the risks associated with the supply chain for this type of product and also to determine the expected value of the losses and their corresponding probabilities. With the focus on three prominent categories of risks including risks of delays, forecast, and inventory, multiple business situations for effective strategic planning and decision making are generated and analyzed

Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain

The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse