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

RFID-Enabled Dynamic Value Stream Mapping for Smart Real-Time Lean-Based Manufacturing System

Lean Manufacturing has become the most popular and dominant management strategy in the pursuit of perfection and in strengthening the competitive edges of manufacturers to face the challenges in the global markets. However, today’s global markets drive manufacturers to create highly customer-oriented job-shop manufacturing systems characterized by high dynamic behavior, uncertainty and high variability, in contradiction to lean being originally designed for high repetitive-production systems with a high-volume low-mix work environment with stable demand and a low degree of customization. Moreover, since the product is the changing agent, another challenging aspect that faces the effectiveness of lean is that the product life cycle is rapidly decreasing; and thus some of the lean initiatives often die after the product life cycle ends. In this regard, in order to constantly cope with the resulting rapid changes and adapt new process designs while reviving lean initiatives and keeping them alive; an effective real-time lean-based IT system should be developed, since lean without a real-time IT system has become impracticable and unthinkable in today’s high-customized manufacturing environments. In this context, due to the special characteristics and superior capabilities of Radio Frequency Identification technology (RFID), it could be the major enabler to support such a real-time IT system with real-time production data. However, RFID remains questionable and doubtable and manufacturers are still quite hesitant to adopt it in their manufacturing systems. This thesis introduces a solid basis for a standard framework of a digitalized smart real-time lean-based system. This framework describes the best practice of RFID technology through the integration of real-time production data captured via RFID with lean manufacturing initiatives in manufacturing systems, in order to overcome today’s lean manufacturing challenges. The introduced framework represents a new kind of smart real-time monitoring and controlling lean-based IT mechanism for the next-generation of manufacturing systems with dynamic and intelligent aspects concerning lean targets. The idea of this mechanism has been derived from the main concepts of traditional value stream mapping (VSM), where the time-based flow is greatly emphasized and considered as the most critical success factor of lean. The proposed mechanism is known as Dynamic Value Stream Mapping (DVSM), a computerized event-driven lean-based IT system that runs in real-time according to lean principles that cover all manufacturing aspects through a diversity of powerful practices and tools that are mutually supportive and synergize well together to effectively reduce wastes and maximize value. Therefore, DVSM represents an intelligent, comprehensive, integrated, and holistic real-time lean- based manufacturing system. The DVSM is proposed to contain different types of engines of which the most important engine is the “Lean Practices and Tools Engine” (LPTE) due to its involvement with several lean modules that guarantees the comprehensiveness of the real-time lean system. Each of these modules is specified to control a specific lean tool that is equipped with suitable real-time monitoring and controlling rules called “Real-Time Lean Control Rules” (RT-LCRs), which are expressed using “Complex Event Processing” (CEP) method. The RT-LCRs enable DVSM to smartly detect any production interruptions or incidents and accordingly trigger real-time re/actions to reduce wastes and achieve a smart real-time lean environment. Practically, the basis of this introduced framework in this dissertation is derived based on a highly customized job-shop manufacturing environment of an international switchgear manufacturer in Germany. The contributions of this dissertation are represented as follows: building the main framework of the DVSM starting with a systematic RFID deployment scheme on the production shop floor; introducing the main components of the DVSM (i.e. Event Extractor-engine, AVSM-engine, VVSM-engine, Real-time Rules-engine, and LPTE); demonstrating the feasibility of the DVSM concerning lean targets through developing a number of Lean Practices and Tools Modules that are supplied with RT-LCRs (e.g. Real-time Manufacturing Lead-time Analysis, Smart Real-time Waste Analysis, Real-time Dispatching Priority Generator (RT-DPG), Real-time Smart Production Control (RT-SPC), Smart-5S, Smart Standardized Work, Smart Poka-Yoke, Real-time Manufacturing Cost Tracking (RT-MCT), etc.); verifying the effectiveness of RT-LCRs in RT-DPG and RT-MCT modules through building simulation models using ProModel simulation software and finally proposing a framework of the tools “Smart-5S, Smart Standardized Work, Smart Poka-Yoke” to be implemented in the switchgear manufacturing environment

Fuzzy Belief Nets

This paper introduces fuzzy belief nets (FBN). The ability to invert arcs between nodes is key to solving belief nets. The inversion is accomplished by defining closeness measures which allow diagnostic reasoning from observed symptoms to cause of failures. The closeness measures are motivated by a Lukasiewicz operator which takes into account the distance from an observed symptom set to the modeled symptom set for all failure combinations. Hypothesized failures are then ranked according to maximum closeness measure and minimum cover, i.e., number of faults. Within the realm of fuzzy logic we show the graphical representation and solution of fuzzy belief nets

Fuzzy Belief Nets

This paper introduces fuzzy belief nets (FBN). The ability to invert arcs between nodes is key to solving belief nets. The inversion is accomplished by defining closeness measures which allow diagnostic reasoning from observed symptoms to cause of failures. The closeness measures are motivated by a Lukasiewicz operator which takes into account the distance from an observed symptom set to the modeled symptom set for all failure combinations. Hypothesized failures are then ranked according to maximum closeness measure and minimum cover, i.e., number of faults. Within the realm of fuzzy logic we show the graphical representation and solution of fuzzy belief nets

Towards Fuzzy Belief Nets

In this paper we investigate how the observation of symptoms which do not completely match a modeled fault can be used to find the most likely fault – and the degree to which this fault occurs. We start out by setting up fuzzy causal diagrams and then show how with the use of a proper operator the arcs of the causal diagram can be reversed. We introduce a graphical representation for fuzzy belief nets (FBN) and show how both AND and OR connected antecedents and consequents of rules can be accommodated. The paper concludes with an illustrative diagnostic example