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

Abstract

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 systems

where 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.