Evaluation of the Return on Investment (RoI) of Digitalization in Supply Chain

Masters Thesis from the year 2021 in the subject Business economics - Supply, Production, Logistics, grade: 2, course: Supply Chain, language: English, abstract: The Supply chain process is an integrated business process that organizes and manages the flow of goods and services from the manufactures to the end-users. It and streamlines the supply chain activities by establishing linkage between suppliers, dealers, manufacturers, service providers, and end-users. The concept of supply chain management is adopted by different industries such as healthcare, retail, food, manufacturing, construction, and textile to enhance the working process and optimize customer value. The working of the supply chain process could be enhanced by adopting advanced technologies and digitalization in its working. While focusing on digitalization in the supply chain, it includes the introduction of digital technologies such as Big Data, the Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, predictive analytics, and blockchain into the supply chain process. The technologies enhance the supply chain management system by coordinating and synchronizing the different supply chain parts such as order, raw materials, production, finished products, warehouse, logistics, packaging, delivery, retailer, and end-user. For instance, the adoption of the blockchain process into the supply chain management process helps in simplifying the complex supply chain work process and transferring paper-intensive work processes into digitalized formats. On the other hand, the introduction of the robotic process helps in digitizing the back-office process by 50% to 80% that helps in faster movement and précised tracking of products. Predictive analytics is based on advanced analytics that facilities the supply chain process by streamlining internal and external demand variables such as social media trends, sensor data, and demand plans. It helps in improving demand prediction accuracy and