Intelligent Decision Support and Big Data for Logistics and Supply Chain Management Minitrack
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Information technology (IT) and information systems (IS) are prerequisites and enablers for successful supply chain management (SCM). With related advances, the logistics and SCM field is developing very dynamically. Business-to-business transactions are made via the Internet and enterprise resource planning (ERP) systems support managing the transactional information within the enterprise. Cooperation and coordination is only possible based on IT and IS. While IT and IS are vital components in supply chains, their successful management rests on intelligent and coordinated decision making throughout the logistics network. Intelligent decision support and decision analytics using advanced decision technologies and analytics methodology are of utmost importance in logistics and SCM. Sensor networks, social network activities, RFID deployment, internet search histories and retail transactions are just a few examples of sources to provide data to support efficient decision analytics. Big data issues are well recognized and offer opportunities long waited for but also provide challenges in handling and decision analytics. Cloud computing allows also small and medium sized enterprises to access resources to support analytics functions. Business intelligence and data mining can be used to store and analyze logistics, product, inventory, and sales information. Simulation and optimization, which can be found in advanced planning and scheduling systems, can be employed for, e.g., inventory, production, procurement, and distribution planning. Intelligent agents can, e.g., communicate with different partners in the supply chain, assist in collecting information, share product information, negotiate prices, and distribute alerts throughout the logistics networks. The design and implementation of intelligent decision analytics tools to support human agents in computational logistics and SCM is a very active field in research, consulting and software development. Many such technologies or systems are continuously being developed, implemented and used in real-world scenarios. We do, therefore, believe that this minitrack will be recognized by both the scientific community and practitioners developing or using logistics and SCM solutions.
We aim at organizing a minitrack consisting of two sessions depending on the number of high quality submissions. We seek papers dealing with decision analytics, business intelligence, big data, cloud computing and decision technologies which contribute to intelligent decision support in the whole field of logistics and in particular in all categories of SCM. This includes but is not restricted to simulation, optimization, heuristics, metaheuristics, agent technologies, decision analytics, descriptive models, and data mining. We are especially interested in real-world applications and in information systems and software solutions which assist in solving decision problems. This is extended towards, e.g., computational logistics, advanced planning systems and the intelligent use of ERP systems. Also conceptual ideas, reports on projects in progress, and case studies are welcome. Moreover, teaching cases both at the university as well as the executive level may be of interest.
Stefan Voß (Primary Contact)
University of Hamburg, Germany
RWTH Aachen University, Germany
University of Southern Denmark
ItemQuantity and Location Decision of Fresh Food Distribution Centers for a Supermarket Chain under Carbon Policies( 2017-01-04)Supermarket chains handle frequent deliveries of fresh food to the stores, which have led to the non-ignorable high transportation cost. Then a question arises that is it possible to reduce cost by establishing more refrigerated distribution centers (DC)? To answer this question, on basis of data from a large supermarket chain in China, we analyze the decision making process to construct new sub DCs. A balance of the DC cost and the transportation cost is achieved to gain the optimal number and location of sub DCs. We also extend the model to situations with carbon policies (carbon tax policy and carbon cap-and-trade policy). The locations of sub DCs remain the same under carbon policies. Furthermore, a carbon tax policy does not change the number of sub DCs and only causes an increase in the total cost. Under a carbon cap-and-trade policy the optimal decision of the DC number is dependent on the carbon selling rule.
ItemBig Data and its Applications in Supply Chain Management: Findings from a Delphi Study( 2017-01-04)Big data and its applications have increasingly received interest by both scholars and practitioners. However, there is still missing evidence regarding how big data is understood as well as its applications in supply chain management (SCM). Empirical contributions are especially limited. This study seeks to address this gap through an explorative Delphi study to understand the terminology of big data and its application in the SCM processes of sourcing, manufacturing, service, logistics, planning, and return. The findings reveal that big data is mostly concerned with data collection and logistics, service, and planning processes are the most applicable processes for deploying big data analytics in SCM. Furthermore, a range of applications have been identified and ranked within each process.
ItemAn Analysis of Digital Transformation in the History and Future of Modern Ports( 2017-01-04)Digital transformation is of utmost importance in the business world with major impacts on any of its sectors. Here we consider ports and logistics within maritime shipping to exemplify those developments. That is, as actors in world-wide supply chains, seaports are particularly affected by technological change. Due to the high requirements in the logistics sector, e.g., regarding costs, efficiency, security, and sustainability, digital innovation is essential to stay competitive. Past developments show how digital innovation can shape the modernization of ports. In order to understand future challenges in this area, it is inevitable to review the outcomes of past developments and their impact on port operations. In this paper, we provide an extensive analysis of digital transformations in seaports. We identify three generations and analyze the stages of respective digital transformations using a well-known model from literature. Based on the observations, we identify important aspects and challenges.