A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics

Digitized VMI – maturity model for the FMCG sector

Optimized stocks and lean processes are prerequisites in order to keep up with the increasing consumption of goods and limited storage areas in cities due to urbanization. This paper, therefore, develops a maturity model to increase the dissemination rate of Digitized VMI to optimize supply chains in the FMCG sector. Firstly, criteria for successful VMI application are identified by literature research and expert interviews. Moreover, the respective target state descriptions are defined. Secondly, an assessment scheme is integrated. Finally, a procedure model to identify company-specific operational factors is developed. The research establishes requirements for successful VMI application in the FMCG sector within four generic categories and a specific one. It has been found that company-specific operational factors are particularly relevant in this context. Therefore, the specific fifth category is described by a procedure model to identify those factors. Current VMI models are generalistic and describe potential components of VMI, contract contents and relevant processes. However, a model addressing the barriers for VMI application ex ante and taking company-specific operational factors into account is pending. The VMI Maturity Model is practically applicable, since it has been tested at a company in the FMCG sector. It turned out, that assessing the VMI Maturity Level supports companies in identifying specific areas for improvements in advance of VMI implementation

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics

Building Models of Global Supply Chains Basic Principles and Requirements

In a market environment of increasing complexity, managing the entire supply chain becomes a critical factor for success in logistics. The requirements on simulation are as ambitious as wide-spread. Simulation models are implemented to evaluate concepts and system designs, analyse, modify and optimise existing systems and control material flows. This work covers two main topics. First, we analyse the properties of existing concepts for simulation models, so as to consolidate present knowledge on the modelling of a supply chain. Therefore, theoretical developments from the field of supply chain optimisation by simulation are revisited. Secondly, we describe and analyse the different key terms and concepts of discrete-event simulation. We characterise and distinguish the key terms for a supply chain simulation model, like objects, states, timeframes and flows and the specific requirements on different kinds of supply chains. Generally, the reasons for modelling a supply chain are quite divergent. Models are often case-based or focus on selected aspects or subsystems of a global network. Finally, we summarise potentials and shortcomings of supply chain simulation and delineate issues for further research

Selecting sustainability key performance indicators for smart logistics assessment

The application of smart technologies and applications is becoming increasingly common in the logistics processes of companies and supply chains. However, standard logistics indicators are still used to evaluate their performance, which contradicts the sustainable development strategy of many industrial enterprises and their supply chains. Thus, the article aims to design a methodology for selecting sustainability key performance indicators (SKPIs) suitable for assessing smart logistics and its technologies and applications. The research relies on cluster analysis of the SKPIs recommended in the relevant literature, frequency analysis of indicators used in practice and their comparison. The cluster analysis showed that the primary attention in the references is given to sustainability's economic and environmental dimensions. Most frequently, the authors highlighted the importance of the following indicators: production-related costs and investments, planning performance and quality, customer satisfaction, energy efficiency, waste intensity and treatment, emissions, and resource efficiency. On the contrary, the frequency analysis corroborated that leading industrial enterprises paid more-or-less balanced attention to all areas of sustainability, but at the company level. The article's primary result constitutes a methodology comprising six steps, respecting the results of the analyses carried out: specialIntscript Sustainability objectives definition; specialIntscript Establishing SKPIs cluster pool; specialIntscript Definition of criteria for selecting SKPIs clusters; specialIntscript Selection of SKPIs clusters; specialIntscript Definition of SKPIs and their parameters; and specialIntscript Development of SKPIs hierarchical structure.Web of Science9447846