Dynamics in Logistics

Technological advancements, increased globalization, political, social, and environmental concerns are changing the world we live in. Firms applying a global strategic management approach to enter a new country have to dynamically assess and align their performance objectives and micro capabilities with the changing macro logistics higher-order capabilities and resources at the regional or country level, and vice versa. Higher-order capabilities are present in a specific location and emerge over time as a result of systematic interactions across firms and institutions, whereas micro capabilities and performance objectives are firm-wide. Previous research on logistics and supply chain performance measurement hasn’t precisely addressed this macro-micro perspective with a global strategic management view. Furthermore, this aspect associating macro and micro capabilities and performance has been only projected with the help of case study-based empirical investigation within the previous research. To investigate the magnitude of this association, this research uses an integrative literature review strategy that includes a qualitative content analysis of the most important/cited supply chain performance measurement literature.With the help of a macro-micro supply chain performance measurement framework adapted from the supply chain performance measurement literature, the findings bring out the link between various macro logistics higher-order resources and capability elements (input) such as collaboration, knowledge sharing between logistics partners, transportation mode and energy efficiency and the strategic firm performance objectives and dynamic micro capabilities (output) such as sustainability and process innovation. It also extends framework propositions to guide future supply chain performance measurement research with a particular emphasis on global supply chain decision-making.

This paper examines the integration of regional food manufacturers into the supply chains of large grocery retailers. It discusses the current integration problems and challenges of those supply chain concepts which are presently used in the food retail sector. In this paper, we assessed direct delivery, milkrun and intermediate stations with regard to their suitability for integrating regional food manufacturers into the structures of large grocery retailers and used an Analytical Hierarchy Process (AHP) approach for the evaluation. The results of the AHP show that milkruns with an intermediate station are best suited for this type of supplier integration.

Globalization and developments in technology have contributed to the growth of numerous industries around the globe which are creating major impacts on today's supply chains. The supply chain has drastically changed under the open-ended influence of globalization. The need for different types of mobilities is increasing due to urbanization and population growth, rapid development in the E-commerce industry, and the growing expectation of customers. Crowd logistics is one of these techniques that is gaining rapid attention in the logistics industries and many start-ups have started using this method in their business models. This paper has investigated the crowd logistics and the challenges like user trust, data safety and security, security of the financial transactions for both the customer and the crowd and tracking service quality. Using the Blockchain technology, an e-commerce crowd logistics conceptual model is proposed. Moreover, through different scenarios the capabilities of the proposed model besides the detailed flow of the business processes have been discussed. The conceptual model of a Blockchain-based crowd logistics has used the functionalities of Blockchain such as the smart contracts and the DApps and especially has increased the flexibility of the crowd logistics system.

The COVID-19 pandemic research has emerged as a rapidly increasing field both from a practitioner’s and an academician’s perspective. In this paper, we present a synthesis of logistics and supply-chain related COVID-19 pandemic research and classify its results through a systematic literature review (SLR). To achieve this, the content of 87 papers was qualitatively assessed and classified according to their research themes and methodological approaches. The SLR findings pertain the changing mechanisms in logistics and supply chain management. The transformational change indicates insights for strategies, services, structures, and social issues. Accordingly, research avenues in logistics and supply chain management field are provided. Finally, propositions referring to research avenues and synthesis of the findings are suggested.

During the COVID-19 pandemic, an uncontrolled spread of the virus worldwide was observed. To reduce the world wide spread of highly contagious viruses in the future, it is essential to target situations with a high risk for spreading contagious diseases. The risk of rapidly spreading of diseases is characterized by the fact that prevention measures cannot be applied as required, e.g., due to faulty execution. This situation has been observed during the COVID-19 pandemic in passenger transportation, especially at airports. Air transportation enabled the rapid exchange of people to and from different locations, which may have contributed to the spread of the virus, especially at the beginning of the pandemic before hygiene measures and vaccines were available. It would be particularly interesting for airports to use innovative guidance and tracking strategies and assistance systems to allow people to travel further and react fast in circumstances similar to the one observed during the COVID-19 pandemic. This paper examines the challenges in preventing the uncontrolled spread of disease in air transportation of people and highlights the current state of the art. Furthermore, a new approach using technical systems is demonstrated and discussed in other scenarios, e.g., rail transport.

Over the last couple of years, there has been an increase in reciprocal discussion within the fields of supply chain resilience (SCRES) and sustainability (SSCM). Although some thematic overlap has been noted, SCRES and SSCM are generally still considered to be two separate domains. However current global SC events and legislative initiatives demonstrate why it is important to view SCRES and SSCM in combination to solve the problem of long-term supply chain risks and disruptions. The purpose of this paper is to explore whether SCRES and SSCM can be integrated, which overlapping elements need to be considered and which main congruent capabilities exist. The exploratory study applies a citation network and main path analysis based on a dataset of two previously conducted systematic literature reviews. The literature review revealed great potential for combining the two research domains based on four identified connecting elements - supply chain capabilities, practices, risks and performance. We also identified great potential for the application of two main congruent capabilities - transparency and big data analytics. The four elements identified provide us with a useful basis for creating a theoretical framework for integrating SSCM and SCRES. We also highlight the importance of the congruent capabilities that are required to overcome paradoxical tensions between resilience and sustainability.

This paper introduces the results of the study of the impact of blockchain technology on supply chain resilience.The study has been conducted using empirical research methodology. The qualitative research method of expert interviews has been applied to collect the data, and the text analysis method - for the data evaluation. Following capabilities of resilient supply chains have been studied: supply chain engineering, collaboration, agility, risk management culture and knowledge management. The experts in blockchain technology from the fields of logistics, production and consumer industry have been interviewed.The main finding of this study is the positive impact of blockchain technology on agility and collaboration in the supply chain.The implication for the supply chain management theory and practice is the understanding of blockchain technology contribution to enhance the resilience of supply chain and of the related challenges.

The production of raw materials has become a backbone of the manufacturing industries and will play a relevant role in the transition process to global sustainability by 2050. Due to its nature, the mining industry (MI) is highly prone to disruptions, by triggering a shortage of mineral raw materials or commodities in the downstream segment of many supply chains (SC). Lately, this latent vulnerability of the early phases of today’s SC has drawn attention to better understand Supply Chain resilience (SCRes) in mining. However, there is no common understanding on the concept of SCRes in the mining industry context in literature. This paper aims to contribute to a conceptualization of SCRes in the mining industry by defining and operationalizing main resilience principles and elements from the mineral SC topics and by taking into account the industrial nature of the MI. The baseline of this paper is a literature review based on 27 relevant articles systematically selected from peer-reviewed papers from Scopus and Web of Sciences.

Social media analytics (SMA) is claimed to be an opportunity for practical inquiry to create new knowledge and possibilities but is only slowly finding its way into practice due to uncertain information quality. Good Operations and Supply Chain Management (OSCM) decisions are just as good as the data they are based upon. A more detailed consideration of data quality is needed, especially when natural language data is processed for decision-making. Motivated by recent calls in the domain, the purpose of this study is to investigate how big data quality is considered in SMA for operations and supply chain performance. The study employs a directed qualitative content analysis of 56 research contributions based on the re-analysis of a previous systematic literature review. The results reveal that within performance-oriented SMA literature, intrinsic and contextual data quality are not comprehensively addressed by OSCM-research to date. More particularly it is shown, that contextual data quality assessment remains a challenge for the analysis of textual social media data. The study contributes by reporting how data quality is considered for SMA in operations and supply chain performance management (OSCPM) literature from an intrinsic and contextual perspective. Based on the results of this analysis, data relevancy and data believability are identified as levers to reduce information uncertainty in SMA-aided decision-making, paving the way for future research on contextual social media data quality in OSCM.

The digital transformation of industry and the accompanying virtualization of supply chains are creating new physical, contractual and organizational boundaries between the physical and virtual world. These interfaces need to be analyzed in order to solve potential challenges at an early stage. The aim of this paper is to provide an overview of future interfaces between smart factories, systems and parties in the supply chain. Furthermore, it is fundamentally investigated how they are managed and communicated. The approach is based on a comprehensive literature review, in which the design preconditions of smart factories and adaptive environments are examined. Subsequently, the review results are analyzed using the three interface management steps definition, control and communication to identify their future controlling. The paper demonstrates how five technology network levels can make a significant contribution to successful operations by handling interfaces and defining responsibilities between transformed processes in the supply chain.

In maritime logistics, mathematical optimization and simulation are widely-used methods for solving planning problems and evaluating solutions. When putting these solutions to test, extensive and reliable data are urgently needed but constantly scarce. Since comprehensive real-life data are often not available or are classified as sensitive business data, synthetic data generation is a beneficial way to rectify this deficiency. Even institutions which already own comprehensive container flow data are dependent on synthetic data, due to the need to adapt and test their business models to uncertain future developments. A synthetic data generator that creates incoming and outgoing containers from the perspective of a maritime container terminal has already been proposed. However, since its publication more than 15 years have passed and the industry has changed. This justifies to rethink, rework, and improve the existing solution. This paper presents a synthetic container flow generator which allows the user to create synthetic but yet realistic data of container flows for maritime container terminals. After the introduction and motivation, this paper provides an overview about the state of the art of synthetic data generators. Then, the conceptual model of the generator is presented. Furthermore, an exemplary visual validation of the generated output data is shown. The paper closes with a discussion and outlook on planned future developments of the software.

Port storage is an important resource in port operations, complex to manage and increasingly scarce. Considering the great disturbance in port operations due to lack of storage, this paper aims to propose a simulation-based approach for dimensioning the storage of port terminals. Discrete event simulation (DES) was applied in a test case of a container terminal, whose scope contains truck gates, container yard and ship berths. First, operational stability regimes were identified, which are dependent on the availability of storage static capacity. This availability, instead of directly affecting mean operational indicators, is related to the probability of storage shortage over time. Later, an approach for dimensioning port storage by means of simulation was proposed, which is based on recurrence time for a maximum required storage in the year. This approach is oriented to statistics and generalizable for port terminals with different characteristics. The results of this work may assist port managers to plan terminals with different cargo types, providing greater assertiveness of investment, minimizing the occurrence of congestion and hence improving port performance.

Maritime container terminals play an important role in global supply chains. In addition to the rapid handling of containers, the reduction of CO2 emissions is also increasingly crucial for terminal operators. This can be achieved by integrating renewable sources such as photovoltaic or wind energy. While energy supply and demand must be in balance, the amount of energy produced through renewable sources cannot be controlled as it depends exclusively on variable weather conditions. One option for efficient use of renewable energy sources is to modify energy consumption by intelligently controlling processes.This study aims to answer the question to what extent energy intensive consumption processes at container terminals can be adapted to a volatile energy supply. A discrete event simulation study is conducted to analyze handling processes by quay cranes as main energy consumers depending on the availability of photovoltaic energy. Therefore, the operating times of quay cranes are partially limited to daylight hours. Only a low number of quay cranes is deliberately deployed when a time window occurs between the predicted end of loading of the vessel and a departure time of the vessel determined by the tidal range.The simulation results show that by flexibly using certain quay cranes only during daylight hours the percentage of energy produced by renewable resources can be increased by up to 50%. As a result, handling-related CO2 emissions can be reduced. The study offers an approach to a sustainable energy supply on terminals by reconciling energy use and environmentally friendly generation.

LNG as ship fuel is an emerging market. LNG infrastructure is a prerequisite for the adoption of LNG technology. One important element of LNG infrastructure is LNG bunker ships. By global comparison, Northern Europe is a region with dense traffic of LNG vessels. The bunker ship fleet in this region consists of several units in operation, with numerous others on order. The operational profile, assessed based on AIS data for the months June to September 2019, confirms that LNG as ship fuel is still a niche market. Ship-to-ship LNG deliveries are concentrated in several hotspots. The fleet of LNG vessels supplied by the Northern European fleet of LNG bunker ships is rather diverse in terms of ship types. The LNG bunker ships with a larger carrying capacity also engage in feedering LNG to smaller-scale LNG terminals. With a future denser net of LNG infrastructure and a larger LNG bunker ship fleet, LNG bunker ships are expected to specialize increasingly with regard to the services they supply.

With its potential of increasing economic efficiency, safety and process reliability automated driving offers substantial benefits for road freight logistics. Limited by technical and regulatory hurdles, first use cases of automated driving in logistics are in pilot testing stage on private areas or include traffic scenarios with fixed, foreseeable environmental conditions. These conditions partially exist in seaports. This article addresses the feasibility and specific requirements of automated trucks when implemented in seaport systems. A hybrid study consisting of a semi-structured interview survey and an extended project review is conducted to analyze the applicability of fully automated vehicles for the transportation of containers in port areas and its resulting requirements. Firstly, a review of currently existing automated road freight projects and their findings about container transportation is given. Secondly, port-related requirements for automated road freight transportation are derived as a result of the interview survey. The authors find main requirements in the technical feasibility, the operation of the vehicles as well as the organizational and process integration into the port system.

Unmanned and autonomous vessels are a multi-disciplinary research topic. The related legal research analyses international maritime law and depicts several legal challenges to be met. The article provides an overview of the legal literature, focusing on three aspects concerning the commercial shipping industry. First, the legal status of unmanned and autonomous vessels in maritime law will be discussed. Almost unanimously, such vessels are considered as ‘ships’ within the scope of maritime law. Second, the minimum safe manning requirement governed by several conventions will be examined. The main issue is whether the statutory provisions prevent unmanned operations at all, or whether a more favourable interpretation can be applied. Third, the duty to maintain a proper lookout will be examined. Scholars are reluctant to interpret the respective rules broadly and call for amendments considering the technical peculiarities.

This study compares carbon dioxide (CO2) emissions in multimodal transport chains. Given the recent pressure towards responsible logistics, the International Maritime Organization (IMO) has given several resolutions aiming to decrease all emission types in sea transports. The study analyzes different combinations of alternative delivery chains of paper rolls from the production facility to the customer. The results show emissions originating from each transport leg. In the case of land transports, the greenhouse emissions from solely truck-based deliveries are two times higher than in intermodal solutions, i.e. different combinations of truck and rail. Electrified rail transportation lowers the overall emissions significantly. This indicates that a potential for electric trucks, particularly in the case of short distance deliveries.

The “Intelligent Container” for remote monitoring of refrigerated transports of fresh fruits already implements typical features of digital twins, including remote sensing and modeling of physical and biological objects. This article asks how the Intelligent Container can be extended to make the best use of digital twin concepts. Existing applications in agricultural science focus on offline simulation models that can predict shelf life and the effects of packaging on cooling but cannot integrate real-time data or correct their current estimates according to those data. This update feature is considered a key component of digital twins. The related challenges and algorithms can be best understood from the viewpoint of systems theory and state-space description. Internal properties of real objects can be either directly measurable, hidden, or unobservable, and implementation of the update process should be adapted accordingly. Using ocean transport and banana processing as an example, this paper demonstrates how models can be made “updateable”, in addition to discussing the necessary steps for linking different sub-models over a standardized platform according to the “publish/subscribe” pattern.

The paper focuses on the static dial-a-ride problem with ride and waiting time minimization. This is an important problem setting of significant practical relevance, as several ridesharing providers launched in recent years in large cities. In contrast to the standard dial-a-ride problem, these providers focus on the general public. Therefore, they are amongst others in competition with taxis and private cars, which makes a more customer-oriented objective necessary. We minimize the sum of relative detours of all customers. The paper introduces upper bounds for the arrival times and an initial lower bound for the objective value. Our approach is tested in a computational study with realistic test instances.

The trends of autonomous transportation and mobility on demand in line with large numbers of requests increasingly call for decentralized vehicle routing optimization. Multi-agent systems (MASs) allow to model fully autonomous decentralized decision making, but are rarely considered in current decision support approaches. We propose a multi-agent approach in which autonomous vehicles are modeled as independent decision makers that locally interact with auctioneers for transportation orders. The developed MAS finds solutions for a realistic routing problem in which multiple pickup and delivery alternatives are possible per order. Although information sharing is significantly restricted, the MAS results in better solutions than a centralized Adaptive Large Neighborhood Search with full information sharing on large problem instances where computation time is limited.

Collaboration in transportation is important to reduce costs and emissions, but carriers may have incentives to bid strategically in decentralized auction systems. We investigate what the effect of the auction strategy is on the possible cheating benefits in a dynamic context, such that we can recommend a method with lower chances for carriers to cheat. We consider both a first-price auction system and a second-price auction scheme. Contrary to what was expected, a second-price auction scheme gives more room for successful strategic behaviour, while it also results in more rejected orders. A first-price auction scheme might be useful in practice if the profit shares that are allocated to the winner of an auction are selected carefully.

Artificial Intelligence (AI) has the potential to solve the sustainability and service issues of Urban Last Mile Logistics (ULML). High delivery costs, noisy and polluting traffic, bad working conditions and failed delivery attempts could be addressed by measures like AI-based demand forecasting, intelligent tour and route optimization or digital delivery assistance. However, there is little empirical evidence on the extent to which AI can do this. Thus, the purpose of this report is to elaborate the relevance of AI for solving ULML problems by identifying use cases, potentials and challenges of implementing AI in ULML planning and execution. Therefore, we conducted 15 explorative expert interviews with ULML companies and analyzed them using qualitative content analysis to obtain an initial orientation in this new empirical research field. The findings indicate, among others, that the ULML industry is in the very early stages of AI implementation and that there is relevant potential for efficiency and service improvement. However, one of the key challenges is the perceived high level of uncertainty about achieving economic benefits while having high investment and AI operating costs. The practical contribution of this paper is to provide guidance for ULML companies starting AI activities. The scientific contribution is to show the practical need for AI implementation and to derive concrete research needs for the development of suitable AI methods and algorithms.

Maritime container transport is the backbone of international trade and supply and distribution processes in global manufacturing networks. However, the level of digital support of container transportation is still lacking, compared to other fields. This article shows how an inter-organizational digital platform can facilitate the digital interaction between industry and logistics stakeholders. It describes four processes where the platform can support transactions between shippers, carriers, and other stakeholders involved in transportation processes. Use of the digital platform can decrease waiting times for process-related documentation and reduce effort, duplication of tasks and inaccuracies, directly increasing the performance and resilience of customers and providers of transportation services.

With the growing carbon-di-oxide (CO2) emissions and road vehicles being responsible for almost 75% of the emissions, it is imperative to put in efforts to reduce CO2, especially in the transportation sector. Ridesharing services enable users to use cars more wisely by filling the vacant spaces with passengers having similar itineraries and time schedules. However, most of the ridesharing services are dependent on a third party for the interaction between the riders and drivers. Relying on a third party and central server can turn out to be expensive since a commission is charged by the third-party; risky since it is more prone to going down and malicious attacks; might not lead to the most appropriate matches; and in case the security of the service provider is not protected and jeopardized, there are high chances of the service being disturbed and the data of the users being disclosed or tampered with. This paper has proposed SRP-A sustainable ridesharing platform that replaces the third party/central server by Blockchain technology. This platform makes use of Blockchain’s capabilities such as consensus mechanism (Proof of Stake); smart contracts; and solvers, making the entire system more secure and less prone to attacks along with tackling the issue of excessive emissions of CO2 in the environment.

Yard management is crucial for logistics and transport operations due to the high influence towards smooth and efficient intralogistics on dedicated depot sites of logistics service providers. Yet, this field has interesting new insights to offer, especially regarding prioritizing and decision-making concepts with the support of Multi-Criteria Decision Analysis (MCDA) tools. Within this publication five critical yard sub-processes are identified and prioritized with the AHP methodology in the following order: management of the shunting system, registration at the gateway, allocation of trucks to gates/parking spaces, removal of a transport unit from the gate and exit control.

Cargo throughput capacity is a very important and basic indicator for port expansion and operation and maintenance. As a non-linear dynamic indicator affected by multiple variables, timely estimation of cargo throughput capacity can schedule the usage of quay cranes, thereby reducing port energy consumption. To solve the estimation of the long-term scale unit, to complete the estimation in the multi-modal situation through the method of system evolution and to include the quay crane scheduled situation into the system, we propose a quantified state filter within partial state delay to estimate the throughput capacity based on the semi-Markov jump systems. The $$H\infty $$ H ∞ stability can be assured under the conditions provided by the Lyapunov approach. Numerical results show that our methods could provide a different prospect and satisfying performance in the throughput capacity quantization estimation.

Flows are central to the study of transport & logistics and telecommunication. The efficient flow of materials, money and information are vital to the success of any transport & logistics system such as a supply chain. Telecommunication relies on accurate data flows to ensure seamless communications. While both these sectors use the term “mobility” to emphasize their field of study, the literature suggests that the level of cross-fertilization is quite limited. This manuscript investigates pixel oriented mobility modelling, which has its roots in telecommunications, as a viable method to study flows in transport & logistics. Breaking down paths and its characteristics into pixels and applying the characteristics of the unit of study allows users to properly model and optimize the system. The manuscript suggests numerous applications of pixel oriented mobility in transport & logistics and encourages its use as an innovative method to expand the body of knowledge. Examples to apply pixel oriented mobility modelling in transport & logistics would include vehicular traffic, port and shipping operations, warehouse operations, freight movements and the aviation sector.

Evolutionary algorithms are a well-known tool for optimising problems that are hard to solve analytically. They mirror the evolutionary approach of recombination and mutation as well as a selection process according to the fitness of an individual. Individuals who violate set search space restrictions are either killed at birth or penalised in their fitness calculation. Which possibility is best to choose depends on the problem at hand and therefore subject to change. Furthermore, restrictions can be vague, for example, when stemming from experiments. We propose a noise-sensitive penalty for violating restrictions and develop a framework where an expert might choose which penalising technique to choose for what kind of restriction. We evaluate our configurable approach against configurations where one technique is used for every type of restriction and find that our approach achieves better results than a strict configuration. Additionally, the noise-sensitive penalising method allows individuals to survive, which may only violate the given restrictions due to a noised testing environment, leading to better results.

This paper proposes a System Dynamics (SD) approach to support decision-making to manage variety induced complexity. Offering product variety leads to increasing internal complexity, which results in higher inventory and increasing setup processes. Managing the trade-off between marketing-, logistics- and product management complicates the decision process in offering sufficient variety to the market. This leads to numerousness of stock keeping units (SKUs), all of which are required to maintain various key performance indicators and inventory levels. Managing this variety induced complexity to optimize the overall business success requires an understanding of its System Dynamics behavior and interrelation. The reviewed literature reveals that existing metrics do not capture the necessary dynamic system behavior sufficiently to measure the impact of long-term strategies. The proposed model combines System Dynamics and the portfolio-fitness index (PFI) metric to capture the required dynamic system behavior. Applying scenarios to a national electronics company through a case study demonstrates the ability to manage complexity using System Dynamics and the PFI metric. The outcome of this research is a System Dynamics model that can manage variety induced complexity by offering scenario analysis to support decision-making. The findings in the case study suggest that reducing the complexity does not automatically lead to competitive advantages. Understanding the dynamic behavior of complexity impacts forms a basis for decision-making. Thus, the model’s findings provides insights to manage complexity in the most efficient manner.

Production planning and control has a great influence on the economic efficiency and logistical performance of a company. In this context, this article gives an insight into the use of simulation as a virtual model of a filling machine in the process industry. Furthermore, it shows the possibilities of a reinforcement learning (RL) approach for dynamic lot sizing. The contribution indicates a possible implementation in an ERP system and shows how a decision support tool can support the planner to save up to 5% of costs compared to a human planner and a heuristic approach proposed by Groff.

The scheduling of complex manufacturing systems requires the integration of production, inventory, and maintenance to obtain a robustness control and performance. In this context, this paper proposes a data-driven simulation-based optimization approach for production scheduling to optimize job shop sequencing decisions considering inventory availability and machine breakdowns. The approach consists in the implementation of a combination of genetic algorithm with discrete-event simulation model aiming to optimize the selection sets of dispatching rules for groups of machines in the job shop to improve the manufacturing system performance. The approach was evaluated in a machining process, part of a job system of a Brazilian industry of the dental and medical sector, achieving better performance for the average lead time with the improvement of 10.27% compared to the current strategy of the company.

Decentral production control plays a crucial role within the paradigm of Industry 4.0. Due to fast and flexible decisions on allocation and sequencing, there is no baseline schedule in advance. Moreover, the fourth industrial revolution modifies the organizational structures in the area of human resources, too. Despite changed tasks, the human is still a key factor with a coordinating, controlling and directing function—but without knowing the exact time of requirement. The workers are not available 24 h a day but are provided individually via personnel schedules. Creating a personnel schedule for the changed tasks without an overall baseline schedule becomes a crux of efficient staff deployment in the vision of Industry 4.0. This article presents the current state of this research aspect and derives a challenge for future research.

Maintenance 4.0 (M4.0) is described as an innovative and optimized maintenance strategy, integrating existing practices with technologies from Industry 4.0. These technologies have been impacting and transforming several production processes and also maintenance management. It is possible to extract increasingly accurate data about the assets through sensors and other technologies in real-time. However, companies still struggle during the Maintenance 4.0 implementation, becoming an open field for research. Based on this, we developed a consistent literature review, followed by a SWOT analysis (strengths, weaknesses, opportunities, and threats) of Maintenance 4.0 implementation. The synthesis of the results was divided into bibliometric, thematic, and SWOT analyses. The bibliometric analysis showed the continuous growth of the theme in recent years. In the content analysis, the perspectives were grouped into (i) conceptual, theoretical studies, (ii) empirical studies/applications. In the SWOT analysis, we demonstrated the main strengths to achieve the benefits of implementing Maintenance 4.0. The weaknesses and threats identified may challenge some organizations. The implications of this study are in the organization of a comprehensive body of knowledge on Maintenance 4.0. From a practical point of view, it is hoped that this research can be used as a guideline, providing decision support to maintenance managers.

There is intense competitive pressure in the third-party logistics industry. As a result, logistics providers have to respond to tenders quickly and with convincing concepts. This article shows how logistics process planning in tender management can be accelerated using methods of supervised learning. Under the premise that similar processes from past projects can be transferred and adapted to a new project, an assistance system suggests appropriate process steps in the form of MTM (methods-time measurement) codes to the planner using N-Gram analysis and a decision tree. This procedure accelerates the process planning and can lead to an increase in the quality of the planned logistics processes.

Packaging is a logistics function which generates significant amounts of waste. The fashion industry is the business area with the highest share of e-commerce shipments and is therefore responsible for substantial amounts of packaging waste. A shift from linear to circular packaging models is therefore highly desirable, especially in the fashion industry. However, although many fashion retailers recognized the importance of sustainability, hardly any company establishes circular packaging solutions. This paper aims to identify the challenges which prevent fashion retailers from introducing circular packaging. Additionally it will be shown which enablers drive the development of circular packaging. Expert interviews were conducted with companies all along the circular packaging value chain in the fashion industry. A PESTEL analysis shows the Political, Economic, Social, Technological, Environmental, and Legal factors which currently inhibit circular packaging. Three types of enablers where found to be relevant, namely profit, consumer and politics. The findings provide valuable input to develop circular business models promoting the use of circular packaging in the future.

The research paper analyses the level of stress and functional state of the drivers in urban traffic congestion. Therefore, the primary objective of this research is to describe patterns to assess fatigue of the driver during urban traffic congestion. The Electrocardiography (ECG) data is used to assess fatigue of the driver. The model comprising of influence of traffic congestion on the functional state of the average driver, allows us to predict changes to the driver’s state depending on the age, the duration of the traffic congestion and initial state prior to congestion. The value of the initial functional state affects the driver’s functional state during his/her stay in a traffic congestion in different ways. The rising of tension during staying in traffic jam is 10–12% after 7–10 min. The research uses system analysis for data analysis; electrophysiological methods in determining the functional state of the driver and mathematical statistics methods were used during the development of model for analysis of the functional state of the driver.

The COVID-19 pandemic brought public life to a near standstill. Precautionary practices, such as face masks and safe distance, were established to protect people. In addition, various tracking methods were developed to detect possible contacts. In this paper, we review suitable technologies to indicate a solution for a people guidance system, which actively prevents these contacts by suggesting routes through large areas (e.g. airport terminals or train stations). By tracking the people and using destination information, e.g. from tickets, the system should be capable of calculating routes and visualise the suggestion to each person individually.

The advent of COVID-19 led to a surge in online shopping. As a result of the health crisis, companies reduced their priorities on environmental issues. However, consumers’ concern for sustainability is on the rise. Thus, the purpose of this paper is to examine the impact of the COVD-19 pandemic on e-commerce consumers’ pro-environmental behavior. Accordingly, we conduct an online survey exploring consumers’ online shopping frequency and engagement in environmentally friendly practices before, during and after the COVD-19 pandemic. Applying the Wilcoxon test to compare these three stages, we are able to investigate the shift in e-commerce consumer pro-environmental behavior triggered by COVID-19. The results indicate that the shopping frequency has increased substantially since the start of the pandemic, but will drop down after the end of the pandemic. Moreover, the COVID-19 pandemic was detrimental to consumers’ pro-environmental behavior: during the pandemic consumers showed a tendency towards less environmentally friendly behavior but they have strong intentions to adopt more eco-friendly practices after the pandemic ends.

Considering the increasing freight traffic and the associated negative effects, (e.g.: congestion, unpunctuality, waste of energy, harmful environmental influences etc.), systems for improving the status quo are being researched and implemented. The understanding and the acceptance of possible future applications in road freight transport must be brought to the users by appropriate means. This paper presents a prototype game on the topic of “Intelligent Transport Systems for Road Freight Transport”. Based on the preliminary work, a prototype game is designed. Questionnaire results show that more than half of the participants (n = 32, average age = 43 years) could not relate to intelligent transport systems. By using the serious game presented here, the understanding of and need for intelligent transportation systems in society can be achieved. The decision to use intelligent transport systems in the logistics industry can be simplified for decision makers by playful interaction and beneficial consequences between the game elements. This paper describes the development stages of a serious game from problem definition to prototyping in the field of intelligent transport systems.