Logistics Management

Digital technologies pervade products, services and companies. Keywords like Internet-of-Things, or big data analytics made their way on to almost every presentation and promotional slogan, seemingly becoming the backbone for every business organization, including supply management functions. However, the understanding of digital and related terms such as digitalization varies significantly. This research addresses the conceptual gap and seeks to identify characteristics and patterns of digital/digitalization. In addition, the terms digital, digitization and digitalization will be delineated. For this purpose, this research reviews existing literature. Digital as a phenomenon is structured through the application of deductive structure-discovering methods. The findings show the discrepancy between a technical and a managerial understanding. This paper proposes a conceptual model that structures the phenomenon into (1) digitization, (2) media convergence, (3) digitalization and (4) digital transformation.

Digital forwarders are a rather new phenomenon in the logistics market. They claim benefits for shippers and freight carriers alike and threaten established players in the market. As a lot of digital forwarders are still situated in a start-up phase, the phenomenon has not yet arrived thoroughly in scientific discussions. Digital forwarders are not necessarily alike each other but try to cover various market segments. To enable a profound analysis of digital forwarders, this paper presents an empirically based taxonomy to serve as a starting point for both theoretical and practical discussions. The taxonomy was created through an iterative process by analyzing major digital forwarders in the market. The taxonomy consists of three major types within the digital forwarding market, being international full service, direct contract trucking and niche digital forwarder.

Digital developments for logistics include many general and specific concepts as for example automation and Industry 4.0, Internet of Things (IoT), Physical Internet (PI) or Cyber-physical Systems (CPS). Overall, the human role in such settings will see profound changes – and many fears from workers are arising especially as there is no positive definition of new human work roles and expectations yet. We analyze the role of human intuition within an IoT and artificial intelligence application environment in logistics and supply chain processes and how it can be developed. Such a positive concept of increased efficiency by human-AI teams is an important cornerstone for digitalization as otherwise obstruction and fear may prevail with logistics and production workers.

Blockchain technology is associated with greatly beneficial applications for supply chain and logistics (SC&L), two of which are to trace goods across many actors, and to decentralize asset transfers without needing an intermediary. As a first use-case, actors from the Port of Hamburg are planning to implement blockchain to improve the sea freight container release by providing a common data platform for sea freight carriers, terminals, truck companies, and freight forwarders. Currently, releasing containers from the port’s terminals to trucks requires proof of ownership for the recipient to take custody. In practice, this proof passes through many hands causing duplication of information flow and ownership evidence. We conducted workshops and short interviews with experts providing first-hand insight into the use-case. Using blockchain in the process provides improvements such as traceable proof of ownership. The technology also faces barriers, with privacy concerns as one of the most prominent obstacles. A decentralized system could lead to business networks and company information being disclosed through data triangulation. We argue that privacy is a vital design consideration that affects the use of blockchain in SC&L generally.

Agent-based software attracts great interest in industry and research, the main reasons being the efficiency, robustness and complexity minimization of such multi-agent systems (MASs). In addition, the application possibilities are varied. This paper presents an overview of the different areas and topics in which MASs are used and specifically addresses the question of how MASs are used in supply chain management (SCM). For this purpose, the identified studies are classified in the supply chain planning matrix and gaps in research are subsequently identified.

Supply Chain disruptions can result in immense financial losses for affected enterprises. Quantitative models which analyze the impact of supply chain disruptions and, in particular, the possible application of mitigation strategies can support the decision making process of practitioners to better cope with disruptions. Since existing approaches have mainly investigated the effects of backup supply and information exchange, further mitigation strategies need to be implemented. Therefore, we present an agent-based model in which the supply chain entities set their prices autonomously and dynamically based on their experienced total costs. We analyze whether dynamic responsive pricing is an appropriate strategy in the event of a disruption in case of price-sensitive customers. Our results illustrate that, in many cases, a dynamic price choice delivers better results than a fixed price choice. The value of optimal price elasticity increases the lower the price sensitivity becomes, but the speed of growth decelerates. However, if the price elasticity is too high, strong costs can occur and fixed prices become advantageous.

Collaborative production networks are becoming a common setting in modern times. Issues of how and where data from IoT devices is stored and how communication between entities is conducted are not fully resolved. Decentralized approaches provide opportunities compared with centralized approaches in this context, especially in terms of trust and data security. One technology that has emerged recently is the blockchain technology. Although the potential benefits are well known, literature on blockchain in supply chain management and manufacturing is scarce. This paper presents a concept for the use of blockchain technology in distributed manufacturing. By utilizing smart contracts, manufacturing processes are executed on shared resources in distributed collaborative production networks. Benefits and risks of the proposed methodology are identified. The paper is completed by a case study with several companies operating in distributed manufacturing.

Robotic Process Automation (RPA) is currently a topic of interest for both research and industry. Many administrative tasks in operations consume a vast amount of time and create little value. The possibility that the responsible employees can quickly and easily automate these processes by themselves makes RPA a promising approach for many processes. However, RPA procedures are only able to automate a process in its present form. In this way, redundancies and excessive process steps are incorporated into RPA process flows. Combining RPA with the popular Business Process Management (BPM) approach poses a useful strategy as the as-is process is optimized first. The RPA procedure is then deployed on the optimized process to reach its full automation potential. This paper proposes a methodology to combine RPA and BPM and demonstrates the potential within a case study. Benefits, limitations, and further research opportunities are likewise addressed.

In the B2C e-commerce market, logistics plays a crucial role, not only as a cost factor, but also as a major success factor for the firms. It is therefore important for e-tailers to know which trends in B2C e-commerce logistics are considered to be the way forward. This also has implications for the directions of future research.

We present a systematic content analysis of 87 non-scientific, practice-oriented articles published on the internet. This results in a comprehensive overview of the trends currently being discussed in B2C e-commerce logistics. An additional correlation/association analysis reveals important relationships between the trends.

A total of 36 trends were identified. Overall, the trend towards faster deliveries was most frequently mentioned in the articles. Followed by more transparency across the processes (track-and-trace), logistics cooperation/outsourcing and more (smaller) urban warehouses.

To the best of our knowledge, this is the first scientific paper that systematically examines current trends in B2C e-commerce logistics practice. The results of this article can serve as an impetus for a variety of research questions, some of which we will touch upon in the course of this article.

This paper presents a review of recent trends in urban fright transportation, especially the typical last mile for logistic service providers. The high increase in shipped parcels over the last and presumably future years makes it necessary to particularly tackle the special logistical issues in large urbanized cities. Of course, one leading factor of this effect is the massive increase in online orders and the resulting deliveries. The advanced delivery concepts, which will be highlighted, try to overcome typical image in most urbanized cities, like congested roads, dangerous parking of delivery vans, high environmental effects, etc. The aim of this work is to present and analyze some of the recent trends in the last mile delivery to show their strengths and weaknesses. Especially, the impact of fast fulfillment offers, parcel lockers or stations, and advanced delivery vehicles like electric vans and bicycles or autonomous vehicles is in the focus.

In the coming years, the growth of e-commerce is expected to continue. Due to the increasing number of parcels delivered and existing capacity bottlenecks within the logistics service providers, concepts are emerging that are newer and more sustainable than traditional home delivery. This article analyzes (1) how customers perceive four alternative concepts (reception box, controlled access systems, trunk delivery, and crowdsourced delivery among friends) and (2) what influences acceptance, which is the decisive prerequisite for continued application. The data of 207 young German consumers were analyzed using descriptive methods and linear regression. The results show that the reception box achieves the highest acceptance level. In addition, perceived usefulness, security, and privacy are crucial factors in the customers’ intention to use delivery concepts. Since the study provides indications of the concepts’ success potential, it is of value to decision-makers from e-tailers, logistics service providers, and politicians that intend to incorporate new delivery concepts.

Lead times and carbon emissions are important factors for the design of supply chain networks since companies’ customers and various other stakeholders are getting more and more sensitive regarding both performance factors. In order to reduce lead times, companies apply different strategies like e.g. using faster logistic modes, locate production facilities and warehouses near customers or keep goods at stock. Nevertheless, these measures can have a high impact on both costs and carbon emissions. This paper provides a multi-layer, multi-product and multi-period supply chain design approach with a carbon cap-and-trade system considering lead times. The bi-objective model aims to minimize delivery lead time and discounted total costs. Furthermore, it is possible to keep units on stock and carbon emissions for all necessary processes to fulfill customer demand are taken into account. A computational study evaluates the solvability of the model and gives insight on the influence of delivery lead time and prices of carbon credits on the configuration of a supply chain.

This study compares emissions and transit times from an environmentally oriented and a time oriented routing of large freight shipments in the European rail/road transportation network. We use the terminal-and-service selection problem (TSSP) to find the optimal routings under the different objectives. We show that substantial differences exist between the emission oriented routing and the time oriented routing. A large-scale simulation study reveals that shipments in the emission minimizing routing emit on average almost half as much emissions as if they were routed with the objective to minimize transit time. At the same time, the average transit time of shipments in the emission oriented routing almost triples compared to the transit time in the time optimal routing. This shows by experiment that substantial emission reductions can be achieved in the European freight transport sector by a corresponding routing of shipments but that this comes at the cost of a much lower service quality.

The question of sustainability evaluation in global supply chains is often answered qualitatively with standards and status evaluations. However, dedicated management requires also quantitative approaches to evaluate the existing situation adequately. By applying a quantitative approach, this research work considers the global automotive supply chains with a Data Envelopment Analysis (DEA) sustainability evaluation and matching key performance indicators. In this setting, 13 automotive companies are analyzed for three different years 2015 to 2017. Results show that different OEMs are featuring very distinctive sustainability settings and results – deriving also optimization potential in comparison to the other companies.

Logistics is an important factor in humanitarian aid operations, whether after the outbreak of a natural disaster or during man-made crises. The speed and effectiveness of the various actors depend, among other things, on the appropriate design of the operations, which is why the scientific interest in this field has increased considerably in recent years. Unfortunately, there is a gap between science and practice [1]. One reason for the lack of applicability in humanitarian logistics research can be seen in the diversity of humanitarian actors. As there is no systematic characterization of humanitarian NGOs regarding aspects relevant from a logistical perspective, this paper aims at providing a suitable overview. A classification scheme is developed that includes the four dimensions of the mandate and sector, as well as the internal network structure and the various forms of on-site cooperation. The results can help to better align scientific research in humanitarian logistics and to foster practical exchange between the pertinent humanitarian organizations.

Urbanization trends confront companies with major challenges in their location management. Facility location planning is a complex task, as it usually involves multiple conflicting objectives. In addition, particularly at a regional decision-making level, companies are confronted with relevant dynamics that cause main changes of location requirements and characteristics over time. In order to ensure optimal long-term location decisions, planning approaches have to consider company-driven and municipal location developments over an extended planning horizon. To date, the variety of dynamic multi-criteria facility location planning approaches is generally scarce and possibilities for location developments are not given. Therefore, an appropriate model formulation for regional facility location planning considering relevant dynamics in location development is missing. In this contribution, the Regional Facility Location and Development Planning Problem (RFLDP) is introduced. For this purpose, an appropriate model is developed that provides an integrated decision-aid for a company’s location selection and development. In doing so, a strategic measure plan is determined through an allocation of company-driven measures under consideration of municipal location developments. The model is evaluated based on an illustrative example.

Customers’ desire for ever shorter delivery times forces companies to produce and deliver customer orders in the shortest possible time. To achieve this goal, companies can follow the distributed manufacturing paradigm and try to move their production sites close to their customers, which automatically leads to a location problem that has to be solved. Another approach is to coordinate production and distribution scheduling in such a way that punctual delivery can take place at the lowest possible cost, whereby stock keeping is largely avoided. The article shows for the first time that the traditionally successively planned problems of location planning and joint production and distribution planning can be combined in an integrated approach. A multi-period MIP model is set up to coordinate both subproblems, so that the overall costs incurred can be minimized. The simultaneous planning approach is illustrated using a numerical example and then evaluated critically.

In make-to-order (MTO) production, decisions are to be made about the jobs which are to be accepted and the sequence in which they are to be carried out. While in practice often rather simple rules like first-come-first-served (FCFS) are used, also strategies from the field of revenue management can be applied to achieve better results. In MTO not only the maximization of short-term profit should be focused on, but also the long-term perspective of performing good service in particular to valuable and returning customers is important. Therefore, in this work a booking-limit approach is combined with an order acceptance and scheduling model for a single machine environment to derive new strategies which take this aspect into account by defining different service levels to be strived at for the different customer segments. These strategies are tested on data settings with three customer segments. It turns out that a newly developed reversed nested booking limit approach (RNBL) leads to the best results regarding the conflicting aims of short-term profit maximization and customer satisfaction, whereas the classical partitioned booking limit (PBL) strategy is not recommendable.

The maximum deviation machine-sequencing problem with set-ups is a short-term decision problem in which a sequence of plastic outer-shell car parts are to be determined for launching in a paint shop. The decision is made under two objectives. On the one hand, the deviation between the original job number in the called off sequence by the customer and the position on which the job is painted should be minimized. On the other hand, the number of set-ups that occur when changing the colour in the paint shop should also be minimized. The situation is taken from a real-world situation of a supplier in the German automotive industry. Due to the large number of authentic influences, the problem is described in detail and classified according to existing characterizations of scheduling problems. An IP formulation is presented that is solved by the \(\epsilon \)-constraint method. Finally, an example is explained and computational results discussed.

Railway crew scheduling is the problem of generating feasible duties for the crews on a train to cover all trips at minimal cost. In Germany, regional passenger transportation consists of many distinct but interlinked networks, each with own crews. For efficiency reasons, we investigate the cost saving potential of scheduling crews collectively across multiple networks operated by the same company. To derive valid estimates, we develop a solution approach for the large-scale multi-network crew scheduling problem considering the network-specific constraints of attendance rates for conductors. Several studies have shown that partitioning large-scale problems improves computational times. We discuss guidelines for a problem-specific decomposition and derive three methods: a graph partitioning algorithm with adjusted edge weights and two variants of a network-based greedy decomposition heuristic. We assess their performance with a 2-phase optimization method using a hybrid column generation genetic algorithm and benchmark the results against a test run without decomposition. The tests show that maintaining the network structure while considering the connectivity between networks achieves the best results.

The locomotive assignment (or scheduling) problem is a highly relevant problem in rail freight transport. For a preplanned train schedule, minimum-cost locomotive schedules have to be created so that each train is pulled by the required number of locomotives (locomotives are assigned to trains). Determining locomotive schedules goes hand in hand with determining the number of required locomotives and this has a significant impact on capital commitment costs. Therefore, this paper proposes an improved heuristic for scheduling locomotives at a European rail freight operator. We show that a transformation of an iterative process to simplify the underlying network into a one-step procedure can significantly reduce computing times of a heuristic. Computational tests are carried out on the real-world instance as well as on smaller instances. The results show that the proposed heuristic outperforms an existing heuristic from literature in terms of both solution quality and computation times and, in contrast to approaches from literature, enables a solution of a practical instance in Europe.