Logistics Management

Despite the growing maturity of Blockchain technology and an increasing deployment in Supply Chain and Logistics, many small and medium-sized enterprises (SMEs) struggle to use the technology for their benefit. Based on 27 expert interviews, we develop a typology of Blockchain adoption approaches for SMEs and discuss their implications. We find that SMEs can approach the technology as either an Observer, a Cooperator, or a Service Provider based on their technological expertise, the expected relevance of the technology for their organization, and their market power.

Digital technologies are omnipresent in today’s world, and successful involvement in the digital transformation becomes increasingly important to companies from all industries. To stay competitive, companies are pressured to rethink their supply chains and adapt. However, the topic of digital supply chains is still in its infancy. The goal of this paper is to shed light on the preconditions and challenges regarding the transformation. The different areas of the digital transformation of supply chains, namely the strategic, organizational, process & method, and technological area are considered. Preconditions and challenges for companies in the digital transformation of their supply chain are extracted from literature with literature reviews and followingly discussed with practitioners to capture the magnitude of the transformation process. Furthermore, differences between academia and practice are revealed, and future research opportunities are identified.

Road freight transport has become a vital part of today’s life, and its importance will only rise in the future due to developments such as increasing e-commerce orders. Transport planning has become more complex and has to deal with many difficulties such as demand volatility, high customer expectations, or the consideration of legal regulations. The fast-changing environment makes dynamic and highly reactive planning, typically done by the transport coordinator, necessary. Incorporating technology is a promising way to deal with the complexity and dynamic environment of transport planning. Indeed, more data and computing power is available than ever before, and developments coined under “digitalization” are transforming transport planning. Before understanding the benefits digitalization can have, examining its influences on the industry, the roles participating in transport planning, and their relationship is necessary. Hence, this paper establishes an overview of roles associated with transport planning, identifies digitalization’s effects on transport planning and specifically the transport coordinator, and then provides an updated overview of roles considering these effects. The results show that new relations and roles are important for transport planning. The role of the transport coordinator itself is transformed mainly due to the emergence of platform-based business models.

So far, the term ‘smart contract’ is mainly reflected in research addressing technology, IT applications or peculiar businesses like stock exchange or bitcoin. However, a smart contract is an issue of purchasing and supply management (PSM) due to its potential to connect suppliers with customers through state-of-the-art digital means. Thus, we identify the need to define the concept of smart contracts within the PSM domain. Therefore, the main research aim is to clarify the construct of a smart contract. The core finding is that a deconstruction of existing definitions reveals peculiarities in the perspectives and domains but gives insufficient validity to PSM analysis. Thus, the result of this research is a definition proposal for smart contract. The implications of this analysis could pave the way for a wider discussion of how smart contracts affect PSM outside the typical blockchain and bitcoin arenas.

The Covid-19 pandemic drives the need for Intelligent Process Automation (IPA). However, the technology’s adoption for purchasing and supply management (PSM) is still in the initial stage and has hardly been explored. Therefore, this empirical multiple case study builds on 19 organizations, including private and public procurement departments, consultancies, and IPA providers, to examine the impact of IPA on the PSM function. The findings provide comprehensive insights and reveal suitable operational and strategic application areas as well as several benefits related to IT systems and data, operational efficiency, process quality, and employee satisfaction. The study also identifies various technological, organizational, and environmental challenges that need to be overcome for further IPA adoption. Therefore, future research directions and managerial implications are outlined.

The digitalization of supply chains offers great opportunities, especial-ly with regard to procurement. To effectively implement innovative concepts, it is necessary to evaluate procurement departments' current situations and create target-oriented recommendations for action. Suit-able maturity models can help to achieve these goals effectively. However, most existing models do not consider the specifics of pro-curement and Industry 4.0 technologies and fail to define realistic digitalization goals. In this paper, a model called MAP 4.0 is proposed to assess procurement organizations' maturity and derive realistic tar-get conditions. A qualitative and deductive methodology is applied to analyze existing maturity models. Based on the findings, the maturity assessment model MAP 4.0 is developed according to the methodolo-gy of de Bruin et al. (2005). By focusing on the specific field of Procurement 4.0, the requirements and needs can be considered in more detail. In addition, target states and recommendations for actions can be devel-oped. The model includes the relevant dimensions of digitalization and defined weighted items for measurement. In this way, the study lays the foundation for future research, provides valuable insights for procurement managers, and can contribute to the digitization of procurement.

In berth allocation planning, container vessels are to be assigned to berthing locations and times at the quay of a container terminal. Terminal operators often aim to provide the best possible service quality to the shipping companies, i.e. especially short waiting times. However, the actual arrival times of vessels are uncertain due to external influences, e.g. wind and current or technical defects, which impedes the planning and may lead to conflicts with respect to scheduled berths. In this work, Machine Learning techniques are applied to enable the determination of patterns in AIS data and hence to develop forecasts of the arrival times. Moreover, with a robust optimization approach based on Dynamic Time Buffers, uncertainty is proactively considered in the planning phase, resulting in a robust berthing schedule. The results of this new approach are evaluated from an ex post perspective using real ship data and actual ship arrival times. It is shown by a numerical study that the average number of conflicts can be reduced significantly by this approach and that the new concept improves the schedules’ robustness.

Marshalling yards are nodes in rail networks where incoming trains are decoupled and sorted to outbound trains. An algorithmic approach to sort wagons of incoming trains is to apply sorting schemes. Well investigated sorting schemes are Sorting by train, Sorting by block, Triangular Sorting and Geometric Sorting. Throughout the applying of sorting procedures greenhouse gases are emitted, e.g. by shunting locomotives. A simulation study is conducted in which emissions in marshalling yards can be calculated. For this purpose a formula based analysis of the sorting schemes is carried out. The simulation shows that Sorting by block performs best w.r.t. less emissions for most scenarios. On the other hand Geometric Sorting is the worst sorting scheme, i.e. for most scenarios emissions are higher in comparison to other sorting schemes.

Although retailers and logistics service providers offer various last mile delivery options, the level of individualisation regarding integrating consumer demands and expectations for home delivery is currently low. In order to offer such individualised delivery options, it is necessary to understand consumers' home delivery needs and wants. Accordingly, this paper examines consumer preferences regarding delivery options, focusing on online grocery retailing using a discrete choice experiment approach. Our results show that consumers can derive high benefits from the individualization of delivery options, especially in the areas of vehicle type and place of delivery. Also, packaging and delivery time are relevant delivery options for consumers but considered to be less useful than transport vehicles and place of delivery, while consumers do not prefer the selection of a time window on the day of delivery.

We study a multi-skilled worker assignment problem with multiple shifts in cell manufacturing. The problem is motivated by a real planning problem in protective device manufacturing. Two assignment decisions must be made simultaneously: Requested orders and multi-skilled workers are assigned to production cells and shifts. Specifically, a certain number of workers process orders in one production cell, whereby the processing times of the orders vary depending on this number of assigned workers. We also take into account family setup times, invalid worker-cell combinations, and a limited number of possible shifts for each order to process. A 0–1 integer linear programming model that minimizes the total number of cells opened for production in all shifts is introduced. The model is tested using real-world data. We show that the generated solutions are suitable to support production planners and can be used to reveal problems of the production such as lack of workers and skills or disruption-prone cells with a high utilization.

Our aim with this paper is to formulate a platoon relocation model and to apply it on a large-scale one-way car sharing system. We analyze the system performance in terms of realized customer trips and relocation costs with regard to conventional, platoon or none relocation tours. We analyze the efficiency potential of platoon relocation on simplified instances that mimic demand and city structures. In these examples, the platoon approach can be up to 60% more efficient in relocating cars compared to the conventional one. We formulate a time-expanded network model for platoon based relocation in station-based one-way car sharing systems and apply it to a self-designed case study from the city of Hanover. In order to cope with runtime restrictions, we delete redundant network arcs. Our results show that in the large-scale Hanover case example, the platoon tours lead to a moderate increase of customer trips and reduced relocation costs.

This paper presents three explorative scenarios for reverse logistics in the textile and apparel industry in Germany for the year 2035. The scenarios build upon six key factors which represent technological, legal, societal, and sustainable perspectives. Scenario “Sustainable policies” refers to companies in the textile and apparel industry changing their production philosophy towards sustainability and producer responsibility due to stricter legal recycling regulations. Scenario “Consumer awakening” assumes a rise in backward flows of recyclable apparel due to consumers demanding sustainable fashion and recycled apparel. The third scenario “Profit over humanity” is negative in its basic tone and describes a profit driven production philosophy where sustainable aspects such as recycling-oriented product development are not considered at all by society.

Different modality concepts for freight transportation have been developed over the past decades. One inherent emission reduction strategy they all include, by definition, is the strategy of modal shift. Several literature reviews are made to identify main characteristics of multi-, inter-, co- and synchromodality with a special focus on the most current concept of synchromodal transportation. The analysis further focuses on the environmental orientation (sustainability, emission reduction and modal shift) of the concepts. The emission reduction importance for modality concepts is analysed, to determine if they can be used by different actors in the transportation chain to reach emission reduction and to ascertain if courses of action for low-emission transportation planning can be deducted. The differences between the modality concepts are not very distinct, emission-reduction is expected to be achieved by using these concepts and courses of action are usually developed in form of methods or tools for specific problems and users. They are difficult to access or transfer onto other cases or user. A standardized process usable for different Logistic Service Provider is needed.

National and international authorities, specifically the European Commission, have set challenging targets for the reduction of carbon emissions. Transportation is an important lever for emission reduction due to the share of over one quarter of the total emissions in Europe. In particular, road transportation, which accounts for 75% of the transported goods, offers saving potential. The improvement of traffic flows has been identified as a field for prospective energy consumption reduction, among others, by automation, especially platooning. Truck platooning has been discussed from a technical perspective, as well as with a focus on advantages. Risks have so far been included by some authors but not examined holistically. To close this research gap, a case study was conducted in this article to investigate the risks associated with platooning. For this purpose, eight semi-structured interviews were conducted with experts from logistics service providers and truck manufacturers. Eighteen risks were identified and classified into six dimensions (economic, social, legal, IT-related, technical, and ecological), resulting in a risk framework. The results extend the research on truck platooning by clarifying the risk perspective of this concept and provide a starting point for a discussion on the feasibility.

Disruptions pose a significant threat to supply chains, as their impact may have devastating consequences. As a result, the research in supply chain resilience has increased immensely over the last years. This growing research’s particular focus is the kind of disruptions supply chains have to face and consequently what capabilities supply chains should inherit to be more resilient. This article focuses on the supply chain structure and investigates how resilience depends on it. Thus, a LR was conducted to identify the vulnerabilities the supply chain structure is exposed to and the strategies that exist to counteract these vulnerabilities and increase supply chain resilience. Findings show that vulnerabilities are manifold and that there is no strategy that in itself leads to supply chain resilience. It is especially crucial first to examine the supply chain structure, identify the specific vulnerabilities to the supply chain and subsequently choose an appropriate strategy.

This paper seeks to complement the supply chain risk monitoring literature by identifying analytics methods and the risk indicators being monitored for this purpose. This includes the underlying supply chain data used for short-term or even real-time monitoring of risks in supply chain risk management. A systematic literature review is carried out in order to identify risk types and underlying factors considered in the context of risk monitoring. Furthermore, the monitored risk indicators and the data analytics methods applied in their generation, monitoring or prediction, as well as the underlying risk data are examined. The identified works focus mainly on micro risks, where supply and transport risks are the most prevalent. A variety of risk indicators is found to be used including both, qualitative and quantitative, which are often used jointly. Identified data sources range from operational databases to IoT and sensor networks. Moreover, first approaches utilizing predictive analytics methods to anticipate risks are identified. The findings are used to derive promising research topics to further explore this largely underrepresented field within supply chain risk management and pave the way for data-driven risk monitoring.