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Order picking has been identified as the most labour-intensive, as well as costly activity within warehouse logistics and is experiencing significant changes due to new technologies in the forms of artificial intelligence (AI) and automation. One fundamental question concerns the employees learning progress in human-robot picking systems compared to existing manual technologies. Therefore, this paper presents an empirical analysis of learning curves in manual pick-by-voice (n=30 pickers) and semi-automated (n=20 pickers) order picking. Aspiring to measure the individual learning progress without a priori assumptions, this publication is the first to apply Data Envelopment Analysis and examine order pickers learning curves in real application scenarios. The findings indicate that automating human work accelerates the individual learning progress in human-robot picking systems.

Methods of multi-criteria decision making (MCDM) from operations research have been applied to provide information for making long-term decisions in the energy sector, and energy policy. For example, in sustainability evaluations, multiple conflicting criteria can be considered. While most MCDM approaches have been applied to evaluate energy systems in a single period, the multi-criteria evaluation of energy system evolution over time has received less attention. To evaluate such transition paths, multi-period MCDM approaches can be used. Because of long-term planning horizons, deep uncertainties need to be considered. Based on prior multi-period MCDM approaches, this paper provides an extension of the outranking approach preference ranking and organization method for enrichment evaluations (PROMETHEE) for multi-period evaluations in deep uncertainty settings. In order to adequately address the consideration of uncertainties and to obtain an additional level of information, a multi-period PROMETHEE approach and scenario planning are combined. In an illustrative example, this method is applied to a case study from the German energy sector regarding a renewable energy transition. This highlights the potential interactions of a multi-period perspective and the consideration of external scenarios in the decision-making process.

The increasing use of information technology (IT) in supply chain management and logistics is connected to corporate advantages and enhanced competitiveness provided by enterprise resource planning systems and warehouse management systems. One downside of advancing digitalization is an increasing dependence on IT systems and the negative effects of technology disruption impacts on firm performance, measured by logistics efficiency, e.g., with data envelopment analysis (DEA). While the traditional DEA model cannot deconstruct production processes to find the underlying causes of inefficiencies, network DEA (NDEA) can provide insights into resource allocation at the individual stages of operations. We apply an NDEA approach to measure the impact of IT disruptions on the efficiency of operational processes in retail logistics. We compare efficiency levels during IT disruptions, as well as ripple effects throughout subsequent days. In the first stage, we evaluate the efficiency of order picking in retail logistics. After handing over the transport units to the outgoing goods department of a warehouse, we assess the subsequent process of truck loading as a second stage. The obtained results underline the analytical power of NDEA models and demonstrate that the proposed model can evaluate IT disruptions in supply chains better than traditional approaches. Insights show that efficiency reductions after IT disruptions occur at different levels and for diverse reasons, and successful preparation and contingency management can support improvements.

Order picking is a crucial but labor- and cost-intensive activity in the retail logistics and e-commerce domain. Comprehensive changes are implemented in this field due to new technologies like AI and automation. Nevertheless, human worker’s activities will be required for quite some time in the future. This fosters the necessity of evaluating manual picker-to-part operations. We apply the non-parametric Data Envelopment Analysis (DEA) to evaluate the efficiency of n = 23 order pickers processing 6109 batches with 865,410 stock keeping units (SKUs). We use distance per location, picks per location, as well as volume per SKU as inputs and picks per hour as output. As the convexity axiom of standard DEA models cannot be fully satisfied when using ratio measures with different denominators, we apply the Free Disposal Hull (FDH) approach that does not assume convexity. Validating the efficiency scores with the company’s efficiency assessment, operationalized by premium payments shows a 93% goodness=of-fit for the proposed model. The formulated non-parametric approach and its empirical application are promising ways forward in implementing empirical efficiency measurements for order picking operations within e-commerce operations.

Although resources are scarce and outputs incorporate the potential to save human lives, efficiency measurement endeavors with data envelopment analysis (DEA) methods are not yet commonplace in the research and practice of non-government organizations (NGO) and states involved in humanitarian logistics. We present a boot-strapped DEA window analysis and Malmquist index application as a methodological state of the art for a multi-input and multi-output efficiency analysis and discuss specific adaptions to typical core challenges in humanitarian logistics. A characteristic feature of humanitarian operations is the fact that a multitude of organizations are involved on at least two levels, national and supra-national, as well as in two sectors, private NGO and government agencies. This is modeled and implemented in an international empirical analysis: First, a comprehensive dataset from the 34 least developed countries in Africa from 2002 to 2015 is applied for the first time in such a DEA Malmquist index efficiency analysis setting regarding the national state actor level. Second, an analysis of different sections in a Rohingya refugee camp in Bangladesh is analyzed based on a bootstrapped DEA with window analysis application for 2017, 2018, and 2019 quarter data regarding the private NGO level of operations in humanitarian logistics.

The COVID-19 pandemic is changing consumer behavior and accelerating the interest for online grocery purchases. Hence, traditional brick-and-mortar retailers are developing omnichannel solutions enabling online purchases in parallel to normal activities. Buy-Online-Pick-up-in-Store concepts are flourishing in this context, and they are the topic of this work.

Die globale humanitare Logistik in Not- und Krisensituationen sieht sich einer Reihe von Herausforderungen wie steigender Anzahl und Schwere von Katastrophensituationen, steigender Komplexitat der globalen Transportnetzwerke sowie gegenuber. In dieser Situation wird in umfassender Weise der Versuch unternommen, erfolgreiche Konzepte der Betriebswirtschaftslehre auch auf wesentliche Bereich der humanitaren Logistik mit in der Regel Non-Profit-Settings zu ubertragen. Dies gilt auch fur den Bereich der betrieblichen Leistungsmessung, der in diesem Kapitel ausfuhrlich in der Bezugsetzung zu den Spezifika humanitarer Logistikkonzepte erlautert wird. Verschiedene Handlungsoptionen und Konzepte in Hinblick auf die besonderen Anforderungen der humanitaren Logistik werden vorgestellt und eingeordnet, offene Forschungsfragen und Entwicklungspunkte benannt. Damit stellt dieses Kapitel ein Beispiel dafur da, wie fundierte betriebswirtschaftliche Forschung einen konkreten Beitrag zur Verbesserung der Situation von Menschen in Notlagen bereitstellen kann.

Digitalization is a major trend and challenge in most industries and sectors of societies. Still, quantitative insights regarding the impacts of digitalization are missing. This chapter is reporting a first approach using Data Envelopment Analysis (DEA) for measuring efficiency results of digitalization steps in a retail logistics context. Aspiring to quantify the performance of professional truck drivers during a digital turnover related to mobile devices, we evaluate truck loading processes. As inputs we use loading time and costs. Outputs are load factor of units, invoice charged to shops, and the value of the damages during truck loading. The findings indicate that a change in the level of digitalization entails a loss of the efficiency level in the first instance, which can be compensated and even surpassed later. When applying linear regression analysis, we prove a low statistical linear relationship of age and efficiency plus a strong statistical linear relationship of employer size and efficiency as well as period of employment and efficiency, always regarding the changing levels of digitalization in the working system of professional truck drivers. For practitioners in retail logistics, we derive the importance of employee retention programs for human resource management, along with a positive working environment provided for truck drivers to reduce fluctuation effects. Furthermore, we advise designing software for truck drivers as commonplace as possible and in the style of widespread smartphone software user interfaces.

Recent advances in the areas of Artificial Intelligence (AI) in the informatics field, Cyber-Physical Systems (CPS) in the production field, and Internet of Things (IoT) in the logistics and transportation field have induced a tremendous growth and innovation potential for global value chain setups. The question is not if further innovation and automation will happen but when—sooner than later—and how. Independent of physical production innovations (additive manufacturing) the information integration and decision autonomy tendencies themselves will drive new supply chain and customer interaction designs and business models. This article presents a technology forecast model based on extensive descriptions of developments by field as well as interaction traits. Results suggest that the crucial element in AI and technology application in logistics will be the human factor and human-artificial cooperation capacities and attitudes.

Digital logistics processes are not only a technological challenge. In addition, the question of human cognitive strain and human-computer interaction are important success factors for digital logistics work concepts. After a background introduction, conceptual and an analytical frameworks for identifying cognitive strain in digital logistics work are presented and applied to specific logistics activities. Characteristic features are the speed and density of work tasks, the requirement for teamwork and inter-organizational cooperation as well as the important role of motivation in the “people business” of logistics. Further research is warranted regarding the specific characteristics of digital logistics work and the impact on cognitive workload of human workers.

Leseprobe Leseprobe 2 ----- Zusammenfassung Die Logistik als globale innovative Branche erfahrt grundlegende Veranderungen durch die fortschreitende Digitalisierung. Es bilden sich neue Geschaftsmodelle heraus und die Arbeitsorganisation wandelt sich. Arbeitsprozesse konnen digital und transparent erfasst und kontrolliert werden. Der Beitrag untersucht die Auswirkungen dieser technologischen Veranderungen auf Logistikmitarbeitende und deren Arbeitsbeziehungen. Ziel ist es, die Digitalisierung in der urbanen Lebensmittellogistik in Hinblick auf die Wahrnehmung von Autonomie und Kontrolle aus Perspektive der Arbeitskrafte ebenso zu analysieren wie die Effekte, die daraus fur die Ausgestaltung der Arbeitsbeziehungen resultieren. Zur Analyse wird auf eine qualitative Studie mit Berufskraftfahrenden sowie Kurierfahrenden in der urbanen Lebensmittellogistik zuruckgegriffen. Im Ergebnis zeigt sich im Alltag die Gleichzeitigkeit von Autonomie und Kontrolle, die auf die Einbindung von neuen Technologien in die Arbeitsorganisation sowie die Rolle von Fuhrungskraften zuruckgefuhrt werden kann. Schlieslich werden Erfordernisse an Digitalisierungsprozesse festgehalten. Schlagworter: Digitalisierung, Autonomie, Kontrolle, urbane Logistik, Lebensmittellogistik ----- Workforce between Autonomy and Control – Effects of Digitalization on Employment Relationships in the Logistics Industry Abstract Logistics as a global innovative industry is experiencing fundamental changes because of digitalization. New business models are emerging and the organization of work is changing. In this way, work processes can be recorded and controlled digitally and transparently. This article examines the effects of these technological changes on logistics workers and their employment relationships. The aim is to analyze the digitalization of urban food logistics regarding the perception of autonomy and control from the worker’s perspective and the resulting effects on the design of employment relationships. The analysis is based on a qualitative study with professional truck drivers and cyclists in urban food logistics. The results show a ambivalence between the concurrence of autonomy and control in daily work, which can be connected to the integration of new technologies into work organization as well as the role of managers. Finally, requirements for a structured consideration of this interrelationship in digitalization processes are presented. Keywords: Digitalization, Autonomy, Control, Urban Logistics, Food Logistics. JEL: L91, L22, J53 ----- Bibliographie: Ruiner, Caroline/Klumpp, Matthias: Arbeitskrafte zwischen Autonomie und Kontrolle – Auswirkungen der Digitalisierung auf Arbeitsbeziehungen in der Logistik, Industrielle Beziehungen. Zeitschrift fur Arbeit, Organisation und Management, 2-2020, S. 141-159. https://doi.org/10.3224/indbez.v27i2.03

In the digitisation development of economy, logistics is of central importance due to the linkage of information and material flow throughout supply chains. However, verifiable effects in terms of efficiency have not been empirically shown in many cases yet. Therefore, this paper develops an efficiency analysis to evaluate the impact of changing levels of digitalisation. An efficiency-centred cross-sectional approach uses Data Envelopment Analysis (DEA) for assessing the performance of 60 truck drivers, as well as Malmquist productivity index for longitudinal analysis of 50 truck drivers between 2012 and 2018. The results of the DEA model indicate that a change in the level of digitalisation entails a loss of the efficiency level in the first instance, which can be compensated and even surpassed later. Findings of the Malmquist model prove positive long term effects of several digitalisation steps on transport logistics efficiency. As the proposed methodologies only allow an a posteriori evaluation for the impact of digitalisation, we develop an a priori simulation approach, based on empirical DEA results, statistical bootstrapping and regression analyses. Both approaches allow logistics researchers and managers to achieve a better understanding regarding the success or failure of digital transformation scenarios from an efficiency perspective.

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.

Current concept, development, and testing applications in production concerning Cyber-Physical Systems (CPS), Industry 4.0 (I40), and Internet of Things (IoT) are mainly addressing fully autonomous systems, fostered by an increase in available technologies regarding distributed decision-making, sensors, and actuators for robotics systems. This is applied also to production logistics settings with a multitude of transport tasks, e.g., between warehousing or material supply stations and production locations within larger production sites as for example in the automotive industry. In most cases, mixed environments where automated systems and humans collaborate (e.g., cobots) are not in the center of analysis and development endeavors although the worker’s adoption and acceptance of new technologies are of crucial relevance. From an interdisciplinary research perspective, this constitutes an important research gap, as the future challenges for successful automated systems will rely mainly on human-computer interaction (HCI) in connection with an efficient collaboration between motivated workers, automated robotics, and transportation systems. We develop a HCI efficiency description in production logistics based on an interdisciplinary analysis consisting of three interdependent parts: (i) a production logistics literature review and process study, (ii) a computer science literature review and simulation study for an existing autonomous traffic control algorithm applicable to production logistics settings with the specific inclusion of human actors, and (iii) a work science analysis for automation settings referring to theoretical foundations and empirical findings regarding the management of workers in digital work settings. We conclude with practical implications and discuss avenues for future research and business applications. peerReviewed

Ein Transfer der innovativen Elemente des Konzepts Industrie 4.0 in den Bereich der Dienstleistungsproduktion am Beispiel der Leitungsprozesse in Hochschulen ist sehr interessant, da der uberwiegende Anteil der volkswirtschaftlichen Wertschopfung in tertiaren Dienstleistungsorganisationen erbracht wird. Daher sind Befassungen mit der Ubertragbarkeit des Konzepts Industrie 4.0 in den Bereich der Dienstleistungen hinein fur alle tertiaren Leistungsprozesse von hoher Bedeutung. Dieses Kapitel gibt hierzu einen qualitativen Uberblick mit einer Konzeptanalyse fur verschiedene Teilbereiche von Hochschulprozessen (Forschung, Lehre) in Bezug auf die Kernelemente von Industrie 4.0. Die Hauptaussagen der Analyse konnen dahingehend zusammengefasst werden, dass im Bereich der Lehre fur die Anwendung von Elementen der Konzeption Industrie 4.0 als theoretische Ableitung insbesondere Effizienzpotenziale identifiziert werden. Demgegenuber werden im Bereich der Forschung im Wesentlichen Qualitats- und Effizienzvorteile erwartet. Eine Pilotierung einzelner Prozessinnovationen durch Anwendung von Industrie-4.0-Elementen im Bereich der Hochschulleistungsprozesse ist als vielversprechend einzustufen und sollte angestrebt werden. Gleichzeitig ist mit den hier dargestellten Uberlegungen auch die fundierte Aufforderung verbunden, Anwendungsbereiche in anderen Feldern der Dienstleistungsproduktion zu identifizieren und zu testen.

PurposeThe purpose of this paper is to establish criteria for evaluating strategic partners in a network of logistics service providers (LSPs) to show how analytical network process (ANP) can be used to identify the weights of these criteria on a case-specific basis, and to investigate whether the ANP model can be used as a starting point to evaluate strategic partners for other LSP networks.Design/methodology/approachBased on a literature review of vertical cooperation, the authors develop an overview of criteria for the evaluation of partners in a network of LSPs. The authors then apply ANP at LSP1 to validate the criteria, identify weights for these criteria and to validate model outcomes. Furthermore, the authors investigate whether the ANP model developed for LSP1 can be applied to another LSP with similar characteristics (LSP2). In-depth interviews are used to draw conclusions on the modeling approach and the model outcomes.FindingsThe research shows that evaluation criteria for partners in vertical partnerships between shippers and LSPs are applicable to LSP partners in horizontal partnership networks. The ANP model with criteria weights provides a good starting point for LSPs to customize the evaluation framework according to their specific needs or operating environments.Originality/valueLimited research is available on evaluating LSP partners in horizontal partnerships. To the best of the authors’ knowledge, this paper is the first to bring forward horizontal LSP partner evaluation criteria to develop an ANP model for LSP partner evaluation and to apply this to two cases, and to provide a starting point for evaluating partners in similar horizontal LSP networks.

Big data applications in transportation and logistics are much discussed before the background of mainly economic improvement potential. For the area of road cargo transportation, this contribution is discussing the use of geospatial data in truck routing especially in the context of autonomous driving and social sustainability concepts. Fleet management and cruise control systems have been established during the last decade in road transportation. However, the stationary vehicle routing before the actual travelled tour is subject only to planning and optimization based on a quite low level of information. For example, geospatial data regarding topography as well as speed limitations and trajectory as well as street elevation and bend characteristics are currently not used but have significant impact on truck speed, fuel consumption and driver workload. Therefore, a conceptual outline as well as a quantitative test simulation for applying geospatial big data in ex ante vehicle routing is provided. This does encompass obvious advantages in economic (reduced transport cost), environmental (reduced transport emissions) as well as social dimensions (reduced driver workload and working time). Further inquiries shall address detailed question as to how geospatial big data could be integrated into the daily routine and processes of vehicle routing in road transportation.

Sustainability performance and efficiency is an important topic in transportation and for forwarders. This is shown, for example, by the fact that major logistics service providers LSP publish sustainability reports, often within the annual legal business report. However, in depth research is missing regarding the efficiency of forwarders regarding the established triple bottom line approach for sustainability, including economic, social, and ecology performance areas. This is especially true for a dynamic time-series perspective, as usually only static analyses for one point in time are presented (in most cases single business years). Therefore, the operations research technique of a data envelopment analysis (DEA) Malmquist index calculation is used in order to provide a longitudinal calculation of efficiency, incorporating multiple objectives regarding the triple bottom line approach for European forwarders. Several indicators are tested, including total revenues and assets as input types, profit (EBIT) and dividend volume (economic dimension), employment and gender equality in management (social), and carbon-equivalent emissions (environmental) as output types.

The objective of green logistics has thus far failed. For example, the share of greenhouse gas emissions by the transportation and logistics sector in Europe rose from 16.6% in 1990 to 24.3% in 2012. This article analyzes the reasons behind this failure by drawing on political, economic and business as well as social motivations and examples. At the core of this analysis are the established theorems of the Jevons paradox and the median voter (Black, Downs) in combination with time-distorted preferences of voters and consumers. Adding to the hurdles of green logistics are the problems of short-term political programs and decisions versus long-term business investments in transportation and logistics. Two cases from Germany are outlined regarding this political “meddling through” with a recent 2015 truck toll decision and the support for electric trucks and vehicles. Finally, the article proposes two ways forward: public control and restriction of carbon raw materials (coal, oil), as well as public investment in low-emission transport infrastructure or biofuels as the more feasible and likely alternative.

In this paper we address a 2-stage capacitated vehicle routing problem (CVRP) in which the demands are probabilistic and can only increase. In this CVRP variant the routes used by the fleet to satisfy the customers must be minimised. The customers' demands may increase with a probability after the beginning of the tours like the unexpected events that happen in a realistic environment. The existence of these events make sometimes the fleet fail to satisfy all the customers at their first try (1st stage). In this case, additional vehicles will be used to cover the rest of the demand (2nd stage). In this paper, an ant colony system is used to generate solutions and the effect of different objective functions used is shown. Conclusions are drawn on which evaluation method leads to near optimum routes and which to near optimum number of vehicles.