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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.

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.

The COVID-19 pandemic is a global challenge to humankind. To improve the knowledge regarding relevant, efficient and effective COVID-19 measures in health policy, this paper applies a multi-criteria evaluation approach with population, health care, and economic datasets from 19 countries within the OECD. The comparative investigation was based on a Data Envelopment Analysis approach as an efficiency measurement method. Results indicate that on the one hand, factors like population size, population density, and country development stage, did not play a major role in successful pandemic management. On the other hand, pre-pandemic healthcare system policies were decisive. Healthcare systems with a primary care orientation and a high proportion of primary care doctors compared to specialists were found to be more efficient than systems with a medium level of resources that were partly financed through public funding and characterized by a high level of access regulation. Roughly two weeks after the introduction of ad hoc measures, e.g., lockdowns and quarantine policies, we did not observe a direct impact on country-level healthcare efficiency, while delayed lockdowns led to significantly lower efficiency levels during the first COVID-19 wave in 2020. From an economic perspective, strategies without general lockdowns were identified as a more efficient strategy than the full lockdown strategy. Additionally, governmental support of short-term work is promising. Improving the efficiency of COVID-19 countermeasures is crucial in saving as many lives as possible with limited resources.

Although technological innovation has enabled a new wave of warehouse automation, human involvement remains necessary for most order picking operations in grocery retailing. This has spawned new forms of interaction between humans, machines, and intelligent software, that is, cyber‐sociotechnical systems. However, scant empirical field‐based research has been conducted on how this transition impacts human learning and the perception of work characteristics. Considering that humans are an essential element of these systems, it is fundamentally important to quantify the impact of these transformations when aspiring to improve performance, quality, and workers' well‐being as primary outcomes of order picking systems. This study utilized a mixed‐methods design, developing and applying parametric and non‐parametric approaches to operationalize learning progress, and semi‐structured interviews were conducted to examine perceived work characteristics. The findings indicate that the perception–cognition–motor–action cycle for learning by doing tasks can be accelerated through real‐time feedback provided by the order picking system. Furthermore, perceived work autonomy and feedback from the picking system are constant or perceived as greater when human decisions are accepted. The results have valuable implications for logistics practitioners, emphasizing the need for human‐centered work system design.

Background: A large proportion of logistics jobs still rely on manual labor and therefore place a physical strain on employees. This includes the handling of heavy goods and physiologically unfavorable postures. Such issues pose a risk for employee health and work capability. This article provides a detailed empirical analysis and a decision process structure for the allocation of ergonomic measures in warehousing and intralogistics processes. Methods: The methodological basis is a load assessment of the musculoskeletal system in retail intralogistics. Based on the established measurements systems CUELA and OWAS, the specific loads on employees are assessed for four typical logistics workplace settings. These are combined with standards for efficient decision rules regarding contracting and developing ergonomic improvements. Results: The results suggest an increased risk of long-term low back injury for the selected four standard work situations in warehousing and likely apply to similar work environments in logistics. Using measures, posture descriptions, and international standards, we show how already few threshold values serve as sufficient conditions to decide if ergonomic interventions are required. Conclusions: The specific contribution is characterized by the combination of literature review results, empirical results, and the identification and discussion of specific mitigation measures. These elements are focused on the highly relevant ergonomic situation of logistics workers and present a unique contribution towards the knowledge base in this field due to the multi-perspective approach.