Aligning MIS5 proxy records from Lake Ohrid (FYROM) with independently dated Mediterranean archives: implications for core chronology

The DEEP site sediment sequence obtained during the ICDP SCOPSCO project at Lake Ohrid was dated using tephrostratigraphic information, cyclostratigraphy, and orbital tuning through marine isotope record. Although this approach is suitable for the generation of a general chronological framework of the long succession, it is insufficient to resolve more detailed paleoclimatological questions, such as leads and lags of climate events between marine and terrestrial records or between different regions. In this paper, we demonstrate how the use of different tie points can affect cyclostratigraphy and orbital tuning for the period between ca. 140 and 70 ka and how the results can be correlated with directly/indirectly radiometrically-dated Mediterranean marine and continental proxy records. The alternative age model obtained shows consistent differences with that proposed by Francke et al. (2015) for the same interval, in particular at the level of the MIS6-5e transition. According to this age model, different proxies from the DEEP site sediment record support an increase of temperatures between glacial to interglacial conditions, which is almost synchronous with a rapid increase in sea surface temperature observed in the western Mediterranean. The results show how important a detailed study of independent chronological tie points is for synchronizing different records and to highlight asynchronisms of climate events

Application of a mathematical model for ergonomics in lean manufacturing

The data presented in this article are related to the research article \u201cIntegrating ergonomics and lean manufacturing principles in a hybrid assembly line\u201d (Botti et al., 2017) [1]. The results refer to the application of the mathematical model for the design of lean processes in hybrid assembly lines, meeting both the lean principles and the ergonomic requirements for safe assembly work. Data show that the success of a lean strategy is possible when ergonomics of workers is a parameter of the assembly process design

The Impact of Product Packaging Characteristics on Order Picking Performance in Grocery Retailing

Increasing labor cost levels and workforce shortages have caused retailers to pay increased attention to their order-fulfillment operations, which continue to largely depend on manual order picking systems. The operations and logistics management literature suggests that optimizing tertiary packaging, which groups products into full unit loads for storage and shipping, is an important way to improve order picking performance. While most retailers handle products at the level of secondary packaging when fulfilling orders, this packaging level remains largely unexplored. To address this gap, we analyze 3,380,596 picks performed by 185 order pickers of 4957 products in a grocery retail warehouse in Germany. Our findings indicate that secondary packaging characteristics directly affect order picking performance and that this effect is moderated by traditional product characteristics (e.g., product weight and volume), as well as elements of warehouse design (e.g., pick and stack levels). From a managerial perspective, our findings may help to bridge the gap between logistics managers and packaging engineers and provoke further research on the trade-off between operational and environmental performance

Enhanced climate instability in the North Atlantic and southern Europe during the Last Interglacial

Considerable ambiguity remains over the extent and nature of millennial/centennial-scale climate instability during the Last Interglacial (LIG). Here we analyse marine and terrestrial proxies from a deep-sea sediment sequence on the Portuguese Margin and combine results with an intensively dated Italian speleothem record and climate-model experiments. The strongest expression of climate variability occurred during the transitions into and out of the LIG. Our records also document a series of multi-centennial intra-interglacial arid events in southern Europe, coherent with cold water-mass expansions in the North Atlantic. The spatial and temporal fingerprints of these changes indicate a reorganization of ocean surface circulation, consistent with low-intensity disruptions of the Atlantic meridional overturning circulation (AMOC). The amplitude of this LIG variability is greater than that observed in Holocene records. Episodic Greenland ice melt and runoff as a result of excess warmth may have contributed to AMOC weakening and increased climate instability throughout the LIG

A continuous training approach for risk informed supplier selection and order allocation

Supplier selection and order allocation, a longstanding challenge in supply chain management, has recently begun incorporating risk minimization alongside cost, reflecting growing interest in supply chain resilience and risk mitigation. In response, hybrid frameworks leveraging artificial intelligence and machine learning have emerged. However, current methods often lack mechanisms to update decisions over time and typically rely solely on demand forecasts. To address these gaps, this study introduces a new hybrid approach that integrates machine learning–based predictions of supplier delivery delays into a linear programming model for multiperiod supplier selection and order allocation. Additionally, the proposed method evaluates a continuous training strategy, wherein predictions and decisions are refreshed as new data become available. Empirical evidence from an automotive case study demonstrates that this approach reduces prediction errors and total costs more effectively than models without continuous training, albeit with increased order allocation instability

Assembly line balancing and activity scheduling for customised products manufacturing

Nowadays, end customers require personalized products to match their specific needs. Thus, production systems must be extremely flexible. Companies typically exploit assembly lines to manufacture produces in great volumes. The development of assembly lines distinguished by mixed or multi models increases their flexibility concerning the number of product variants able to be manufactured. However, few scientific contributions deal with customizable products, i.e., produces which can be designed and ordered requiring or not a large set of available accessories. This manuscript proposes an original two-step procedure to deal with the multi-manned assembly lines for customized product manufacturing. The first step of the procedure groups the accessories together in clusters according to a specific similarity index. The accessories belonging to a cluster are typically requested together by customers and necessitate a significant mounting time. Thus, this procedure aims to split accessories belonging to the same cluster to different assembly operators avoiding their overloads. The second procedure step consists of an innovative optimization model which defines tasks and accessory assignment to operators. Furthermore, the developed model defines the activity time schedule in compliance with the task precedencies maximizing the operator workload balance. An industrial case study is adopted to test and validate the proposed procedure. The obtained results suggest superior balancing of such assembly lines, with an average worker utilization rate greater than 90%. Furthermore, in the worst case scenario in terms of customer accessories requirement, just 4 line operators out of 16 are distinguished by a maximum workload greater than the cycle time

A two-step methodology for product platform design and assessment in high-variety manufacturing

The delayed product differentiation (DPD) recently rose as a hybrid production strategy able to overcome the main limits of make to stock (MTS) and make to order (MTO), guaranteeing the management of high variety and keeping low storage cost and quick response time by using the so-called product platforms. These platforms are a set of sub-systems forming a common structure from which a set of derivative variants can be efficiently produced. Platforms are manufactured and stocked following an MTS strategy. Then, they are customized into different variants, following an MTO strategy. Current literature proposes methods for platform design mainly using optimization techniques, which usually have a high computational complexity for efficiently managing real-size industrial instances in the modern mass customization era. Hence, efficient algorithms need to be developed to manage the product platforms design for such instances. To fill this gap, this paper proposes a two-step methodology for product platforms design and assessment in high-variety manufacturing. The design step involves the use of a novel modified algorithm for solving the longest common subsequence (LCS) problem and of the k-medoids clustering for the identification of the platform structure and the assignment of the variants to the platforms. The platforms are then assessed against a set of industrial and market metrics, i.e. the MTS cost, the variety, the customer responsiveness, and the variants production cost. The evaluation of the platform set against such a combined set of drivers enhancing both company and market perspectives is missing in the literature. A real case study dealing with the manufacturing of a family of valves exemplifies the efficiency of the methodology in supporting companies in managing high-variety to best balance the proposed metrics

A hybrid approach integrating genetic algorithm and machine learning to solve the order picking batch assignment problem considering learning and fatigue of pickers

Modeling human behaviors has become increasingly relevant to improving the performance of manual order-picking systems. However, although a vast corpus of literature has recently started to consider the human factors in these systems, several gaps remain uncovered. Specifically, mental and physical human factors, like learning and fatigue, and quantitative and spatial features of picking orders have never been considered jointly to estimate the time a human order picker requires to execute a specific picking mission. Furthermore, little attention has been given to assigning and sequencing orders to pickers to minimize the picking time acting on their individual learning and fatigue characteristics. This study thus proposes a novel approach integrating machine learning and genetic algorithms to solve the problem. A non-linear machine learning-based predictive model has been adopted to predict the picking time of batches of orders based on quantitative and spatial features of batches and learning and fatigue indicators of pickers. These predictions have thus been adopted to guide a genetic algorithm to find the best assignment of future planned batches of orders to pickers. One year of picking data collected from the warehouse of a grocery retailer has been adopted to investigate the potential of the proposed approach. Furthermore, multiple comparisons have been performed. First, the advantages of predicting the batch-picking time with the proposed non-linear model have been compared with predictions executed based on linear models. In addition, an ablation analysis has been performed to investigate the advantages of predicting the batch picking time while simultaneously considering the quantitative and spatial features of batches and the learning and fatigue indicators of pickers. Moreover, the advantages of the proposed batch assignment strategy, which considers learning and fatigue indicators, have been compared with an assignment strategy that does not optimize these elements. Lastly, an explainability analysis of the predictive model has been performed to understand how and how much quantitative and spatial features of batches and learning and fatigue indicators of pickers affect the batch picking time

Predictive maintenance: a novel framework for a data-driven, semi-supervised, and partially online prognostic health management application in industries

Prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers’ perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals

Managing Mass Customization through Delayed Product Differentiation: a bi-objective model for product platforms design

In recent years, the heterogeneity of customer needs caused a wide proliferation of product variants, asking industrial companies to adopt new strategies to remain competitive in the transition from mass production to mass customization. Traditional production strategies such as Make-to-Order (MTO) and Make-to-Stock (MTS) are no longer adequate for the efficient manufacturing of multiple product variants. In such a scenario, the Delayed Product Differentiation (DPD) rose as a hybrid strategy overcoming the main limitations of traditional production strategies, best balancing high product variety and quick response time with low storage cost through the so-called product platforms. These platforms are subsystems of components, forming a common base structure from which a stream of product variants can be efficiently derived. Platforms are produced and stocked in advance, following an MTS strategy, and customized into different variants after the order arrival, according to an MTO strategy. Platforms dimension affects in an opposite manner their storage cost and their customization time, as platforms made of few components require long time for customization activities, reducing the storage cost. The best platform design and association to product variants are open topics in current literature, having major impact on the trade-off between platform storage cost and customization time. This paper contributes to applied research in mass customization, proposing a bi-objective optimization model for platform design and association to product variants, determining at the same time the best production strategy among MTO, MTS and DPD for each product variant to best balance platforms storage cost and customization time. The model is applied to a reference case study providing a multi-scenario analysis about the main effects of the limitation in the number of possible platform types on platform configurations, customization tasks and their correspondent customization time and storage cost