Analytic model to predict productivity in divisional Seru production environment

Advanced production environments emerged as the good solution to address the modern market challenges asking for a wide product mix and low time to market. Within cellular systems, made of independent, modular and flexible working areas, tailored on families of similar products, Serus are of increasing adoption for both manufacturing and assembly tasks. Among them, the so-called divisional Serus are the first step to move from the traditional production lines to a production environment made of a set of identical working areas, parallelising activities and enabling potential productivity increase. Despite their adoption in industry, starting from the electronic sector and moving forward, reference analytic models to predict divisional Seru productivity are rare in the literature, while their formulation and application is a gap to fill. This paper addresses this gap in theory, supporting the transition toward Seru production environment by proposing and proofing the analytic closed-form expressions getting the expected productivity of a divisional Seru made of a generic number of workers and a) one (base case), b) two (extension) and c) a generic number (general case) of product types to produce. Together with the steps to get the productivity expressions for these three cases of immediate practical applicability and not yet proposed by the literature, a case study and sensitivity analysis on the divisional Seru dimension showcase the proposed model industrial use and impact on the expected productivity. Key results highlight a stationary behaviour of the working time for all workers making the Seru productivity dependent on the sum of the workers speed and the product type workloads

How Cultural Transmission Through Objects Impacts Inferences About Cultural Evolution

The cross-fertilisation between biological and cultural evolution has led to an extensive borrowing of key concepts, theories, and statistical methods for studying temporal variation in the frequency of cultural variants. Archaeologists have been among the front-runners of those engaging with this endeavour, and the last 2 decades have seen a number of case studies where modes of social learning were inferred from the changing frequencies of artefacts. Here, we employ a simulation model to review and examine under-discussed assumptions shared by many of these applications on the nature of what constitutes the 'population' under study. We specifically ask (1) whether cultural transmission via 'objects' (i.e. public manifestations of cultural traits) generates distinct patterns from those expected from direct transmission between individuals and (2) whether basing inference on the frequency of objects rather than on the frequency of mental representations underlying the production of those objects may lead to biased interpretations. Our results show that the rate at which ideational cultural traits are embedded in objects, and shared as such, has a measurable impact on how we infer cultural transmission processes when analysing frequency-based archaeological data. At the same time, when cultural transmission is entirely mediated by the material representation of ideas, we argue that copying error should be interpreted as a two-step process which may occur in either one or both of embedding information in objects and retrieving it from them

Crowd Logistics: A Survey of Successful Applications and Implementation Potential in Northern Italy

Nowadays, last-mile logistics represents the least efficient stage of supply chains, covering up to 28% of the total delivery cost and causing significant environmental emissions. In the last few years, a wide range of collaborative economy business models has emerged across the globe, rapidly changing the way services were traditionally provided and consumed. Crowd logistics (CL) is a new strategy for supporting fast shipping services, entrusting the management of the last-mile delivery to the crowd, i.e., normal people, who agree to deliver goods to customers located along the route they have to travel, using their own transport means, in exchange for a small reward. Most existing studies have focused on evaluating the opportunities and challenges provided by CL through theoretical analysis and literature reviews, while others have proposed models for designing such emerging distribution networks. However, papers analyzing real successful applications of CL worldwide are lacking, despite being in high demand. This study attempted to fill this gap by providing, at first, an overview of real CL applications around the globe to set the stage for future successful implementations. Then, the implementation potential of CL in northern Italy was assessed through a structured questionnaire delivered to a panel of 214 people from the Alma Mater Studiorum University of Bologna (Italy) to map the feasibility of a crowd-based system in this area. The results revealed that about 91% of the interviewees were interested in using this emerging delivery system, while the remaining respondents showed some concern about the protection of their privacy and the safeguarding of the goods during transport. A relevant percentage of the interviewees were available to join the system as occasional drivers (ODs), with a compensation policy preference for a fixed fee per delivery rather than a variable reward based on the extra distance traveled to deliver the goods

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

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

Ergonomic Design of an Adaptive Automation Assembly System

Ergonomics is a key factor in the improvement of health and productivity in workplaces. Its use in improving the performance of a manufacturing process and its positive effects on productivity and human performance is drawing the attention of researchers and practitioners in the field of industrial engineering. This paper proposes an ergonomic design approach applied to an innovative prototype of an adaptive automation assembly system (A3S) equipped with Microsoft Kinect™ for real-time adjustment. The system acquires the anthropometric measurements of the operator by means of the 3-D sensing device and changes its layout, arranging the mobile elements accordingly. The aim of this study was to adapt the assembly workstation to the operator dimensions, improving the ergonomics of the workstation and reducing the risks of negative effects on workers’ health and safety. The case study of an assembly operation of a centrifugal electric pump is described to validate the proposed approach. The assembly operation was simulated at a traditional fixed workstation and at the A3S. The shoulder flexion angle during the assembly tasks at the A3S reduced between 18% and 47%. The ergonomic risk assessment confirmed the improvement of the ergonomic conditions and the ergonomic benefits of the A3S

ENVIRONMENTAL ASSESSMENT OF AN INNOVATIVE PLANT FOR THE WASTEWATER PURIFICATION IN THE BEVERAGE INDUSTRY

Nowadays, efforts to reduce the resource depletion and environmental emissions from the anthropic activities, are mandatory for sustainable development pattern. Among the key resources to save, pure water is as important as critic due to its scarcity and its essential role for life and growth. Furthermore, during the last decades, rising attention from institutions and industries is toward solutions for the water intensity decrease and wastewater recovery. This paper proposes the environmental assessment of an innovative wastewater collection and purification plant tailored to a mid-size beverage industry aiming at locally closing the loop of the water chain, allowing its recirculation and local reuse. After the description of the functional module features, sizes and design, based on a prototype actually working in Italy, the paper follows the ISO 14040 standards to develop an environmental assessment of the industrial system, quantifying the impact rising from the manufacturing and the assembly phases

Feature-based multi-class classification and novelty detection for fault diagnosis of industrial machinery

Given the strategic role that maintenance assumes in achieving profitability and competitiveness, many industries are dedicating many efforts and resources to improve their maintenance approaches. The concept of the Smart Factory and the possibility of highly connected plants enable the collection of massive data that allow equipment to be monitored continuously and real-time feedback on their health status. The main issue met by industries is the lack of data corresponding to faulty conditions, due to environmental and safety issues that failed machinery might cause, besides the production loss and product quality issues. In this paper, a complete and easy-to-implement procedure for streaming fault diagnosis and novelty detection, using different Machine Learning techniques, is applied to an industrial machinery sub-system. The paper aims to offer useful guidelines to practitioners to choose the best solution for their systems, including a model hyperparameter optimization technique that supports the choice of the best model. Results indicate that the methodology is easy, fast, and accurate. Few training data guarantee a high accuracy and a high generalization ability of the classification models, while the integration of a classifier and an anomaly detector reduces the number of false alarms and the computational time

a tailored maintenance management system to control spare parts life cycle

Abstract The maintenance of complex production systems became increasingly crucial to ensure the competitiveness of companies and service level to their clients. Because of product customization the number of mechanical and electrical components and functional groups of manufacturing lines enhanced with their complexity. To face this concern, the physical and logical design of such systems is typically partitioned among several groups of engineers and designers. Consequently, a holistic awareness of the whole project is lacking and the maintenance of such systems becomes even more challenging. In view of this, new tailored support-decision tools able to manage and control the life cycle of spare parts from their design, throughout the run time, and to their failure and replacement are necessary. This paper illustrates an original maintenance management system (MMS) resulting by the combination of different computerized tools able to integrate the information flow behind the life cycle of a generic component. The proposed system supports coordination among groups of engineers and practitioners through graphic user interfaces (GUIs) and performance i.e. cost, reliability, dashboards, which lead decision-making from the design phase to the planning of maintenance tasks along the life of the manufacturing line. These tools are validated with a real-world instance from the tobacco industry which allows assessing how components belonging to the same functional group may differently behave over their life cycle. The results suggest that the holistic awareness on the whole manufacturing system provided by the proposed MMS can support task design and schedule of maintenance actions providing the reduction of more than 20% of the total cost and time for maintenance actions. The practical example shown contributes to shed light on the potentials of new paradigms for maintenance management in the industry 4.0

Uso do ácido indol butírico na estaquia de Tibouchina sellowiana (Cham.) Cogn. no outono e inverno de 2004.

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