Analysis of Production Process Performance Using Computer

Production and service processes always introduce discrepancies between real and expected parameters. Accurate modelling of uncertainties in manufacturing processes has great impact on logistics related factors. In this representation, instead of a deterministic analysis of manufacturing processes with uncertain quality of components, decision making is described with stochastic decision trees. The objective of this work is to show how to include the uncertain quality of components into the optimization of manufacturing processes. This paper proposes a new decision tree model with stochastic parameters that can determine the optimal operations depending on the quality of components to be assembled. After a careful literature review, this paper introduces a model to formulate the problem of decision making in manufacturing and assembly processes depending on the quality of produced batches of components. The model seeks the optimal pre-assembly operation, like testing or repairing while taking into account specific costs, prior and posterior probabilities and likelihoods. Next, we demonstrate an enhanced stochastic decision tree with cumulative hypergeometric probability distributions to find the minimum of expected cost based objective function. Numerical results demonstrate how the proposed model supports the decision making process and increase the efficiency, flexibility and reliability of the manufacturing process. DOI: DOI: http://dx.doi.org/10.5755/j01.ee.30.1.19324

Increasing the efficiency of tapping the aquatic biological resources by fishing vessels directly depends on the improvement of production and technological activities in the unpredictable field conditions. Like any production process, the production and technological activity of fishing vessels is associated with the occurrence of costs at all stages (phases) of the production process, the aggregate and structure of which depends on the fishing technological system of the fishing vessel. The stages of the production process form the main material flow, ensuring its end-to-end promotion. Its efficiency significantly affects the costs and management of field resources and should be achieved by minimizing material losses, primarily in the process of extracting raw materials. For this, it is necessary to study the field conditions in which the production and technological process takes place within the framework of the production vessel, determine their structure, variability, analyze the structure and composition of production costs at all phases of the production and technological process, develop a situational and scenario approach to the formation of production and technological activities of fishing vessels on a fishing trip. The costs arising in all subsystems (phases) and operating on a constant basis throughout the entire fishing period are proposed to be divided into several types of costs. Their interrelated and interdependent influence can give both a general determinism to the production process and increase its imbalance, increasing the total fishing costs of a producing vessel due to the emergence of additional costs associated with an unstable fishing situation in the fishing area. In this regard, the production activity on the fishing vessel is carried out according to the formed fishing and technological scenarios. The developed block diagram of the totality of fishing costs during the movement of the main material flow through the phases of the fishing and technological system of a fishing vessel gives a complete picture of the total fishing costs of a fishing vessel when it simultaneously performs the processes of extraction and processing of aquatic biological resources with a situational-scenario approach to their formation.

In a manufacturing process, data analysis is conducted to identify defective products in real time to lower their massive production and improve the rate of efficient production. In the production process, it is difficult to find defective products mixed with normal products. Therefore, it is necessary to detect defective products generated in the production process and reduce the risk of their production. Consequently, this study proposes the Mask R-CNN-based occlusion anomaly detection method in consideration of the orientation of manufacturing process data. The proposed method uses Mask R-CNN to find abnormal objects, such as occluded objects, in a manufacturing process line. In the manufacturing process, some products are hidden. Accordingly, preprocessing in consideration of multiple orientations is applied to generate data. The generated data is performed to detect occlusions and anomalies using Mask R-CNN. The mean of IoU was compared to evaluate the detection accuracy of YOLO and Mask R-CNN. YOLO showed excellent performance when there was a constant distance and orientation and no occluded object. However, Mask R-CNN performed excellently when there was any occluded object and the orientation was considered. Therefore, for occlusion anomaly detection in a manufacturing process, Mask R-CNN can reduce the production rate of defective products.

Abordaje de la complejidad psicosocial en pacientes con cáncer

Biopharma Is Going Digital … Bit by Bit

Modern production processes' complexity increases steadily. Therefore, virtual planning has been prevailed as a method used to evaluate risks and costs before the concrete realization of production processes. In doing so, virtual planning uses a number of numerical simulation tools that differ in the simulated production techniques as well as in the considered problem domains. Users may choose between tailor-made, thus costly, simulation tools delivering accurate results and off-the-shelf, thus less costly, simulation tools causing post-processing efforts. Thereby, simulating a whole production process is often hardly realizable due to insufficient prediction accuracy or the missing support of a production technique. The supposed solution of interconnecting different simulation tools to solve such problems is hardly applicable as incompatible file formats, mark-up languages and models describing simulated objects cause an inconsistency of data and interfaces. This paper presents the architecture of a framework for adaptive data integration that enables the interconnection of such numerical simulation tools of a specific domain.

Due to the government pressure and public awareness, industries are bound to incorporate sustainability in their manufacturing process. In this context, the concept of lean manufacturing and value stream mapping (VSM) process has been used widely in various manufacturing industries to minimize the waste in their production process. The objective of this study is to propose a conceptual model for the integration of VSM tool integrated with various sustainability indicators. The proposed model is capable to assess the manufacturing process into three sustainability dimensions such as economic, social, and environmental. This methodology was applied to two different manufacturing industries such as automotive component manufacturing organization and PVC pipe manufacturing organization, situated in India. The result demonstrated that the proposed methodology identified the areas of improvement after applying these integrated methodologies and clearly enabled the opportunities for improvements in both manufacturing organizations.

The decarbonization of the industrial sector plays a crucial role in a successful energy transition. This transformation is very costly and complex, as many of the existing production processes and plants will have to be partially or completely replaced to reduce carbon dioxide (CO2) emissions. This raises questions about how significant reductions in CO2 emissions resulting from decarbonization will affect the use of resources to produce a certain product and the overall value of sustainability. This article considers the relationship between CO2 reduction and the impact on the resource efficiency of an industrial production process. For this purpose, a methodology was developed that holistically assesses the decarbonization pathway of an industrial sector. This holistic assessment takes into account the energy carriers, raw materials, and auxiliary and construction materials used for the operation and building of the significant plant components and summarizes them as a total use of resources. For this purpose, the use of resources is represented by the thermodynamic quantity exergy, which takes into account both the energy and material components of a production process. The energy and material streams in a production process are balanced by applying exergetic analysis. This methodology is used for current state-of-the-art and future decarbonized production processes in order to quantify the effects of the decarbonization process. By comparing the calculated resource efficiencies, the thermodynamic impact on the sustainability of decarbonization paths can be set in relation to the amount of CO2 saved. For validation, the developed methodology is applied to a conventional and a decarbonized ammonia production process. The conventional production route represents the production of ammonia by methane steam reforming, and the decarbonized production route is represented by synthesis gas production via water electrolysis and an air separation unit. The resource efficiency of the conventional ammonia production route, taking into account the energy sources, raw materials, construction materials, and auxiliary materials used, is 59%, producing a total of 1539 kg of CO2 emissions per ton of ammonia. The decarbonized process has a resource efficiency of 45%, while no CO2 emissions are produced in this manufacturing process. This means that the decarbonization of the production process reduces resource efficiency by 14%. In relation to the reduced amount of CO2, specific resource efficiency decreases by 9.09%/tCO2. The decline in resource efficiency is mainly due to the high level of heat and energy recovery in the conventional process and the very electricity-intensive hydrogen production in the decarbonized production process.

Мета роботи. Надати документальні докази, які підтвердять, що процес виробництва препарату «Уронефрон» у формі таблеток, вкритих плівковою оболонкою, приводить до отримання продукту встановленої якості.Матеріали і методи. Валідаційні дослідження проводили на 3 серіях препарату.Валідація процесу виробництва таблеток «Уронефрон» проведена на всіх стадіях технологічного процесу з визначенням критичних параметрів, а саме: підготовка сировини, отримання маси для таблетування, зберігання таблетмаси, таблетування, нанесення плівкової оболонки на таблетки-ядра.Результати й обговорення. Для визначених критичних параметрів технологічного процесу отримання продукту проводили валідаційні випробування, які дозволяють визначити допустимі межі відхилення цих параметрів.Результати проведення валідаційних випробувань на кожній стадії технологічного процесу виготовлення таблеток «Уронефрон» відповідають критеріям прийнятності.Висновки. Валідація процесу виробництва таблеток «Уронефрон» підтвердила кваліфікацію виробничого персоналу і дотримання ним прописаних параметрів; показала, що технологічне обладнання ділянки в змозі забезпечити відтворюваність параметрів технологічного процесу виробництва, що призводить до очікуваного рівня якості напівпродукту і готової продукції; продемонструвала відтворюваність і стандартність показників якості напівпродукту і готової продукції відповідно до нормативної документації.

The OEE is a mathematical calculation that allows measuring and evaluating a productive process of a manufacturing company, this article presents the results of the research project “Design and development of a functional prototype aimed at improving the productive process of Mypimes manufacturing and his foray into the dynamics of Industry 4.0, endorsed by MinCiencias. In this, a bibliographic review is developed around the OEE and Industry 4.0, on the other hand, the results of the survey carried out to manufacturing Mypimes companies in Risaralda are shown, evidencing the lack of measurement of productivity indices in these. Finally, a development of technological innovation is proposed, for the measurement and evaluation of the OEE (Overall Equipment Effectiveness), in the production process, where the behavior of production can be observed in real time, in such a way that it serves to make timely and early decisions.

Energy efficiency of industrial facilities is a set goal towards developing a sustainable future. Many production facilities though are still not operated at a highly efficient rate in terms of energy use. The complexity of industrial environments requires an integrated analysis of all subsystems – production processes, logistics, building and technical building services – to grasp full optimization potentials. The use of simulation tools provides significant benefit in energy demand modeling and prediction, making its application essential for planning and management of energy efficient industrial facilities. Building Energy Modeling (BEM) and Manufacturing Process Simulation (MPS) have been used by researchers for analyzing mostly building related and process related conditions respectively. A novel approach is the holistic assessment by combining capabilities of BEM and MPS into one simulation environment. Such a hybrid simulation application has been developed within the research project Balanced Manufacturing (BaMa), addressing all subsystems of a production plant. However, the actual condition of a facility, the type and requirements of the manufacturing process, the level of implemented automation as well as the available infrastructure are decisive for the application of the appropriate simulation-based optimization method. Focusing on the subsystem of the building, this paper examines its function within two different approaches for energy demand optimization, through the use cases of industrial facilities with different characteristics. The first case focuses on the building and its components, utilizing BEM, the second on the production processes, utilizing the BaMa method. Thereby the use of Building Information Modeling (BIM) as knowledge database for the simulations models is discussed and the integration of all industrial subsystems is analyzed. The role of the building in each case is highlighted, addressing its benefits and drawbacks.

PurposeWhen is it more profitable for multinational manufacturers to manufacture in high-cost environments and when in low-cost environments? While the literature offers many cues to answer this question, too little empirical research directly addresses this. In this study, we quantitatively and empirically investigate the financial effect of companies' production footprint in low-cost and high-cost environments for different types of production networks.Design/methodology/approachUsing the data of 770 multinational manufacturing companies, we analyze the relationship between production footprints and profitability during four calendar semesters in 2018 and 2019 (N = 2,940), investigating the moderating role of companies' production network type.FindingsWe find that companies with networks distinguished by both high levels of product complexity and process sophistication profit the most from producing to a greater extent in high-cost countries. For these companies, shifting production to low-cost countries would be associated with negative performance implications.Practical implicationsOur findings suggest that the production geography of companies should be attuned to their network type, as defined by the companies' process sophistication and product complexity. Manufacturing in low-cost countries is not always the best choice, as doing so can adversely affect profits if the products are highly innovative and the production processes are complex.Originality/valueWe contribute to the scarce empirical literature on managing global production networks and provide a data-driven analysis that contributes to answering some of the enduring questions in this critical area.

Service business-to-business (b-to-b) companies provide a wide range of services characterized by a high level of variability and complexity, very often due to the so called ‘service customization’. In this context, the need to manage the trade-off between customer satisfaction and cost control emerges: therefore the choice of the right cost accounting system acquires an important role in order to identify and manage the links between the services offered to the customers and the costs of their production processes. In this article the two main cost accounting systems are analyzed (cost centers system and activity-based costing system), focusing in particular on the need to identify the final object of cost measurement (service component). The activity-based costing system together with the introduction of new cost containers (cost destinations), allows to accurately calculate the costs of the services provided and to identify the causal relationships between costs and services. The article analyzes the case of a b-to-b company which supplies IT services to banks.

A critical point for manufacturing companies has always been the optimization of their production processes to increase the productivity and minimize the related costs, chasing an efficiency principle. Thanks to advent of Industry 4.0 (I4.0) era, some technologies, perceived as far away few years ago, now can be reached by everyone. The use of these technologies allows factories to acquire and evaluate their production data (and much more) and carry out complex analyses in short time making possible the improvement of the production processes. Aim of this paper is to propose a methodological framework in which, starting from an existing process, where workers and machines have already been assigned to each station of a production line, the use of Industrial Internet of Things (IIoT) devices, such as wearable ones, allows to carry out analyses of the performance parameters of the production lines by means of an event-based simulation. In this way it is possible to evaluate if improvements are possible by re-balancing or re-scheduling the line. A case study, regarding a manual task of a real manufacturing production line, is presented to demonstrate the applicability and the effectiveness of the described framework.

Large scale/mega projects are lengthy complex endeavors that require significant planning by management, engineers and construction personnel to ensure the success of the project. When we examine the state of mega projects today, we are faced with a real crisis. Companies, both client and contractor, are reporting significant cost and schedule overruns. Lack of project scope definition and planning are the primary characteristics of this problem. Computer simulation is a powerful tool for analyzing complex and dynamic scenarios. It provides an appealing approach for the analysis of repetitive processes. Simulation helps decision makers identify different possible options by analyzing enormous amounts of data. Hence, computer simulation can be used effectively to analyze the resource loading and manpower requirements needed to complete a task in a given time frame, based on current progress levels. This paper discusses a special- purpose simulation (SPS) tool for optimization of manpower forecast loading and resource leveling. The simulation model is capable of optimizing resource requirements for a petrochemical project, based on standard discipline requirements and involvements. Tests of this simulation tool have produced exceptional results; currently, the system is being modified to incorporate historical data within the simulation.