Industry 4.0 for Electronics Assembly

Process and product engineering – achievements, present and future challenges

Process and Product Engineering: Achievements, Present and Future Challenges

Research in interactive Design – Vol. 4 presents the most recent successful developments in mechanics, design engineering and advanced manufacturing for innovation in product, process and production engineering. The readers are invited to discover new techniques, methods, models and tools based on integrated and interactive design approaches. In a context of worldwide competition, there is an unquestionable need for such approaches to improve the reactivity, flexibility and capacity for innovation of engineers faced with particular challenges in enhancing the development of transport systems, the production of energy, the manufacture of consumer goods, the building engineering or the product design. But these approaches are also of great interest to academics, to improve the content of their curriculum and education programmes and to define novel research roadmaps in the field of mechanics, design engineering and advanced manufacturing.

Purpose Previous studies have focused on explaining the developmental paths and the relevant skills necessary for smart factories, based on an extensive review of the literature. Unfortunately, there is a deficit of empirical analyses that present an in-depth overview of smart factory development. Although the literature supports the benefits of smart factories, it remains unclear whether there should be government intervention (GI) to facilitate or regulate such adoption. This study will provide an in-depth empirical analysis of smart factory adoption (SFA) and its role in manufacturing performance (MP) and sustainable manufacturing (SM). Design/methodology/approach This study used non-probability convenience and referral sampling techniques for data collection. This approach considered production managers from each company that participated in the survey questionnaire; thus, each production manager represented one firm. A total of 240 managers from several manufacturing companies participated in the study. This study employed direct and moderating hypotheses tested using PROCESS Macro, which Andrew Hayes developed for SPSS. Findings The study identified three fundamental elements of a smart factory: manufacturing big data (MBD), process automation (PA) and supply chain integration (SCI) and analyzed them individually to see how they affect MP. According to the results, building a smart factory has positive and significant impacts on MP and SM. Furthermore, this study explores the role of GI in promoting smart factory deployment for both production performance and sustainable production. The study found that GI did not have a significant moderating effect but did have a positive relationship with SM. Research limitations/implications This study contributes to the literature on smart factories by examining the impact of SFA on MP and SM. This provides a more comprehensive overview of the potential benefits of smart factories across various aspects, such as the application of big data, adoption of automation technology and integration of the supply chain. This study suggests that managers and decision-makers consider investing in smart factory implementation to improve factory productivity and enhance sustainability. Policymakers and government officials can promote the adoption of smart factories by providing incentives, funding, and resources to manufacturing firms. Originality/value There is a scarcity of research measuring the actual performance of manufacturing firms that have already adopted smart factories, and this study seeks to address this gap in the literature. This study focuses on the implementation of manufacturing big data, process automation and supply chain integration and how the adoption of these technologies improves MP and provides a SM environment by conducting a real-time study of manufacturing organizations. This study presents an initial effort to explore the role of government involvement in promoting smart factories.

PurposeIndustry 4.0 has put forward a smart perspective on managing supply chain networks and their operations. The current manufacturing system is primarily data-driven. Industries are deploying new emerging technologies in their operations to build a competitive edge in the business environment; however, the true potential of smart manufacturing has not yet been fully unveiled. This research aims to extensively analyse emerging technologies and their interconnection with smart manufacturing in developing smarter supply chains.Design/methodology/approachThis research endeavours to establish a conceptual framework for a smart supply chain. A real case study on a smart factory is conducted to demonstrate the validity of this framework for building smarter supply chains. A comparative analysis is carried out between conventional and smart supply chains to ascertain the advantages of smart supply chains. In addition, a thorough investigation of the several factors needed to transition from smart to smarter supply chains is undertaken.FindingsThe integration of smart technology exemplifies the ability to improve the efficiency of supply chain operations. Research findings indicate that transitioning to a smart factory radically enhances productivity, quality assurance, data privacy and labour efficiency. The outcomes of this research will help academic and industrial sectors critically comprehend technological breakthroughs and their applications in smart supply chains.Originality/valueThis study highlights the implications of incorporating smart technologies into supply chain operations, specifically in smart purchasing, smart factory operations, smart warehousing and smart customer performance. A paradigm transition from conventional, smart to smarter supply chains offers a comprehensive perspective on the evolving dynamics in automation, optimisation and manufacturing technology domains, ultimately leading to the emergence of Industry 5.0.

The allocation of resources to process activities can have a huge influence on overall performance, in particular, if resources are costly and limited in their availability. Rule-based allocations can lead to unnecessarily low resource utilization rates, high costs, and large delays. In this paper, we present a framework allowing for optimized resource allocations by extending a traditional Business Process Management System by a new component that we call the Resource Manager. Our framework allows a process designer to specify resource requirements which are used by the Resource Manager to decide on allocations of resources to process activities. We describe the functionality of the Resource Manager, its interaction with the process engine, and the data needed. The framework is implemented by extending an open-source process modeler and engine, and applied to a use case concerning the last mile delivery.

Numerous approaches for assisting Smart Factory implementations in production companies have been published over the last few years. However, guidelines for calculating the profitability of these efforts have barely been addressed by scientific approaches so far. This paper aims to close this research gap using the Smart Factory application Condition Monitoring as an example. Therefore, a framework of the expense structure, as well as a framework of accompanying effects on business processes and resources, are presented. Based on these frameworks a procedure to support the financial assessment prior to the actual implementation is proposed. This procedure enables decision-makers to follow a deductive approach when identifying the economic relevant factors of smart factory applications. The authors argue that the shift towards a descriptive character of effect assessment simplifies and precises the profitability calculation. The construct validity of the frameworks and the usability of the proposed approach are confirmed in two case studies in separate production plants of ZF Friedrichshafen AG.KeywordsSmart factoryCondition monitoringEconomic assessment

Electric machines are important players in factories. They service manufacturing industries, automation, and moving materials. Ensuring these machines work effectively is significant to energy savings and ensuring things run efficiently. This paper looks at modern developments associated with electric machine performance. It examines the technologies that save energy, approaches to make electric machines more reliable and efficient, and tools for smart monitoring, data gathering, and data analysis. It also deliberates on how artificial intelligence (AI), machine learning (ML), digital twins (DT), and the Internet of Things (IoT) can aid with maintenance and diagnosing problems, allowing for improved operational performance and product quality. This review highlights best practices and barriers faced in most smart factories in the transition to Industry 4.0. It aims to give suitable guidelines to researchers and engineers in power and production engineering.

This research is conducted in Locarvest. Established in 2015, Locarvest is an online platform company that selling agricultural products based in Bandung. They see the importance of agricultural products for humans and people who like to shop online as an opportunity for them to sell local agricultural products online. Currently, they have been developing a website to change their selling platform which will be launched soon in the late of July 2019. They assume that the website can create a simpler selling process. However, they do not have their written business process where many companies have become increasingly interested in business processes due to the need for effective and efficient activities (Dave, 2017). In here, the objectives of this research are to analyze and model the current business process of Locarvest and propose a new business process to solve Locarvest’s current problem using the concept of Business Process Management (BPM) and the design is modeled using Business Process Modelling Notation (BPMN). To model the current business process, interviews with the employees of Locarvest and observation in the office were conducted. The result of this paper is proposing some improvements within the business process by eliminating some activities and create some new activities for Locarvest. The design also has been approved by the employees of Locarvest.
 Keywords: Business process, Business Process Management, Business Process Modelling Notation, case study, agriculture

Business Process Management (BPM) is the factual criteria for business process modeling. Although it is widely used in various fields, when required When using business processes to express big data business processes, the existing business process specifications do not support it. Big data business can only be simulated as multiple manual tasks before execution, which is not friendly to non-big data technology practitioners, and will The non-standardized business process and poor reusability lead to a reduction in the efficiency of business completion. On the other hand, while supporting big data technology brings flexibility to BPM, it consumes a lot of process engine resources when executing big data business processes, and the execution efficiency is low, which brings greater burden to the process engine. Aiming at the above deficiencies, this paper carries out the following work: (1) Extend the existing business process modeling, and define a new category of " Big Data Service Task" by extending the activity elements in BPMN2.0 (2) Propose a compatible big data technology The task scheduling strategy in the business process includes a single big data task scheduling decision and an intensive big data task scheduling decision.

Unplanned Electrical Submersible Pump (ESP) failures can have a profound impact on well production and overall field economics; therefore, it is important to reliably predict the onset of damaging operating conditions and take proactive action to prevent early failure of an ESP. Most operators rely on production engineers to monitor and optimize equipment performance with remote monitoring and surveillance technologies that provide threshold-based diagnostics to detect electrical or mechanical problems. Due to inherent limitations in exception-based monitoring, it is extremely difficult to reliably predict the root cause of equipment failure and remaining useful life (RUL) of an ESP. A major operator in the Americas was experiencing similar operational challenges in three of its producing assets. Unplanned failures happen frequently since they are hard to predict ahead of time with the data available to production engineers. This case study demonstrates how Advanced ESP Predictive Failure Analytics (PFA) technology has helped this operator to detect such events and extend ESP run life. Methods, Procedures, Process PFA is an innovative technique that leverages artificial intelligence (AI), life data analysis, physics, and knowledge-based methods to predict electrical and mechanical events and provides an estimate of RUL of an ESP. The data-driven models are trained using sensor time-series of historical failures and entail advanced data processing, interpolation, quality evaluation and feature engineering. The trained models are deployed to predict short term damage events that may lead to immediate failure, such as broken shaft, short-circuit, grounded downhole sensor failure, as well as long term events which build up over time, such as sand, and scale deposition. Results, Observations, Conclusions For one ESP, PFA detected scale deposition and predicted a sharp decline in RUL. After confirming the decrease in production fluid, and other surface and downhole sensor trends indicating scale deposition in the ESP, the production engineer applied chemical injection and avoided the failure. For a second ESP in this case study, PFA detected a grounding condition and predicted sudden decline in RUL. The production engineer noticed motor amps increased beyond the recommended threshold and performed electrical optimization to reduce motor amps. The ESP ran for another year and eventually failed due to grounded downhole sensor failure, which PFA had detected two weeks prior to the failure.

Within the framework of Industry 4.0, the transition from stand-alone systems to an interconnected digital enterprise plays a crucial role. Increasing demands for flexibility, quality, and shorter product development cycles drive the adoption of advanced digital solutions such as product data management (PDM) systems, manufacturing execution systems (MES), advanced planning and scheduling (APS) tools, and the digital twin (DT) concept. At the same time, digitalisation trends (e.g., Smart Factory) reveal critical weaknesses of manufacturing companies, with insufficient planning and scheduling capabilities being one of the most common issues. While medium- and long-term planning is typically handled within ERP systems, operational scheduling is often still managed through inflexible spreadsheet-based solutions. APS systems are therefore gaining importance as effective tools with relatively fast return on investment, although their deployment requires alignment between ERP and MES layers as well as a thorough understanding of business and production processes. The combined implementation of PDM, APS, MES, and DT enhances decision-making, enables rapid adaptability to change, and supports the development of learning organisations. The article focuses on key principles and technologies of digital transformation with emphasis on data integration using selected tools.

The Internet of Things (IoT) is the cornerstone of smart applications such as smart buildings, smart factories, home automation, and healthcare automation. These smart applications express their demands in terms of high-level requests. Application requests in service-oriented IoT architectures are translated into a business process (BP) workflow. In this paper, we model such a BP as a virtual network containing a set of virtual nodes and links connected in a specific topology. These virtual nodes represent the requested processing and locations where sensing and/or actuation are needed. The virtual links capture the requested communication requirements between nodes. We introduce a framework, optimized using mixed integer linear programming (MILP), that embeds the BPs from the virtual layer into a lower-level implementation at the IoT physical layer. We formulate the problem of finding the optimal set of IoT nodes and links to embed BPs into the IoT layer considering three objective functions: i) minimizing network and processing power consumption only, ii) minimizing mean traffic latency only, iii) minimizing a weighted combination of power consumption and traffic latency to study the trade-off between minimizing the power consumption and minimizing the traffic latency. We have established, as reference, a scenario where service embedding is performed to meet all the demands with no consideration to power consumption or latency. Compared to this reference scenario, our results indicate that the power savings achieved by our energy efficient embedding scenario is 42% compared with the energy-latency unaware service embedding (ELUSE) reference scenario, while our low latency embedding reduced the traffic latency by an average of 47% compared to the ELUSE scenario. Our combined energy efficient low latency service embedding approach achieved high optimality by jointly realizing 91% of the power and latency reductions obtained under the single objective of minimizing power consumption or latency.

Several reports indicate that the highest business priorities include: business improvement, security, and IT management. The importance of security and risk management is gaining that even government statements in some cases have imposed the inclusion of security and risk management within business management. Risk assessment has become an essential mechanism for business security analysts, since it allows the identification and evaluation of any threats, vulnerabilities, and risks to which organizations maybe be exposed. In this work, a framework based on the concepts of Model-Driven Development has been proposed. The framework provides different stages which range from a high abstraction level to an executable level. The main contribution lie in the presentation of an extension of a business process meta-model which includes risk information based on standard approaches. The meta-model provides necessary characteristics for the risk assessment of business process models at an abstract level of the approach. The framework has been equipped with specific stages for the automatic validation of business processes using model-based diagnosis which permits the detection of the non-conformance of security objectives specified. The validation stages ensure that business processes are correct with regard to the objectives specified by the customer before they are transformed into executable processes.

This work considers the optimization and development of business processes in investment. The conditions for the digitalization of the economy actualize the issue of introducing information technologies at all its levels and links. It is proved that information technology is an effective tool for reducing the time for the implementation of individual business processes and in investment. Its use expands the possibilities of exchanging information and its dissemination to the general public, improves the quality of investment tasks and the objectivity of investment decision-making results. Experimental studies have confirmed that the result of its application is the efficiency of obtaining, processing, and analyzing information when making management decisions. The visual content of the results of investment analysis, obtained using automation technologies, facilitates the perception of information, improves the quality of its transmission, and the value of ideas. The continuous development of information technologies in the field of investment is the basis for further scientific and practical research in this area. Based on such considerations, the feasibility of modifying the methodical toolkit of financial calculations in the process of analyzing the effectiveness of investment implementation is justified. Models of the modified internal rate of return on investment and the rate of return on financial management are proposed. Based on their definition, the objectivity of evaluating investments increases, the effectiveness of cash flow management during their implementation is proved. Strengthening the manifestation of crisis phenomena actualize the study of the nature of the occurrence of risks and the degree of their controllability. The approach to assessing investment risks using the β coefficient, the calculation technology, which is a universal tool for assessing the impact of systemic risks at the macro and micro levels, is substantiated.