Process Works Research Articles

Predicting Work-in-Process in Semiconductor Packaging Using Neural Networks: Technical Evaluation and Future Applications

This review paper focuses on the application of neural networks in semiconductor packaging, particularly examining how the Back Propagation Neural Network (BPNN) model predicts the work-in-process (WIP) arrival rates at various stages of semiconductor packaging processes. Our study demonstrates that BPNN models effectively forecast WIP quantities at each processing step, aiding production planners in optimizing machine allocation and thus reducing product manufacturing cycles. This paper further explores the potential applications of neural networks in enhancing production efficiency, forecasting capabilities, and process optimization within the semiconductor industry. We discuss the integration of real-time data from manufacturing systems with neural network models to enable more accurate and dynamic production planning. Looking ahead, this paper outlines prospective advancements in neural network applications for semiconductor packaging, emphasizing their role in addressing the challenges of rapidly changing market demands and technological innovations. This review not only underscores the practical implementations of neural networks but also highlights future directions for leveraging these technologies to maintain competitiveness in the fast-evolving semiconductor industry.

Unequal area facility layout problem considering transporters interaction– a queuing theory and machine learning approach

ABSTRACT This study presents a novel analytical framework that merges queueing theory with deep neural networks to optimize facility layout and transporter selection in manufacturing systems. It addresses critical factors such as stochastic service times of facilities, random demand, transporter capacity, speed, and transportation batch size. Three objectives are considered: minimizing material handling costs (MHC), work-in-process (WIP), and the interaction probability of transporters (IP). The latter objective focuses on reducing instances where transporters cross paths to prevent accidents or disruptions. WIP is computed using a multi-class open queueing network model, while IP is determined using a deep neural network. The model facilitates the identification of facilities and the assignment of suitable transporters, considering empty transporter travels to minimize MHC and WIP. Results from the model are compared to a simulation model for validation across various scenarios, demonstrating acceptable accuracy. Additionally, a multi-objective meta-heuristic optimization algorithm is employed to solve the model. The effectiveness of the optimization method is evaluated against other approaches, highlighting its applicability in enhancing manufacturing system performance.

Internet of things-based smart medical waste management system

Smart medical waste management is significant during the ongoing COVID-19 pandemic. This research presents a smart medical waste management system with real-time monitoring of medical waste data and an end-user application. The functional and technical design of a smart trolley by integrating various sensors for authenticated automatic lid opening and closing, waste level, weight, and storage time monitoring are discussed. Subsequently, the implementation of a cloud server and web application that provide automatic notifications to stakeholders are also presented. The users can monitor the entire process to take actions and approvals whenever necessary. The experiments are conducted to test the functionalities of the smart trolley and web application prototype. The results showed that the sensing system is efficient and avoids manual checking by sending necessary notifications. Healthcare institutions can adopt the proposed system for medical waste transfer to ensure process transparency, worker safety, and environmental cleanliness.

A hybrid model for shop floor simulation and work-in-process feedback control

In this paper, a smart hybrid model for shop floor simulation and control is proposed based on dynamic modelling and control theory’s analytical tools. The model is smart because the production levels are automatically adjusted based on feedback loops, considering shop floor current states (i.e., it is a closed-loop model). It is hybrid because it deals with discrete and continuous transitions, and integrates two approaches: switched arrival systems (SAS) and bond graphs (BG). This novel integrated SAS + BG model is applied to a production system in the food industry and four production control rules (well-known from the operations management literature) are tested: pure push and pull rules and extended push and pull rules. The results showed that the SAS is effective to dynamically represent intermittent production systems, and its use, coupled with bond graphs, allows the definition of capacity adjustment and production control rules that are effective to control the work in process (WIP). The extended pull rule outperformed the remaining ones, corroborating prior literature. This paper contributes with a novel approach that is modular and can be extended to represent the closed-loop dynamics of different configurations of multi-product multi-station production systems, with a disaggregated level of detail.

단수 휴가와 일량 제어정책을 갖는 이산시간 Geo/G/1 시스템의 대기시간 분석

In this paper, we derived the steady-state waiting time distribution of a discrete-time Geo/ G/1 queuing system under single vacation and D-policy. Customers such as WIP(work-in- process), parts, packets arrive at the system with Bernoulli process, and the server takes a single vacation with length of V when there are no more customers to service in the system. The restart condition is met when the total service time (workload, unfinished work) of customers waiting in the system at the end of vacation exceeds the workload threshold D. If the total amount of work in the system is less than or equal to D at the end of vacation, the server waits until the restart conditions are met and serves waiting customers. Otherwise, it starts to be busy as soon as the vacation ends. The average waiting time was also derived as a performance measure. Additionally, the computability of theoretical results was confirmed through a numerical example. The server control policy covered in this study can be applied in situations where it is desired to reduce operating costs per unit time due to high server restart costs (or energy consumption) in production and communication systems.

Effectiveness of Trained Community Lay Workers on Glycemic Control, Knowledge, and Self-Efficacy Among Agricultural Workers with Diabetes in the Texas Panhandle

Systemic health barriers, economic challenges, and lack of follow-up care exacerbate self-management of chronic diseases like diabetes among Hispanic agricultural workers. The primary objective of this pilot project was to determine the benefit of using community coaches to decrease A1C levels and increase diabetes knowledge among agricultural workers with diagnosed diabetes in the Texas Panhandle. A longitudinal study design with two phases was used to create, deliver, and evaluate a diabetes coaching program. Phase 1 was the development of the program and community coaches training (n = 4). In Phase 2, the coaches then delivered the program over 12 weeks to thirteen clients. Phase 1: All coaches were Hispanic females, 28.3 (SD 3.8) years of age, half had at most a high school education level and the other half had a vocational certification (n = 4). Mean DKQ-24 score was 54.2% (SD = 29.7) at baseline and 75.0% (SD = 31.4) after training (t (4) = 4.6, P < 0.05). We observed a very large difference between mean baseline and exit DKQ-24 scores relative to the pooled standard deviation, resulting in an effect size estimate of 0.59 indicative of a medium to large learning effect. Phase 2: Clients were Hispanic Spanish-speaking, predominantly female (55%), 44.4 (SD 6.8) years of age with at most a high school level of education (88.9%) and occupations varied from dairy farm worker (33.3%), meat processing worker (33.3%), and other agriculture or manufacturing position (33.3%). The mean SKILLD score was 40.0% (SD = 28.7) at baseline, increasing to 72.2% (SD = 25.4) at 12 weeks upon completion of the coaching program (t (9) = 2.956, P < 0.05). We observed a very large difference between mean baseline and exit SKILLD scores relative to the pooled standard deviation, resulting in an effect size estimate of 1.13 indicative of a large learning effect. The mean A1C levels at baseline screening was 8.3% (SD = 3.0) and 7.6% (SD = 3.0) at exit screening, representing a 0.7% decrease (p = 0.4730). No statistically significant differences were observed between depression (p = 0.786) or anxiety (p = 1.000) measures at baseline compared to exit. Training and coaching programs for hard-to-reach agricultural and meat processing workers must be culturally, linguistically, and literacy appropriate for both coaches and clients. The program must be feasible and sustainable, focus on empowering community members, capitalize on technological advances and persisting new-normals from the COVID-19 pandemic as well as dismantle common systemic barriers to health and understanding lived-experiences of agricultural working populations in rural regions.

MapReduce Algorithm on a Serverless Platform

Using the MapReduce (MR) programming technique, large-scale data processing tasks can be divided into tasks that are easier to manage and independent. The serverless implementation's performance is better than the non-serverless MR model when the model parameters are altered in the experiment. Optimizing the use of the MR model on the serverless platform can be achieved by taking into account the relationship between implementation efficiency and platform settings. Additionally, it can act as a source of inspiration for future serverless hardware support configurations. Also, the serverless platform demonstrates how it improves the effectiveness of resource utilization in machine learning training. The intended result can then be achieved by combining the results of these concurrently operating jobs on server clusters. This work adapts MR, a popular big data processing framework, to the serverless platform, emphasizing realization simulation principles and services, and then uses the results in a word count experiment. The experiment uses a word count of about eleven thousand words to evaluate how well MR is implemented on Alibaba Cloud. It is validated for execution time on the platform with varying Central Processing Unit (CPU) core counts, memory configurations, and worker counts. By testing several platform configurations, it is found that the memory configuration has very little impact on the model's execution time, while the size of the CPU core has a considerable impact on reaction time relative to the number of workers.

A Comprehensive Analysis of Sensitivity in Simulation Models for Enhanced System Understanding and Optimisation

This article delves into sensitivity analysis within simulation models of real systems, focusing on the impact of variability in independent input factors (x) on dependent system outputs (y). It discusses linear and nonlinear regression to analyse and represent relationships between input factors and system responses. This study encompasses three sensitivity analysis areas: factor screening, local sensitivity analysis, and global sensitivity analysis, highlighting their roles in understanding the significance of factors in simulation models. The practical application of sensitivity analysis becomes clear through a case study in a manufacturing system. The case study utilises the Simio simulation system to investigate the impact of input factors on production lead time and work in process (WIP). The analysis uses regression to quantify the impact of seven factors, showcasing the most significant ones with tornado charts and emphasising the application of sensitivity analysis to optimise system responses.

Work Standardization in Lean Manufacturing for Improvement of Production Line Performance in SME

Lean manufacturing is a management philosophy and production system that provides all humanly conceived soft technology to remove waste in any production line. One of the requirements of lean is work standardization. Work standards impose a 'rule of law' in a production line by restricting operators to how long they should work on a single unit. The standards consist of the standard workflow, standard work time, and standard quantity that must be adhered to to meet the customers' demands. The main objectives of this study were to improve the performance of a production line, say SW660 x 600, in terms of weekly output, lead time, and work-in-process (WIP). By applying certain lean elements to the production line, this work indeed improved the performance of the production line. In this work, 5S, PDCA cycle, SPC, and VSM tools were used together to solve the production line problems systematically. It was noticed that the low weekly output and long cycle time at each workstation resulted in delays in the delivery of products to customers. The study managed to increase the weekly output from 61.75 per week to 128.75, an improvement of more than 108.50 percent. Next, in terms of lead time, with the help of a simulation exercise, this work also managed to reduce the lead time from 5 to 1 hour, a reduction of 80 percent. Lastly, the WIP also recorded a reduction from an average of 17 to 9 units weekly, which was an improvement of 47 percent on the weekly WIP. Therefore, this work showed that with the introduction of work standards on the production line, the organization could meet the customers' demands and deliver on time as promised. The study can also be used in other production lines of this and other organizations to help improve their production line performance in the long run.

An effective approach for total completion time minimization subject to makespan constraint in permutation flowshops

This article addresses the permutation flowshop scheduling problem with the objective of minimizing the total completion time subject to a makespan constraint. The makespan is related to system utilization while the total completion time is related to the waiting time, and hence to the work in process (WIP); in real contexts, focusing on both total completion time and makespan allows a good trade-off between WIP and utilization to be found. Two local search algorithms are developed in the paper and, by using an extensive computational experience on literature benchmark instances, they are proved to be able to find good solutions both for regular and no-wait flowshops.

Resource integration and firm performance through organizational capabilities for digital transformation

PurposeDigital transformation is a foundational change in how firms operate and deliver value to customers by using digital technologies to create new business opportunities. The purpose of this study is to offer a conceptual framework by reorganizing the elements of digital transformation, including resources, technology, capabilities and performance, into a workable process and investigating how firms integrate these resources, build new capabilities and transform them into enhanced performance.Design/methodology/approachThis framework builds three blocks: resource integration, organizational capabilities and outcomes, exploring the impact of resource integration on outcomes through organizational capabilities. For resource integration, this study adopts a resource-based view (RBV) and service-dominant logic (SDL) to integrate organizational resources, including information technology (IT)-based resources, which play a role in moderating the effect of resource integration. Moreover, the author argues that firms’ capabilities have two levels: higher-order capabilities and lower-order capabilities, which will convert these resources through the capabilities into organizational performance.FindingsThis framework is built to understand the process of digital transformation and its antecedents for firms’ performance in business environments. Drawing on RBV, it provides a more holistic perspective that has been linked to resource integration, organizational capabilities and outcomes at the firm level. In this way, the theoretical basis for diminishing implicitness associated with the current perspective of digital transformation can be strengthened.Originality/valueThis paper offers a coherent discussion of digital transformation and explains the process of digital transformation, thus advancing prior work. The major contribution is connecting the process of digital transformation through which firms integrate resources, i.e. digital technologies and valuable, rare, inimitable and nonsubstitutable (VRIN) and nonVRIN resources as well, to build organizational dynamic capabilities based on RBV and SDL.

Lateness in production systems - In a nutshell: How to determine the causes of lateness at work systems?

Adherence to customer due dates is the yardstick for the performance of manufacturing companies. In the era of same-day delivery, consumers expect reliable delivery of ordered goods and short delivery times. Also, in the field of business-to-business supply, it is evident that adherence to delivery dates is a fundamental logistical objective for companies. Contract manufacturers, in particular, are confronted with significant challenges: strong fluctuations in customer demand, shorter requested delivery times, and high competitive pressure require appropriate organisation, planning and control of production. However, companies often miss their schedule reliability targets and fail to identify the right causes for these failures. This raises the question of what factors influence the failure to meet schedule reliability targets, how to identify such factors, and what options are available to counteract them. This contribution addresses this issue and focuses on ways to analyse the emerging lateness at work systems in production areas as a deviation of the actual form the planned throughput time. We present existing approaches to analysing the lateness behaviour at work systems and extend the current theory of logistical modelling to determine the three drivers of the so-called relative lateness – planning influences, variance of work-in-process (WIP) and sequence deviations – at work systems systematically. Through this analysis, we enable the practical applicator to initiate target-oriented countermeasures to improve the schedule reliability of their work systems with acceptable analysis expenditure.

Third mortality follow-up of the Mallinckrodt uranium processing workers, 1942–2019

Introduction Mallinckrodt Chemical Works was a uranium processing facility during the Manhattan Project from 1942 to 1966. Thousands of workers were exposed to low-dose-rates of ionizing radiation from external and internal sources. This third follow-up of 2514 White male employees updates cancer and noncancer mortality potentially associated with radiation and silica dust. Materials and methods Individual, annualized organ doses were estimated from film badge records (n monitored = 2514), occupational chest x-rays (n = 2514), uranium urinalysis (n = 1868), radium intake through radon breath measurements (n = 487), and radon ambient measurements (n = 1356). Silica dust exposure from pitchblende processing was estimated (n = 1317). Vital status and cause of death determination through 2019 relied upon the National Death Index and Social Security Administration Epidemiological Vital Status Service. The analysis included standardized mortality ratios (SMRs), Cox proportional hazards, and Poisson regression models. Results Vital status was confirmed for 99.4% of workers (84.0% deceased). For a dose weighting factor of 1 for intakes of uranium, radium, and radon decay products, the mean and median lung doses were 65.6 and 29.9 mGy, respectively. SMRs indicated a difference in health outcomes between salaried and hourly workers, and more brain cancer deaths than expected [SMR: 1.79; 95% confidence interval (CI): 1.14, 2.70]. No association was seen between radiation and lung cancer [hazard ratio (HR) at 100 mGy: 0.93; 95%CI: 0.78, 1.11]. The relationship between radiation and kidney cancer observed in the previous follow-up was maintained (HR at 100 mGy: 2.07; 95%CI: 1.12, 3.79). Cardiovascular disease (CVD) also increased significantly with heart dose (HR at 100 mGy: 1.11; 95%CI: 1.02, 1.21). Exposures to dust ≥23.6 mg/m3-year were associated with nonmalignant kidney disease (NMKD) (HR: 3.02; 95%CI: 1.12, 8.16) and kidney cancer combined with NMKD (HR: 2.46; 95%CI: 1.04, 5.81), though without evidence of a dose-response per 100 mg/m3-year. Conclusions This third follow-up of Mallinckrodt uranium processors reinforced the results of the previous studies. There was an excess of brain cancers compared with the US population, although no radiation dose-response was detected. The association between radiation and kidney cancer remained, though potentially due to few cases at higher doses. The association between levels of silica dust ≥23.6 mg/m3-year and NMKD also remained. No association was observed between radiation and lung cancer. A positive dose-response was observed between radiation and CVD; however, this association may be confounded by smoking, which was unmeasured. Future work will pool these data with other uranium processing worker cohorts within the Million Person Study.

공정과 작업자 특성을 고려한 데이터 기반 작업자 배치 최적화

공정과 작업자 특성을 고려한 데이터 기반 작업자 배치 최적화

Data-Driven Production Planning Models for Wafer Fabs: An Exploratory Study

Cycle time, which is of order of ten weeks for most products in semiconductor wafer fabrication facilities (wafer fabs), must be explicitly considered in production planning models. Cycle times depend nonlinearly on the resource workload in a wafer fab. Different data-driven (DD) production planning formulations are studied in this paper. Such formulations are based on a set of system states representing the congestion behavior of a wafer fab with work in process (WIP) and resulting output levels. Different WIP-output relations in the DD formulations are investigated. In addition, different methods are proposed to gather representative sets of system states. The DD formulations are compared with the Allocated Clearing Function (ACF) formulation with respect to profit and cost values using simulation models of a scaled-down and a large-sized multiproduct wafer fab, respectively. The simulation results show that some DD variants are able to outperform the ACF formulation under several experimental conditions.

Simulation and Genetic Algorithm-based approach for multi-objective optimization of production planning: A case study in industry

To stay competitive on the constantly changing and demanding market, production systems need to optimize their performance daily. This is particularly challenging in labour-intensive industries, which is characterized by highly volatile customer demand and significant daily variability of available workers. The Uncertainty related to the key production parameters in the industry is causing disruptions in long-term production planning and optimization, which leads to the long lead production times, operational risks and accumulation of inventory. To address these challenges, production systems need to ensure adequate operational production planning and optimization of all variables that are influencing the productivity of their systems on a daily basis. To tackle the problem, this study elaborates the application of discrete event simulations and genetic algorithm, using the Tecnomatix Plant Simulation software, to support decision-making and operational production planning and optimization in the industry. The simulation model developed for this purpose considers: customers demand changes, variable production times, operationally available resources and production batch size, to provide an optimal production sequence with the highest number of produced pieces and the lowest total work in process (WIP) inventory per day. To demonstrate the efficiency of the methodology and prove the benefits of the selected optimization approach, a case study is conducted in the textile factory.

A process oriented psychology (POP) approach to processing collective trauma in an Irish context

The island of Ireland and Irish people have experienced a vast array of social, political, and military and paramilitary events and occurrences throughout history. Many have given rise to different forms of collective trauma. This has influenced the development of a national psychology and the many sub-psychologies of different groups of Irish people. Unprocessed, this trauma is transferred to subsequent generations at an individual, group, and collective level. This article considers an integrative approach to working with collective issues within groups of people who have a relationship with Irishness. This is heavily based on process oriented psychology (POP) or its larger scale application, worldwork. Mapping and following the different roles that may emerge within the process is key. We combine this approach with the spirit or essence of ancient Irish storytelling in hosting community-based dialogue interventions. These groups explore ‘the experience of being Irish in 2022 or different relationships with, or to, Irishness in today’s world’. The different themes and topics that emerged across the initial groups are discussed with further reflections from participants and more in-depth commentary from the perspectives of the group facilitators. The next stages of this group-based psychotherapeutic work are considered within an Irish framework; as well as the potential benefit of this kind of work at an international level, given the many collective psychosocial challenges we face across a very interconnected globe.

Radicalization from a societal perspective.

Studies on radicalization tend to focus on the dynamics of extremist groups and how they exploit grievances of vulnerable individuals. It is imperative, however, to also understand the societal factors that lead to such vulnerabilities and grievances. Our social environment plays a key role in how we view the world and shape our beliefs. By understanding the social dynamics, we can gain insight into the motivations that drive people to extremism. Throughout this paper, we examine the societal factors and processes such as discriminative institutional structures and social norms/practices that can make an individual vulnerable and serve as a driving force for them to join a radical group. To do that, we use the process-oriented psychology of Arnold Mindell and the phenomenology of whiteness of Sara Ahmed as our theoretical framework. These frameworks help us map out the societal dynamics causing individuals to carve social niches out of their current social group and into an extremist group. We use interviews with ex-militants of the radical group, Islamic State of Iraq and Syria, to show how certain societal dynamics, such as social injustice, misuse of power, marginalization and discrimination, served as key factors that led these individuals to identify and sympathize with radical ideology. The aim of this paper is to emphasize that, to develop effective preventative measures against recruitment into extremist groups, it is imperative to have a profound understanding of the social dynamics that make an individual susceptible to radicalization in the first place.

Material flow control in Remanufacturing Systems with random failures and variable processing times

Material flow control in remanufacturing is an important issue in the field of disassembly. This paper deals with the potential of autonomous material release decisions for remanufacturing systems to balance the uncertainties related to changing bottlenecks, to maximise throughput (TH) and to minimise work-in-process (WIP). The goal is to achieve the highest possible throughput rate using real-time data while keeping costs to a minimum. Unlike traditional production systems, remanufacturing must consider and handle high uncertainties in the process. Up to now, classical methods such as CONWIP, Material Requirement Planning (MRP) and Kanban have been used for material flow control. However, these methods do not perform well in a system with high variation and uncertainties such as remanufacturing as they aim to find solutions for static environments. Crucial for optimal production in stochastic environments is finding the optimum pull or release rate which can vary over time in terms of maximising TH and minimising WIP. We propose a deep reinforcement learning approach that acts on the environment and can adapt to changing conditions. This ensures that changing bottlenecks are taken care of and that there is a minimum WIP in the system.

REDUCING WORK IN PROCESS (WIP) USING LINE BALANCING TECHNIQUE IN MANUFACTURING INDUSTRY

For organizations looking to increase productivity and efficiency in the manufacturing sector, reducing Work In Process (WIP) is a crucial goal. The deployment of line-balancing techniques to accomplish this goal is the main topic of this research article. The introduction discusses the negative consequences of WIP, including productivity loss, leads times that are too long, and bottlenecks. The application of lean manufacturing principles, workflow optimization, WIP limits establishment, improved communication and collaboration, prioritization and sequencing of work, identification and removal of bottlenecks, and performance monitoring are just a few of the methods for reducing WIP that are covered. The importance of line balancing in maximizing efficiency, effectiveness, and productivity in manufacturing output is emphasized in the study. The literature review explores the use of worksharing and line-balancing approaches to increase productivity as well as the advantages of line-balancing in eliminating operational inefficiencies. The procedure for data collection and analysis, which includes calculating cycle time and takt time, is described in the methodology section. To reduce WIP and streamline processes, the suggested methodology advises allocating greater resources to bottleneck stations. The outcomes show how the suggested methodology works to decrease WIP and boost productivity. The paper concludes by comparing the advantages of line balancing in reducing workstations, cycle time, balance delay, idle time, and overall line length. Present. The references provided offer relevant studies on line balancing and assembly line basics. This research contributes valuable insights and practical implications for improving productivity and reducing WIP in the manufacturing industry.