Cycle Time Reduction Research Articles

Selection of Artificial Intelligence Technology For Digitalization of Order Fulfillment on Drug Factory Using Multi Criteria Decision Making

Objective: This paper aims to identify the most suitable Artificial Intelligence (AI) technology for the order fulfillment process in pharmaceutical factory, along with the key criteria and sub-criteria for successful AI implementation. Theoretical Framework: The study integrates theories of digital transformation, process optimization, and decision-making. Method: The research assesses digital readiness to evaluate a company's preparedness for digital transformation. The as-is process is mapped using BPMN to identify potential AI applications. AI technologies are then ranked using BWM and PROMETHEE methods, followed by scenario analysis to determine the optimal technology. Results and Discussion: Demand Forecasting is the top AI technology for adoption in the pharmaceutical factory's order fulfillment process, reducing cycle time by 29.50% to 21.80 hours. Research Implications: The research contributes to the understanding of selecting the most effective AI technology for optimizing the order fulfillment process in pharmaceutical companies, providing valuable insights for improving efficiency. Originality/Value: The study proposes a perspective on applying AI to optimize the order fulfillment process in pharmaceutical factory, offering valuable insights for improving operational efficiency through digital transformation.

Design of Connector Assembly Equipment for the Automotive Industry

The automotive industry is one of the most demanding sectors of all manufacturing industries due to its competitiveness. It is necessary to innovate through the implementation of automated and robotic equipment, leading to cycle time and labor cost reduction. This work aims to design semi-automatic equipment to assemble cabling connectors used in the automotive sector, replacing a manual process currently taking place in an automotive components company. In the proposed equipment, the operator places a connector in the equipment, and the components (pins and seals) are automatically inserted. A vision sensor with artificial intelligence then confirms the correct application. The equipment operation defined as Finite Element Method (FEM) was applied for structural verification; the materials and fabrication processes were detailed; the associated costs were calculated, and the equipment subsets were validated. The design was successfully accomplished, and the imposed requirements were fulfilled, with significant advantages over the current process, providing new knowledge on how semi-automatic systems can be deployed to enhance the productivity and quality of manufacturing processes. The design principles and insights gained from this work can be applied to other automation challenges, particularly where manual processes need to be replaced by more efficient semi-automatic or automatic systems. The modularity of the overall solution and the design concepts of the component inserter, component feeder, and assembly process allow for its use in different assembly scenarios beyond the automotive sector, such as electronics or aerospace, providing a contribution to increased competitiveness and survival in the global market.

Cycle time reduction using intermittent DC internal heating in vapour phase drying of cellulose-based insulation used in transformers

ABSTRACT For the commissioning of a new transformer, a delay in delivery caused owing to the long-drying process at the manufacturer is not acceptable in order to avoid erratic power supplies and extensive down times. To increase productivity, ensure on-time delivery, avoid unplanned power outages, reduce the cost, increase the efficiency of energy utilisation and maintain the quality parameters of the transformer insulation thereby increasing its service life, cycle time of insulation drying at the manufacturer needs to be reduced. As of today, the latest technology for insulation drying of transformers at the manufacturer’s factory is the vapour phase drying (VPD) method. The cycle time in VPD may span for 4–5 days for a power transformer. This establishes the need to reduce the cycle time in VPD of transformer insulation. In this work, experimentation was carried out on 11 kV transformers for developing a system to reduce the cycle time in VPD.

Quality improvement approach to reduce patient cycle time at a student-run free healthcare clinical network

BackgroundThe University of Florida (UF) Equal Access Clinic Network (EACN) is the largest student-run free healthcare clinic network in Florida. The UF EACN serves those who are underinsured or uninsured...

Fischer-Tropsch Hydrocarbons as Processing Aides in Injection Molding

Polymer producers and converters are continuously evaluating options to reduce costs by producing faster, reducing energy consumption, reducing scrap and improving finished article mechanical and aesthetic properties. Recently, however, sustainability and overall environmental impact have also become prominent themes for converters. Processing aides are commonly used in blown film extrusion and injection molding. Specifically in injection molding silicone spray, fluoropolymers and oleochemicals are used. An alternative is to use higher molecular weight Fischer-Tropsch (FT) hydrocarbons as a polymer processing aide and mold release agent due to its good compatibility with the polymer compound matrix. Formulating with these hydrocarbons allows the converter to reduce cycle time, produce faster to reduce labor, to reduce energy consumption and improve certain properties of the injection molded article. Ultimately FT as a polymer processing aide could be an important tool to a converter to reduce manufacturing costs and improve quality.

Optimizing Quality Control on Electric Vehicle Production Lines with AI and Machine Learning

The study explores the use of AI and ML technologies to optimize quality control in EV production lines. By applying neural networks and machine learning algorithms, the research achieved significant improvements: a 74.4% reduction in torque deviation, a 75.9% enhancement in speed consistency, and a 70.8% decrease in defect rates. These gains also resulted in a 16.0% reduction in production cycle time and a 50.0% decrease in downtime, leading to an 8.4% increase in Overall Equipment Effectiveness (OEE). The methods employed included AI-driven predictive maintenance, real-time monitoring, and statistical process control (SPC). Despite the clear benefits, challenges such as integrating these technologies with existing systems and ensuring robust data infrastructure remain. Future research should focus on refining these approaches and extending their application across the automotive industry.

An Innovative Vision-Guided Feeding System for Robotic Picking of Different-Shaped Industrial Components Randomly Arranged

Within an industrial plant, the handling of randomly arranged objects is becoming increasingly popular. The technology industry has introduced ever more powerful devices to the market, but they are often unable to meet the demands of the industry in terms of processing times. Using a multi-component feeder, which facilitates the automatic picking of objects arranged in bulk, is the ideal element to speed up the identification of objects by the vision system. The innovative designed feeder eliminates the dead time of the vision system since the feeder has two working surfaces, thus making the viewing time hidden in relation to the total handling cycle time. In addition, the step feeder integrated into the feeder structure allows for control over the number of objects that fall onto the work surface, optimizing the material flow. The feeder was designed to palletize aluminum hinge fins but can also handle other products with different shapes and sizes. A two-dimensional (2D) vision system is integrated into the robotic cell to identify the components to be palletized, obtaining a reduced cycle time. The innovative feeder is fully adaptable to industrial applications and allows for easy integration into the robotic cell in which it is installed; by testing its operation with different aluminum fins, male and female, significant results were obtained in terms of cycle times ranging from 1.44 s to 1.68 s per piece, with an average productivity level (PL) of 1175 pcs every 30 min.

METHODS FOR MITIGATION OF GEOMETRIC DEFECTS GENERATED DURING POLYMER INJECTION MOLDING

Injection molding is one of the most used processes in the polymer parts forming industry, which consists of raising the temperature of a polymeric material to its melting point and introducing it under pressure into the cavity of a mold where it is It will cool to a suitable temperature so that the pieces can be extracted without deforming. This article presents a review of the state of the art of scientific works that state techniques, methods and commercial software, used to analyze, understand and in some cases mitigate failures in the polymer injection process, which will reduce cycle times and production costs. Likewise, during the search, the polymers were delimited: polypropylene (PP), polyvinyl chloride (PVC), polyethylene terephthalate (PET) and for thermostables, acrylonitrile butadiene styrene (ABS), since they are the ones that showed the most cases with failures in the injection molding process. An overview of the use of methods that can be used to analyze the parameters that affect the quality of formed parts is also shown, such as Analysis of Variance (ANOVA), Artificial Neural Networks (ANN), Taguchi, Response Surface Methodology (RSM). It is also presented which are the Finite Element Analysis (FEA) and computer-aided engineering (CAE) software that stand out the most in their use to better understand the physical phenomena that occur during the injection molding process.

Measuring process innovation outputs and understanding their implications for firms and workers: Evidence from Pakistan

New processes significantly affect firms and workers; however, due to a lack of quantitative metrics, our understanding of the measures, determinants, and impacts of new processes remains limited. Drawing on unique data from Pakistan, we analyze five different metrics of process innovation output: cost reduction, defect rate reduction, reduction in production cycle time, increase in production capacity, and improvement in product quality. We find that the breadth and depth of innovative capabilities, level of competition, and availability of market sources of knowledge are important inducers of process innovation, and that smaller firms are more likely to introduce new processes and are better able to transform them into higher output. All five process innovation outputs are associated with higher labor productivity and sales expansion; however, there is considerable heterogeneity in the size of these impacts. We did not find that adopting new processes led to lower employment.

Redesign of Wallet Production Facility Layout Using ARC and Simulation at Fanri Collection

Abstract. The development of technology at this time causes competition in the accessories and fashion industry. This situation has caused an increase in the number of requests for products at Fanri Collection, a producer of leather craft accessories. To survive and win the competition, companies need to increase productivity. One way of resolving the issue is by designing the layout and production facilities to minimize the distance of material handling in the production process. This research aims to design layout and production facilities on wallet products with the Discrete Event Simulation (DES) method using FlexSim 21.0 software and Activity Relationship Chart (ARC) to analyze the amount of output and get a proposed facility layout. Based on the simulation results, in producing 200 units of wallets, the initial total material handling distance is 20.667 meters with a cycle time of 1 day, 5 hours, and 3 minutes. After making improvements with ARC, the total material handling distance is reduced to 17.475 meters with a cycle time of 1 day, 4 hours, and 48 minutes. Thus, the improvement can minimize material handling distance by 3.192 meters and reduce cycle time by about 15 minutes.

Optimization of GMAW Processes in Robotic Welding: A Case Study from Automotive Manufacturing

The optimization of Gas Metal Arc Welding (GMAW) processes in robotic welding has become crucial for enhancing efficiency and quality in automotive manufacturing. This paper explores the integration of advanced robotic welding technologies, focusing on the use of the KUKA KR8 L8 Arc HW robot in GMAW applications. By leveraging artificial intelligence and sensor integration, this study demonstrates significant improvements in welding precision and process stability. Key advancements include the use of geometric and process sensors for real-time adaptive control, and the implementation of Fronius CMT for superior arc control and penetration. Detailed case studies from automotive manufacturing illustrate the practical benefits of these technologies, showcasing enhanced productivity, reduced cycle times, and improved weld quality. The findings underscore the importance of continuous innovation and optimization in robotic welding processes to meet the evolving demands of the automotive industry.

In-situ wet milling for particle size control in continuous crystallization: Expanding the attainable region

Continuous mixed-suspension mixed-product removal crystallizer cascades are robust and proven options for continuous manufacturing. However, when applied to a late-stage drug substance the desired particle size could not be achieved due to rapid crystal growth and slow secondary nucleation kinetics. This necessitated a batch terminal milling operation to achieve particle size control (Johnson et al., 2021). To overcome this and to intensify the process, an in-situ high-shear wet mill was integrated into the continuous equipment train to achieve the target physical properties of the drug substance that met the quality and manufacturability criteria needed for the drug product. This article describes the workflow used to optimize the equipment set and operational parameters. Incorporation of a high-shear wet mill operated intermittently in the continuous drug substance process greatly expanded the attainable particle size region, improved product quality, reduced cycle time, and simplified process development and scale-up compared to the process of batch milling after continuous crystallization. Small scale batch milling experiments were used to characterize milling performance at lab and production scales and inform operational space. Several lab-scale continuous runs were then used to define the process operating space and validate the process parameters necessary to meet the particle specifications of the product. Scale-up of the equipment set for in-situ milling was also demonstrated. This approach was tested at lab-scale on representative material operating for 70 h while changing the particle size of the product in real time. This illustrated the potential of intermittent milling for model-free particle size control.

P-507 Prediction of clinical efficacy of PGT-A based on maternal age and embryo quality in patients with recurrent implantation failure (RIF) or recurrent pregnancy loss (RPL)

Abstract Study question Can the utility of PGT-A be predicted by selecting embryos based on both maternal age and embryo quality in patients with RIF or RPL? Summary answer The predictive value of PGT-A can potentially be determined by assessing maternal age and embryo quality of experiencing patients with RIF or RPL. What is known already Embryo quality and euploid status are crucial determinants in selecting embryos for euploid embryo transfer to achieve a successful pregnancy. This is because embryos with high morphological scores may still exhibit aneuploidy, making morphologic evaluation alone limited. This limitation is particularly important in patients with a history of RIF and RPL. Aneuploidies occur during meiosis in the embryo and increase significantly with age, becoming a major contributor to implantation failure, leading to unsuccessful pregnancies, miscarriages, and congenital birth defects. Study design, size, duration This retrospective study, conducted at a single center, assessed embryo quality in 927 blastocysts derived from 221 cases of patients with RIF and 863 blastocysts derived from 204 cases of patients with RPL. The study spanned from January 2022 to November 2023. Blastocysts were scored using the KIDScore provided by the Embryoscope® time-lapse system (Vitrolife, Göteborg, Sweden) following the manufacturer’s protocols and subsequently underwent next-generation sequencing (NGS)-based PGT-A. Participants/materials, setting, methods A total of 927 RIF and 863 RPL patients’ blastocysts were analyzed, resulting in 256 and 242 euploid embryos, respectively, using NGS. Euploid embryos were classified by morphokinetics (good quality and medium quality) and maternal age groups (≤35, 36-37, 38-39, 40-41, 42-43, 44-45, ≥46). In RIF, 62.9% (161/256) of euploid embryos were classified as good quality, and 37.1% as medium quality. In RPL, 59.1% (143/242) were classified as good quality, and 40.9% as medium quality. Main results and the role of chance The euploid embryo rate decreases in both good- and medium-quality embryos as the patient’s age increases. For RIF patients: ≤35 (47.1% and 22.7%), 36-37 (46.7% and 19.8%), 38-39 (37.5% and 16.4%), 40-41 (37.5% and 11.5%), 42-43 (31.8% and 10.0%), 44-45 (0% and 11.1%), ≥46 (0% and 0%). For RPL patients: ≤35 (46.0% and 25.4%), 36-37 (45.9% and 25.0%), 38-39 (43.8% and 15.1%), 40-41 (27.9% and 18.0%), 42-43 (26.3% and 11.7%), 44-45 (0% and 12.5), ≥46 (0% and 0%). Moreover, ongoing pregnancy or live birth rates also decrease as the patient’s age increases, while the miscarriage rate increases. For RIF patients: ≤35 (57.8% and 16.1%), 36-37 (50.0% and 18.8%), 38-39 (46.2% and 25.0%), 40-41 (43.5% and 28.6%), 42-43 (36.4% and 33.3%), 44-45 (0% and 100%). For RPL patients: ≤35 (59.5% and 12.0%), 36-37 (52.0% and 18.8%), 38-39 (47.8% and 31.3%), 40-41 (43.5% and 37.5%), 42-43 (40.0% and 42.9%), 44-45 (0% and 100%). However, embryo transfer was not possible for those over 46 due to all embryos being aneuploidy. Therefore, PGT-A is valuable for patients under 42, proves beneficial for reducing the abortion rate, and achieving a high pregnancy rate for those aged 42-43, and should be considered for patients over 44. Limitations, reasons for caution This data is from a retrospective study, and well-designed prospective studies with data from multiple centers, larger samples, and extended study periods are needed to validate the current results. Additionally, obtaining information on demographic characteristics, detailed embryo information, and overall clinical outcomes can further enhance the current results. Wider implications of the findings These findings suggest that individualizing each case based on both maternal age and embryo quality is helpful in predicting the utility of PGT-A. Such personalized considerations can result in significant benefits for patients by optimizing outcomes, reducing cycle times, and consequently lowering related costs. Trial registration number not applicable

Optimization of novel two-step curing method for die stack epoxy bonding to reduce voids in Ball Grid Array packages for high-density interconnect applications

This research explores the optimization of epoxy curing parameters to minimize void formation in 3-IC-Chip-MAPBGA packages, a subset of BGA packages, crucial components in high-density interconnect applications. The study utilizes a systematic approach involving design of experiments (DOE) assisted by statistical JMP tool to manipulate curing profiles, aiming to achieve void reduction while preserving adhesion properties. Various analytical techniques, including X-ray imaging, differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), die shear strength tests, and C-Sam analysis for delamination, are employed to analyze void formation, material characteristics, mechanical properties, and structural integrity. The findings demonstrate that the sample with a 2nd step curing profile, identified as sample#3, which includes a ramp time of 15 min, a 1st step curing temperature of 90 °C with a soak time of 20 min, and a 2nd step ramp time of 20 min, exhibits the most favourable outcome in void reduction. This sample shows a notably lower void presence of 3.66 % and the highest die shear strength of 126 MPa. In contrast, the control sample, serving as a reference, displays a void percentage of 7.28 %, nearly twice as high as that of sample#3, and much lower die shear strength of 80 MPa at 25 °C. Adopting the curing profile of sample#3 also leads to a substantial 18.75 % reduction in cycle time compared to the control sample. The study highlights the importance of balancing curing parameters to mitigate void formation and maintain optimal mechanical properties, offering valuable insights for improving the reliability of high-density interconnect applications.

Use of simulation to optimize a sweet corn breeding program: implementing genomic selection and doubled haploid technology.

Genomic selection and doubled haploids hold significant potential to enhance genetic gains and shorten breeding cycles across various crops. Here, we utilized stochastic simulations to investigate the best strategies for optimize a sweet corn breeding program. We assessed the effects of incorporating varying proportions of old and new parents into the crossing block (3:1, 1:1, 1:3, and 0:1 ratio, representing different degrees of parental substitution), as well as the implementation of genomic selection in two distinct pipelines: one calibrated using the phenotypes of testcross parents (GSTC scenario) and another using F1 individuals (GSF1). Additionally, we examined scenarios with doubled haploids, both with (DH) and without (DHGS) genomic selection. Across 20 years of simulated breeding, we evaluated scenarios considering traits with varying heritabilities, the presence or absence of genotype-by-environment effects, and two program sizes (50 vs 200 crosses per generation). We also assessed parameters such as parental genetic mean, average genetic variance, hybrid mean, and implementation costs for each scenario. Results indicated that within a conventional selection program, a 1:3 parental substitution ratio (replacing 75% of parents each generation with new lines) yielded the highest performance. Furthermore, the GSTC model outperformed the GSF1 model in enhancing genetic gain. The DHGS model emerged as the most effective, reducing cycle time from 5 to 4 years and enhancing hybrid gains despite increased costs. In conclusion, our findings strongly advocate for the integration of genomic selection and doubled haploids into sweet corn breeding programs, offering accelerated genetic gains and efficiency improvements.

Transforming Tablet Production in Indonesia with 66% Faster Manufacturing

This study addresses the high production costs of tablet products at PT. XYZ, a pharmaceutical company in Indonesia, due to long cycle and takt times. Using Causal Branching and KATA methods, data were collected over eight months through observations, interviews, and quantitative analysis. The study achieved a 43% reduction in reject foils from destripping, a 40% decrease in total cycle time, and a 66% reduction in takt time. These results highlight effective strategies for optimizing manufacturing processes and reducing costs in the pharmaceutical industry. Highlights: Cost Reduction: 43% reject foils, 40% cycle time reduction. Methodology: Used Causal Branching and KATA for problem-solving. Data Collection: Eight-month study with observations, interviews, quantitative analysis. Keywords: Tablet production, Cycle time reduction, Takt time optimization, Causal Branching, KATA method

EFFECTS OF COOLING CHANNEL DIAMETER, DISTANCE, AND PITCH IN PLASTIC INJECTION MOLDING

A straight drilled or conventional cooling channel is a linear cooling pathway that circulates around the mold cavity to regulate temperature during the plastic injection molding process. The cooling phase accounts for 70% of the plastic injection molding process, making it crucial to design the cooling channel for reducing cycle time while upholding part quality. This study aims to analyze the effects of cooling channel parameters on the product cavity. The parameters under consideration are the cooling channel's diameter, pitch, and distance. Various designs of cooling channels were examined. This study employed a food container lid made from polypropylene (PP) as a product case study. A 3D model of the food container lid and Straight Drilled Cooling Channel (SDCC) was created using CATIA V5, and these models were subsequently imported into Autodesk Moldflow Insight for simulation analysis. Four results were obtained from the simulation analysis, namely time to reach ejection temperature, average temperature, volumetric shrinkage, and deflection. The results indicate that increasing the diameter and pitch of cooling channels leads to a decrease in the average part temperature and the time required to reach ejection temperature. Conversely, increasing the distance between cooling channels results in an increase in the average part temperature and the time required to reach ejection temperature.

More is better? The role of strategic data management in a lean manufacturing process

Purpose Lean manufacturing has been pivotal in emphasizing the reduction of cycle times, minimizing manufacturing costs and diminishing inventories. This research endeavors to formulate a lean data management paradigm, through the design and execution of a strategic edge-cloud data governance approach. This study aims to discern anomalous or unforeseen patterns within data sets, enabling an efficacious examination of product shortcomings within manufacturing processes, while concurrently minimizing the redundancy associated with the storage, access and processing of nonvalue-adding data. Design/methodology/approach Adopting a lean data management framework within both edge and cloud computing contexts, this study ensures the preservation of significant time series sequences, while ascertaining the optimal quantity of normal time series data to retain. The efficacy of detected anomalous patterns, both at the edge and in the cloud, is assessed. A comparative analysis between traditional data management practices and the strategic edge-cloud data governance approach facilitates an exploration into the equilibrium between anomaly detection and space conservation in cloud environments for aggregated data analysis. Findings Evaluation of the proposed framework through a real-world inspection case study revealed its capability to navigate alternative strategies for harmonizing anomaly detection with data storage efficiency in cloud-based analysis. Contrary to the conventional belief that retaining comprehensive data in the cloud maximizes anomaly detection rates, our findings suggest that a strategic edge-cloud data governance model, which retains a specific subset of normal data, can achieve comparable or superior accuracy with less normal data relative to traditional methods. This approach further demonstrates enhanced space efficiency and mitigates various forms of waste, including temporal delays, storage of noncontributory normal data, costs associated with the analysis of such data and excess data transmission. Originality/value By treating inspected normal data as nonvalue-added, this study probes the intricacies of maintaining an optimal balance of such data in light of anomaly detection performance from aggregated data sets. Our proposed methodology augments existing research by integrating a strategic edge-cloud data governance model within a lean data analytical framework, thereby ensuring the retention of adequate data for effective anomaly detection.

Recent Advancements towards Sustainability in Rotomoulding.

Rotational moulding is a unique low-shear process used to manufacture hollow parts. The process is an excellent process method for batch processing, minimal waste and stress-free parts. However, the process has drawbacks such as long cycle times, gas dependency and a limited palette of materials relative to other process methods. This review aimed to shed light on the current state-of-the-art research contributing towards sustainability in rotational moulding. The scope of this review broadly assessed all areas of the process such as material development, process adaptations and development, modelling, simulation and contributions towards applications carving a more sustainable society. The PRISMA literature review method was adopted, finding that the majority of publications focus on material development, specifically on the use of waste, fillers, fibres and composites as a way to improve sustainability. Significant focus on biocomposites and natural fibres highlighted the strong research interest, while recyclate studies appeared to be less explored to date. Other research paths are process modification, modelling and simulation, motivated to increase energy efficiency, reduction in scrap and attempts to reduce cycle time with models. An emerging research interest in rotational moulding is the contribution towards the hydrogen economy, particularly type IV hydrogen vessels.

Non-Cutting Moving Toolpath Optimization with Elitist Non-Dominated Sorting Genetic Algorithm-II

Path planning (PP) is fundamental in the decision-making and control processes of computer numerical control (CNC) machines, playing a critical role in smart manufacturing research. Apart from improving optimization in PP, enhancing efficiency while decreasing CNC machine cycle time is important in manufacturing. Many methods have been offered in the literature to improve the cycle time for obtaining optimal trajectories in toolpath optimization, but these methods are mostly considered for improvements in path length or machining time in optimal PP. This study demonstrates a method for creating a smoothing path. It aims to minimize both cycle time and toolpath length, while demonstrating that the non-dominated sorting genetic algorithm (NSGA-II) is efficient in addressing the multi-objective PP problems within static situations. Pareto optimality for performance comparisons with multi-objective genetic algorithms (MOGAs) is presented in order to highlight the positive features of the non-dominant solving generated by the NSGA-II. According to the comprehensive analysis results, the optimization of the path carried out with the NSGA-II emphasizes its shorter and smoother attributes, with the optimal trajectory achieving approximately 30% and 7% reductions in path length and machining cycle time, respectively.