Process Control Capabilities Research Articles

Effect of plasma treatment on LMPAEK/CF tape and composites manufactured by automated tape placement (ATP)

Automated tape placement (ATP) process is widely used in aerospace for its advanced process control and multi-axis capabilities but faces issues like limited choice of materials and suboptimal tape consolidation. This study investigates air plasma treatment on ATP carbon fiber thermoplastic feedstock tape to address these challenges. The effects on low melt Polyaryletherketone/carbon fiber unidirectional tape (LMPAEK/CF UD tape) were analyzed. Treated and untreated tapes were used to fabricate composites and evaluated for physical, thermal, mechanical, and interfacial properties. Atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared (FTIR) analyses revealed surface roughness changes (on LMPAEK), extent of oxidation, and the presence of hydroxyl/carboxyl groups. Composites from plasma-treated tapes showed a 7.6% increase in tensile strength, 8% in tensile modulus, 18% in flexural strength, and 8.3% in flexural modulus. The interlaminar shear strength improved by 18.7%. Failure analysis showed untreated composites failed via inter-ply and fiber-matrix delamination, while treated composites experienced matrix cracking and fiber breakage. This study highlights atmospheric plasma treatment as a solution to ATP’s limitations, significantly enhancing LMPAEK/CF UD tape composites’ properties.

Rapid total sialic acid monitoring during cell culture process using a machine learning model based soft sensor.

Total sialic acid content (TSA) in biotherapeutic proteins is often a critical quality attribute as it impacts the drug efficacy. Traditional wet chemical assays to quantify TSA in biotherapeutic proteins during cell culture typically takes several hours or longer due to the complexity of the assay which involves isolation of sialic acid from the protein of interest, followed by sample preparation and chromatographic based separation for analysis. Here, we developed a machine learning model-based technology to rapidly predict TSA during cell culture by using typically measured process parameters. The technology features a user interface, where the users only have to upload cell culture process parameters as input variables and TSA values are instantly displayed on a dashboard platform based on the model predictions. In this study, multiple machine learning algorithms were assessed on our dataset, with the Random Forest model being identified as the most promising model. Feature importance analysis from the Random Forest model revealed that attributes like viable cell density (VCD), glutamate, ammonium, phosphate, and basal medium type are critical for predictions. Notably, while the model demonstrated strong predictability by Day 14 of observation, challenges remain in forecasting TSA values at the edges of the calibration range. This research not only emphasizes the transformative power of machine learning and soft sensors in bioprocessing but also introduces a rapid and efficient tool for sialic acid prediction, signaling significant advancements in bioprocessing. Future endeavors may focus on data augmentation to further enhance model precision and exploration of process control capabilities.

Process Capability Control Charts for Monitoring Process Accuracy and Precision

Process capability index (PCI) is a convenient and useful tool of process quality evaluation that allows a company to have a complete picture of its manufacturing process in order to prevent defective products while ensuring the product quality is at the required level. The aim of this study was to develop a control chart for process incapability index , which differentiates between information related to accuracy and precision. Index measures process inaccuracy as the degree to which the mean departs from the target value, while index measures imprecision in terms of process variation. The most important advantage of using these control charts of , , and is that practitioners can monitor and evaluate both the quality of the process and the differences in process capability. The and charts were instead of Shewhart’s and chart since the process target values and tolerances can be incorporated in the charts for evaluation as a whole, which makes the charts capable of monitoring process stability and quality simultaneously. The proposed , , and control charts enable practitioners to monitor and evaluate process quality as well as differences in process capability. The control charts are defined using probability limits, and operating characteristic (OC) curves used to detect shifts in process quality. The method proposed in this study can easily and accurately determine the process quality capability and a case is used to illustrate the application of control charts of , , and .

Real Time Automation and Ratio Control Using PLC & SCADA in Industry 4.0

Industrial Control Systems (ICS) and SCADA (Supervisory Control and Data Acquisition) systems play a critical role in the management and regulation of critical infrastructure. SCADA systems brings us closer to the real-time application world. All process and equipment control capability is typically provided by a Distributed Control System (DCS) in industries such as power stations, agricultural systems, chemical and water treatment plants. Instead of control through DCS, this paper proposes a SCADA and PLC (Programmable Logic Controller) system to control the ratio control division and the assembly line division inside the chemical plant. A specific design and implementation method for development of SCADA/PLC based real time ratio control and automated assembly line system in a chemical plant is introduced. The assembly line division is further divided into sorting stage, filling stage and the auxiliary stage, which includes the capping unit, labelling unit and then the storage. In the ratio control division, we have defined the levels inside the mixer and ratio of the raw materials through human machine interface (HMI) panel. The ratio of raw materials is kept constant on the basis of flow rates of wild stream and manipulated stream. There is a flexibility in defining new levels and the ratios of the raw materials inside the mixer. But here we taken the predefined levels (low, medium, high) and ratios (3:4, 2:1, 2:5). Control valves are used for regulating the flow of the compositions. In the assembly line division, the containers are sorted on the basis of size and type of material used i.e., big sized metallic containers and small sized non-metallic containers by inductive and capacitive proximity sensors. All the processes are facilitated with laser beam type or reflective type sensors on the conveyor system. Building a highly stable and dependable PLC/SCADA system instead of Distributed Control System is required to achieve automatic management and control of chemical industry processes to reduce waste manpower and physical resources, as well as to improve worker safety.

(Digital Presentation) Advanced Process Control of Dual Patterns for FinFET Mass Production

ABSTRACT Advanced Process Control (APC) has been widely applied in state-of-the-art semiconductor industries for the everlasting pursuit of cycle time reduction, higher yield and zero scrap. Inline metrology is thus valuable input feeds for the improvement of wafer-to-wafer and within-wafer CD variations originated from either abnormality or noise of different processes. Implementing a suitable APC scheme to process flow remains a challenging task for engineers. In most cases, it is enough to implement the APC to only one single pattern on Chip. However, for multi-purpose chip design, the CD control of multiple patterns is becoming important. In this paper, we presented a workflow of APC to control the CD of two different patterns. It is shown that, with the same amount of APC process layers, we were able to improve from single pattern CD control to dual pattern CD control. The final process variations of two patterns can be controlled within ±0.5 nm. INTRODUCTION Abnormality and noise are two inevitable root causes of systematic yield loss and mis-process during semiconductor manufacturing processes [1]. Within-wafer uniformity is another important indication of process control capability. Lam tools implement Hydra Electro Static Chuck (ESC) combining the 4 radially-tunable zones with grid-heating elements, where additional CDU tuning is made possible for radial and non-radial uniformity [2].APC schemes are normally applied to a single pattern as the anchor point to control the CD and CDU. However, for multi-purpose chip layouts, areas of different CDs and pitches are designed. For instance, CD controlling of both Logic and SRAM are important for yield improvement and device performance. When APC is implemented to different processes across the production flow, it is possible to utilize properly the inline metrology data of different patterns across processes for a dual pattern control. For this purpose, The APC CD control scheme of dual patterns is demonstrated in this work. SETUP The process flow is divided into 4 stages. In stage 1, a hard mask (HM) is patterned with a Lithograph-Etching (LE) process. An added CD trimming process of stage 2 is added to cover the LE etch bias (EB) incapability. Pattern B is covered by photoresist materials to trim the CD of pattern A HM to the target value. In stage 3, a thin Atomic Layer Deposition (ALD) layer is deposited on the HM of both patterns A and B for etching protection. In stage 4, the final etch process is completed with the HMs of target CDs. RESULTS and DISCUSSIONS For single pattern APC, pattern A is designated as the anchor pattern for this scheme. For stage 1, the Hydra function is applied to pattern A to improve its CDU. Here we assume that the Hydra function barely improves the CD or CDU of pattern B, due to the fact that pattern A and pattern B have different line CDs and Pitches. In stage 2, APC is turned on using inline data of pattern A from stage 1. Pattern A is exposed for the isotropic plasma dry etching process until target CD is reached. Pattern B is covered in photoresist materials, and its CD is not changed during this process. And the photoresist materials are removed afterward. In stage 3. Fixed-amount ALD process is applied for HM protection on both Pattern A and B. In stage 4, HM is used to etch the pattern into substrate Si with Hydra function turned on for pattern A only. During this process flow, CD and CDU APC are applied to pattern A only. The process variation of stage 1 on pattern B is transferred all the way down to the final stage 4 without any APC functioning on Pattern B.Figure 1 shows the new APC flow. In stage 1, HM of pattern A and pattern B are etched with Hydra function working on pattern A only. However, inline metrology is applied to pattern B for APC of stage 3. In stage 2, Pattern A trimming changed from APC-mode to fixed time mode. The APC function is turned on for stage 3 for a global CD control of both pattern A and pattern B using ALD. And Hydra function is turned on for stage 4 for a CDU control on pattern A. It is shown that the CD variation originated from stage 1 is compensated on stage 3. With the same amount of APC stages, the effect of the APC can be improved from pattern A CD/CDU only, to pattern A CD/CDU and pattern B CD control. Figure 1

Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory

In the manufacturing industry, the process capability index (Cpk) measures the level and capability required to improve the processes. However, the Cpk is not enough to represent the process capability and performance of the manufacturing processes. In other words, considering that the smart manufacturing environment can accommodate the big data collected from various facilities, we need to understand the state of the process by comprehensively considering diverse factors contained in the manufacturing. In this paper, a two-stage method is proposed to analyze the process quality performance (PQP) and predict future process quality. First, we propose the PQP as a new measure for representing process capability and performance, which is defined by a composite statistical process analysis of such factors as manufacturing cycle time analysis, process trajectory of abnormal detection, statistical process control analysis, and process capability control analysis. Second, PQP analysis results are used to predict and estimate the stability of the production process using a long short-term memory (LSTM) neural network, which is a deep learning algorithm-based method. The present work compares the LSTM prediction model with the random forest, autoregressive integrated moving average, and artificial neural network models to convincingly demonstrate the effectiveness of our proposed approach. Notably, the LSTM model achieved higher accuracy than the other models.

Systematic assessment of process analytical technologies for biologics.

The application of process analytical technology (PAT) for biotherapeutic development and manufacturing has been employed owing to technological, economic, and regulatory advantages across the industry. Typically, chromatographic, spectroscopic, and/or mass spectrometric sensors are integrated into upstream and downstream unit operations in in-line, on-line, or at-line fashion to enable real-time monitoring and control of the process. Despite the widespread utility of PAT technologies at various unit operations of the bioprocess, a holistic business value assessment of PAT has not been well addressed in biologics. Thus, in this study, we evaluated PAT technologies based on predefined criteria for their technological attributes such as enablement of better process understanding, control, and high-throughput capabilities; as well as for business attributes such as simplicity of implementation, lead time, and cost reduction. The study involved an industry-wide survey, where input from subject matter industry experts on various PAT tools were collected, assessed, and ranked. The survey results demonstrated on-line liquid Chromatography (LC), in-line Raman, and gas analysis techniques are of high business value especially at the production bioreactor unit operation of upstream processing. In-line variable path-length UV/VIS measurements (VPE), on-line LC, multiangle light scattering (MALS), and automated sampling are of high business value in Protein A purification and polishing steps of the downstream process. We also provide insights, based on our experience in clinical and commercial manufacturing of biologics, into the development and implementation of some of the PAT tools. The results presented in this study are intended to be helpful for the current practitioners of PAT as well as those new to the field to gauge, prioritize and steer their projects for success.

A Novel Multi-Objective Process Parameter Interval Optimization Method for Steel Production

Customized small batch orders and sustainable development requirements pose challenges for product quality control and manufacturing process optimization for steel production. Building a multi-quality objective process parameter optimization method that converts the original single target optimization into multi-objective interval capability optimization has become a new method to ensure product quality qualification rate and reduce production costs. Aiming at the multi-quality objective control problem of plate products, we proposed a novel multi-objective process parameter interval optimization model (MPPIO) with equipment process control capability and parameter sensitive analysis. The multi-output support vector regression method was used to establish a multi-quality objective prediction model, which was settled as a verification model for the process parameter optimization results based on the particle swarm optimization algorithm (PSO). The process control capability functions of key parameters were fitted based on the real data in production. With these functions, each optimized particle of the classical PSO was converted into the particle beam of the MIPPO. The iteration process was weight controlled by calculating the Morris sensitivity between each input parameter and output index in the multi-quality objective prediction model, and finally the processing control window of each key parameter was determined according to the process parameter optimization results. The experimental results show that the MPPIO model can obtain the optimal parameter optimization results with the maximum processing capacity and meet the customized processing range requirements. The MPPIO model can reduce the difficulty of control and save production costs while ensuring the product properties is qualified.

(Invited) Characterization of ALD in Semiconductor Device Fabrication

The CMOS industry continues to push the boundaries of what is possible. Indeed, today’s manufacturing can be described as atomic level engineering, with an example lying in the use of Atomic Layer Deposition (ALD). The development and fabrication (fundamental R&D through to manufacturing) of highly repeatable structures required by the semiconductor industry also necessitates the use of state-of-the-art materials characterization (“if you can’t see it, you can’t develop it”) and process control capabilities (“if you can’t see it, you can’t control it”). This talk will cover the more commonly used materials characterization capabilities used within the industry. This includes, but not limited to: Transmission Electron Microscopy, Atomic Force Microscopy, and X-ray Photoelectron Spectroscopy. Following this, the use of Secondary Ion Mass Spectrometry (SIMS) in the form of Self Focusing SIMS (SF-SIMS), in area selective ALD R&D will be demonstrated.

Fuzzy-inference-based decision-making method for the systematization of statistical process capability control

This paper proposes a decision-making method based on fuzzy inference to facilitate process capability analysis based on the knowledge and experience of experts, and implement systematized statistical process capability control. Data screening is implemented in the form of a rule-based decision-making tree to perform normality testing, R- or s-control chart testing, and x-bar control chart testing on process data to determine whether a process is in a state of statistical control. After setting the improvement direction of the process using a four-block diagram, the processes with a high probability of defect leakage due to large dispersion compared to the specification are reexamined after the fundamental improvement is completed by reinforcing the technology. Additionally, an optimal process capability index is selected using fuzzy inference by considering the degree of bias in a distribution and the differences between short- and long-term process capabilities. The feasibility of the proposed method was verified by applying it to a process for manufacturing home gas boilers. The method enables process control engineers to examine the results of the statistical analysis and the priority of the process to be improved, which are visualized in real time using a dashboard. These results are subsequently used for decision-making.

Statistical process control and process capability analysis for non-normal volumetric modulated arc therapy patient-specific quality assurance processes.

Applying statistical process control (SPC) to intensity-modulated radiotherapy (IMRT)/volumetric modulated arc therapy (VMAT) patient-specific quality assurance (PSQA) program was recommended by the American Association of Physics in Medicine Task Group 218 report, but a comprehensive analysis of PSQA processes with non-normal distributions is lacking. This study investigates SPC and process capability analysis (PCA) methods for non-normal IMRT/VMAT PSQA processes. 1119 VMAT PSQAs were performed on three beam-matched linear accelerators (linacs), using gamma analysis. The Anderson-Darling statistic was used to test normality. The control charts for each PSQA process were obtained using three non-normal-based methods and compared with the conventional Shewhart method. The ability of each PSQA process to produce an output within the specification limit was measured using the index; in this study, the index was calculated using two transformation methods and compared with that calculated using the conventional method. The performances of the three linacs were assessed using SPC and PCA methods. All three PSQA processes were non-normal (P<0.005). Compared to the non-normal-based SPC and PCA methods, the false alarm rates of the conventional method for linac1, linac2, and linac3 were 0.83%, 3.77%, and 4.95% respectively; the minimum overestimated values were 0.59, 0.87, and 1.49, respectively. The process capabilities of the three beam-matched linacs were at different levels. For non-normal VMAT PSQA processes, the conventional SPC and PCA methods increase the false alarm rates and overestimate process capabilities. Instead, non-normal-based SPC and PCA methods are more reliable and accurate in non-normal PSQA processes. Statistical process control and PCA are useful tools for assessing the performance of beam-matched linacs.

Monitoring and detection of meltpool and spatter regions in laser powder bed fusion of super alloy Inconel 625

Additive manufacturing is being adopted to produce metal parts directly from digital design with applications in automotive, aerospace, and biomedical products. Laser powder bed fusion (LPBF) process is a viable technology for this purpose that utilizes a high-power laser beam which follows layer-to-layer scanning of predefined paths on a metal powder bed. In situ monitoring of LPBF process is essential to detect the localized meltpool, its vicinity, and material spatter around it with a goal to control the health of the meltpool. Among many alternatives, high-fidelity video monitoring can provide cost-effective insights to the on-going process to detect changes in meltpool and its vicinity that can further be integrated into an adaptive control system. For this purpose, a high frame rate camera was employed for in situ viewing of meltpool regions during laser fusion of a super alloy, Inconel 625, powder material to be able to improve the process control capability. The size and shape of the meltpool and the heat affected region detected via in situ viewing represent the sources of information to detect possible anomalies and defects. These acquired video volumes were processed and analysed using statistical process control (SPC) charts. The results indicate that some occurrences of undermelting, overmelting, and material spatter can be detected that can then be correlated to localized defects, delamination, and layer separation.

Optimizing the Analytical Value of Oncology-Related Data Based on an In-Memory Analysis Layer: Development and Assessment of the Munich Online Comprehensive Cancer Analysis Platform.

BackgroundMany comprehensive cancer centers incorporate tumor documentation software supplying structured information from the associated centers’ oncology patients for internal and external audit purposes. However, much of the documentation data included in these systems often remain unused and unknown by most of the clinicians at the sites.ObjectiveTo improve access to such data for analytical purposes, a prerollout of an analysis layer based on the business intelligence software QlikView was implemented. This software allows for the real-time analysis and inspection of oncology-related data. The system is meant to increase access to the data while simultaneously providing tools for user-friendly real-time analytics.MethodsThe system combines in-memory capabilities (based on QlikView software) with innovative techniques that compress the complexity of the data, consequently improving its readability as well as its accessibility for designated end users. Aside from the technical and conceptual components, the software’s implementation necessitated a complex system of permission and governance.ResultsA continuously running system including daily updates with a user-friendly Web interface and real-time usage was established. This paper introduces its main components and major design ideas. A commented video summarizing and presenting the work can be found within the Multimedia Appendix.ConclusionsThe system has been well-received by a focus group of physicians within an initial prerollout. Aside from improving data transparency, the system’s main benefits are its quality and process control capabilities, knowledge discovery, and hypothesis generation. Limitations such as run time, governance, or misinterpretation of data are considered.

A Visualization and Control Strategy for Dynamic Minimization of Chemical Process Releases.

Our societal needs for greener, economically viable products and processes have grown given the adverse environmental impact and unsustainable development caused by human activities, including chemical releases, exposure, and impacts. To make chemical processes safer and more sustainable, a novel sustainability-oriented control strategy is developed in this work. This strategy enables the incorporation of online sustainability assessment and process control with sustainability constraints into chemical process operations. Specifically, U.S. Environmental Protection Agency (EPA)'s GREENSCOPE (Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a multi-Objective Process Evaluator) tool is used for sustainability assessment and environmental release minimization of chemical processes. The multivariable GREENSCOPE indicators in real time can be represented using a novel visualization method with dynamic radar plots. The analysis of the process dynamic behavior in terms of sustainability performance provides means of defining sustainability constraints for the control strategy to improve process sustainability aspects with lower scores. For the control tasks, Biologically Inspired Optimal Control Strategy (BIO-CS) is implemented with sustainability constraints so that the control actions can be calculated considering the sustainability performance. This work leads to a significant step forward towards augmenting the capability of process control to meet future demands on multiple control objectives (e.g., economic, environmental, and safety related). The effectiveness of the proposed framework is illustrated via two case studies associated with a fermentation system. The results show that the proposed control strategy can effectively drive the system to the desired setpoints while meeting a preset sustainability constraint and improving the transient sustainability performance by up to 16.86% in terms of selected GREENSCOPE indicators.

Automated Optical Inspection (AOI) for FOPLP with Simultaneous Die Placement Metrology

Abstract As the final step of IC fabrication, packaging is the process to encapsulate the chip and provide the interconnections for the I/O of the final form factor. The demand for increasingly hig...

Standards-based integration of advanced process control and optimization.

Integration of process control with optimization is critical to Smart Manufacturing (SM). Oftentimes, however, the process control solutions from one vendor do not interoperate with the optimization solutions of another. Incompatibilities among the representation and format used by the vendors can impede interoperability. Without this interoperability, it is impossible to achieve the higher level of decision support essential to SM. We believe that an emerging standard, ISO 15746, can facilitate semantic interoperability and enable the integration of process control with optimization. This paper reports the implementation and validation of ISO 15746, Automation systems and integration - Integration of advanced process control and optimization (APC-O) capabilities for manufacturing systems. Guided by the standard, we modelled major components of a typical APC-O system using tools from different vendors, implemented the information models defined in the standard, and integrated key system functions such as process optimization, process control, and user interface. A chemical process case based on the Tennessee-Eastman problem is used to demonstrate the implementation and validation of the standard. We developed a simulation of the chemical process and integrated it with the APC-O system. We discuss the standard validation experience and the findings will be used to guide advance development of the standard.

SBEMimage: Versatile Acquisition Control Software for Serial Block-Face Electron Microscopy.

We present SBEMimage, an open-source Python-based application to operate serial block-face electron microscopy (SBEM) systems. SBEMimage is designed for complex, challenging acquisition tasks, such as large-scale volume imaging of neuronal tissue or other biological ultrastructure. Advanced monitoring, process control, and error handling capabilities improve reliability, speed, and quality of acquisitions. Debris detection, autofocus, real-time image inspection, and various other quality control features minimize the risk of data loss during long-term acquisitions. Adaptive tile selection allows for efficient imaging of large tissue volumes of arbitrary shape. The software’s graphical user interface is optimized for remote operation. In its user-friendly viewport, tile grids covering the region of interest to be acquired are overlaid on previously acquired overview images of the sample surface. Images from other sources, e.g., light microscopes, can be imported and superimposed. SBEMimage complements existing DigitalMicrograph (Gatan Microscopy Suite) installations on 3View systems but permits higher acquisition rates by interacting directly with the microscope’s control software. Its modular architecture and the use of Python/PyQt make SBEMimage highly customizable and extensible, which allows for fast prototyping and will permit adaptation to a wide range of SBEM systems and applications.

Distributed Control System Application for Distillation Column

The entangled nature of various continuous production processes, such as chemical processing, oil and gas, and water treatment, continue to require the advanced process control capabilities of the Distributed control system (DCS) in order to aggregate and analyse a large amount of data at the same instant of time. This paper aims to enhance the flexibility in controlling and monitoring of distillation column by configuring and developing a HMI using DCS with SCADA. DCS provides high scalability, solutions to complexity, redundancy, high adaptability to frequent process changes, hybridized to incorporate PLCs and PCs to control complex functions and to provide reporting services. Hence the optimized control of distillation column has been achieved via DCS.

Retrospective analysis for phase I statistical process control and process capability study using revised sample entropy

This study explored a new nonparametric analytical method for identifying heterogeneous segments in time-series data for data-abundant processes. A sample entropy (SampEn) algorithm often used in signal processing and information theory can also be used in a time series or a signal stream, but the original SampEn is only capable of quantifying process variation changes. The proposed algorithm, the adjusted sample entropy (AdSEn), is capable of identifying process mean shifts, variance changes, or mixture of both. A simulation study showed that the proposed method is capable of identifying heterogeneous segments in a time series. Once segments of change points are identified, any existing change-point algorithms can be used to precisely identify exact locations of potential change points. The proposed method is especially applicable for long time series with many change points. Properties of the proposed AdSEn are provided to demonstrate the algorithm’s multi-scale capability. A table of critical values is also provided to help users accurately interpret entropy results.

The effectiveness analysis of the practices in five quality management stages for SMEs

For most countries around the world, small and medium-sized enterprises (SMEs) play an important role in the economic development, but they are usually embedded with weak competition as opposed to big enterprises. The pursuit of quality is often adopted by SMEs as the main strategy to raise their competitive advantage. In order to reveal the SMEs’ implementation status of quality practices, and also to find out where to be improved, we first develop a ‘systematic framework of quality management with five stages’ for SMEs, and then conduct the related empirical study to investigate the implementation status of the practices in these five stages for SMEs. We also analyse the implementation performance of the practices by using a developed implementation-effectiveness analysis model. SMEs usually implement more practices belonging to the lower stages, but the adoption of total quality management (TQM) is an exception, since the governments in Taiwan provide financial motivation to support SMEs to implement the quality practices of TQM. Based on the analytical results of the implementation-effectiveness analysis model, SMEs need to raise the implementation levels on the critical practices which will result in significant effectiveness degree, for examples, lean production, process capability control, customer relationship management, and performance management.