Integrating Multiobjective Optimization With The Six Sigma Methodology For Online Process Control

Over the past two decades, the Define-Measure-Analyze-Improve-Control (DMAIC) framework of the Six Sigma methodology and a host of statistical tools have been brought to bear on process improvement efforts in today’s businesses. However, a major challenge of implementing the Six Sigma methodology is maintaining the process improvements and providing real-time performance feedback and control after solutions are implemented, especially in the presence of multiple process performance objectives. The consideration of a multiplicity of objectives in business and process improvement is commonplace and, quite frankly, necessary. However, balancing the collection of objectives is challenging as the objectives are inextricably linked, and, oftentimes, in conflict. Previous studies have reported varied success in enhancing the Six Sigma methodology by integrating optimization methods in order to reduce variability. These studies focus these enhancements primarily within the Improve phase of the Six Sigma methodology, optimizing a single objective. The current research and practice of using the Six Sigma methodology and optimization methods do little to address the real-time feedback and control for online process control in the case of multiple objectives. This research proposes an innovative integrated Six Sigma multiobjective optimization (SSMO) approach for online process control. It integrates the Six Sigma DMAIC framework with a nature-inspired optimization procedure that iteratively perturbs a set of decision variables providing feedback to the online process, eventually converging to a set of tradeoff process configurations that improves and maintains process stability. For proof of concept, the approach is applied to a general business process model – a well-known inventory management model – that is formally defined and specifies various process costs as objective functions. The proposed iv SSMO approach and the business process model are programmed and incorporated into a software platform. Computational experiments are performed using both three sigma (3σ)-based and six sigma (6σ)-based process control, and the results reveal that the proposed SSMO approach performs far better than the traditional approaches in improving the stability of the process. This research investigation shows that the benefits of enhancing the Six Sigma method for multiobjective optimization and for online process control are immense

Online Inventory Control Using A Multiobjective Six Sigma Approach

In industrial manufacturing and service environments, it is important to develop or modify objectives for operating a process in order to incorporate the changes that contributed to the improvement; however, these objectives are usually conflict with each other. Additionally, in order to maintain consistency in process performance and sustain improvements, a reliable process control plan is required. This paper utilizes an innovative integrated multiobjective Six Sigma optimization approach for online inventory management. The approach provides automated feedback for out-of-control events while attempting to balance two common, relevant and conflicting objectives - minimization of the inventory holding cost and minimization of the inventory ordering cost

Multiobjective Optimization And Six Sigma For Online Process Control And Decision-Making

Industrial manufacturing and service organizations are interested in improving their processes by decreasing process variation. The primary goals of robust process design are to: (1) drive mean process performance toward target values and maintain the performance within desired limits, and (2) reduce the variation of process performance. The Six Sigma DMAIC methodology and a host of statistical tools can be brought to bear on the process improvement efforts to achieve these goals. Robust process design problems are often complex in nature, as they typically involve more than one design objective. The process design objectives may conflict, and the tradeoffs between these conflicting objectives must be considered when generating process design alternatives. In recent years, researchers and practitioners believe that it is possible to enhance the Six Sigma DMAIC methodology by integrating it with other improvement approaches. This research explores the integration of one such approach - multiobjective process design optimization - with the DMAIC methodology in order to improve the online process control. Specifically, an innovative approach is presented that automatically provides the best compromised process design specifications such that the two primary goals of robust process design are achieved

Development of a Multifunctional Wet Laid Nonwoven from Marine Waste <i>Posidonia oceanica</i> Technical Fiber and CMC Binder

A Posidonia oceanica waste marine plant was used to produce a wet-laid nonwoven web for multifunction applications. To study the effect of some parameters related to the web characteristics (sheet weight, binder ratio, and pulp ratio) on the mechanical and physical properties of the web, we used a Box–Behnken design plan with three levels. The diagram of the superposed contours graphic method was used to find the optimum parameters of the process for the application of the Posidonia nonwoven fiber on an insulation field. With the measurement of the thermal conductivity properties using the box method, the results demonstrated that the nonwoven fiber from Posidonia oceanica marine waste had good insulation properties in comparison with other classical natural fibers (hemp, flax) used in the field of insulation with the big advantage of being a natural product

Structure and Performance Attributes Optimization and Ranking of Gamma Irradiated Polymer Hybrids for Industrial Application

The selection of suitable composite material for high-strength industrial applications, from the list of available alternatives, is a tedious task as it requires an optimized structural performance-based solution. This study aimed to optimize the concentration of fillers, i.e., vinyl tri-ethoxy silane and absorbed gamma-dose, to enhance the properties of an industrial scale polymer, i.e., ultra-high molecular weight polyethylene (UHMWPE). The UHMWPE hybrids, in addition to silane, were treated with (30, 65, and 100 kGy) gamma dose and then tested for ten application-specific structural and performance attributes. The relative importance of attributes based on an 11-point fuzzy conversation was used for establishing the material assessment graph and corresponding adjacency matrix. Afterwards, the normalized values of attributes were used to establish the decision matrix for each alternative. The normalization was performed after the identification of high obligatory valued (HOV) and low obligatory valued (LOV) attributes. After this, suitability index values (SIVs) were calculated for ranking the hybrids that revealed hybrids 65 kGy irradiated the hybrid as the best choice and ranked as first among the existing alternatives. The major responsible factors were higher oxidation strength, a dense cross-linking network, and elongation at break. The values of the aforementioned factors for 65 kGy irradiated hybrids were 0.24, 91, and 360 MPa, respectively, as opposed to 0.54, 75, and 324 MPa for 100 kGy irradiated hybrids, thus placing the latter in second place regarding higher values of Yield Strength and Young Modulus. Finally, it is believed that the reported results and proposed model in this study will improve preoperative planning as far as considering these hybrids for high-strength industrial applications including total joint arthroplasty, textile-machinery pickers, dump trucks lining ships, and harbors bumpers and sliding, etc

Integrated Optimization Model for Maintenance Policies and Quality Control Parameters for Multi-Component System

The practical applications of integrated maintenance policies and quality for a multi-component system are more complicated, still rare, and incomplete to meet the requirements of Industry 4.0. Therefore, this work aims to extend the integration economic model for optimizing maintenance policies and quality control parameters by incorporating the Taguchi loss function for a multi-component system. An optimization model is developed based on preventive maintenance, corrective maintenance policies, and quality control parameters with the CUSUM (Cumulative Sum) chart, which is widely used for detecting small shifts in the process mean. The model was developed to minimize the expected total cost per unit of time and to obtain the optimal values of decision variables: the size of samples, sample frequency, decision interval, coefficient of the CUSUM chart, and preventive and corrective maintenance intervals. The solution steps were employed by selecting a case study in the Alahlia Mineral Water Company (AMWC). Then, the design of experiments based on one-factor-at-a-time was used to evaluate the effect of selected decision variables on the expected total cost. Finally, sensitivity analysis was performed on the selected decision variables to demonstrate the robustness of the developed model. A predictive maintenance plan was developed based on the optimal value of preventive maintenance interval, and the results showed that the performance of the maintenance plan realizes the full potential of the integrated model. In addition, the case study results indicate that the extended integrated model for multicomponent is the new standard for the quality production of multi-component systems in future works