Mohammed Alkahtani

Modeling and performance analysis of a closed-loop supply chain using first-order hybrid Petri nets

Green or closed-loop supply chain had been the focus of many manufacturers during the last decade. The application of closed-loop supply chain in today’s manufacturing is not only due to growing environmental concerns and the recognition of its benefits in reducing greenhouse gas emissions, energy consumption, and meeting a more strict environmental regulations but it also offers economic competitive advantages if appropriately managed. First-order hybrid Petri nets represent a powerful graphical and mathematical formalism to map and analyze the dynamics of complex systems such as closed-loop supply chain networks. This article aims at illustrating the use of first-order hybrid Petri nets to model a closed-loop supply chain network and evaluate its operational, financial, and environmental performance measures under different management policies. Actual data from auto manufacturer in the United States are used to validate network’s performance under both tactical and strategic decision-making, namely, (1) tactical decision—production policies: increase of recovered versus new components and (2) strategic decision—closed-loop supply chain network structure: manufacturer internal recovery process or recovery process done by a third-party collection and recovery center. The work presented in this article is an extension of the use of first-order hybrid Petri nets as a modeling and performance analysis tool from supply chain to closed-loop supply chain. The modularity property of first-order hybrid Petri nets has been used in the modeling process, and the simulation and analysis of the modeled network are done in MATLAB® environment. The results of the experiments depict that first-order hybrid Petri nets are a powerful modeling and analysis formalism for closed-loop supply chain networks and can be further used as an efficient decision-making tool at both tactical and strategic levels. Unlike other researches on modeling supply chain networks that focus on evaluating individually cost, operational, or environmental aspects, the research here shows how first-order hybrid Petri nets can be extended to assess simultaneously operational, financial, and environmental network’s performance measures at different managerial decision-making levels. The results particularly are compelling for researchers and industrial practitioners who can use the same methodology in evaluating their network’s performance and making educated management decisions based on the performance results and the impact of their selected supply chain and manufacturing strategies.

Rotary ultrasonic drilling of Ti6Al4V: Effects of machining parameters and tool diameter

This article presents the use of the rotary ultrasonic machining process for drilling holes in Ti6Al4V alloy which is regarded as a difficult-to-cut material due to its high-temperature strength and low thermal conductivity. This research presents an experimental investigation on the effect of the key rotary ultrasonic machining input parameters including ultrasonic power, spindle speed, feed rate, and the tool diameter on the main output responses including cutting force, hole cylindricity and overcut errors, and tool wear. No previous reports were found in literature to experimentally investigate the effect of the rotary ultrasonic machining parameters and the tool diameter on tool wear, surface integrity, and the accuracy of the drilled holes in Ti6Al4V alloy. The results showed that the rotary ultrasonic machining input parameters within the current ranges can significantly affect the quality of the drilled holes. Through proper selection of input parameters, holes could be drilled in Ti6Al4V alloy with smoothed surface morphology, low tool wear (0.7 mg) and very low cylindricity (2 µm) and overcut (120 µm) errors. Moreover, it was found that the selected level of any input parameter has the ability to significantly affect the influence of the other input parameters on the output responses.

A study on the effect of main process parameters of rotary ultrasonic machining for drilling BK7 glass

BK7 glass is an important engineering material with extensive applications in high-quality and precision transmissive optical components. However, BK7 glass is considered to be a difficult-to-cut material due to its high brittleness and nonconductivity. This article presents the use of rotary ultrasonic machining process for drilling holes in BK7 glass. No previous reports have been found in the literature to experimentally investigate the response of the BK7 glass to rotary ultrasonic drilling. The experimental investigations take into account the effect of the key rotary ultrasonic machining input parameters including the ultrasonic power, spindle speed and feed rate on the output responses of cutting force, exit chipping, surface roughness, hole cylindricity and overcut errors, and surface integrity. The results show that the input parameters within the current ranges can significantly affect the quality of the drilled holes. Moreover, the selected level of any input parameter has the ability to significantly affect the influence of the other input parameters on the output responses. Through proper selection of input parameters, holes could be drilled in BK7 glass with less fractured topography, low surface roughness (1.32 µm), low exit chipping size (0.85), and very low cylindricity (3 µm) and overcut (73.6 µm) errors.

Development of a device to reduce the risk of injury in handling unstable loads

BACKGROUND Manual material handling (MMH) task is the most common cause of work-related musculoskeletal disorders (MSDs). Operators carrying unstable loads were recently shown to be at greater risk of back injury compared to workers carrying stable loads. OBJECTIVE This study focused on developing a device to minimize trunk muscle activity and cardiovascular demand while handling a 19-liter bottle. METHOD After evaluating several designs, one was selected to be developed, manufactured and tested through an experimental study. Healthy participants (n = 42) manually carried a 19-liter bottle. The carrying technique (i.e., carrying a lateral load while holding the load using the dominant hand, pulling the load using the developed device, carrying the load on the back using the developing device) was the independent variable. The muscular activities (e.g., neck extensor, upper trapezius, pectoralis major, deltoid medial, rectus abdominis, and erector spinae muscles of the dominant side), cardiac costs, plantar pressures, walking speeds, and subjective measures were the dependent variables. RESULTS Results show that carrying the developed device like a backpack significantly reduced trunk muscle activity, cardiovascular demand, and plantar pressure compared to the usual practice. The present results suggest that carrying a 19-liter water bottle using the developed device is likely to contribute to lower MSDs. CONCLUSION Implementation of the develop device recommended to lessen the risk of injury when handling unstable loads such as liquids.

A Decision Support System based on Ontology and Data mining to Improve Design using Warranty Data

Abstract Analysis of warranty based big data has gained considerable attention due to its potential for improving the quality of products whilst minimizing warranty costs. Similarly, customer feedback information and warranty claims, which are commonly stored in warranty databases might be analyzed to improve quality and reliability and reduce costs in areas, including product development processes, advanced product design, and manufacturing. However, three challenges exist, firstly to accurately identify manufacturing faults from these multiple sources of heterogeneous textual data. Secondly, accurately mapping the identified manufacturing faults with the appropriate design information and thirdly, using these mappings to simultaneously optimize costs, design parameters and tolerances. This paper proposes a Decision Support System (DSS) based on novel integrated stepwise methodologies including ontology-based text mining, self-organizing maps, reliability and cost optimization for identifying manufacturing faults, mapping them to design information and finally optimizing design parameters for maximum reliability and minimum cost respectively. The DSS analyses warranty databases which collect the warranty failure information from the customers in a textual format. To extract the hidden knowledge from this, an ontology-based text mining based approach is adopted. A data mining based approach using Self Organizing Maps (SOM) has been proposed to draw information from the warranty database and to relate it to the manufacturing data. The clusters obtained using SOM are analyzed to identify the critical regions, i.e., sections of the map where maximum defects occur. Finally, to facilitate the correct implementation of design parameter changes, the frequency and type of defects analyzed from warranty data are used to identify areas where improvements have resulted in the greatest reliability for the lowest cost.

Six Sigma DMAIC phases application in Y company: a case study

Six Sigma has been used by large organisations to improve the performance of their manufacturing processes. The objective of this paper is to analyse the problem of increasing demand of flour in wafer biscuit production in Y Company. This problem leads to decrease profit and external customer satisfaction. In this study, define, measure, analyse, improve and control (DMAIC) methodology of Six Sigma is used to develop the solutions for the problem. Root causes are determined by using cause-and-effect analysis. Pareto chart helped to focus attention on the major important root causes. Moreover, a brainstorming concept was conducted with experts from the production, maintenance and quality control departments, which helped in formulating the solutions. The study presents the applicability, feasibility and impact of DMAIC methodology on the company, which carried out the reduction in deviation rate of flour material usage from approximately 27.55-10.45% on the production line.

Multitasking in healthcare systems

M in health care refers to the physician and their assistant dealing with more than one task simultaneously over the same period during surgery or in other operations of the health care system. The multitasking in healthcare system is also a big challenge and has esteem importance. Physicians, Nurses, Dispenser (Medicine collector) and patients are the key stakeholders for this system. The aim of this study is to determine the impact of multitasking by hospital physicians, nurse and operator performance. The method to evaluate the performance of the each stakeholder is done using a time and motion study in King Khalid university hospital. The performance measures multitasking activities during medicine distributions, nursing and surgery/operations. The effect of these input factors is assessed through the satisfaction of patients. Ten stakeholders (surgeon, nurses, medicine distribution unit at pharmacy and patients) were observed. The patients are observed after the activity is performed through an interview. Then the response of each patient is calculated on a likert scale (from 1 to 5) and is evaluated and analyzed. The research highlights for each category of multitasking is proposed at the end. Physician surgery supposed to be the most caring area and need more attention during multitasking. Nurse’s patient caring and medicine distribution are least area with respect to danger. Each area has its own importance; especially in cardiac surgery when number of patients waiting are too large and surgeons are limited. In these types of cases, they have to do multitask to serve more patients corresponding to decrease the death rate.T traditional desk-based design of work in many organizations, even those that outwardly claim to embrace the principles of ergonomics reflects a design paradigm in which workers are constrained by organizational factors rather than human factors, stifling workers’ potential for innovation and productivity. The traditional approach to workplace design reflects an organizational fear of uncertainty. This workplace is a product of the desire of its owners to control and direct human activity to achieve a predefined goal; it is essentially a command-and-control work system, in which uncertainty is a form of future threat to be negated by the application of tighter controls, more rigorous planning, micro-management, and by increasing centralization of decision-making and power. Such an approach is inefficient and unrewarding, both for the employee and for the employer. Uncertainty is, however, a significant component of profit, and it has long been argued that there are economic advantages for organizations that embrace, rather than resist uncertainty. In the uncertainty-embracing workplace, people may express their potential for working in a more co-operative manner with more fluid social engagement, resulting in greater problem solving, peer-to-peer encouragement, teaching, learning, ingenuity, and innovation. Based on recent collaboration between professionals in workplace design, management consulting and human factors, this presentation advocates a design approach that facilitates the embracing of uncertainty, thereby releasing the innovation and profit potential of all employees.The aim of this paper is to reassess the way management technology was carried out in the manufacturing industry and establish “New JIT, New Management Technology Principle”. New JIT consists of the Total Development System (TDS), the Total Production System (TPS) and the Total Marketing System (TMS), which are the three core elements required for establishing new management technology principles for sales, R&D, design, engineering, and production, among others. To realize manufacturing that places top priority on customers with a good QCD in a rapidly changing technical environment, the author proposes a high linkage model, employing a structured “integrated triple management technologies system Advanced TDS, TPS & TMS” for expanding “uniform quality worldwide and production at optimum locations”.S chain (SC) is a system with multi-layer entities, multi-layer processes, to convert raw material into products. There are three levels of planning issues in a supply chain, i.e., strategic, tactical, and operational levels. They differ from each other based on their considerations and time effects. The strategic level planning deals with issues in the design stage of a supply chain, tactical level considers utilizing the SC resources, and the operational level deals with daily or weekly scheduling issues. SC optimization and its responsiveness are greatly influenced by inventory and as such inventory and amount of safety stock are important issues in a supply chain to manage demand uncertainties and maintain customer service level at lowest possible cost and shortest responsive time. In this paper, the multi-echelon safety stock optimization (MESSO) for tactical supply chain planning (SCP) in manufacturing systems is addressed. The problem is formulated as a multi-objective mixed integer non-linear programming (MINLP) model. Unlike other works that consider serial, assembly, or distribution system only, our model considers general supply chain topology. In a competitive global market, different objectives should be optimized simultaneously to avoid conflicting decisions. In the SC planning context, two methods are often used to optimize multi-objective models. The first one is to convert the multi-objective problem into a single-objective using a weighted sum or weighted goal programming method. The main drawback of this method is the subjectivity and bias in weight setting. The other method is known as e-constraint method in which one objective is optimized while the others are used as un-equality constraints. The same approach is applied to every objective leading to a set of solutions called Pareto optimal solutions or nondominated points which form the Pareto-Optimal (Pareto-Efficient) Frontier. However, this method again involves subjectivity in determining which frontier point is selected eventually. For this reason, the modified Chebyshev programming (MFC) method is adopted to solve the multi-objective model. Unlike the conventional weighted sum method that subjectively assigns weight to each objective, the weights in the MCP method are automatically selected based on the importance order of the objectives, the model and input data. Application of the proposed model and solution method are illustrated by solving an example problem and compared with the traditional weighted-goal programming method.T concern to develop wind turbines as a low carbon source of renewable energy is paramount to the fast expansion of renewable energy for power production. Proper analytical condition monitoring technique to observe the state of system could minimize damage to the whole turbine. Faults are inevitable in automated system of which failures could be severe depending on the kind of machine and the circumstances of the fault interruption. The main aim is to increase reliability, cost effective for modern dynamic systems could improve industrial automatic system availability as well as achieving a better system performance. The main challenge in model-based fault detection and diagnosis FDD is to diagnose incipient and abrupt faults in complex dynamic systems considering a practical system input and output measurements. Several techniques have been proposed to improve the performance of fault diagnosis is a captivating research challenge to advance of the robustness to fault detection. Model-based FDI has been studied for over 20 years; this can be used to either boost safety or accomplish a more reasonable current level of safety. A genetic algorithm (GA) is an artificial intelligence to increase the FDD performance to optimize an observer. A better analysis have been identified to demonstrate the propose approach.M and reliability are playing vital roles in all engineering disciplines. As the demand for systems with better performance at minimum cost increases, there is a requirement to minimize the failure probability and the need to the system recovery mechanisms and fault tolerance. However, history witnesses that maintainability design is lagging behind structure design. To solve this problem, VR based maintainability design method is proposed that validates maintainability design of a structure. This presentation aims at providing an overview on maintainability design and VR technology. Also, the required platform for integrating maintainability information into structured information, and limitations & complications will be discussed. Finally, the processes and feature/benefit matrix and maintenance metrics will be presented.A sequence planning is a typical of representative combinatorial optimization problems in manufacturing. The general methods are used to generate a large number of feasible assembly sequences and find the best sequence through evaluation. A lot of computation time and memory space are needed and many methods usually find a local optimum. To reduce the hardness of assembly sequence planning, the assembly model is converted into a directed weighted graph considering the assembly constraints which are classified into the qualitative and quantitative constraints. The qualitative constraints including the topological and geometrical assembly constraints between parts are adopted to configure out the feasible assembly sequences and they are represented as the directed edges in the weighted graph. A portion of process constraints is also used as the qualitative constraints. The other process constraints, such as the assembly tolerance, assembly stability, assembly directions and tools, are quantified with the fuzzy analytical hierarchy process method and attached to the edges. The weights are taken as the heuristic information to find the optimal fragments of the optimal or near-optimal assembly sequences. With the assembly weighted graph, the optimal or near-optimal assembly sequences will be searched in the generation of the feasible sequences and the search space of the best solution will be decreased. A branch and bound algorithm is designed to find the optimal sequence with the weighted graph. The results illustrate that the optimal assembly sequences are found quickly. The method is expected to apply to the complex products.