Automation and Artificial Intelligence System - Research Paper Example

INDUSTRIAL SYSTEMS AND ENVIRONMENT Student’s Name Course Professors’ Name University City (State) Date Table of Contents Chapter 1: Introduction 1.0 Background Information………………………………………………………………4 1.1 Selection of System……………………………............................................................4 Chapter 2: Characterization of the System under Study 2.0 Overview………………………………………………………………………………5 2.1 System Objectives / Purposes…………………………………………………………5 2.2 The Environment of the System………………………………………………………7 2.3 Sub-System Objectives……………………………………………………………….7 2.4 Elements of the Sub-Systems and their Attributes……………………………………8 2.5 System Relationships………………………………………………………………….9 2.5.1 System: between each of its sub-systems…………………………………………...9 2.5.2 Sub-System: between each of it's attributes………………………………………..10 2.6 System Complexity…………………………………………………………………..12 2.7 Dynamics of the System……………………………………………………………..12 Chapter 3: Three: Problem/Challenge Identification 3.0 Challenges identification and stakeholders………………………………………….13 3.1 Addressing the Necessity of Cognitive Power Tools………………………………..14 3.2 Addressing the Challenge in Healthcare…………………………………………….14 3.3 Addressing the Challenge in Healthcare…………………………………………….15 Chapter 4: Methodology 4.0 Selection of System Methodology……….………………………………………….15 4.1 Hard versus Soft System Methodologies……………………………………………16 Chapter: 5 Application of System Methodology 5.1 Description of the System Methodology…………………………………………….17 5.2 Examination of RTC system methodology stages in the context of challenges……..18 5.3 Implementation Issues……………………………………………………………….19 Chapter 6: Conclusion 6.0 Conclusion…………………………………………………………………………...19 Reference List……………………………………………………………………………………20 An Automation and Artificial Intelligence System Chapter One: Introduction 1.0 Background Information The necessity to understand the history, philosophy and systems approach practices is fundamental to solving contextualized engineering challenges through application and management of information technology for a better, sustainable environment. Industrial systems and environment require the development of life cycle system directed towards co-operating IT policies to manage complex, real-world challenges. Further, the field demands the ability to exposit the legal, social and environmental interests while upholding the ethical considerations and conjecture of stakeholders (Blanchard 2004). 1.1 Selection of System Therefore, this paper will analyze "automation and artificial intelligence" as an industrial system and its application to the environment. The system will be characterized into five sub-facets so as explore its complexity and dynamics, describe its challenges and finally apply the system methodologies in stages for implementation of issues. The system "artificial intelligence" integrates artificial entity of computer modules and its used in modern day fields such as engineering, medicine and even military; hence, the justification for analyzing its compounding implementation to the environment. Further, the increasing use of automation artificial intelligence in labor industry through replacing human manual labor by robots and automobiles such as; self-drive cars increases the necessity to understand the IA topic. Chapter Two: Characterization of the System under Study 2.0 Overview The automation and artificial intelligence system in an engineering environment are beneficial to the simplification of operations. A system is meant to regulate the quantity of manual involvement needed to operate the system, subsystems as well as the elements involved in the production. The prerequisite to simplify engineering operations proliferates with the addition and upgrade of both the system software and hardware while engaging personnel and data-processing system developers. By streamlining the operations, an automated artificial intelligence system will ensure that the requisite service levels, costs and the efficiency of the system operations will be attained. 2.1 System Objectives/ Purpose Improve Network and System Availability The automation and artificial intelligence system will aim to improve the processes in the production of new services. By making processes, automated, it is easy, accurate and quick to respond to numerous orders and help network all the subsystems of the entire production system. Besides, in the case of both planned and unplanned engineering outages, the automated system can reduce the recovery process during operations. Further, the automation of the system will reduce the probabilities for operator malfunctions. In engineering fluidity in producing and ensuring that all mechanisms are efficient, it requires an artificial intelligence system to supplement the manual operations. During operations, human errors are bound to occur, and failures lengthen the recovery procedure. As a result, the automation of the system will look to correct all the manual errors while reducing the use of complex commands in syntax the system. With an automated engineering system, there is the substitution of automatic responses with operator typified algorithms. As a result, where there is the need for human intervention, the system intelligence will be in a suitable position to simplify the tasks, cut on the proliferous of errors and create a continuation of similar commands during the occurrence of comparable incidences. Remove Developmental Restrictions On the backdrop of the engineering system, reducing the constraints on the growth and development of production operations will be essential. For instance, unmanaged data levels in a system may culminate into growth constraints as their loopholes that deter the efficiency of both manual and automated labor. As a result, the system will increase in capacity with the development of the engineering environment that is adequately supplemented by automatic and artificial intelligence techniques. Therefore, the automated process will reduce the suppression and blocking of the system by artificially highlighting the errors before the damage escalates. Automatic Alert Response In engineering, the rate of error occurrence during production can be of subsequent damage in a short period. Further, to solve the manifestation of system errors, the automation process is vital in managing the operations through the integration of artificial system derived using operator commands that are executable. Moreover, the system will help in specialization with one player capable of running a couple of sequences from a centralized location. In addition, the complexity issue in an engineering system calls for the implementation of an automated system able to detect any errors. Moreover, the automation of the system will reduce the complexity of task management by supplementing the activities of the operators through network configurations to provide specific commands to the artificial intelligence to execute with precision. Therefore, the automation and artificial intelligence will help manage the system networks; hence, proliferating growth. Increase Operative Efficiency The automation of the system will encourage operationally efficient through specialization. Since the system is artificial and equipped with intelligence software, the prevalence of operational errors will be minimal as well as ensure that performance is at the optimum level due to timely alerts during operations. Besides, the coding process provides for the restructuring of transactions to ensure that useful reviews contribute to system updates and upgrades. As a result, there is a practical problem solution and operations. 2.2 The Environment of the System The level of industrial growth articulates a fundamental impact on the automation and artificial intelligence system design that can accelerate engineering mechanisms in modern production. The scope of automation has a broad range attributes aimed at influencing the big view mechanical and technical aspects of operations are accomplished. Automation and artificial intelligence will operate in an environment where industrialization looks to centralize while supporting the human labor in ensuring effectiveness and efficiency. 2.3 Sub-System Objectives Cost effectiveness: the subsystem takes into consideration the aspects of speed, time, and the immense quantity of workload a computerized machine can process, artificial intelligence offers an alternative. When employee function encounters conflicting burdens to provide more material while in the same period aims to reduce operational overhead costs, an electronic sub-system can become progressively cost effective since, in the long run, automation becomes cheaper as equipment costs slump. Effective human resource performance: perchance more digital automation provides potential efficiency through computerization as more people experience a reduction in labor work through the availability of an automated system. Improved accuracy: the importance of a subsystem is to ensure that it contributes to the provision of precision during the command process of the automatic system. The implementation of an artificial intelligence system provides room for coding specific commands that can yield accurate results. 2.4 Elements of the Sub-Systems and their Attributes Automated Guided Vehicle (AGV) Transportation of materials Automated component restructuring Lean Manufacturing (LM) Timely production Accuracy Flexible Manufacturing System (FMS) Control systems Cabling and energy management Automated Storage and Retrieval System (ASRS) Information design plans Employee data sources Robotics Manual replication Sophisticated pneumatics 2.5 System Relationships 2.5.1 System: between each of its sub-systems The link between the automation and artificial intelligence system with the AGV sub-system lies with the incorporation of a new mechanism of having a remote guided motor mobiles, which substitute standard motors. As the system provides for command coding, employees can create appropriate commands that are executable by the system to provide real-time results with limited risks or errors. Along with lean manufacturing, precision is paramount to ensure that custom made products meet the requirements of the customer and the purpose they are meant to accomplish. Automation provides only essential that provide cutting edge technological innovations during operations. The relationship between FMS and the system revolves around the computerization of the aspects of production. As the mechanical operations dwindle, replacement with advanced controls and simplified controls compels the system to be optimized during the numerous activities in engineering. In engineering, data storage and retrieval is very vital in the designing, implementation, and testing of new as well as old technologies. As such, the mechanics employed in the automation system through artificial intelligence, integrate with the parent system to ensure standard command channels for all sub-systems to coordinate in a proper way. Robotics in the automation and artificial intelligence systems ensure that there is reliability and should ensure that it constitutes of optimum requirements needed for competence. In mechanical engineering the risk of injuries is an enormous challenge, however, with automated machines can act on commands without engaging the human labor in a direct way, but as a substitute to ensure proficiency. 2.5.2 Sub-System: between each of its attributes The automated guided vehicle sub-system considers two elements; transportation of materials as well as necessary component restructuring, which determine the structural design of the entire facility to foster the movement of labor and factors of production around. Moreover, the robotics in the system replicate the manual handling and maintainability of the whole artificial intelligence system that requires the articulation of controlled commands that pave the way for dynamism. The primary role of the AGV subsystem to create an integral for the operations of the following subsystems as they align in the same simultaneous order for the cordial ingenuity of the entire system. In lean processing, the automation procedure compiles all the system software that build complete structures within the conceptual framework of the intelligence artificial system. The final commands of the manufacturing process are affected by the cabling process as there is a need for ensuring that all connections in machinery are automatic when the uphold of commands commences. Cyberccompistion in intelligence, innovation requires the timely accuracy in planning, production procedures and ensure that mechanics are available to perform at full capacity. As a result, the relationship between lean manufacturing incorporates the need to consider timely production in an accurate approach for the sophisticating of great engineering prowess (Hitchins 2013). Flexible manufacturing system ensures proficiency in managing labor commands when ordering for the design and testing of engineering outcomes. After the planning of a proper lean process, it calls the control procedure that interlinks the different subsystem attributes to form a single element so as to run the complete parent system. The control characteristics of the system call for professional ingenuity to ensure that the interface coordinates with the system. Further, the cabling of the subsystem is comparable to the other subsystems since a slight glitch can impend the development of the entire system, therefore, during procedural designs the system with promulgating the cabling in the system. Automated storage and retrieval system create a corresponding integral between the chain of command from the artificial intelligence with the mechanical system elements that stores all information about the procedure. In designing the system, there must be a way to formulate and update new commands from the employees to ensure that the correspondence between the subsystems remains on par with the central system. As a result, custom-made automated machines have a database to access all the dynamic design and implementation information for the attributes of the subsystems that connote the entire project to ensure that automation is prevalent in all situations. Therefore, the connection between the characteristics of the ASRA subsystem definite staple out the system mechanism that is followed to propel all elements to connect and operate in the same environment. The robotics subsystem encompasses manual replication of manually designed labor, equipment and sophisticated material that support engineering projects and intelligence in an automated system. As the machinery era comes to an end in the engineering world, the thought of engaging in innovative intelligence essentials capable of supporting and creating reliability in the production process is proving vital to the automation field. In the case of sophisticated pneumatics modeling in the automation field, the machinery has to be custom designed to meet the needs of the production process. The relationship held in an automated system can only rely on the optimum output of all components of the subsystems to ensure reliability and fluidity during the automation process, which encompasses the engineering process of the system. Therefore, for any system to perform to its required capacity, the design should have all the essential premises of the entire process to make it feasible for the reliability of the entire automation and artificial intelligence system. 2.6 System Complexity The automation and artificial intelligence system in manufacturing are one of the most elaborate schemes in understanding and assembling workers as well as machines. The complexity provides an overview of how the different system components integrate to produce a corporate interactive environment for sophisticated software using the knowledge that addresses the principalities of automation. The challenge with engineering systems lies in the corresponding relationship of getting to influence the capabilities and the integration of new non-traditional innovations. In order to get around the complexity of the automation and artificial intelligence, the design has to conceptualize the basis of the mechanics of the parent system (Jackson 2000). 2.7 Dynamics of the System Automation specialization has a set of dynamics it follows in the implementation of engineering designs, underline bodies as well as the simulation of the complete system environment. The system dynamics will reduce the effects of residual sound and noise vibrations to the workforce and ensure that the technological solutions function at the optimum as instructed. Further, the electrical aspects of the system will follow a computerized wiring that connects the subsystems through a software based mechanism to avoid remodeling in the automation system. Fig 1: Diagram Showing the Relationship between the System and its Subsystems Source: Engineering Management Forum Chapter Three: Problem/Challenge Identification 3.0 Challenges identification and stakeholders Automation and artificial intelligence system are revolutionizing almost all sectors of the business operations through sensing, synthesizing, and modeling data to reduce the chance of environmental challenges. The applications, in this case, range from the routine to revolutionary modules such as cybernetics, decision making on textual information, and developing the automobile. Further artificial intelligence subsystems are employed in organizations to induce transcend conventional performance and achieve high levels of efficiency and quality. Therefore, this section will analyze some of the challenges that are ramified through the automation of artificial intelligence. 3.1 Addressing the Societal and Business Challenges Numerous societal and business organizations are increasing their demand for intelligence information and automation of machines; hence, the concept of artificial intelligence is employed. First talent demographics are changed through the creation and opening of automated alternatives. This is important in some sector of the business and environment that are increasingly facing the shortage of manual laborers. Moreover, some nations and regions such as Germany have an aging population that is unable to meet efficiently the production demands and exploit the available resources. Therefore, the cost of labor in such areas has risen to level that investors resort to automated machines and intelligent integration of computers to meet the economic compounds. One such region with the high cost of labor is China that completely relays on the building its manufacturing industry through significant but low-cost labor force. 3.2 Addressing the Necessity of Cognitive Power Tools The amount of data created in the modern era is accelerating highly more than human consumption. For example, in the medical field, the amount of data generated is double the ability of expertise use, and the rate is steadily increasing every five years. Further, the amount of information generated daily through social interaction platforms and learning modules on the internet are increasing since most of them are not erased; there for the backlog of such data renders the online information platform redundant and too broad to offer practical answers to humans. No wonder the increase in search engines on online information gathering and filter pages. The challenge of retroactive data and jammed information systems need to be addressed through power tools. In the same way, physical power tools enable the manual workers and machine operators to increase production efficiency; the information systems should also develop that use ballooning volumes of information. These applications can be on investment, physicians, and the engineering field to help the automate structures, modules and relevant data for ease access (Nicholas 2004). 3.3 Addressing the Challenge in Healthcare The healthcare sector is one of the most crucial sections of human existence and demands quick, diagnosis, response as well as post treatment comfort. However, clinicians in this sector waste a lot of time especially on medical insurance policies to understand patients' situations and integrate them with hospital policies. Further, the compensation period is comparatively rigorous with some bureaucratic steps. Therefore, the healthcare needs an automated system that can compound the requirements of patients and integrate them with hospital policies within the short period, while delivering efficient services. Cognizant of automation intelligence to the healthcare insurance policies, care providers such as WellPoint that caters for more than 40 million Americans have embraced an IBM-powered system. The system combines clinicians' treatment in unstructured form to present medical policies and provide responses for information analysis. Moreover, the system is cost effective to WellPoint and faster than relying on human labor for insurance analysis (Kasser 1995). Chapter Four: Methodology 4.0 Selection of System Methodology The selection of system method is necessary since it outlines the frameworks and the strategies used in the planning, development, control and implementation process of information's systems. Numerous system methodology approaches have evolved over the years in the race to induce efficient human performance, cut the cost of operation and mitigate work environment challenges; however, each of these methodologies has strengths as well as weakness. Moreover, the applications of the systems are distinctive to in nature since some systems are not useful in other fields of operation depending on technical developments, organization structures, and environment of operation. Therefore, selection of appropriate system methodology must integrate the dependent variables of projects objectives, technical operation environment as well as the linear and iterative structures of an organization. 4.1 Hard versus Soft System Methodologies Systems methodology selection must distinguish between the two compounding verities of hard systems (HSM) or soft systems (SSM). The prior technique involves the use of computer interface programs to simulate research and provided solutions. According to Cavaleri (1993), complicated systems method employs selected options of analysis to justify quantifiable solutions and is characterized by statistical probabilities and application of deterministic fixed inputs to obtain outputs. Hence, the systems are often applied in project management, numerical programming, simulation, theories in the decision as well as forecasting. Contrarily, the simple system methodologies are employed analysis before application of strategies and it is often used in unquantifiable programs. The systems provide solutions for understanding human perspectives such as motivations, ideas, and human-related activities. Therefore, the preceding section will analyze the application and implementation of a compact systems methodology used in engineering to develop a complex, but automated intelligence machine control system called real-time control system (RTCS). Chapter Five: Application of System Methodology 5.0 Description of the System Methodology The Hard system methodology (HSSM) selected in this section is real-time control system (RTCS) used to develop a complex, but automated intelligence machine control program. The RTC system methodology operates through a comprehensive integration of algorithm models. The models then identify data in real-time computer software so as to illuminate critical components of intelligence machine control domain. The process is achieved through the use of superior software engineering that can be compatible with the algorithm models. The RTC methodology is divided into two sections of the software and hardware; since the control of intelligence in machine demand ascendancy of both manual labor units and computer software programs. The hardware articles include machines, manual inputs, actuators while the software sections incorporate the components of methodology design such as; computer interface and models of information (Clayton & Radcliffe 1996). The RTC system method applies to a broad array of engineering spectrum such as; robotic controls to achieve accurate results in high-speed control machines. Further, the devices that require close contact with human input can likewise integrate the loop control models of RTCS methodology to reduce uncertainty and noise such as; in aircraft, submarines, and ships. Further, RTCS methodology employs the use of sensory –feedback in information transfers that can reduce obstruction to system engineering programs such as; communication networks and attention applications. 5.1 Examination of RTC system methodology stages in the context of challenges The RTCS methodology is objectively designed to address the challenges of managing complex, intelligent software, ability to induce a robust real-time performance, improve design result in human understanding. Stage 1; The servo stage The stage designs the sensor level of the methodology system that acts as the drivers of the entire program. Here the task commands are integrated into dynamic models and voltages to increase the processing speed of the software and application. Further, sensors integration performs interpolation that reduces the complexity in software. Complexity management, in this case, infers to the design of easily understandable modular that that can limit the necessity of time, and input resources in computer enabled machines. The RTC system methodology servo stage is those efficient in mitigating the complexity challenge. Stage 2; Interface and Communication Stage The interface and communication phase includes designing of software systems and manual control panels that are visible to human. For example, the screen of machines and the batons. Through the Real-time methodology system, the spatial and temporal facets are integrated into algorithms thus improving the human design. Therefore, the interface stage is crucial invisible elements of intelligence machines since they are automated. Stage 3; System Reuse Since the real-time model falls under complicated systems, the step uses automated algorithms and templates to compute scientific programs. Further, the scientific programs complete different trajectories by updating the files in a computer system, and they can be reused. This is important in mitigating the challenge of machines operations within a specified period. For example, automated generators are programmed to detect power shortage and operate immediately; the same was the RTCS methodology works. 5.2 Implementation Issues The implementation of this kind of rigorous system method is limited by the availability of input resources including human labor, skills, and capital since it is expensive. The system methodology likewise has to comply with policies within system engineering and artistic modeling. Further, the operating environment of the system demands qualified personal operate it so as to ensure efficiency and avoid breakdowns. All these variables are limited; hence, the implementation real-time control systems can be involved in both the business sector and technical design field such as; architecture and structural engineering. Chapter Six: Conclusion 6.0 Conclusion Industrial systems approaches are developing due to the improved technology; hence, increasing the level of individual operation in both technical and streamlined sectors. Further, these developments have increased the amount of complex data and applications available for solutions to challenges in the modern world. Further, users find it retroactive and difficult to get the appropriate information and channels for challenges. Hence, the necessity to automate intelligent systems through substantial financial investments, innovation, and redesigning of existing model so as to fuel organizational performance. Therefore, improving the artificial intelligence, automation, and robotics especially through complicated systems methodology is the only solution to environmental challenges. Reference List Blanchard, BS. 2004. System engineering management, 3rd Ed. Hoboken, NJ: John Wiley & Sons. Blanchard, BS. & Fabrycky, WJ. 1998. Systems engineering and analysis 3rd Ed. Englewood Cliffs, NJ: Prentice hall. Cavaleri, S. & Obloj, K. 1993. Management systems: A Global Perspective. Belmont, CA: Wadsworth. Clayton, AMH. & Radcliffe, NJ. 1996. Sustainability: A Systems Approach. London: Earthscan. Flood, RL. & Jackson, MC. 1991. Creative problem solving: Total systems intervention. Chichester: Wiley. Hitchins, DK. 2013. Putting systems to work. New York: John Wiley & Sons. Jackson, MC. 2000. Systems approach to management. New York: Kluwer Academic. Kasser, J. 1995. Applying total quality management to systems engineering. Boston: Artech House. Nicholas, JM. 2004. Project management for business & engineering: Principles & practice 2nd Ed. Burlington, MA: Elsevier Butterworth Heinemann. Read More