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Computer Manufacturing Enviroments Essay, Research Paper

Computerized Manufacturing Applications

Manufacturing Information Systems & Production Control Systems

In computer integrated manufacturing environments, dependability is a crucial attribute for the production management and control information system, which should be carefully assessed during system design. A global approach is taken for assessment of the dependability of industrial information systems[1]. Manufacturing environments are changing constantly and the technology surrounding and supporting them is often changing faster. Solving manufacturing problems by acquiring a variety of incompatible hardware and software from multiple vendors has created a complex application and device integration problem. Approaches taken are driven toward a more application independent platform[2].

Manufacturing companies throughout the world in many industries are adopting lean manufacturing methods, a fundamental shift from traditional mass production. The original model for lean manufacturing is the Toyota Production System. Toyota runs their system with remarkably little information technology and relies heavily on simple, visual, manual signals to manage scheduling and material flow. Central tenets of lean manufacturing include:

1. Takt time and continuous flow-all operations should ideally build at the pace of customer demand.

2. Demand is leveled by creating an inventory buffer and replenishing that buffer using a leveled schedule.

3. Since simplicity in manufacturing is a virtue, visual systems should be used wherever possible.

Information systems can be a powerful tool as long as they are subordinate to the physical systems and not designed to replace them. As other companies in the modern

computer age have been adopting lean methods the question arises: In what ways can appropriately applied information technology significantly enhance the performance of lean systems?[3]

Central tenets of lean manufacturing include:

Takt time and Continuous Flow-All operations should ideally build at the pace of customer demand. Continuous flow is the ideal, building one piece at a time, which tends to minimize waste, with all operations building to takt time. Pull systems should be used when continuous flow is not feasible. In this case a small buffer is set up between operations and the feeder operation replenishes what is taken away by the

downstream operation. Again, only the final is scheduled and then all upstream processes build to replenish what has been consumed by their immediate customer.[3]

Lean manufacturing deals with this through heijunka, i.e., leveling demand by creating an inventory buffer and replenishing that buffer using a leveled schedule. That simplicity was to use visual systems where ever possible. Thus, the signals used to trigger more production were cards or kanban. They could be color coded, they traveled with the material so it was apparent if a kanban was missing, and operators and material handlers had to do something deliberate and manual to order parts. Kanban gave operators control over the scheduling process. As vehicles became more complex ultimately thousands of parts all have cards attached to them. Toyota is able to handle that complexity adding only an automatic card sorter to sort out cards coming back from suppliers. The cards now have bar codes on them so that when they are read in that automatically updates a database that a transaction has occurred and accounts payable and receivable can be updated by computer[3].

Part of the problem with systems like MRP in the past is that they were used as execution systems to set schedules for individual operations. Aside from the fact that they did not include data on capacity and thus put out unrealistic schedules, scheduling individual operations violates a central tenet of lean manufacturing-that operations should build to customer pulls[3].

Let us consider each of the main features of synchronous material flow in lean and how they can be enabled by APS:

Takt Time and Balanced Operations-Machines may be dedicated to a product

family and their cycle times matched to the takt time. There are at least two

conditions in which a takt time analysis is not straight forward and can be aided by


1. If the takt time changes over time, new calculations are needed to

rebalance the system. As long as the takt time can be predicted accurately

and smoothed over a time period the manual calculations can easily be

performed. But when the customer demand rate changes there are a

myriad of calculations necessary to identify the implications for the

entire product flow.

2. Takt time calculations are straightforward when there is a dedicated product line with product variations which have the same routing and work content and all machines are dedicated to that product and run in the same amount of available time[3].

Some operations may run 3 shifts, others 2 shifts, some with breaks, and others automatically without breaks. While in principle we would like a continuous flow with dedicated machines running the same amount of time and a product going through the same process, this is often not practically feasible. APS can shine in calculating takt times for the total line and for individual items and developing optimal plans for the system as takt times change. It can go as far as to have individual work elements in the system and identifying optimal job designs to load jobs to takt time[3].

Pull Systems-The key decisions include the pack size, how many to put in a container, the marketplace size, how many kanban to put in the system, and the frequency of the replenishment cycle by material handling. APS can be very good at-using data that resides in the database instead of manually assembling the data each time. And APS can

look more broadly than between a feeder operation and the consuming operation to consider stability in supply through the value chain to identify appropriate marketplace sizes and kanban quantities. For example, the safety margin built into the marketplace is based not just on characteristics of one consuming operation and one producing operation, but on variances in the arrival of supplied parts and variation in quality of

parts coming in[3].

Production Leveling-While there has been a lot of discussion in the “bull whip effect” literature on using current and accurate information to mitigate its effects, Toyota’s

solution is to level production at each stage of the process and develop stable manufacturing processes that can build to the leveled schedule. The assembly plant uses a leveling algorithm to take the demand (actual orders + forecast) and create a levelized sequence to spread out all variations of product across the day so the producers of components for that product see a level stream of orders coming to them. As a general rule they assume the suppliers should plan for fluctuations of +/- 10% deviations from the

levelized schedule in a given day[3].

APS can calculate the leveled schedule, considering many factors, while visual control systems can be used to execute the plan. For example, more advanced lean plants are using load-leveling (heijunka) boxes to visually present the leveled schedule. The box has slots for different products and different times and kanban are loaded for each time slot to spread out the building of products over the day. The material handler withdraws the cards at each time period and this sets the assembly schedule. It is a system brilliant in its simplicity as an execution tool, but can be made more efficient and effective with APS in the background helping to specify how to load the box[3].

In manufacturing we make physical products using physical production systems. Information systems can be a powerful tool as long as they are subordinate to the physical systems and not designed to replace them[3].

Multimedia is an application of real time-critical computing. In a networked multimedia system such as video conferencing, real-time image communication is the key for its success. The application of multimedia in the manufacturing industry has received significant attention since the emergence of the Internet and World Wide Web[5].

The open hypermedia approach to information management and delivery allows a single multimedia resource base to be used for a range of applications, and permits a user to have controlled access to the required information, in an easily accessible and structured manner. It is contended that with the integration of open hypermedia, and knowledge-based systems together with network technology giving access to external databases, the concept of industrial strength hypermedia can be realized[6].

Implementation of formal quality policy in manufacturing environments requires extensive knowledge in many different fields:

1. Knowledge of the problems that can be found.

2. Knowledge of the methods and procedures that can definitely improve the process.

3. Knowledge of the quality techniques that can be used.

4. How to implement these techniques in manufacturing environments.

Different experts are able to provide such knowledge, ranging from operators to quality engineers, which for example provide knowledge about specialized quality tools. Intelligent decision support systems can be used to make quality expertise available to people who face quality problems every day[7].

Manufacturing systems are often described as being complex. The dynamic nature of the manufacturing environment greatly increases the number of decisions that need to be made and system integration makes it difficult to predict the effect of a decision on future system performance. An understanding of the effects of integration on the system complexity is essential for realizing the full potential of manufacturing systems, their successful deployment in industry, and the economic justification of new technologies[8].

A complex system may refer to one whose static structure or dynamic behavior is unpredictable. It may also refer to a system which has patterns of connections among subsystems such that the prediction of system behavior is difficult without substantial analysis or computation, or one in which the decision making structures make the effects of individual choices difficult to evaluate. Algorithmic complexity is often used for classifying manufacturing planning and control problems. In fact, the question, “Does a system fundamentally change or become simpler if a better algorithm is invented for solving the problem at hand?”[8].

The complexity of a physical system can be characterized in terms of its static structure or time dependent behavior. Static complexity can be viewed as a function of the structure of the system, connective patterns, variety of components, and the strengths of interactions. Dynamic complexity is concerned with unpredictability in the behavior of the system over a time period. The manufacturing environment consists of physical systems in which a series of sequential decisions need to be made in order to produce finished parts. The sequence and nature of these decisions are not only dependent on the system capabilities but also on the products being manufactured in the system. Hence, any measure of system complexity should be dependent on both the system and the product information. The difficulty in making production decisions arises from the number of choices available at each decision point and the unpredictability of the effects of each choice on the system performance[8].

As more office-grade PCs are placed in manufacturing environments, the machines are being exposed to environmental extremes they were never designed to handle. Office PCs typically last a little more than a week in a factory setting, before their components start failing. Computer hardware on the shop floor is often subjected to extreme heat and cold, dust, sprayed and dripped liquids, vibration and shock, power surges, electromagnetic and radio-frequency interference, and even security issues[4].

Robotic & Programmed Machine Control/Quality Control

As robots assume more important roles in flexible manufacturing environments, they are expected to perform repeated motions while being able to quickly adapt to changes in the assembly line. It can not be ignored that the parameters of the robot influence the cost per cycle[9].

In robotics, “open” means interfacing. System integrators and end users can bring robotic manufacturing systems into production in a timely and cost-effective manner using standard components. The real value of “open” systems, we believe, is in interfacing to external cell devices and the information systems they provide[10].

Typical robotic systems use industry-standard I/O interfaces. Robot, CNC, and PLC control vendors supply whatever interface the customer specifies. These bus implementations connect virtually all the discretely controlled devices found in a manufacturing environment. Robot programs written by the customer or systems integrator can directly access any discrete I/O point using standard programming tools provided with the robot[10].

Robot controllers can have Ethernet hardware on the motherboard, with standard FTP, TCP/IP, and BOOTP protocols communicating with the robot. An integral Pentium PC comes with expansion slots for third-party devices. Integral or external PCs and their software can monitor robot systems and call objects from customer developed Visual Basic or C++ software. In combination, these products allow users to develop their own Graphical User Interfaces for robotic applications[10].

Robotic suppliers can and do supply these algorithms and guarantee the integrity, reliability, and safety of the robot motion control system. To guarantee the performance of the robot in advanced applications, and to minimize the risk that an errant task or

hardware malfunction will affect the motion and/or safety of the robot, we believe the robot manufacturer must limit access to the real-time motion and process control processor[10].

Robot control suppliers have invested heavily in a strategy that “opens” the controller to external peripheral devices and PCs while maintaining the integrity and safety of the motion control system. Users get the reliability and safety they expect along with the flexibility that PCs and commercial hardware and software can offer[10].

Scheduling, Inventory & Process Control Information Systems

Production planning and scheduling models arising in automated manufacturing environments exhibit several features not encountered in models developed for traditional production systems. For instance, models of automated facilities typically include tooling constraints which reflect the possibility for a machine to use different tools in order to perform successive operations, within limits imposed by the size of the tool magazine. Also, these models often account for the existence of flexible material handling systems whose activities must be synchronized with the machining operations in order to optimize system utilization[11].

In many scheduling problems, a newly released job must be stored in an input buffer while it waits to begin processing. The lack of attention given to these buffers in the classical scheduling literature results from the implicit assumption that they have infinite capacity. In modern manufacturing environments, however, there are several important reasons for limiting buffer capacity. Nonpreemptive single machine dynamic scheduling problems are studied under the assumption that some jobs may be lost, either because of insufficient input buffer capacity, or because due dates cannot be met[12].

We consider deterministic scheduling problems where decisions involving input buffers are important. Consider a situation in which n jobs with varying release dates are to be processed nonpreemptively on a single machine. When a job is released, it can either be processed immediately, stored in an input buffer while it awaits processing, or discarded. The usual assumption is that the input buffer has infinite capacity. However, we assume that the capacity of the input buffer is finite[13].

Scheduling problems with finite capacity input buffers arise in several manufacturing environments such as production processes with physical space limitations. Finite capacity input buffers model limited warehouse space for components that await processing. In modern manufacturing systems, the provision of buffer space including input buffers is regarded as expensive. Moreover, holding products in a buffer incurs costs due to insurance, record-keeping, and deterioration. Also, the opportunity cost of financing the inventory is often considerable[13].

A job can be lost if it cannot fit into the buffer or if it cannot complete by its due date. We seek a nonpreemptive schedule that minimizes the total weight (or cost) associated with lost jobs. For the case of input buffers, Nawijn(1992) studies a constrained problem in which the jobs that are not lost are processed in their order of arrival. Also, Nawijn et al.(1994) derive a polynomial time algorithm for minimizing the number of lost jobs when the buffer capacity is constant, and establish that the problem of minimizing the weighted number of lost jobs is intractable. Hall et al. (1997) describe algorithms and complexity results for several preemptive single machine scheduling problems with finite capacity input buffers[13].

IT departments everywhere need people with more or less the same skills. But so many manufacturing companies are using enterprise resource planning(ERP) and supply chain management systems that these systems are profoundly changing the face of IT in the manufacturing sector[14].

IT professionals in manufacturing say ERP systems are blurring the lines between IT and users. There is huge demand for users or line-of-business people who also have professional-level IT skills. But traditional IT types who know only about technology and nothing about the business are not needed as they once were. “Understanding the business is probably the most critical [aspect],” says Joan Cox, CIO at the Space and Strategic Missiles Sector of Lockheed Martin. “It’s more important to understand how you want things to flow though the factory than [to have] the skill of programming-except for the few places where SAP doesn’t do what’s needed, so you need coders.” Thomas says, “What manufacturers are going to need in the future are some real visionaries who still understand the supply chain and profitability, and who could potentially influence the future direction of the company in big ways. There could be a huge change in the supply chain and the cost structure.”[14]

Manufacturing Equipment(CAD/CAM)

Manufacturers can avoid production problems by including manufacturing information in PDM systems along with product design data like drawings, CAD models, and engineering specifications[15].

Manufacturing companies, even those that have invested heavily in advanced

computer-based engineering and production systems, often fail to systematically manage the tooling, fixtures, molds, dies, and CAM programs required to produce parts on the factory floor[15].

Information waste time, and tooling mistakes on the shop floor can be avoided by including this manufacturing information in PDM systems along with product design data like drawings, CAD models, and engineering specifications. In PDM, this information comprises the overall product definition, and adding categories to include tooling information usually is relatively easy. The system should be implemented with input from the factory personnel, of course, who know their operation better than anyone and who will eventually have to use the PDM system in their jobs[15].

Such a system can have an enormous impact for a manufacturer, not only in more efficient factory operations but also in heading off problems caused by faulty production information. In this way, PDM can help avoid serious production problems that could cripple an otherwise smooth-running operation[15].

Today, in order to meet growing competitive pressures, there is an increased use of the computer to aid in the design process. Computer-aided design and computer-aided manufacturing tools are providing the capability to meet the reduced time-to-market window of opportunity. Thus, the process of transferring design information to manufacturing production equipment, as in the past, becomes a major factor in the success or failure of a project to meet customer or company management expectations. Over the years, many standards have evolved to address the situation of design-to-manufacturing data transfer. The Institute for Interconnecting and Packaging Electronic Circuits has a suite of standards that deals with board, photo-tool, electrical test, and assembly descriptions[16].

The industry has faced design-to-manufacturability relationships since the days of the first Winchester rifle-designers were interested in providing the best design for the hunters of that era, and manufacturers were tasked to produce parts for the Winchester that were interchangeable[16].

The electronics industry is under a similar challenge as that of our forefathers of the industrial revolution. The difference in today’s world is that, in order to meet growing competitive pressures, there is an increased use of the computer to aid in the design process. Computer-aided design (CAD) and computer-aided manufacturing (CAM) tools are providing the capability to meet the reduced time-to-market window of opportunity. Thus, the process of transferring design information to manufacturing production

equipment, as in the past, becomes a major factor in the success or failure of a project to meet customer or company management expectations[16].

The advent of the first CAD systems was based within the large OEM structures. IBM, RCA, PhilcoFord, Raytheon, Northern Telecom, and Hewlett-Packard all had systems that their engineers used in conjunction when designing electronic printed boards. The industry learned to rely on computer algorithms to place components and interconnect them on one, two or several layers. These initial systems provided machine language to photoplotters that created the tools to produce the conductor images[16].

As CAD systems continued to evolve, more computer power was expected of them. Design methodology received a great deal of attention from companies who became automation tool suppliers to the industry as opposed to OEMs who created their own internal systems. Users of these products provided input to the tool developers, and a great deal of concentration was exerted on the computer algorithms needed to provide quick and easy solutions to the design problem. CAD programs were expected to perform

analyses that determined if timing and speed of circuit signals were consistent with the engineers desire. CRT browsers were provided to assure easy access to modify the data or implement user modifications to the resulting CAD solution. While the systems for CAD improved, the transfer of data to the manufacturing floor stalled[16].

Fortunately, CAM tools have provided analyses and functions that have addressed the ambiguities in the machine language provided by the customer. While design complexity has increased with more layers, more parts and faster turnaround required by the customer[16].

Although design and engineering is usually 100 completed by the CAD system, the manufacturing processes, even with improved tools, are only 70 realized. Manufacturers indicate that they can’t trust what they’ve received-data files are unreadable, information is incomplete and communication is not two-way-thus, data is reengineered on the

manufacturing floor. In addition, due to the competitive nature of the printed board industry, the manufacturer would rather fix the problem and charge for the services than contact the customer to imply that the data provided was insufficient. This is not just an problem for the United States, but a global problem for the industry[16].

In 4,000 hours, Culin/Collela produced miles of curved molding and other custom millwork using a CNC (computer numeric controlled) wood router driven by a personal computer. This was about half the time it would have taken to make all these products using traditional methods. The router improved work accuracy by a factor of 10. The Mamaroneck, New York, company delivered curved molding, cabinets and bookshelves that drew praise from the project architect. Creating large curved wood pieces by hand requires making a trammel and physically swinging an arc to calculate curve radii. Instead, Culin/Collela created shop drawings in its CAD (computer-aided design) system. CAD data was transferred to the router’s CAM (computer-aided manufacturing) system to create toolpaths for the router. Without CNC equipment, Culin/Collela could not have even bid on this contract, much less won it[17].

Culin/Collela executives realized that CAD/CAM technology could make some of this work easier. But computer-controlled woodworking machines cost around $60,000, a sum impossible to justify with the firm’s existing workload. Then came news of the Techno Series III PC-driven CNC wood router. It costs less than $16,000, yet can do production routing and drilling on a wide variety of materials: solid wood, mediumdensity fiberboard, plastic, solid-surfacing materials and nonferrous metals[17].

Textile design remained a manual process until initial changes occurred 30 years ago. This was the beginning of computer-aided design/computer-aided manufacturing (CAD/CAM) for textiles. But even through the 1970s and into the 1980s, computer and graphic processor were primitive by today’s standards. PCs, desktop workstations, powerful graphic processors and a lot of software developed over the last 15 years changed all that. Rapid development of CAD systems was spurred by user-friendly operating systems and the introduction of peripheral equipment such as color scanners and printers. Other significant factors include innovative software engineers, a big contribution from textile manufacturers and the synergy from both. CAD/CAM simulates the manual processes in creating a design and getting the pattern information to the production machine. It saves a lot of time, while contributing to higher efficiency and productivity. Ways that a number of textile companies are using CAD/CAM applications for design are discussed[18].

What an end user needs depends on what the plant produces and the degree of that plant’s product complexity. A great deal of hardware and software is available for textile design, color matching, color separation, yarn and fabric simulation, 3D presentations and fashion design[18].

A system can be as simple as a regular PC with a keyboard, a mouse and a few thousand dollars spent on software. Further up-scaling requires a digitizer pad and pen, color flatbed scanner and color printer, each costing several hundred dollars. Other end users will need a number of more expensive desktop workstations, color flatbed and drum scanners, high-tech printer and more extensive software package[18].

CAD/CAM simulates the manual processes in creating a design and getting the pattern information to the production machine. It does not replace the designer’s creativity, skill or experience. But it does save a lot of time, while contributing to higher efficiency and productivity, from design concept all the way to the finished product[18].

JIT, MRP, & Integrating JIT/MRP


The main goal of just-in-time (JIT) implementation is to solve problems and to find solutions. Most of the time, JIT is described as waste elimination at all levels, as a means of maximizing high-added value activities payoff or to minimize low-added value activities impact. The firm that wants to implement JIT also wants to know the cost and length of this process and its related activities. However, above all, the firm certainly wants a successful implementation at the first attempt and an implementation process that perfectly suits that firm’s needs. Project management provides a framework capable of monitoring the complex JIT implementation process: it is a management tool developed for planning, controlling, and monitoring an intricate set of non-repetitive activities[19].

The quest by suppliers, vendors and discounters to apply just-in-time delivery principles that reduce inventory and transportation costs while making merchandise available to consumers immediately after it is produced is important. One key best practice in the JIT arena entails exploring new logistics arrangements that fall outside the traditional direct store delivery or distribution center model. For manufacturers, this sometimes means shipping to consolidation centers operated by 3rd-party firms or by retailers. But no matter what the extent of supply chain responsibilities assigned to 3rd-party logistics companies by vendors and retailers they serve, entities that want to move forward with JIT are delving into using at least some form of sophisticated technology as a springboard[20].

A large order of General Electric telephones, bound for units of a mass merchant, leaves the manufacturer’s warehouse. But rather

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