Computer Manufacturing Enviroments Essay Research Paper Computerized

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].

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