Programmable Automation Technologies. Daniel Kandray
that it is ready to process more material and then prepare itself to receive material by opening safety gates, moving tooling out of the way, and opening the lathe chuck. Next, it must inform the PLC that it is ready to accept material for processing. Once the material is loaded, the CNC lathe must recognize that it is loaded and then process the material. When the processing is complete, it must prepare itself to be unloaded. Finally, it will inform the PLC that it is ready to be unloaded. The robot functions similarly. It receives input from the PLC that the lathe is ready to be loaded. It then executes a sequence of movements to pick up the raw material and load it into the lathe. It will then inform the PLC that it has loaded the material and is ready for the next instruction. Thus, the PLC is, essentially, the brain of the cell. It controls the timing and sequence of all events that occur within the cell. It monitors the status of the cell and informs each piece of equipment when and where actions are to be performed.
Figure 1-13 depicts another manufacturing cell. This cell consists of a hydraulically actuated press, a shuttle system, and a robot. In this cell, products (round disks) are molded inside the press then moved outside the press (with the shuttle system), where the robot unloads and finishes the product. A PLC controls the cell. Note that even though a CNC machine was not part of this particular cell, CNC technology had an impact on the cell. A CNC machine was used to produce the mold in the press and the gripper on the end of the robot arm.
Figure 1-13 Press manufacturing cell
Thus, as shown in these two examples, it is easy to envision how programmable automation is present, either directly or indirectly, in almost all modern automation systems.
1.4 Manufacturing Performance Measures
In order to understand when and where to apply automation in general and programmable automation in particular, it is essential to comprehend how manufacturing production performance is measured. From these performance measures one can evaluate and justify the use of automation. As will be seen in the next chapter, the performance measures used most often to quantify production include:
Production rate—measure of products per hour (pc/hr).
Setup time—measure of the amount of time to prepare a machine or process to make a product (hrs).
Production capacity—measure of the maximum amount of product that can be produced by a manufacturing facility, system, cell, or process in a specified period of time (output units/time period).
Utilization—ratio of the actual amount of output from a manufacturing facility, system, cell, or process in a specified period of time to the production capacity over the same time period (%).
Manufacturing lead time—total time to process a product through a manufacturing facility, system, cell, or process.
Each of these measures provides a picture of how certain individual aspects of the manufacturing process and system are performing. These are vital and critical measures to be evaluated when one considers automation. However, each only provides a small segment of the overall picture. Thus, the measures need to be evaluated collectively to effectively evaluate and justify the use of automation. There are other factors as well that should be considered, including burden rates of equipment and labor costs. Thus, it may be difficult to get a clear comprehensive picture of the performance of a process or system. However, there is one measure that effectively combines and summarizes many of the individual measures into one all-encompassing metric: That measure is productivity.
1.4.1 Productivity
The term “productivity” is often cited in the news media as an important economic indicator of the health of the nation’s economy. The U.S. Department of Labor, Bureau of Labor Statistics, collects and publishes productivity data for many elements of the U.S. economy. Per the Bureau of Labor Statistics website (www.bls.gov/bls/productivity.htm):
Productivity and related cost measures are designed for use in economic analysis and public and private policy planning. The data are used to forecast and analyze changes in prices, wages, and technology. (p. 1)
Thus, the measure plays an important role in the development of private sector and government economic policies. The interaction of productivity measurements on price changes, wages, and technology may be complicated, but the definition of productivity is not. While the term may have a slightly different connotation to an economist compared to an industrial engineer/technologist, the basic definition is clear. This simple definition is why this measurement, used as a means of quantifying manufacturing production, can be used in all levels of manufacturing. It is the author’s contention that the productivity measure is perhaps the best indicator of when and where to utilize automation.
As David J. Sumanth observed in Productivity Engineering and Management:
Productivity is concerned with the efficient utilization of resources (inputs) in producing goods and/or services (output.) (p. 4)
Note that “inputs” refer to all the resources (labor, material, capital, etc.) that go into producing the goods or service, and “output” is whatever is produced by the system under consideration. Thus, the productivity of a manufacturing system can be determined simply by the ratio:
productivity = output/input
For the purposes of this text, output will be expressed in units of parts/hr and input in terms of $/hr.
In order for the productivity ratio to rise, either the output must increase (more parts/ hour) or input must decline (amount of product remains the same with less resources). Productivity increases are positively viewed because they indicate that more is produced with less dollar input to the system. Therefore, if the selling price of the product does not change, the producer realizes an increase in profit. Conversely, a decrease in productivity occurs when not as much product is being produced and/or the input cost increases. Typically, all inputs (labor, raw materials, and energy costs) will increase over time. To prevent these increases in inputs from being passed along to the consumer as a price increase, more products must be produced. Note that this is the link of productivity to inflation. Therefore, it is obvious that improving productivity is of vital importance to manufacturing. Equally as obvious is how automation can improve productivity by allowing more products (output) to be made or by reducing the cost (input) of production.
The way a company can use productivity and the other manufacturing measures to justify automation will be addressed in detail in Chapter 2.
Specific reasons to automate one process may be very different from the reasons to automate another. However, the goal of any automation is to produce a tangible benefit. Common to all automation is the benefit of productivity improvement. Listed below are seven common reasons to automate:
Increase labor output
Increasing labor output has a direct effect on increasing productivity. If, through automation, the amount of product is increased, the productivity also increases. Essentially, automation focused on improving the effectiveness of the labor, thereby increasing the amount of product made over a specific time period, will lead to increases in labor output and, thus, productivity improvements. Examples include addition of a robot to handle material or use of a PLC to control a manual process. Each is intended to “free up” the worker from a task, thereby enabling him or her to produce more.
Reduce labor cost
Reducing labor cost also has a direct effect on increasing productivity. Because labor cost is an