Bar Code Technology & Fdc

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Shop Floor Control and Automatic Identification Techniques

in identifying problem areas in the plant that adversely affect achieving the master pro-

duction schedule.

There are a variety of techniques used to collect data from the factory floor. These

techniques range between clerical methods that require workers to fill out paper forms

1~a are later compiled, and fully automated methods that require no human participatio~

' J

he term

factory data collection system

is sometimes used to identify these techniques.J/

25.2

FACTORY DATA COLLECTION SYSTEM

The factory data collection (FOC) system consists of the various paper documents, ter-

minals, and automated devices located throughout the plant for collecting data on shop

floor operations, plus the means of compiling and processing the data, usually by com-

puter. The factory data collection system serves as an input to the order progress module

in shop floor control, as illustrated in Figure 25.2. Using our feedback control system

analogy of Figure 25.1, the FDC system is the sensor component of the shop floor control

system. Examples of the types of data on factory operations collected by the FOC system

include piece counts completed at a certain work center, direct labor time expended on

each order, parts that are scrapped, parts requiring rework, and equipment downtime.

The data collection system can also include the time clocks used by employees to punch

in and out of work. .

On-line versus batch systems

The purpose of the factory data collection system is twofold: to supply data to the order

progress module in the shop floor control system, and to provide current information to

production foremen, plant management, and production control personnel. To accomplish

this purpose, the factory data collection system must input data to the plant computer

system. This can be done in either an on-line or off-line mode. In an on-line system, the

data are entered directly into the plant computer system and are immediately avfHlable

to the order progress module. The advantage of the on-line data collection system is that

the data file representing the status of the shop can be kept current at all times. As changes

in order progress are reported, these changes are immediately incorporated into the shop

status file. The personnel with a need to know can access this status in real time and be

confident that they have the most up-to-date information on which to base any decisions.

In the off-line data collection system, the data are temporarily stored in either a

storage device or a stand-alone computer system to be entered and processed subsequently

by the plant computer in a batch mode. In this mode of operation, there is a delay in the

data processing. Consequently, the plant computer system cannot provide real-time in-

formation on shop floor status. This delay, and the requirement for a separate data storage

system. are the principal disadvantages of this configuration. The advantage of an off-

line collection system is that it is generally easier to install and implement.


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Data input techniques

The techniques of factory data collection include manual procedures, computer termina

located in the factory, and other technologies. The following paragraphs discuss th

various categories.

The manually oriented techniques of factory data collection are those in which t

production workers must fill out paper forms indicating order progress data. The form

are subsequently turned in and compiled, using a combination of clerical and comr.nerized

methods. The manual/clerical techniques include [7,10]:

Job traveler.

This is a log sheet included in the shop packet that

travels

the order through the factory. Workers who spend time on the order are required to reco

their times on the log sheet together with other data, such as the date, piece coun

defects, and so on. The job traveler becomes the chronological record of the processin

of the order. The problem with this method is its inherent incompatibility with t

principles of real-time data collection. Since the job traveler

II10VC\

wul. tht: Job, it

not readily available for compiling current order progress.

Employee time sheets.

In. the typical operation of this method, a daily time she

is prepared for each worker and the worker must fill out the form to indicate

h e

wo

that was accomplished during the day. Data entered on the form include the order numbe

operation number on the route sheet, the number of pieces completed during the da

time spent, and so on. Some of these data are taken from information contained in t

shop packet for the order. The time sheet is turned in daily, and order progress informatio

is compiled (usually by a clerical staff).

Operation tear strips.

With this technique, the shop packet includes a set

preprinted tear strips that can easily be separated from the packet. The preprinted da

on each tear strip include order number, route' sheet details, and so on. When a work

finishes an operation or at the end of the shift, one of the tear strips is torn off, pie

cou ..t and time data are recorded by the worker, and the form is turned in to report ord

progress.

Prepunched cards.

This is essentially the same technique

:is

the tear strip metho

but prepunched computer cards are included with the shop packet instead of tear strip

The prepunched cards contain the same type of order data, and the workers must wr

the same kind of production data onto the card. The difference in the use of prepunche

cards is that in compiling the daily order progress, mechanized data processing procedure

can be used to record some of the data.

There are problems with all of these manually oriented data collection procedure

They all rely on the cooperation and clerical accuracy of factory workers to record da

onto a paper document. There are invariably errors in this kind of procedure. Error rat

associated with handwritten entry of data average 1/30 [ lo]. Some of the errors can

detected by the clerical staff that does the compilation of order progress. Examples

detectable errors include wrong dates, incorrect order numbers (the clerical staff know


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which orders are in the shop and they can usually determine when an erroneous order

number has been entered by a worker), and incorrect operation numbers on the route

sheet (if the worker enters a certain operation number but the preceding operation number

has not been started, an error has been made). Other errors are more difficult to identify.

If a worker enters a piece count of 150 pieces which represents the work completed in

one shift when the batch size is 250 parts, this is difficu t for the clerical staff to verify.

If a different worker on the following day completes the batch and also enters a piece

count of 150, it is obvious that one of the workers overstated his/her production; but

which one?

Another problem is the delay in submitting the order progress data for compilation.

There is a time lapse in each of the methods between when events occur in the shop and

when the data representing those events are submitted. The job traveler method is the

worst offender in this regard. Here the data might not be compiled until the order has

been completed, too late to take any corrective action. This method is of little value in

a shop noor control system. The remaining manual methods described above suffer a

one-day delay since the shop data are generally submitted at the end of the shift, and a

summary compilation is not available until the following day at the earliest.

In addition to the delay in submitting the order data, there is also a delay associated

with compiling the data into useful reports. Depending on how the order progress pro-

cedures are organized, the compilation may add several days to the reporting cycle.

Because of the problems associated with the manual clerical procedures, techniques

have been developed that use data collection terminals located in the factory. The col-

lection terminals require the workers to input data relative to order progress. The various

input techniques include manual entry by simple pushbutton keypads or typewriteriike

keyboards. The keyboard entered data are subject to error rates just like the manual

clerical data collection techniques. However, the error rate for keyboard data entry is

approximately 1/300 [16], substantially lower than for handwritten entry. Also, error-

checking routines can be incorporated into the entry procedures to detect syntax and

certain other types of errors.

The data-entry methods also include more automated input technologies, such as

magnetic card readers or optical bar code readers. Certain types of data, such as iden-

tification of order, product, and even operation sequence number, can be entered with

the automated techniques using magnetized or bar-coded cards included with the shop

packet. Figure 25.3 illustrates one type of factory data collection terminal that combines

keypad entry with bar code technology.

There are various numbers and arrangements of keyboard-based terminals possible

in the factory. These include:

One centralized terminal. In this arrangement there is a single terminal located

centrally in the plant. This requires all workers to walk from their workstations to the

central location when they must enter the data. If the plant is large, this becomes incon-

venient. Also, use of the terminal tends to increase at the time of a shift change, and

this results in significant lost time for the workers.


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Automatic Identification Methods

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FIGURE 25.3 Data collection terminal with keypad entry and hand-held wand-

type bar code reader. Courtesy of Computer Identics Corp.

Satellite terminals. In this configuration, there are multiple data collection ter-

minals located throughout the plant. The number and locations are designed to strike a

balance between minimizing the investment cost in terminals and maximizing the con-

venience of the workers in the plant.

Workstation terminals. The most convenient arrangement for the workers is to

have a data collection terminal at each workstation. This minimizes the time lost in

walking to the satellite terminals. However, it seems to be justified only when the number

of data transactions is relatively large and when the terminals are also designed for

collecting certain data automatically.

The trend in industry is toward more use of automation in factory data collection

systems. Although the term

automation

is used, many of the techniques require the

participation of human workers. The next three sections discuss the various automated

and semiautomated methods of acquiring data from the shop floor.

25.3

AUTOMATIC IDENTIFICATION METHODS

The field of automatic identification is often associated with the material handling industry.

In fact, the industry trade association, called the Automatic Identification Manufacturers

(AIM), is an affiliate of the Material Handling Institute, Inc. Many of the applications


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of this technology relate to material handling. We are covering the subject here because

it is an emerging technique for tracking materials in shop floor control systems.

Automatic identification is a term that refers to various technologies used in au-

tomatic or semiautomatic acquisition of product data for entry into a computer system.

These technologies are mostly sensor-based methods that provide a means of reading data

that are coded on a document, product, component, container, and so on, without the

need for human interpretation of the data. Instead, the computer system interprets and

processes the data for some useful application. The applications of automated identification

systems are numerous; they include retail sales, warehousing (semiautomated storage and

picking), product sortation and tracking, shipping and receiving, and shop floor control.

Some of the automated identification applications require workers to be involved

in the data collection procedure, usually to operate the identification equipment in the

application. These techniques are therefore semiautomated rather than automated methods.

Other applications accomplish the identification procedure with no human participation.

The same basic sensor technologies may be used ,in both cases. For example, certain

types of bar code readers are operated by people while other types are operated auto-

matically.

There are some very good reasons for using automatic identification techniques.

(j)First and foremost, the accuracy of the data collected is improved, in many cases by a

significant margin. To illustrate, the error rate in bar code technology is approximately

10,000 times lower than in manual keyboard data entry. The rate of 13000000 is used

as an error rate for comparison with the handwritten and keyboard entry methods [16]. ,

The error rates of most of the other technologies is not as good as for bar codes, but still

better than manual-based method ~ second reason for using automatic identification

techniques is to reduce the time required by human workers to make the data entry. The

speed of data entry for handwritten documents is approximately 5 to 7 characters per

second, and it is 10 to 15characters per second (at best) for keyboard entry [16]. Automatic

identification methods are capable of ,reading hundreds of characters per second. This

comparison is certainly not the whole story in a data collection transaction, but the time

savings in using automatic identification techniques can mean substantial labor cost ben- ,

efits for large plants with many workers.

The technologies available for use in automatic identification systems at the time

of this writinginclude:

Bar codes

J

Radio frequency systems,

Magnetic stripe

Optical character recognition

Machine vision

~

The use of bar codes in factory data collection systems is predominant and growing,

and we devote a separate section to this technology. The other techniques are either used


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Automatic Identification Systems

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in special applications in factory operations, or they are widely applied outside the factory.

For completeness, we include brief discussions of them in the paragraphs that follow.

Radio frequency (RF) systems rely on the use of radio frequency signals similar to

those used in wireless television transmission. Although the type of signal is the same,

there are differences in the use of RF technology in product identification. One difference

is that the communication is in two directions rather than one direction (as in TV). Also,

the signal power is substantially lower in factory identification applications (ranging from

several milliwatts to 7 watts [9-] .

Radio- frequency identification systems consist of the identification tags on the items

to be identified, an antenna at some location where data are to be read, and a reader that

interprets the data. The identification tag is a transponder, a device that is capable of

emitting a signal of its own when it receives a signal from an external source. It is attached

to the product, truck, railway car, or other item. The term tag is misleading, since the

term refers to a small but rugged boxlike container that houses the electronics for data

storage and RF communication. The container may be as much as 2.5 x 2.5 x 7.5 in.

in size and be capable of withstanding temperatures from - 40 to +400F [9]. The tags

are usually read-only devices that contain up to 20 characters of data representing the

item identification and other information that is to be communicated. Recent developments

in the technology have provided much higher data storage capacity and the ability to

change the data in the tag (read/write tags). This opens many opportunities for incor-

porating much more status and progress information into the automatic identification

system.

The antenna is located at an identification station and listens for the RF signal from

the identification tag that uniquely indicates the item to which it is attached. The signal

is then fed to a reader that decodes and validates the signai prior to transmission of the

associated data to the data collection computer system. The hardware required for an RF

identification system has tended to be more expensive than for most other data collection

technologies. For this reason, RF systems have generally been appropriate for data col-

lection situations in which environmental factors preclude the use of optical techniques

such as bar codes. For example, RF systems are suited for identification of products with

high unit values in manufacturing processes (such as spray painting) that would obscure

any optically coded data. They are also used for identifying railroad cars and in highway

trucking applications where the environment and conditions make other. methods of iden-

tification infeasible.

Magnetic stripes (the term magnetic strip is also used) attached to the product or

container can also be used for item identification in factory and warehouse applications.

These are the same kinds of magnetic stripes that are used to encode identification data

onto plastic access cards for use in automatic bank tellers. Their use seems to be declining

for shop floor control applications because they are more expensive than bar codes and

cannot be scanned remotely. Two advantages they possess is their larger data storage

a acity and the ability to alter the data contained in them.

Optical character recognition (OCR) techniques refer to a specially designed

alphanumeric character set that is machine readable by an optical sensor device. The


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substantial benefit offered by OCR technology is that the characters and associated text

can be read by human beings as well as machines. The list of disadvantages, at least for

factory and warehouse applications, includes the requirement for near-contact scanning,

lower scanning rates, and a higher error rate compared to bar code scanning.

. Machine vision systems are used principally for automated inspection tasks, as

indicated in Chapter 18. The applications also include certain classes of automatic iden-

tification problems, and these applications may grow in Ilumber as the technology ad-

vances. For example, machine vision systems are capable of distinguishing between a

limited set of products moving down a conveyor so that the products can be sorted. The

recognition task is accomplished without requiring that a special identification code be

placed on the product. The recognition by the machine vision system is based on the

inherent geometric features of the object.

.:; 25.4 BAR CODE TECHNOLOGY J

Bar code technology has become the most popular method of automatic identification in

retail sales and in factory data collection. The bar code Itself consists of a sequence of

thick and narrow colored bars separated by thick and narrow spaces separating the bars.

The pattern of bars and spaces is coded to represent alphanumeric characters. Bar code

readers interpret the code by scanning and decoding the sequence of bars. The reader

consists of the scanner and decoder. The scanner emits a beam of light that is swept past

the bar code (either manually or automatically) and senses light reflections to distinguish

between the bars and spaces. The light reflections are sensed by a photodetector that

converts the spaces into an electrical signal and the bars into absence of an electrical

signal. The width of the bars and spaces is indicated by the duration of the corresponding

signals. The procedure is depicted in Figure 25.4. (The decoder analyzes the pulse train

to validate and interpret the corresponding data)

.Bar code Light beam

Correspond ing

electrical

signal

FIGURE 25.4 Conversion of bar code into pulse train of electrical signals.


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Bar Code Technology

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Certainly, a major reason for the acceptance of bar codes is their widespread use

in grocery markets and other retail stores. In 1973, the grocery industry adopted the

Universal Product Code (UPC) as its standard for item identification. This is a lO-digit

bar code that uses five digits to identify the product and five digits to identify the

manufacturer. The U.S. Department of Defense provided another major endorsement in

1982, by adopting a bar code standard (Code 39) that must be applied by vendors on

product cartons supplied to the various agencies of DOD.

The bar code symbol

The Universal Product Code is only one of many bar code formats in commercial use

today. The bar code standard adopted by the automotive industry, the Department of

Defense, the General Services Administration, and many other manufacturing industries

is

Code

39, also known as

AiM USD 2

(for Automatic Identification Manufacturers

Uniform Symbol Description-2), although this is actually a subset of Code 39. We

describe this format as an example of bar code symbols [2,3,5].

Code 39 uses a uniquely defined series of wide and narrow elements (bars and

spaces) to represent 0-9, the 26 alpha characters, and special symbols. The wide elements

are equivalent to a binary value of one and the narrow elements are equal to zero. The

width of the narrow bars and spaces, called the X dimension provides the basis for a

scheme of classifying bar codes into three code densities (this scheme applies to the other

bar code standards as well as Code 39):

High density: X dimension is 0.010 in. or less

Medium density: X dimension is between 0.010 and 0.030 in.

Low density: X dimension is 0.030 in. or greater

For bar codes with X ~ 0.020 in., the wide elements must be printed with a width of

anywhere between 2

x

and 3

x

(two to three times the X dimension). For bar codes with

X

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Bar Code Technology & Fdc