Information management of automatic data capture: an overview of technical developments

Automatic identification and data capture/collection (AIDC) systems are one of the most widely used and under-recognized IT strategic assets in use in the global economy. Data collection and integration strategies are essentia l to enterprise resource management systems as well as warehouse management systems. The development of innovation through the development and marketing of products and services has been a key source of competitive advantage for many large and small manufacturing firms and is greatly aided by AIDC technologies. Management needs to control quality, cost, schedule, location of warehouses and plants, inventory levels, pricing, shipment, and a vast host of factors that are based almost entirely on the volume and quality of data and data collection . A discussio n of types of bar coding technologies and their associated software/hardware requirements is included, with a mobile communication s example. bypass distributors. It did so by helping corporations with installation and training. The dependency of management on good data capture and reporting systems An inspection of technological changes in terms of supply chain management over the last 20 years has illustrated that there have been tremendous changes in the area of physical distribution or supply chain management systems through global businesses (Ross et al., 1996). Information technology and automated inventory control systems have changed payables, receivables, and the asset side of inventory. Typically, at the highest level, an enterprise resource planning (ERP) system manages the data, while bar code scanners collect the needed information and pass it on up to the ERP system (Benfield, 2001; Bensaou and Earl, 1998). Good decision-making techniques coupled with good leadership in knowledge management are always in demand in business and engineering situations (Adams, 1990; Finch, 1999; Putman, 1987). Certainly, the inherent requirement for good decisionmaking techniques is good data. Unfortunately, most decision-making is made based on historical data. Historical data may be measured in terms of weeks or years old. For example, even under the best of conditions, hourly data from a production process are usually recorded manually by a machine operator, then these data concerning parts or products, rejected or accepted, and related process parameters are typically stored for evaluation at the end of the current shift. Tally sheets are created, signed by appropriate tactical managers ± usually shift foremen ± and eventually sent to a data processing department for data processing into databases like Microsoft Access. At predetermined intervals, reports are produced and sent to manufacturing management and production administration. Hopefully, after all these required steps, decisions are made about the process and products, and corrective action, if necessary, is made. Unfortunately, the data associated with them are possibly outdated before they are processed. In this typical manufacturing example, time certainly has an impact on the quality of data that management eventually receives from the plant. Making decisions based on data requires that management have confidence in the quality of data collected. Obviously, to obtain this degree of confidence in data, the data must be accurate, timely, and reliable. In addition, management ± for control purposes for all business transactions ± needs good data and its collection. Management needs to control quality, cost, schedule, location of warehouses and plants, inventory levels, pricing, shipment, and a vast host of factors that are almost based entirely on the volume and quality of data and data collection. Unfortunately, to complicate this situation, employees usually hired to fill data entry roles are minimally skilled, poorly paid, and frequently are associated with high employee turnover rates. In addition, the greater the complexity of the data entry task, such as reading accurately the 17 alphanumeric character vehicle identification number (VIN) found on all passenger vehicles today, the greater the probability of making an error. Hence, to eliminate the human element in data collection, some type of automatic identification and data capture system is necessary to ensure the level of accuracy needed to support managerial decision-making systems. The robustness of some of the various bar code symbologies (the set of rules that define the makeup of each character in the code) tested by the bar code industry is quite remarkable (Fales, 1992; Harmon, 1996), and have extremely small error rates as compared to human-keyed data. The codes were continuously scanned from a low of about 12 million for bar code UPC-A, to a high of over 16 million for bar code Code 16K. The worst-case scenario, assuming a 95 percent confidence interval, is 1 error in 394,000 (for UPC-A) to 1 error in 5,400,000. Certainly, when one compares this rate to the commonly held error rate for data entry by humans to be 1 in 300, the advantage of automatic identification systems is quite obvious. Hopefully, with increasing data collection accuracy, there should be an increase in a firm’s productivity level as well. IT overview of AIDC systems IT systems components of AIDC AIDC systems, as with any IT system, are composed of both hardware/software and personnel elements. The major elements of an automatic identification include: physical object (the object or information to be processed); code (the type of symbology that identifies the major characteristic of the object or information to be coded); reader (the device that reads the code on the item and transmits that information); computer hardware (the device that receives the information from the keyless reader); software (the computer processing package(s) that organizes the [ 110] Alan D. Smith and Felix Offodile Information management of automatic data capture: an overview of technical developments Information Management & Computer Security 10/3 [2002] 109±118 automatically read information into human-usable form); display/printer (device that displays the information read and can print reports as well as actual codes to be placed on the objects to be coded for keyless data entry); and personnel (the human element that installs and manages the entire information flow scheme). The major types of automatic identification can range from the relatively old technology of bar coding (Kern-Rugile, 1998) for keeping track of inventory to more high technology such as biometrics. Although the use of modern biometric devices to control access to sensitive nuclear and defense facilities has been in existence for over a decade, it has only recently become available to private business. All of this growth in the AIDC industry is in response to the need for quality information. The most common types of AIDC systems in use today include the following: bar coding, optical character recognition, magnetic stripe, voice data entry, radio frequency identification, smart cards, biometrics, and touch memory. Bar coding and related technologies Bar coding technology is one of the earliest forms of AIDC technologies. Bar coding technologies have applications in ensuring accuracy and speed in time and attendance records, inventory control, document tracking, shipping and receiving, production control, and quality assurance, to name a few applications: Bar codes come in many shapes and sizes and consist of light and dark bars printed according to industry specifications. Optical device, usually called scanners, `̀ read’’ the bar code-symbologies, or languages, and translates that information into usable data. There are about 225 known bar codes symbologies, but only a few ± such as the UPC (universal product code) symbol on a cereal box or the maxicode symbol on United Parcel Service packages ± are widely used (Kern-Rugile, 1998, p. 54). The potential for bar code use is evident in all aspects of transactions in human society. Bar codes are essential on every conceivable product, from machine parts to food items. As previously discussed with all types of automatic identification systems, bar codes are especially popular due to their enormous savings in time and money, and guaranteed high levels of accuracy. Figures 1-4 illustrate common types of bar code symbologies that are used in a variety of transactions. Bar code symbology basically refers to the bars and spaces encoded according to established rules for each bar code language. Essentially, symbologies are machine-readable or scannable, and thus are examples of keyless data entry. Figure 3 illustrates UPC (universal product code) and EAN (European Standards) bar codes; Figure 4 displays UCC (Uniform Code Council)/EAN-14 in interleaved 2 of 5 (ITE) bar code; and Figure 5 is an example of UCC/EAN-14 in Figure 1 Universal product code (UPC) and EAN (European Standards) bar codes Figure 2 Uniform Code Council (UCC)/EAN-14 in interleaved 2 of 5 (ITE) bar code Figure 3 An example of UCC/EAN-14 in UCC/EAN-128 bar code symbology [ 111 ] Alan D. Smith and Felix Offodile Information management of automatic data capture: an overview of technical developments Information Management & Computer Security 10/3 [2002] 109±118 UCC/EAN-128 bar code symbology. The bar codes illustrated in the figures may be applicable at the item level (namely EAN/ UPC as shown in Figure 3) or at the shipping or fixed content-container level (namely UCC/EAN-14) level. Bar codes are an essential component in the product identification and flow of goods and information in electronic commerce. This flow of goods and data through the supply chain and the management of this flow through electronic data interchange (EDI) systems are heavily regulated by governing agencies at the commercial and industrial levels. The Uniform Code Council (UCC), in conjunction with its European counterpart (EAN) provides standardization in the industry and issues guidelines and company identification numbers. The UCC issues are based on the binary system. Bar code symbology is based on character set, type, element widths, length or density, with self-checking techniques. Although there are many symbologies available, only a few are in common use, and are regulated by industrial specific organizations (see Table I for common symbologies for bar coding). The character set can be numeric only, as in the case of UPC, or alphanumeric with punctuation, as in the case of Code 39. Some codes have full ASCII character set with control characters, while others contain special international characters and special use characters. The character type can be discrete, with space between each character, or continuous, with no spaces between characters. The discrete characters typically present the primary production identification of product or container, item numbers, and level of packaging. Most of the work of the UCC is in bar coding standards. With the help of such industrial organizations as the Automatic Identification Manufacturer’s (AIMUSA) Association, the UCC provides standards and policies to ensure adherence to rules for quality in specification and continuous and scannable bar coded symbols. Standards exist for simple bar code symbols, two-dimensional bar coding schemes, and quality specifications for bar code symbols. The UCC deals with both the data content (information content refers to the structure of numbers or letters being encoded) and symbology (which, as previously discussed, refers to the bars and spaces encoded according to established rules for each bar code to be scanned in machine-readable form). The identification codes are keys to product attributes, as identification numbers are keys to information, by linking product, attributes, and messages together. The types of bar code symbologies can be classified as bar codes and matrix codes (Burke, 1984; Palmer, 1991; Philpot, 2002). Bars and/or space widths, bar height, bar position, and distances of adjacent bars, can Figure 4 Examples of new linear symbologies utilizing reduced space symbology (RSS) Figure 5 Basic concepts and two-dimensional component associated in a composite symbol (CS) bar code symbol Table I Common symbologies for bar coding UPC Commercial, retailing Code 39 Industrial, military, health Interleaved 2 of 5 (ILF) shipping, distribution Code 128 Distribution, health care PDF 417 Portable data file was in medical records, product documentation Data matrix Component marking, hazardous materials Maxicode Shipping and distribution, high speed sorting [ 112] Alan D. Smith and Felix Offodile Information management of automatic data capture: an overview of technical developments Information Management & Computer Security 10/3 [2002] 109±118 characterize bar codes. Matrix codes are primarily based on dot location with a matrix. Of course, all bar codes are character independent, with each character starting and ending with a bar, with inter-character gaps. The continuous character type, on-theother-hand, is character dependent, with each character starting with a bar, and ending with a space, or vice versa. Also, the continuous character type has no character gap. However, the bar code and related symbols are only a small part in total AIDC systems. The other elements, as previously discussed, include the scanner, decoder, computer hardware/software, and the various human interactions. The bar code scanner in this system is the device that converts the visual image of the bar code symbol into an electronic signal. Typically, the scanner and the symbol decoder are packaged in the same device, and are difficult to separate into the two components. In fact, the term scanner commonly refers to just the scanning function, while the decoder simply takes the electrical signal from the scanner and then compares the long and short signal lengths to the various possibilities of encoded information. Once the decoder determines what symbology is being used and encodes the electronic signal, it either stores or transmits the data. Automatic identification and data capture systems and related IT technologies are essential for an organization’s ability to access its tangible and intangible assets for sustainable competitive advantage. Major innovations or technological changes require new skills, market knowledge processing abilities, and systems throughout the organization. These trends in information transfer are especially true in knowledge markets, which are greatly aided by the timeliness and accuracy of the associated automatic identification and data capture systems. All bar codes in common use today are inherently self-checking, and usually occur at the character, word, or message level. At the word level, for example, quiet zones and start and stop characters are at both ends, with data check characters in between these characters. Quiet zones, for example, are usually 10£ (that is, ten multiplied by the smallest bar width). Bar codes are designed to be scanned bi-directional and read at very high speeds. Although bar coding schemes have been used widely in the industry since the early 1970s, there have been significant changes in the type of bar codes and its applications in industry. For example, as supply chains develop among users, many applications require additional data to be encoded with a very limited space, such as random weight, credit/debit information, dates, places of origin and destination, and other valuable information. In response to these changes and new requirements, new reduced space symbologies (RSS) and composite symbologies (CS) have emerged in the area of space-constrained product marking. These symbologies may be stacked or truncated to allow for more data entry. Figure 4 illustrates common examples of the recently adapted reduced space symbologies (RSS) and composite symbologies (CS) as accepted and promoted by the Uniform Code Council. RSS-14 is a typical example of this new reduced space technology. RSS-14 enables the full 14-digit UCC/EAN numbering of items. It is smaller than UPC-A or EAN-13, and provides a linkage flag to indicate the existence of a two-dimensional composite component. These twodimensional components add supplementary application identifier data to typical UCC/ EAN linear symbologies. The CC-A composite, for example, can encode up to 53 characters and comes in three widths (two, three, or four) and from three to 12 row combinations. RSS stacked symbology can encode application identifier element strings with the capacity of up to 74 characters. In addition, stacked RSS provides a linkage flag to indicate the existence of a two-dimensional composite component and has omnidirectional scanning capability. Stacked RSS can be printed in stacked rows with an even number of segments in each row, which can be used to fit a narrow space or print head. Figure 5 illustrates both the basic concepts associated in the steps to truncate and the eventual stacking of an RSS-14 bar code symbol. Other versions of the composite components within a stacked RSS symbology can represent substantial data compaction. For example, the CC-A composite component can encode up to 338 characters, while the CC-C composite component can encode up to 2,361 characters. These data compaction and reserved code word schemes certainly can enhance the flow of information via bar coding. RSS-14 symbologies are basically designed for small item collection and provide a linkage flag for two-dimensional composite components in bar coding schemes. Typical hardware/software configurations The bar code scanner in this system is the device that converts the visual image of the bar code symbol into an electronic signal. Typically, the scanner and the symbol decoder are packaged in the same device, and are difficult to separate into the two [ 113 ] Alan D. Smith and Felix Offodile Information management of automatic data capture: an overview of technical developments Information Management & Computer Security 10/3 [2002] 109±118 components. In fact, the term scanner commonly refers to the scanning function alone, while the decoder simply takes the electrical signal from the scanner and then compares the long and short signal lengths to the various possibilities of encoded information. Once the decoder determined what symbology is being used and encodes the electronic signal, it either stores or transmits the data. Table II illustrates some of the common data transmission formats used by the decoder, with more details supplied in Table III. Symbol density is the range of resolution in which the scanner can read symbols printed in the codes. The limiting factor is, in general, that the smaller the viewing area, the greater the effect of defects in the printing medium or substrate. Table IV displays basic terms associated with symbol density, from ultra high density to very low density. The primary scanning technologies for automatic identification and data capture are wand, charged couple devices (CCD), laser, and video configurations. The wand scanner and its machine-mounted counterparts function in a configuration very similar to a camera or photographic film. The scanner illuminates a large portion of the symbol and reads a relatively small portion of the symbol area. The scanning optics are usually provided by a simple lens system. The common source of illumination is light emitting diodes (LED), but other light sources have been used. Wands mostly have digital output, use analog coding in symbol verifiers, and use digital signals for decoding and transmitting data to a central hub in most local area networks (LANs). Wand scanners have many advantages, including being lightweight, relatively low cost, fairly rugged, and having low power requirement (3mA) (Burke, 1984; Bushnell and Pearce, 1997; Harmon, 1994; La Moreaux, 1995). Of course, as with all scanners, issues of training, efficiency, and amount of contact scanning are important, especially in the case of wand scanners due to their limited scanner field. Another type of scanning device is the CCD scanner. CCD scanners are characterized by wing video technology, mostly on-line connection, no moving parts, use flood illumination (LEDs), and have a pixel count usually from 2,048 to 3,648. Basically, a CCD scanner functions similar to an on-line TV camera, providing a moving beam. Unlike laser scanning, the moving beam is the return or moving beam, not the illumination beam. The return light from the symbol passes through a simple lens system and is focused on the CDD imaging element. The CDD imager then converts the reflected light to an electronic signal that is amplified and digitized by the appropriate electronics. The advantages of CCD include moderate costs, ease of use, no moving parts, rugged, lightweight, moderate power requirements (50 to 200mA), and typically decoded output. However, it is limited in its depth of field as compared to lasers (Bushnell and Pearce, 1997; Collins and Whipple, 1994; Harmon, 1994; La Moreaux, 1995). Table II Common data transmission formats for bar coding applications Data transmission format Specifications RS-232 50 feet or less distance (bit rate is independent of distance) RS-422, RS-485 Higher distance communications. Noise canceling feature allows RS-422A to transmit data 100X faster than unbalanced RS-232. Please see Table III for more important details concerning RS-422 and other formats OCIA Keyboard emulation RF data communication Radio frequency bandwidths Un-decoded emulation Source: Adapted from Stallings and Van Slyke, 1999 Table III Detailed listing of data transmission specifications of RS-422 and RS-423 as a function of length and bit rate Signal type Length (m) Maximun transmission rate (bps) RS-423A/V.10 10 100,000 100 10,000 1,000 1,000 RS-422A/V.11 (Un-terminated) 10 1,000,000 100 100,000 1,000 10,000 RS-422A/V.11 (Terminated) 10 10,000,000 100 1,000,000 1,000 100,000 Source: Adapted from Stallings and Van Slyke, 1999 Table IV Associated terms in descriptions symbol density Density term Minimum symbol within mils (0.001 inch) Ultra high density Less than 4 Very high density 4 to less than 7.5 High density 7.5 to less than 10 Medium density 10 to less than 15 Low density 15 to less than 25 Very low density 25 and greater [ 114] Alan D. Smith and Felix Offodile Information management of automatic data capture: an overview of technical developments Information Management & Computer Security 10/3 [2002] 109±118 Extremely popular laser scanners are optically the reverses of wand and CCD scanners in terms of internal mechanics. The laser beam is a very focused beam of light and illuminates a very small portion of the symbol to be read. The reading of the symbol is accomplished by the return optics scanning the entire symbol defined by the `̀ quiet zones’’. The light reflected back to the scanner is proportional to the reflection area of the symbol being illuminated by the latter. When the beam illuminates a space, a much larger percentage of the light is reflected back to the scanner optics. The laser light intensity is usually less than 1mW and the devices are considered intrinsically safe. Most in-service lasers operate by mechanically moving the laser beam to create motion. Features of the laser scanner include point illumination, light collection through mirror movement, He-Ne instead of diode usage, and can we line roster, or starburst laser configurations. Advantages of laser devises are increased depth of field, since they are usually non-contact scanners, and wide acceptance (Adams, 1990; Halsall, 1996; Polizzi, B., 1997; Polizzi, T., 1997). Video scanners, on the other hand, are more complex than laser technology, and combine two-dimensional CDD with ambient illumination (pre-existing lighting). Their electronics provide for extremely high-speed data communications and have a large depth of field, high-speed image, processing, and two-dimensional scanning capabilities. Unfortunately, they are relatively of high cost and represent fairly new and unfamiliar technology. Video scanners function in a manner similar to a TV camera and send the information to the decoding function within the scanner as a series of data streams. The decoding function must also perform complex image processing to electronically orient the symbol before it is eventually decoded. Of course, selection of the appropriate technology is highly dependent on the people who use the scanners. Issues of scanner performance, costs, special needs of operators and scanning environment, as well as human factors must be considered (Harmon and Adams, 1989, Polizzi, B., 1997; Polizzi, T., 1997). In the USA, it has been estimated that about 60 per cent are lasers and about 15 per cent are CCDs. In Asia, for example, the figures are almost reversed, with CCD scanners dominating (Philpot, 2002). Of course, human and cultural factors equally play roles in the selection and use of magnetic strip, voice recognition systems, and other types of automatic identification and data capture systems. Although bar coding and related technologies are certainly the dominant technologies in current use in the AIDC industry, other technologies previously discussed in the areas of radio frequency, magnetic stripe, voice recognition, smart cards and biometrics are increasingly making greater impacts in the industry. Obviously, magnetic stripes have been used by people for at least as long as bar codes, and unlike barcodes, which are used primarily to automate material handling, magnetic stripes have been used extensively in financial and point-of-sale, identification and access control, and in stored-value debt in closed systems. The magnetic stripe itself is similar to the magnetic tape used with many computers. The essential operational characteristics include that the magnetic head must remain in continuous contact with the stripe during read/write operations, the magnetic head must move along the length of the stripe, but not necessarily at constant speed, and the card itself must be made of non-magnetic material. The total magnetic stripe card market is over 50 billion dollars, and, although not growing as fast as the chip card market, is far less expensive than most technologies (Philpot, 2002; Schantz, 1982). Voice recognition and radio frequency systems, on the other hand, represent more recent technological developments in the automatic identification and data capture industry than magnetic stripe, and voice recognition and radio frequency systems are rapidly growing as well. The rapid changes in inventory policies, JIT and MRP system planning have encouraged the growth of online and wireless technologies to encourage easier operator interface, easier warehouse management systems, and hands-free applications. The radio frequency and voice recognition systems are growing extremely fast, with pay-back-on-investment to occur during small time intervals. Voice recognition terminals are usually wearable, rugged, hands-free, and eyes-free, resist temperature extremes, contain rechargeable batteries, and are usually speaker dependent as opposed to independent recognition for better security. Voice systems integration operates with 2.4GHz spread spectrum radio frequency networks and can interact with proprietary or package WMS/MES. Other characteristics include on-line database file exchanges and data transfer. Applications for voice recognition applications include warehouse automation in order of selection, replenishment, cycle count/inventory levels, shipping verification, and returns. In terms of manufacturing, voice recognition systems [ 115 ] Alan D. Smith and Felix Offodile Information management of automatic data capture: an overview of technical developments Information Management & Computer Security 10/3 [2002] 109±118 have applications in finished goods inspection, inventory and quality control. Future applications of voice recognition and radio frequency systems include remote databases, e-mail access, retail shelf price audit, retail stock check, and voice over IP-LAN access telephone. Of course, benefits of voice recognition and radio frequency systems include elimination of label costs, increased worker safety and comfort and minimal training time, since worker literacy and non-English language skills are not problems with voice recognition systems (Polizzi, B., 1997; Polizzi, T., 1997; Satterfield, 1999). For example, Vocollect, Inc., a Pittsburgh headquartered and privately held company, promotes voice recognition technology and focuses on logistics and industrial implementations. Billions of dollars of goods are picked by voice each year, with ease of warehouse management and radio frequency systems integration as hallmarks of the system. Vocollect’s customers include Wal-Mart, Kroger, and Ford Motor Company, to name a few, and many specifications of voice recognition systems can be found at the company’s Website at www.vocollect.com Typical network configurations in AIDC systems Wireless area networks: an example in mobile communications Typically most AIDC systems are characterized by local area networks (LANs) in bar coding and most other technologies including magnetic stripes, biometrics, and optical character recognition systems. Wireless area networks using time-division multiple access (TDMA) or code-division multiple access (CDMA) is very common in voice recognition radio frequency applications. Especially in voice radio frequency applications, mobile communications are needed since communication may take place in hostile environments, must be quickly deployed, and need to be broadcasted to several locations (Stallings and Van Slyke, 1999). Unfortunately, especially in a manufacturing environment, wireless communications operate in a less controlled environment with more susceptibility to interference, noise, and less security with extremely limited distances, and legal restriction on frequency usage. Therefore, in voice and radio frequency applications the data-transmission rates are relatively slow and limited to short burst data flows. Usually tags or transponders are the data carrier. Antennas with read/write head incorporated into systems with a controller are major components in the line system. Typically, in radio frequency applications unwired LANs are unbounded. Wired LANs, such as those used in bar coding systems, use RS-232 and related formats, with each network node having a unique media address and all nodes listening to the data traffic. The typical wired LAN concept, setting baud rate and other characteristics associated with the software wedge found in most AIDC systems, involves the simple use of software wedge, the simplest method of data collection. This method emulates keystrokes on a standard workstation located between the scanner and CRI, for example, which can reduce data errors and speed up communications many orders of magnitude over human data entry methods. Radio frequency and voice systems are unbounded. Cable transmission range is dependent upon frequency and power, which is largely governed by interference of its physical surroundings. Unfortunately, coverage is generally inconsistent and unpredictable for these types of automatic identification and data capture systems. Of course, due to the unpredictability factors, specifications alone are not enough to ensure system performance. A site survey is frequently needed, especially for large working areas. Generally speaking, typical coverage areas are 2.4GHz for up to 350,000 square feet, and 900Mz for up to 100,000 square feet of floor space. Shared media require rules for sharing, with carrier sense multiple access/collision avoidance (CSMA/ CA) as the standard in voice and radiofrequency application. Since most applications are transactional in nature, usually limited to request and response, the media speed, message size, and transaction rate are the major input characteristics used to model system requirements by standard queuing theory in operations research. Thus, response time and capacity are dependent on transaction rate, not population size: 900MHz. (230kps) for 30 per second and 2.4GHz (1mbps) for 110 per second. Standards are IEEE 802.11 for both direct sequence and frequency hopping techniques for radio media options. System configurations for radio frequency and voice systems are usually based on corporate LANs connecting multiple access points to provide overlapping coverage areas, allowing radio frequency and voice devices to roam freely from area to area. Small areas use peer-to-peer configurations, using an ad hoc configuration as the basic building block containing the radio and wired network connection. Those access points act as a [ 116] Alan D. Smith and Felix Offodile Information management of automatic data capture: an overview of technical developments Information Management & Computer Security 10/3 [2002] 109±118 media bridge and allow a distribution system to interconnect cells to form a network. Usually LAN speeds exceed ratio frequency transmission rate; so access points must filter messages. Only messages for local devices are sent over air and access points must know local devices. Registration protocols include validation of access. Sleeping and roaming devices are entirely based on network association. The typical steps are: the station selects the access point; the access points send probe response and the station selects the best access point; then the station sends a network association request for information to the selected access point. Once the initial connection is established, the outbound management travels to the corporate LAN. However, sleep modes enable conservation of battery life, thus the result is the mobile device is not always on-line and a buffer system is required for transparent operation. Specialized link management systems are required, since wired LANs typically do not deal with intermittence of devices. For example, address is not equal to location; thus location tracking is required for sleeping terminals and inter-access point communication must be supported. IEEE 802.11 defines the over-the-air protocol, but 802.11 does not define access-point-toaccess protocols for distributed systems. In general, 802.11 does not address how access points communicate to each other, message filtering, message buffering, message forwarding, de-registration notification, roaming address issues, authentication process, and full system security and key management issues (Polizzi, B., 1997; Polizzi, T., 1997). Therefore, there are several possible configurations of automatic identification and data capture systems outside the traditional wired LANs, MANs and WANs are extremely customized. General conclusions and IT implications of AIDC systems AIDC systems are essential building blocks for sustainable competitive advantages and superior profitability. The relatively easy application of the software wedge, for example, the simplest method of data collection, which emulates keystrokes on a standard workstation located between the scanner and CRI, can reduce data errors and speed up communications many orders of magnitude over human data entry methods. Although many wired LANs, MANs, and WANs are in typical use in bar coding applications, there are much-needed regulations and standards in the growing wireless network applications of voice recognition and radio frequency applications and their integration configurations. With out these technologies and safeguards, e-commerce as we know it simply would not exist. Because the need for capturing human knowledge and commitment is extremely important, the process of system design should involve non-specialists, not IT specialists alone. This is especially true for new product manufacturability, as well as both incremental and radical innovations, which are greatly aided by the timeliness and accuracy of the associated AIDC systems. AIDC systems integration is essential to effective management of information flows. These concepts are illustrated in a number of related studies. McDermott (1999), for example, suggested that, especially through informal networks, manufacturing generalists on radical new product development teams could help in the above key insights and to implement them within the context of the organization. The research by Swink (1999) reinforces the notion that development team integration processes are important to new product manufacturability. In general, project complexity and design appear to raise the level of difficulty in manufacturing, but development team integration outweighs and may alleviate the negative aspects of these influences. Good management practices as defended by Hout (1999) should provide insight to the complexity and interaction of traditional manufacturing variables with the desire to promote a positive organizational culture of sharing and improvement that is fostered by