Automatic identification and data capture (AIDC) refers to the methods of automatically entering them directly into a computer systems, without human involvement. Technologies typically considered as part of AIDC include bar codes, Radio Frequency Identification (RFID), biometrics (like iris and facial recognition system), magnetic stripes, Optical character recognition (OCR), smart cards, and voice recognition. AIDC is also commonly referred to as “Automatic Identification”, “Auto-ID” and "Automatic Data Capture".

 AIDC is the processor means of obtaining external data, particularly through analysis of images, sounds or videos. To capture data, a transducer is employed which converts the actual image or a sound into a digital file. The file is then stored and at a later time, it can be analyzed by a computer, or compared with other files in a database to verify the identity or to provide authorization to enter a secured system. Capturing of data can be done in various ways; the best method depends on the application.

In biometric security systems, capture is the acquisition of or the process of acquiring and identifying characteristics such as finger image, palm image, facial image, iris print or voice print which involves audio data and the rest all involves video data.

Radio-frequency identification is relatively a new AIDC technology which was first developed in the 1980s. The technology acts as a base in automated data collection, identification, and analysis systems worldwide. RFID has found its importance in a wide range of markets, including livestock identification and Automated Vehicle Identification (AVI) systems because of its capability to track moving objects. These automated wireless AIDC systems are effective in manufacturing environments where barcode labels could not survive.

Nearly all of the automatic identification technologies consist of three principal components, which also comprise the sequential steps in AIDC.

Data encoder:  A code is a set of symbols or signals that usually represent alphanumeric characters. When data are encoded, the characters are translated into a machine-readable code. A label or tag containing the encoded data is attached to the item that is to be identified.

Machine reader or scanner:  This device reads the encoded data, converting them to alternative form, usually an electrical analog signal.

Data decoder:  This component transforms the electrical signal into digital data and finally back into the original alphanumeric characters.

AIDC is used in a variety of areas to support processing and picking throughout a facility:

Assembly: Verification that the right component is selected for assembly

Order Picking: Providing confirmation that the right item and quantity has been picked

Quality control: Directing a worker to a location and/or to perform a task and requiring confirmation of both

Replenishment: Confirmation that the right items are being restocked in the right

Storage: Verification that items are being stored in the correct location

Benefits of AIDC

There are a number of benefits obtained from using AIDC. The main ones include:

·         Control: AIDC technologies help maintain control over inventory from time of receipt through processing and to outbound shipping. Also facilitate and simplify cycle counting.

·         Identification: AIDC makes it easy to identify products or items by stock keeping unit (SKU) number.

·         Inventory Accuracy: Because AIDC enables real-time monitoring of the number of the number of units in a facility, it maintains an accurate count of inventory.

·         Location: Data collected by AIDC yields information about the location of stored products as well as open space available for storage.

·         Picking accuracy: By confirming the accuracy of items picked to fill orders, AIDC reduces errors.

·         Picking efficiency: Because AIDC eliminates paper-based picking, workers can fill more orders faster.

·         Visibility: AIDC aids in monitoring the completion of a process.

Problems with manual collection and data entry:

·         Human errors: When data are collected and entered manually

·         Time factor: Time delay between the occurrence of activities and events and entry of associated data Manual methods are inherently time consuming

·         Labor cost: Cost of full-time the attention of a human worker

AIDC is used in a variety of industries, including:

·         Asset Tracking

·         Automotive

·         Beverage

·         Chemical

·         Consumer goods

·         Electronics

·         Food

·         Healthcare

·         Library

·         Manufacturing

·         Medical devices and equipment

·         Paper

·         Pharmaceutical

·         Plastics

·         Point of sale

·         Retail

·         Transportation and logistics

Warehousing and distribution