Intended learning outcomes: Produce an overview on Automatic identification and data capture (AIDC), bar codes, RFID. Describe the principle and issues of rough-cut data collection.
Manual shop floor data collection, which uses operation cards, parts requisitions or picking lists, is slow, particularly for short operation times. Prompt recording of transactions requires additional administrative personnel in the job shops. In addition, there is a great danger of erroneous data entries. For these reasons, one tries to record shop floor data automatically.
Automatic identification and data capture (AIDC) is a set of technologies that collect data about objects and send these data to a computer without human intervention. Examples are:
Bar codes: Information is coded in a combination of thick and thin lines. A light-sensitive pen reads and transfers this information to a computer.
Radio Frequency Identification (RFID) is an automatic identification technique, relying on storing and remotely retrieving data using RFID tags as transponders. A transponder is an electronic transmitter. An RFID tag can be attached to or incorporated into an object product, animal, or person for the purpose of identification using radio waves. Electronic product codes (EPCs) are used with RFID tags to carry information on the product to support warranty programs.
Badges: A badge is generally a card with a magnetic strip. The strip contains information that can be read with a device and sent to a computer.
The solutions developed thus far focus on the following techniques:
- The use of bar codes or RFID to identify the operation or the allocation directly on the shop order routing or picking list. The use of operation and parts requisition cards is reserved for unplanned issuances or operations. The human operator is identified by means of his or her badge. This is usually the same magnetic card used for measuring the employee’s work hours.
- A clock in the data processing system runs together with the transaction and determines the actual time used through automatic recording of the start time and end time for the operation. The difference between start time and end time yields time used, or the actual load. However, an unplanned issued quantity must still be recorded by hand. With this, a small source of error remains. In contrast to the grocery trade, for example, issuances in industrial production are not in units; under certain circumstances a large set of units may be issued instead.
- Linking the data collection system to sensors that automatically count the goods produced or taken from stock. Such systems can be valuable for any kind of line production as well as for CNC or robot-supported production.
Rough-cut data collection takes into account the fact that the results of the entire operation are more important than the success of a single order.
The costs of data collection must stand in healthy relation to the benefits of data collection itself — namely, better control of the production and the procurement process. This condition is difficult to meet for all extremely short operations where the administrative time needed to record the operation is in the same range as the operation time itself:
- Collective data collection for entire groups of short operations is possible. However, this requires the recording of the operations represented by this group or by collective data collection, so that the time recorded can ultimately be distributed among the individual operations according to a key. Since we often cannot determine this grouping in advance, it must be recorded at some point during the process. This quickly results in a quantitative data collection problem.
For group work, the recording of the actual processing time is often possible only for rough-cut operations, that is, for a combination of individual operations. This can only deal with all participating persons together and includes interoperation times as well.
- This combination may correspond to a rough-cut operation, which is sufficient for long- or medium-term planning. It may, however, be even rougher and cover operations for multiple orders, as was shown above for short operations. In all these cases, accounting for individual orders is questionable. Instead, accounting for the entire group over one time period replaces this; the presence times of the group members and the actual times for the rough-cut jobs delivered are placed in relation to the corresponding standard times. This is also precise enough for payroll purposes (compensation); moreover, “success” is measured not only in terms of actual processing times, but also includes interoperation times.
- For the detailed operation, it is not possible in this way to compare the standard load to the actual load. In the case of well-tuned production — or procurement — with frequent order repetition this is actually not necessary, not even for cost estimation. The measure of success becomes the efficiency rate of the entire group (which is all the standard load divided by all the actual load; see Section 1.2.4), and not the costing of single jobs.
For machine-oriented work centers, especially for NC, CNC, and flexible manufacturing systems (FMS), as well as for automated stock transport systems, the solution for the future lies in inexpensive sensors and in the link to the computer that performs shop floor control.
For manual work centers, it is important that the workers do not need to leave their posts for data entry purposes and that they do not need to enter their identification anywhere. The company can introduce inexpensive data collection units that make use of bar code readers or transponders. These data collection units should be located right at the workstation and linked to an intranet. The employee badge identifies the individual employee.
There is an observation with all the techniques used for measuring job shop processes: Collection of excessively detailed data can influence processes to such an extent that without measurement the outcome as a whole would be different. This type of measurement falsifies the process (by slowing it down, for example) and should not be implemented.
Course section 15.3: Subsections and their intended learning outcomes
15.3 Order Monitoring and Shop Floor Data Collection
Intended learning outcomes: Describe recording issues of goods from stock and completed operations. Produce an overview on progress checking, quality control, report of order termination, and automatic and rough-cut data collection.
15.3.1 Recording Issues of Goods from Stock and the Backflush Technique
Intended learning outcomes: Differentiate between unplanned issuances and planned issuances. Produce an overview on backflush technique and critical point backflush technique.
15.3.2 Recording Completed Operations, and the Demonstrated Capacity
Intended learning outcomes: Describe the data collected after the completion of an operation. Identify demonstrated capacity and downtime.
15.3.3 Progress Checking, Quality Control, and Report of Order Termination
Intended learning outcomes: Produce an overview on progress checking, quality control and the quality control sheet. Identify the anticipated delay report and the order termination report.
15.3.4 Automatic Data Collection and Rough-Cut Data Collection
Intended learning outcomes: Produce an overview on Automatic identification and data capture (AIDC), bar codes, RFID. Describe the principle and issues of rough-cut data collection.
