The standard data method assumes that many movements are common, known and sleepy and already measured. The challenge of this method is to create a standard database on all these recurring and repetitive tasks.
It thus presents for advantage:
- Provide a catalogue of movement and time.
- Allows you to focus on improvements.
- Provides a fast and reliable method for evaluating standards.
- Eliminates the problem of subjective evaluation of operator performance.
Building the Database
1. Choose the working perimeter
It is hard to believe that all operations can be catalogued. In practice, the data bank will be all the more effective as it must be restricted to a reasonable number of tasks, for example related to a sector of activity or a type of product.
You have to prioritize and choose a perimeter. It can be ” geographical ” (for example only for the packaging workshop), or a perimeter linked to tasks that can be found similar throughout the factory.
2. Cut out the work in sub-task
It is necessary to choose the different ” movements ” that one will analyze. For this, we cut out the different operations in basic movements adapted to the situation. Thus, for a packing operation, if on the factory, all the cartons and all the contents are identical, the movement can be defined in one block rather than to cut it into a subset like ” grab the carton “, ” position the Carton” …
3. Choose the data sources
The reliability of the data will be all the greater as the accumulation of it will be done over time and with the maximum of reading. The source and factors taken into account, such as effort or working conditions, are conditions for reliability.
Methods of measurement
Over time, a data bank can be built on the basis of the chronometric measurements made (Chrono Analysis or Sampling analysis). This method has the problem of being restrictive in the sense that you can only measure what you see. For example, if a person walks 10m, we will be able to make measurements and return them to the data bank. However, in case the person walks 25m, there will be no data.
It is better to develop mathematical models to infer data. For example, you can do different walking measurements for 10, 20, 30, or 40 metres and determine whether there is a linear relationship. Once the model is established, it will be possible to deduct all the walking lengths and have an efficient data bank.
The data bank can rely on time estimation tables (table MTM, Most…). Thus, one can cut the work into a sub-group of movement and build the table of the associated times. Simpler and less costly to implement, this method is also more effective than chronometric measures for small movements.
It is possible to use external data to build the data bank. Some sectors have developed data banks taking into account the specificity of the sector. For example, General Sewing Data is found for the seam trades. This technique has the advantage that these data banks are updated regularly and thus allow to have reliable data.
4. Determine the factors
Depending on the activities, there are many influences that need to be determined. For example, for walking, the distance and weight factors transported are to be taken into account.
G. Kanawary (1992) – Introduction to work study
O. D. Salih, A. Raouf, J. D. Campbell (1999) – Planning and control of maintenance systems: Modeling and analysis
G. Salvendy (2001) – Handbook of Industrial engineering: Technology and Operations management