After reading this article you will learn about:- 1. Definition and Concept of Standard Data 2. Types of Standard Data 3. Developing the Standard Data 4. Comparison with Individual Time Studies 5. Use.
Definition and Concept of Standard Data:
Standard data is a catalogue of ‘normal time’ values for different elements of jobs or for minute movements involved in different jobs. This catalogue is prepared by compiling the timings of a number of standard elements.
The necessity of preparing such a time catalogue or standard data arose because (in an industry), generally, similar elements or motions are involved in many jobs. (As an example, drilling holes is a common feature of many machine shop jobs). If time study is to be conducted for every new job, it is naturally wasteful to retime those elements of the new job which are in common with the previously timed jobs.
In such cases it is always economical to use the previously timed and compiled data, called Standard Data. Once the standard data is ready, one requires to list the job elements or the minute motions of an operation, read their times from the standard data catalogue and add them up. The total time thus obtained is an estimate of normal time for a job which can be converted into standard time by adding proper allowances.
Types of Standard Data:
Two types of standard data are used and each is calculated by a different method:
(1) Standard Data (Macrodata):
Standard data (Macrodata) is based upon elements of a job, is also known as ‘Element Standard Data’ and is compiled for a representative group of elements by macroscopic methods. It is for families of jobs and gives normal time for various elements of jobs.
The time values are procured from the actual stop watch (or other) measurements of the tasks (within the job family) carried out previously. This type of data is restricted to particular operations such as machining on lathe, etc.
Operations are broken down into elements; when are then, timed to get a system of data showing normal element time for any and all jobs (completed on that lathe but) having different sizes, materials, feed, speed, depths of cut, and method Of holding the job, etc. Thus compiled large data helps considerably in timing a new job, without going into any more time study. This shortens considerably the amount of time and labour needed to find the standard time for a new job.
(2) Universal Standard Data (Microdata):
Universal standard data (Microdata) is based upon minute movements (i.e., therbligs-reach, carry, hold, etc.) involved in an operation and is compiled by microscopic methods. The methods, lie on the principle that all jobs consist of very little movements called therbligs or in other words, all jobs can be broken into therbligs.
Microdata compiles normal time for a work cycle or a task by analysing the fundamental types of motions (therbligs). This analysis is carried out by frame to frame study of the film of the work cycle recorded by movie camera (Micromotion Analysis). M-T-M (Method-Time-Measurement) and Work factor system are examples of universal standard data.
Macrodata deals with (big) elements and microdata with (minute) motions. Macrodata is collected by time study (say stop watch study) whereas microdata is the result of micromotion study and analysis; but both lead to normal time for a work cycle.
Developing the Standard Data:
The steps involved in developing standard data are given below:
(1) Decide the range of applicability of the standard data.
(2) Break the jobs into elements. There are three types of elements, namely constant elements, variable elements and machine elements.
(3) Obtain or conduct time studies for wide varieties of jobs/job families under different sets of parameters and conditions.
The conditions and characteristics of job, method and equipment, with which time study values are obtained, should be same for the jobs for which this standard data is to be used.
(4) Summarize time studies using a summary form.
(5) Separate constant elements from variable elements.
(6) Using statistical methods calculate the average standard time for constant elements.
(7) Explore the job characteristics leading to variability in elements. A graph can be plotted between normal time value of each element and the dimension of variable (say size of item) and a smooth curve can be fitted (Fig. 9.16).
(8) Compile the standard data, and
(9) Test the data for its correctness and accuracy.
Comparison of Standard Data with Individual Time Studies:
Whether a new job should be timed by a new detailed time study or by adding time values taken from standard data, it depends upon a number of considerations. Conducting a time study for every job may be more economical than compiling a large standard data if the number of jobs involved are a few.
But, if the numbers of jobs to be handled are large, decidedly going for compiling the standard data is economical and advantageous in following respects:
1. Standard data eliminates the need for large number of lime studies.
2. Standard data being collected from a large number of observations is naturally more reliable. It possesses greater accuracy and scope of coverage.
3. Being more accurate, it gives a better estimate of production times.
4. Production schedules for incoming jobs can be better planned.
5. Standard data finds universal application.
Use of Standard Data:
Standard data is used for the following:
(1) To estimate standard time for new jobs of repetitive of non-repetitive nature, quickly and eco-nominally,
(2) For estimating production times for pricing inquires made by customers or for quotation purposes,
(3) In job design, process planning and scheduling,
(4) To measure productive labour for cost checks,
(5) For balancing production operations,
(6) As a realistic basis for incentive plans,
(7) For constructing time formulae,
(8) To calculate the number of automatic machines which an operator can handle effectively.
(9) To find percentage efficiency of manual operations,
(10) In machine (or other) shops where similar jobs are manufactured in different sizes.
(11) To find standards for short runs of custom order products, i.e., for production runs too small to employ time study,
(12) Repair and maintenance,
(13) Building construction,
(14) Machining and assembly,
(15) Typing and clerical jobs,
(16) Filing,
(17) Warehousing,
(18) Packaging, and
(19) Planning team work activities as in garment making industries.