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OCTOBER 19-20, 2012 - YMCA University of Science & Technology

OCTOBER 19-20, 2012 - YMCA University of Science & Technology

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Proceedings <strong>of</strong> the National Conference on<br />

Trends and Advances in Mechanical Engineering,<br />

<strong>YMCA</strong> <strong>University</strong> <strong>of</strong> <strong>Science</strong> & <strong>Technology</strong>, Faridabad, Haryana, Oct <strong>19</strong>-<strong>20</strong>, <strong>20</strong>12<br />

3.3. Gap analysis<br />

Following work has been carried out to identify GAP:<br />

1. Comparison <strong>of</strong> existing status with the requirements <strong>of</strong> lean manufacturing.<br />

2. Using expert opinion and clarify the gap as HIGH , MEDIUM, or LOW<br />

Where<br />

HIGH – very large gap, need immediate action.<br />

MEDIUM – Large gaps, urgent action required.<br />

LOW- insufficient gap does not require immediate action.<br />

Table II shows the gap between the existing status and requirements <strong>of</strong> lean manufacturing as per above<br />

classification.<br />

4. Development <strong>of</strong> generalized approach<br />

For developing a generalized approach, the cost associated with a provision has been taken as the most important<br />

input. In the development <strong>of</strong> approach experts play important role. Experts are employees <strong>of</strong> the organizations, in<br />

all , a total <strong>of</strong> ten experts consisting <strong>of</strong> two managers, three supervisors, three operators, one inspector and one<br />

mechanic were selected. The experts were briefed about the findings <strong>of</strong> the root cause <strong>of</strong> wastes for the purpose<br />

<strong>of</strong> getting their feedback.<br />

A. Factors and Parameters influencing Development <strong>of</strong> a Generalized Approach<br />

The experts after discussion and brain storming conversed on the following factors influencing development <strong>of</strong> a<br />

generalized approach.These qualities scores were then converted into quantative score using the scoring scale<br />

and the number <strong>of</strong> responses to a choice. The scoring scale is shown in table III<br />

Table IV depicts the summary <strong>of</strong> responses received from experts. In this table number <strong>of</strong> experts responding to<br />

a particular choice in a factor or parameter has been compiled from the individual responses <strong>of</strong> the experts.<br />

Number <strong>of</strong> responses to a particular choice has been, then, multiplied by the score <strong>of</strong> that choice in every factor<br />

as listed in table III. Those weighted scores have been summed up against each generalized control or provision<br />

and have benn listed in the last column <strong>of</strong> table IV. The highest score <strong>of</strong> a provision in the column, depicts that<br />

considering all the above eight factors and parameters, this generalized provision should be taken up for<br />

implementation.<br />

847

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