In this paper, the modified S-curve membership function methodology 
is used in a real life industrial problem of mix product selection. 
This problem occurs in production planning management 
where by a decision maker plays an important role in making decision 
in a fuzzy environment. As an analyst, we try to find a good enough solution 
for the decision maker to make a final decision. 
An industrial application of FLP through the S-curve membership function 
has been investigated using a set of real life data collected 
from a Chocolate Manufacturing Company. 
The problem of fuzzy product mix selection has been defined. 
The objective of this paper is to find an optimal units of products 
with higher level of satisfaction with vagueness as a key factor. 
This problem has been considered because all the coefficient 
such as technical and resource variables are uncertain. 
This is considered as one of sufficiently large problem involving 29 
constraints and 8 variables. Since there are several decisions 
that were to be taken, a table for optimal units of products respect 
to vagueness and degree of satisfaction has been defined 
to identify the solution with higher level of units of products 
and with a higher degree of satisfaction . It is to be noted 
that higher units of products need not lead to higher degree of satisfaction. 
Optimal units of products and satisfactory level 
have been computed using FLP approach. 
The fuzzy outcome shows that higher units of products need not lead 
to higher degree of satisfaction. 
The findings of this work indicates that the optimal decision is depend on 
vagueness factor in the fuzzy system of mix product selection problem. 
Further more the high level of units of products obtained 
when the vagueness in the system is low.