This paper discusses use of data analysis using  statistical methods for prediction of the Golden Batch in a sample industrial dataset ( Wine manufacturing).The Golden Batch identification is extremely important as it allows us to reach , a plant process operating curve, which delivers the most optimal product mix, along with enhanced  quality of final product. PCA (Principle Component Analysis) and PLS (Partial Least Square Regression) analysis are the tools used for analyzing the data set. These Data sets are the telemeter and archived process/plant parameters from a large number of such product runs. Principal Components Analysis (PCA) is used for dimension reduction and Partial Least Square Regression (PLS) is used for predictive analysis, RMSE is used to get the error between actual and predicted dataset. Business Managers, Process and control engineers can use this method to detect batch to batch error and recipe of the Golden batch which when implemented in other batches, results in optimal plant and product output, thus increasing efficiency of the manufacturing and increasing profits.