Balaji Wafers And Namkeen Case Study

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Balaji Wafers & Namkeen is founded by Virani family in the year of 1976. The company started with a small canteen in the local movie theater. To expand their business they set up cooking pans at their home itself. As the business started booming, Virani family set up their first factory in the year 1989; but this was spiced up with never ending technical difficulties. Though new machinery resolved these difficulties and also helped them to expand. Balaji to face the fierce competition efficiently and extend their reach they continuously bought in new technology. In 2003 they raised their capacity of processing potatoes to 5000kgs per hour with fresh machinery imported from Germany. As the demand increased even more, they took a step further …show more content…
While as a customer, I consider products with the highest quality and quantity, after all we live in 21st century where resources should be allocated to the highest efficiency. So in future I hope to play the role of a manufacturer and consumer as well, I choose to explore machine’s efficiency in my Math IA. Further I selected to explore machines used in wafer making. With the help of Balaji Wafers and Namkeen I offer to calculate the extent of perfection that the machinery is capable of with the use of statistics and modeling. Thus I choose to take wafers packing machine, Ishida Atlas to …show more content…
But after looking at the set of data, I thought to explore the performance of this machine further. Thus to explore the extent of perfection by this machine. To investigate further I formulated a problem, follows.
What is the probability of me picking up a ‘Simply Salted’ packet from the whole produce of ‘Simply Salted’ wafers in the month of February with the least amount of wafers contained in it?
Through this question, I could explore the efficiency of this machine. After I obtain the probability, I could use this probability to compare the probability of other months and set a standard. This could help Balaji to set their export standards.
I.e. – Lower the average probability means better efficiency.

To start investigating I plotted the provided data. I plotted the ingredient weight on the x-axis and the amount of packets produced for a specific weight on the y-axis. The result is shown below. The outcome was a bell shaped curve as the data was more centric, with the top most point being on (16.54, 320000). Which is also the mode of the given

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