“An Empirical Investigation on Production System and Automation Technology of Bonn Manufacturing Biscuit Industry” A Study of the Biscuit Manufacturing Industry in Ludhiana (Punjab).

The biscuit manufacturing industry has changed dramatically over the past three decades, becoming more complex and multifaceted, particularly the costing process, which has decreased significantly due to the introduction of advanced automated systems where most of its sub-processes are integrated into automation. This study provides an in-depth analysis and detailed structured review of the production system applied by Bonn Biscuit, systematically evaluating the key automation technologies that are critical to improving total manufacturing productivity. Drawing on extensive primary data, a series of citation-rich figures from numerous secondary references, and descriptive parameters, we aim to provide a balanced analysis across the study boundaries.
The results from this project show that the business has efficiently set up a strong and repetitive production system, strongly backed by numerous advanced automation machines, industrial-size mixers, tunnel ovens, conveyor belts, and packing. The ultimate solution to meet such needs now and for the foreseeable future is an automated production line manufacturing system, which can not only optimise but also increase the value of money spent, translating into better quality and quantity of production output.

Biscuit manufacturing, automation, production system, food processing, industrial technology

1. Badri, M. A., & Davis, D. (2018). Manufacturing strategy and organizational performance in food processing industries. International Journal of Operations & Production Management, 38(6), 1456–1475. [Google Scholar] [Crossref]

2. Basu, P., & Roy, S. (2019). Impact of automation on productivity in the Indian FMCG manufacturing sector. International Journal of Production Research, 57(12), 3812–3825. [Google Scholar] [Crossref]

3. Bozarth, C. C., & Handfield, R. B. (2020). Introduction to operations and supply chain management (5th ed.). Pearson Education. [Google Scholar] [Crossref]

4. Campilho, R. D. S. G., & Silva, F. J. G. (2023). Industrial process improvement by automation and robotics. Machines, 11(11), 1011. [Google Scholar] [Crossref]

5. Chopra, S., & Meindl, P. (2021). Supply chain management: Strategy, planning, and operation (8th ed.). Pearson Education. [Google Scholar] [Crossref]

6. Dinlersoz, E., & Wolf, Z. (2024). Automation, labor share, and productivity: Plant-level evidence from U.S. manufacturing. Economics of Innovation and New Technology, 33(4), 604–626. [Google Scholar] [Crossref]

7. George, A. S. (2024). Leveraging Industry 4.0 for efficiency gains in food production. Partners Universal International Research Journal, 3(1), 86–108. [Google Scholar] [Crossref]

8. Gunasekaran, A., Subramanian, N., & Rahman, S. (2017). Agile manufacturing practices and the role of information technology in improving manufacturing performance. International Journal of Production Economics, 189, 1–10. [Google Scholar] [Crossref]

9. Haque, R. (2023). Automation in manufacturing: A systematic review of advanced time management techniques to boost productivity. American Journal of Scholarly Research and Innovation, 2(1), 50–78. [Google Scholar] [Crossref]

10. Heizer, J., Render, B., & Munson, C. (2020). Operations management: Sustainability and supply chain management (13th ed.). Pearson Education. [Google Scholar] [Crossref]

11. Krajewski, L. J., Ritzman, L. P., & Malhotra, M. K. (2021). Operations management: Processes and supply chains (13th ed.). Pearson Education. [Google Scholar] [Crossref]

12. Liu, H., Wang, Y., & Yan, Z. (2024). Artificial intelligence and food processing firms’ productivity: Evidence from China. Sustainability, 16(14), 5928. [Google Scholar] [Crossref]

13. Luthra, S., Govindan, K., Kannan, D., & Mangla, S. K. (2019). Identifying barriers to sustainable supply chain management in emerging economies: A case study of the Indian food industry. Journal of Cleaner Production, 142, 2673–2687. [Google Scholar] [Crossref]

14. Palčič, I., & Prester, J. (2024). Effect of usage of industrial robots on quality, labor productivity, exports and environment. Sustainability, 16(18), 8098. [Google Scholar] [Crossref]

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