Opportunities and Challenges for Implementing Automation among Selected SMEs of Food Manufacturing Industry

Abstract

The key objective of automation is to minimize human intervention in the workplace. Automation improve not only production productivity but also strengthen company competitiveness. This research presents an empirical study that uses qualitative research methods for investigating the automation level among the Small Medium Enterprises (SMEs) in the food manufacturing industry. This research investigates the opportunities of applying automation and identifying the challenges confronted the SMEs in realizing automation in the food manufacturing process. Semi-structured interviews were conducted to examine the level of opportunities and challenges of implementing automation among selected food manufacturing SMEs. The findings show that automation enhances productivity, improve product quality and increase manufacturing process efficiency. However, this research also reveals that costing, training and lack of flexibility were the impeding factors for the studied SMEs to implement a fully automated process in food production. Despite the constraints, the companies strive to automate parts of their food production processes by using second-hand automation machines instead of investing in brand new automation equipment, hiring expert technicians and implementing the lean principle. This research presents several innovative recommendations for food manufacturing SMEs to automate their production process in order to increase competitiveness in the industry.

‘Mover’ a Centralized Job Scheduling and Rapid Routing Solution

“…There are various modes of implementing automation in manufacturing sector (2019; Radke et al 2020), one of its mode is automating logistics within the industrial premises for material handling and transportation (Zsifkovits et al 2020). Automated Guided Vehicles (AGV) are used for material handling in most of the automated industries nowadays, the mobile robot moves autonomously and take the raw product for manufacturing from one node to another (Saffar et With the invent of machine learning based smart autonomous manufacturing systems, AGV's are also made capable of taking decisions based on set parameters.…”