Analysis of Mass Personalization Manufacturing with Industry 4.0 in the Production of Beverage

Lim, Shan Pin (2023) Analysis of Mass Personalization Manufacturing with Industry 4.0 in the Production of Beverage. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

It is well known that "Industry 4.0," a futuristic phrase, was coined to describe a new industrial revolution. However, since Industry 4.0 is such a broad and complex idea, businesses are encountering a wide range of difficulties as they attempt to apply it. Additionally, Industry 4.0 has ushered in a time of astounding developments thanks to digital transformation. Nearly all company sectors are forced to satisfy individual customers’ needs at a price that is comparable to mass manufacturing due to intense market rivalry and high consumer expectations. As a result, mass personalisation as a new manufacturing paradigm is established. Hence, this project’s results first aim to show how one can map all of a use case’s Industry 4.0 components and processes into an Industry 4.0 model which is the Reference Architecture Manufacturing Industry 4.0. (RAMI 4.0). The results have successfully assigned the technologies present in the use case to the corresponding levels and layers in the RAMI 4.0 framework based on their area of usage throughout the production line. The results then serve as a blueprint for individuals looking to manage their networked device collections on the use case. Moreover, this project outcome also serves to analyse the performances of the use case by utilising a performance indicator known as Overall Equipment Effectiveness (OEE). The simulation model of the line is modified, validated through Analysis of Variance test and utilised to obtain the parameters required for the analysis. Different constraints are applied to the simulation runs to represent one off production, batch production, mass production and also mass personalization. Results showed that with the same 350 bottles output, mass production and mass personalization had the shortest total production times which are 476.10 mins and 491.94 mins respectively and lower downtime which are 139.37 mins and 130.11 mins respectively, but their OEE was lower with a value of 43.88% and 44.84% due to decreased availability and performance efficiency metrics. In the other hand, one off production without personalization had the highest OEE with a value of 65% but required significant longer production time which is 1468.59 mins. Moreover, it is noted that the implementation of personalization is justified despite a slight decrease in OEE if it meets the specific needs and requirements of the business. Hence, it is recommended that businesses should carefully consider their production goals and customer demands when choosing a suitable production method. Future work could aim to optimize the use case by updating sensors and actuators for real-time integration and addressing high work in progress through layout optimization, reducing changeover times, and improving efficiency of the use case.