Abstract
The food and beverage (FOODBEV) manufacturing industry is a significant contributor
to global economic development, but it is also subject to major global competition. Manufacturing
technology evolution is rapid and, with the Fourth Industrial Revolution (4IR), ever accelerating.
Thus, the ability of companies to review and identify appropriate, beneficial technologies and forecast
the skills required is a challenge. 4IR technologies, as a collection of tools to assist technological
advancement in the manufacturing sector, are essential. The vast and diverse global technology
knowledge base, together with the complexities associated with screening in technologies and the
lack of appropriate enablement skills, makes technology selection and implementation a challenge.
This challenge is premised on the knowledge that there are vast amounts of information available on
various research databases and web search engines; however, the extraction of specific and relevant
information is time-intensive. Whilst existing techniques such as conventional bibliometric analysis
are available, there is a need for dynamic approaches that optimise the ability to acquire the relevant
information or knowledge within a short period with minimum effort. This research study adopts
smart knowledge management together with artificial intelligence (AI) for knowledge extraction,
classification, and adoption. This research defines 18 FOODBEV manufacturing processes and adopts
a two-tier Natural Language Processing (NLP) protocol to identify technological substitution for
process optimisation and the associated skills required in the FOODBEV manufacturing sector in
South Africa.