UJ’s Prof Arnesh Telukdarie on the need for business systems optimisation for the 4IR
Date: Sep 3, 2021 | 4th Industrial Revolution, Faculty of Engineering and the Built Environment, News
The modern business has adapted and survived based on evolutionary demands.
Demands to change in the form of environmental challenges, safety challenges, skills challenges, automation challenges and “other” significant challenges. The modern business has adopted and will continue to adopt to survive. The Fourth Industrial Revolution (4IR) delivers various tools in the form of automation and systems, proving to be both another challenge and an opportunity for contemporary business.
This was the key research focus of Arnesh Telukdarie, a Professor and Research Chair at the Postgraduate School of Engineering Management, at the University of Johannesburg (UJ).
Titled ‘Business systems optimisation for the Fourth Industrial Revolution’, Prof Telukdarie reflected on how companies sought to implement 4IR strategies, systems, solutions and tools to maximize business sustainability.
“Implementation of 4IR proves to be a multi-dimensional challenge, including skills limitations, resources constraints, time challenges, design knowledge, business readiness, implementation constraints, change management, and general understanding of complex technological landscapes. The ability to decide on the right system and quantify business impacts is a challenge.”
Prof Telukdarie’s inauguration was held at the University’s Council Chambers, Madibeng Building, Auckland Park Kingsway Campus and streamed online on Wednesday 01 September 2021.
At UJ he has worked on various initiatives including GES4.0, Accenture work readiness, Devland 4IR facility and the News 24 building. He has completed various industrial projects and currently has a research chair with the Food and Beverage Manufacturing SETA. He has secured prestigious internal funding such as the Newton fund via the Royal Academy of Engineers.
During his address, Prof Telukdarie indicated that an analysis of global literature provides insights into 4IR tools and systems, implementation challenges, limitations, and current maturity.
“Global literature on implementation strategies and case studies are maturing but the implementation of 4IR is not a one size fits all. Very importantly information is king, and forecasting is ideal in order to understand business impact. The development of the right design and implementation strategies are important to business executives investing in 4IR.”
Prof Telukdarie also serves as an adjunct Professor at Johns Hopkins University and as a visiting Scholar at London South Bank University. The research he presented during his address attempts to resolve the multifaceted challenges facing a modern business.
“The approach is based on the digital representation of activities performed at the business. The researchers adopt Business Process (BP) science to replicate the detailed activities of the business. BP’s are ideally positioned to represent business activities ready for systems implementation and also provide sufficient details of business activities, so as to render simulation models of 4IR implementation. The research team advances the adoption of BP for business simulation and optimisation. Various global best practices are included in the model constructs of a business with all variables affecting businesses included.”
His research interest focuses on modelling and optimization of businesses, including large corporates, and Small, Medium and Micro enterprises. The research portfolio presents an expanded adoption of the modelling of businesses to include Triple Bottom Line predictions and optimisation, based on technological and other determinants.
“The models are expanded into data generation and analysis tools advancing the adoption of data analytics for strategic management of the business. This includes the adoption of the data to determine data centric Key Performance Indicators (KPI’s).”
He concluded: The research and future work include the collective interaction of the multiple models in a network of models, adapted to predict nodal supply and demand. The research team also adopts the design principles of this research in a macro design of 4IR maturity implementation.
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