ABSTRACT
The ongoing COVID-19 pandemic has manifold disastrous effect on different service and production sectors, and manufacturing industries are no exception. Emerging technologies (ETs) can play a pivotal role in reviving these ailing manufacturing industries. However, the cost of implementing and operating ETs is a prime concern. Nonetheless, the important attributes that will minimize the cost and harness the advantages of the technology are still to be explored. To address this gap, this research seeks to examine the critical attributes required for the effective and efficient deployment of ETs. At first, a detailed literature review was conducted to identify and sort the attributes influencing the effective use of ETs in manufacturing industries. After that, Fuzzy-TODIM (Portuguese abbreviation of "Interactive and Multi-Criteria Decision Making ") method was used to rate the importance of the attributes. The result reveals that, "Inventory and Resource Management " tops the attribute list responsible for exploiting the optimal usage ETs, followed by "Development of Skilled Workforce " and "Supplier and Service Management Capability, " respectively. This paper will assist industry professionals not only in using ETs but also getting the best yield from them strategically and practically.
ABSTRACT
The novel corona virus (COVID-19) has badly affected many countries (more than 180 countries including China) in the world. More than 90% of the global COVID-19 cases are currently outside China. The large, unanticipated number of COVID-19 cases has interrupted the healthcare system in many countries and created shortages for bed space in hospitals. Consequently, better estimation of COVID-19 infected people in Sri Lanka is vital for government to take suitable action. This paper investigates predictions on both the number of the first and the second waves of COVID-19 cases in Sri Lanka. First, to estimate the number of first wave of future COVID-19 cases, we develop a stochastic forecasting model and present a solution technique for the model. Then, another solution method is proposed to the two existing models (SIR model and Logistic growth model) for the prediction on the second wave of COVID-19 cases. Finally, the proposed model and solution approaches are validated by secondary data obtained from the Epidemiology Unit, Ministry of Health, Sri Lanka. A comparative assessment on actual values of COVID-19 cases shows promising performance of our developed stochastic model and proposed solution techniques. So, our new finding would definitely be benefited to practitioners, academics and decision makers, especially the government of Sri Lanka that deals with such type of decision making. © 2022 the Author(s), licensee AIMS Press.