Non-oil exports have been seen to be very vital in economic growth and development, especially for developing economics. Despite the poor contribution of non-oil exports to economic growth in Nigeria, this study is inspired by the inconsistencies in empirical findings regarding the connection and effect of non-oil exports on the economy. The objective of the study was to determine the effect of non-oil exports on economic growth in Nigeria. An ex-post facto research design was adopted. The time frame of thirty three (33) years, from 1986 to 2018 was adopted to allow for a large number of observations which will improve the robustness of the results. The data was obtained from the Central Bank of Nigeria (CBN) statistical bulletin of 2017. The Ordinary Least Square (OLS) estimation technique was applied in guesstimating the models. E – views 9.0 was the econometric software used for the analysis. The result revealed that non-oil exports have no significant effect on the growth rate of real gross domestic product, agricultural contribution to real gross domestic product is not significantly affected by exports of non-oil products even though there is evidence of a positive but insignificant correlation between them. Manufacturing capacity utilization is not significantly influenced by variation in Nigeria’s non-oil exports. Non-oil exports are positively associated with manufacturing capacity utilization. Economic growth in Nigeria has not been significantly affected by non-oil exports despite the various non-oil promotion strategies by the government. We recommend that cost and access to financial services for non-oil exporters be moderate or relaxed.

Some recent technological advances in line with the fourth industrial revolution (4IR) are rapidly transforming the industrial sector. This work explores the prospect of robotic and additive manufacturing solutions for mass production in the rail industry. It proposes a dual arm, 12-axis welding robot with advance sensors, camera, and algorithm as well as intelligent control system. The computer-aided design (CAD) of the robotic system was done in the Solidworks 2017 environment and simulated using the adaptive neuro-fuzzy interference system (ANFIS) in order to determine the kinematic motion of the robotic arm and the angles of joint. The simulation results showed the smooth motion of the robot and its suitability to carry out the welding operations for mass production of components during rail car manufacturing. In addition, the ability to fabricate several physical models directly from digital data through additive manufacturing (AM) is a key factor to ensuring rapid product development cycle. Given that AM is embedded in a digitally connected environment, flow of information as well as data processing and transmission in real time will be useful for massive turnout during mass production.