Determinants of Industrial Automation Adoption in Bangladesh’s Manufacturing: An Empirical Study
Mohammad Mohidul Islam *
School of Economics, Huazhong University of Science and Technology, Wuhan, China.
*Author to whom correspondence should be addressed.
Abstract
This study analyzes the potential factors that determine the adoption of industrial automation in the manufacturing industry in Bangladesh using modern time series econometric methodologies from 1991 to 2022. The empirical results indicate that the production volume is a potential determinant of automation adoption in the manufacturing sector. However, increasing per capita income levels may limit the adoption of automation. On the other hand, rapid industrial automation is expected to increase import volume while necessitating significant investment. This study suggests that although automation will improve export performance, further improving manufacturing export performance through product diversity is a prerequisite for an import-dependent economy, as frequent adoption of automation will result in increased import volume, and higher exports will help to stabilize the country’s trade imbalance. For responsible implementation of automation technology in Bangladesh, it is necessary to prioritize strategic planning, invest in education and training, and implement supportive policies. Collaboration between the government, businesses, and educational institutions is crucial to creating a favorable environment for adopting automation technologies. By doing so, Bangladesh can balance the challenges and opportunities for industrial automation utilization in manufacturing industries.
Keywords: Industrial automation adoption, manufacturing industries, income, productivity
How to Cite
Downloads
References
Rüßmann M, Lorenz M, Gerbert P, Waldner M, Justus J, Engel P, et al. Industry 4.0: The future of productivity and growth in manufacturing industries. Boston consulting group. 2015;9(1), 54-89.
Wisskirchen G, Biacabe BT, Bormann U, Muntz A, Niehaus G, Soler GJ, et al. Artificial intelligence and robotics and their impact on the workplace. IBA global employment institute. 2017;11(5):49-67.
Acemoglu D, Restrepo P. Artificial intelligence, automation, and work. SSRN Journal. 2018:(197-236). DOI: 10.2139/ssrn.3098384
Kazmi SJA, Abbas J. Examining the impact of industry 4.0 on labor market in Pakistan. Handbook of smart materials, technologies, and devices: applications of Industry 4.0. 2020;1-11.
Manyika J, Chui M, Miremadi M, Bughin J, George K, Willmott P, et al. A future that works: AI, automation, employment, and productivity. McKinsey global institute research [tech rep]. 2017;60:1-135.
Kurt R. Industry 4.0 in terms of industrial relations and its impacts on labour life. Procedia Comput Sci. 2019;158:590-601. DOI: 10.1016/j.procs.2019.09.093
Rodrik D. New technologies, global value chains, and developing economies. National Bureau of Economic Research; 2018 (No w25164).
Bhuiyan AB, Ali MJ, Zulkifli N, Kumarasamy MM. Industry 4.0: Challenges, opportunities, and strategic solutions for Bangladesh. Int J Bus Manag Future. 2020;4(2):41-56. DOI: 10.46281/ijbmf.v4i2.832
Swazan IS, Das D. Bangladesh’s emergence as a ready-made garment export leader: An examination of the competitive advantages of the garment industry. Int J Glob Bus Compet. 2022; 17(2):162-74. DOI: 10.1007/s42943-022-00049-9
Javaid M, Haleem A, Singh RP, Suman R, Gonzalez ES. Understanding the adoption of Industry 4.0 technologies in improving environmental sustainability. Sustain Oper Comput. 2022;3:203-17. DOI: 10.1016/j.susoc.2022.01.008
Bührer C, Hagist C. The effect of digitalization on the labor market. The Palgrave Handbook of managing continuous business transformation. 2017; 115-37.
Yang F, Gu S. Industry 4.0, a revolution that requires technology and national strategies. Complex Intell Syst. 2021;7(3):1311-25. DOI: 10.1007/s40747-020-00267-9
Farole T, Cho Y. Bangladesh jobs diagnostic. Washington: World Bank, DC eBooks; 2017.
Kergroach S. Industry 4.0: new challenges and opportunities for the labour market. Foresight STI Gov. 2017;11(4):6-8. DOI: 10.17323/2500-2597.2017.4.6.8
Acemoglu D, Restrepo P. The wrong kind of ai? artificial intelligence and the future of Labour Demand. Camb J Reg Econ Soc. 2020;13(1):25-35. DOI: 10.1093/cjres/rsz022
Hahn CH, Narjoko DA. Impact of globalization on labor market. [retrieved May 31, 2019]. Economic Research Institute for ASEAN and East Asia [ERIA Research Project report]; 2013.
Falck O. Introduction to the issue on European labor markets: how can we effectively manage technological and structural change? In: Econ Pol Forum (Vol. 23, No. 5, pp. 3-3). Munich: CESifo GmbH; 2022.
Autor DH. Why are there still so many jobs? The history and future of workplace automation. J Econ Perspect. 2015; 29(3):3-30. DOI: 10.1257/jep.29.3.3
Bessen J, Goos M, Salomons A, van den Berge W. What happens to workers at firms that automate? Rev Econ Stat. 2019;1-45. DOI: 10.1162/rest_a_01284
Bordot F. Artificial Intelligence, robots and unemployment: Evidence from OECD countries. J Innov Econ Manag. 2022;N°(1):117-38. DOI: 10.3917/jie.037.0117
Kromann L, Skaksen JR, Sørensen A. Automation, labor productivity and employment–a cross country comparison. CEBR. Copenhagen Business School; 2011.
Barcia de Mattos F, Dasgupta S, Jiang X, Kucera D, Schiavone AF. Robotics and reshoring: employment implications for developing countries. International Labour Organization; 2020.
Anzolin G. Automation and its employment effects: A literature review of automotive and garment sectors. JRC Working Papers Series on Labour, Education and Technology, No. 2021/16. Joint Research Centre (JRC), Seville: European Commission; 2021.
Parschau C, Hauge J. Is automation stealing manufacturing jobs? Evidence from South Africa’s apparel industry. Geoforum. 2020;115:120-31. DOI: 10.1016/j.geoforum.2020.07.002
Dai Z, Niu Y, Zhang H, Niu X. Impact of the transforming and upgrading of China’s labor-intensive manufacturing industry on the labor market. Sustainability. 2022; 14(21):13750. DOI: 10.3390/su142113750
Aghion P, Antonin C, Bunel S, Jaravel X. What are the labor and product market effects of automation? New evidence from France. CEPR Discussion Pap. 2020; (DP14443).
Barbieri L, Mussida C, Piva M, Vivarelli M. Testing the employment impact of automation, robots and AI: A survey and some methodological issues. SSRN Journal. 2019;12612. DOI: 10.2139/ssrn.3457656
Aghion P, Antonin C, Bunel S, Jaravel X. The effects of automation on labor demand: A survey of the recent literature. In: Robots and ai. Routledge. 2022; 15-39.
Chiacchio F, Petropoulos G, Pichler D. The impact of industrial robots on EU employment and wages: A local labour market approach.. Bruegel working paper. 2018;2018(02).
Kromann L, Malchow-Møller N, Skaksen JR, Sørensen A. Automation and productivity— A cross-country, cross-industry comparison. Ind Corp Change. 2020;29(2):265-87. DOI: 10.1093/icc/dtz039
Jung JH, Lim DG. Industrial robots, employment growth, and labor cost: A simultaneous equation analysis. Technol Forecasting Soc Change. 2020;159: 120202. DOI: 10.1016/j.techfore.2020.120202
Johansen S. Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica. 1991;59(6):1551-80. DOI: 10.2307/2938278
Engert W, Hendry S. Forecasting inflation with the M1-VECM: Part Two (No. 1998-6). Bank of Canada; 1998.
Engle RF, Granger CWJ. Co-integration and error correction: Representation, estimation, and testing. Econometrica. 1987;55(2):251-76. DOI: 10.2307/1913236