Prioritization of barriers for Industry 4.0 adoption in the context of Indian manufacturing industries using AHP and ANP analysis

Abstract

Industry 4.0 has gained enormous attention from the academic and industrial community in recent years and lacks empirical research. This makes it imperative to reveal and perceive barriers for Industry 4.0 adoption. The article explores the prioritization of industry 4.0 adoption barriers in the context of Indian manufacturing industries utilizing the Analytical hierarchy process (AHP) and Analytic network process (ANP) analysis and validation through sensitivity analysis state-of-the-art in the area of Industry 4.0. According to the literature review and validated by specialists from industries and academicians, 15 barriers have been considered and categorized into four criteria. Both models provide enterprises with a direct and trouble-free approach to prioritize the barriers for Industry 4.0 adoption. The two approaches illustrated the impression of being influential in selecting industry 4.0 adoption barriers with high and low priority. The two methods achieved almost similar findings. Adopting such AHP and ANP models on Industry 4.0 barriers in manufacturing industries will enable executives, specialists, and decision-makers of Industry 4.0 to effectively categorize these barriers and concentrate on critical barriers when implementing Industry 4.0 in their respective industries.

Exploring the influencing factor of urban industry development: An order parameter method

“…In terms of technology, scholars have made the following research contributions. Nimawat and Gidwani (2021) used analytic hierarchy process and network analysis to explore the priority of adopting industry 4.0 in the Indian industry. Dalenogare et al (2018) studied the potential impact of "industry 4.0" related technology on industrial performance.…”

Investigating the barriers to Quality 4.0 adoption in the Indian manufacturing sector: insights and implications for industry and policy-making

“…This variability facilitates deep exploration into the impact of factors like industry type, firm size and regional disparities on Quality 4.0 adoption. The dynamic environment, shaped by ongoing digital transformation and governmental "Make in India" strategies, further allows for studying policy and infrastructure-related effects (Nimawat and Gidwani, 2021). We can better understand barriers and facilitators in transitioning economies by exploring these factors.…”

The Development Trends of Computer Numerical Control (CNC) Machine Tool Technology

“…However, the analysis of data from the literature lacks the demands of real industrial development [6]. In the context of Industry 4.0, to determine the development sequence of the manufacturing industry, multi-criteria decision-making methods such as the Analytic Hierarchy Process (AHP) and the Analytic Network Process (ANP) can be used for analysis and validation to obtain the order of priorities [7,8]. Alternatively, the more accurate fuzzy analytic hierarchy process (FAHP) [9] can be employed to calculate weights [10], construct an importance ranking, determine the priority of each indicator, and make decisions based on the order [11].…”

“…Then, use fuzzy theory to build triangular fuzzy numbers for each evaluation criterion, establish a fuzzy reciprocal matrix, and set the minimum value (Li), middle value (Mi), and maximum value (Ri) of each criterion as the triangular fuzzy numbers for that evaluation element. The calculation formulas are shown as Equations ( 5)- (7), where h represents the h-th expert, i represents the i-th evaluation criterion element, and n represents the total number of experts:…”