Managing the barriers of Industry 4.0 adoption and implementation in textile and clothing industry: Interpretive structural model and triple helix framework

Highlights

• Barriers of Industry 4.0 adoption and implementation in textile and clothing industry are modelled.

• Forty experts representing various sectors of Indian textile industry participated in the survey.

• Lack of trained staff; lack of commitment of top management; lack of government support; and poor R&D are major barriers.

• A triple helix based framework is proposed to overcome these barriers.

Abstract

Manufacturing industries worldwide are on the cusp of Industry 4.0 revolution which will create a paradigm shift by integrating the physical and virtual worlds. This study attempts to identify the barriers of Industry 4.0 adoption and implementation in Indian textile and clothing industry. Fourteen barriers were shortlisted through literature review, followed by questionnaire survey among industry experts. Interpretive structural modelling (ISM) was used to elicit the contextual driver-driven relationships among these barriers. It was found that lack of trained staff; lack of understanding and commitment of top management; lack of government support and policies; and poor research and development are the driving barriers. High implementation cost; fear of failure; and seamless integration and compatibility issues are the driven barriers. To overcome these barriers, a triple helix based framework has been proposed for collaborative activities among the actors of textile and clothing organisations, academia and government. This study is the first of its kind to identify and analyse the Industry 4.0 barriers in textile and clothing industry and to propose their mitigation strategies. The outcome of this study will help the policy makers to devise strategies for Industry 4.0 adoption and implementation in Indian textile and clothing industry.

Introduction

Industry 4.0 was conceptualised by Henning Kagermann, Wolf-Dieter Lukas and Wolfgang Wahlster to usher in smart manufacturing for maintaining the future competitiveness of the German industries (Kagermann et al., 2011). It was propounded as the beginning of the fourth industrial revolution during Hannover Messe 2011, one of the world’s largest trade fair. Soon after, in 2013, the German government took it as a strategic initiative to revolutionise the manufacturing industry. Unlike the first three industrial revolutions, which were realised and termed after they happened, Industry 4.0 has been coined before it has actually come into existence. The first industrial revolution (Industry 1.0), which started at the fag end of 18th century, paved the way for steam power and mechanised production. The latter also included spinning jenny and power loom for weaving fabrics (Ghobakhloo, 2018). The next two industrial revolutions, which started around 1870 and 1970, respectively, were propelled by mass production and information technology, respectively (Xu et al., 2018). Industry 4.0 is expected to be realised by the amalgamation of a set of emerging technologies like cyber-physical systems (CPS), Internet of things (IoT), radio-frequency identification (RFID), big data, artificial intelligence, smart sensors, cloud computing, 3D printing, additive manufacturing, etc.

With the aid of vertical, horizontal and end to end integration, Industry 4.0 is expected to change the face of manufacturing industries and augment the productivity up to 55 % and profit up to 15 % (Raj et al., 2020; McKinsey Digital, 2016). Physical machines and components will be complemented by sensors, actuators and embedded software systems, giving them the ability to process and communicate data faster and in real-time. The decision making will happen impromptu at the level of shop-floor with the aid of artificial intelligence and machine learning (Müller et al., 2018). Thus, Industry 4.0 will lead to virtualisation and modularisation of the production processes, achieving flexibility based on CPS and IoT in conjunction with enterprise resource planning (ERP), supply chain management, product lifecycle management, and other software systems (Chen and Xing, 2015; Galati and Bigliardi, 2019). It will also be possible to monitor, control and self-optimise machines and to have real-time communication between different machines. From input resources to work-in-progress to finished goods inventory to product sales and recovery, a digital tracking will be realised which will facilitate the sustainable development and circular economy (de Sousa Jabbour at al., 2018; Stock et al., 2018; Rajput and Singh, 2019a).

However, the manufacturing industries, specially those which fall under the ambit of small and medium enterprises (SMEs), are not well-prepared to adopt and implement Industry 4.0 (Horvath and Szabo, 2019; Türkes et al., 2019; Branco et al., 2019; Pacchini et al., 2019). A survey conducted by McKinsey in 2016 revealed that only 16 % of the Japanese companies felt that they have made good progress in implementing Industry 4.0 while 36 % felt that they are well-prepared for Industry 4.0 (McKinsey Digital, 2016). Though SMEs have more flexibility than large corporates in terms of organisation structure, process and technology, the former face the scarcity of financial resources and skilled manpower (Ghobakhloo, 2018; Rauch et al., 2019). However, Büchi et al. (2020) found that smaller local firms may have greater opportunities in Industry 4.0. Therefore, there are opportunities as well as roadblocks for the SMEs with respect to Industry 4.0 adoption and implementation.

Textile and clothing industry is one of the important industries in most of the developing economies. This industry leapfrogged during Industry 1.0 with a series of inventions. In recent years, due to low cost of production, most of the clothing production has shifted to Asian countries such as China, India, Bangladesh, Pakistan, Cambodia, Vietnam, etc. (Kemper et al., 2017; Majumdar et al., 2020). This industry plays a very important role in the Indian economy by contributing 4 % to the GDP and 14 % to the industrial production. The present value of the Indian textile sector is around $ 200 billion, and is anticipated to grow at a compounded annual growth rate of 12 % to $ 350 billion by 2024. Textile and clothing industry contributed around 80 % and 43 % to the export revenue of Bangladesh and Sri Lanka, respectively, in late 2000s. It is a labour oriented industry and employs 35 million and 4 million people in India and Bangladesh, respectively (Majumdar and Sinha, 2019). There are some areas of textile and clothing production where the Industry 4.0 is already in practice, though in bits and pieces (Küsters et al., 2017). For example, in sizing, which is a preparatory process for weaving, smart beams with RFID tags are being used. Smart beam not only contains all the information about the work in progress material, but the software also suggests the process parameters based on the input data. Similar RFID tags are being used in yarn cones used in knitting machine (Simonis, 2016). Picanol, a Belgian loom manufacturing company, has developed OmniPlus air-jet weaving technology based on four principles, namely smart performance, sustainability (reduction in air consumption), data driven (combined use of many sensors) and intuitive control (Butler, 2017; Kemper et al., 2017;). Image processing and artificial neural network based fabric defect identification system has been developed by Uster Technologies (Meier et al., 2000).

Thus, the possibility of implementation of automation and CPS is immense in textile and clothing industry. However, most of the clothing industries fall under the SMEs which have scarcity and problems related to financial resources, information technology (IT) infrastructure, and skilled human resources (Horvath and Szabo, 2019; Rauch et al., 2019). Unless these barriers are identified and strategies are developed to overcome them, the textile and clothing industry will not be able to utilise the advantages and benefits of Industry 4.0. Therefore, it is important to investigate the barriers of Industry 4.0 adoption and implementation in textile and clothing industry and develop a framework to overcome these barriers. This research makes contribution by reporting the first study on the barriers of Industry 4.0 in the context of textile and clothing industry. The developed digraph, which reveals the contextual relationships among various barriers by converting the mental perception of practitioners, can be utilised by the policy makers. Besides, it will not be possible for the textile and clothing industry to overcome the barriers by working in silos. They need support and handholding from the government and academia. Therefore, a triple helix based framework has been proposed where the industry, academia and government should collaborate to overcome these barriers for the realisation of true potential of Industry 4.0.

Rest of the paper is organised as follows. Section 2 presents a brief literature review focussing on the barriers of Industry 4.0. Section 3 describes the research methodology which includes data collection methodology and ISM. Section 4 presents the results and discussion. Finally, conclusions and future scope of research are presented in section 5.