CAPTURING THE REALITY OF INDUSTRY 4.0 READINESS DIMENSIONS AND INDICATORS IN A DEVELOPING COUNTRY: AN ANALYSIS OF APPLYING I4.0 IN INDONESIA

Indonesia has the lowest Industry 4.0 (I4.0) readiness in South East Asia, and uses the INDI 4.0 instrument (Indonesia Industry 4.0 readiness index), which is not as comprehensive and accurate as it could be. An Initial survey confirmed that only 56.86% of respondents agreed that INDI 4.0 accurately measured readiness in manufacturing operations. Unlike the primary I4.0 indices, INDI 4.0 lacks comprehensive I4.0 dimensions and characteristics. This paper aims to identify the dimensions and indicators of I4.0 readiness to enhance INDI 4.0 through an exploratory mixed-method research approach with a multiphase research design. The first phase consisted of a qualitative approach through a documentary review, interviews, and observations to explore the dimensions and indicators of I4.0 readiness. This phase successfully identified four dimensions that experts, academics, and practitioners validated: technology, data life cycle, I4.0 design principles, and smart factory. These dimensions were broken down into 27 indicators of I4.0 readiness, then validated again through a statistical approach before being affirmed by a survey with a Pearson’s


Industry 4.0
Dr Johanes Helbig, in the document Germany vision in I4.0 [1], said: Implementing the [Industry 4.0] vision will enable employees to control, regulate, and configure smart manufacturing resource networks and manufacturing steps based on situations and context-sensitive targets. Employees will be freed up from performing routine tasks, enabling them to focus on creative, value-added activities. They will thus retain a key role, particularly in quality assurance. Simultaneously, flexible working conditions will enable excellent compatibility between their work and personal needs.
There is no consensus about a definition of the Industry 4.0 (I4.0) concept, except that it provides benefits, vision, and technology. Still, much of the literature mentions that I4.0 consists of four design principles in implementing I4.0: interconnection, information transparency, decentralised decisions, and technical assistance. It was first proposed by Mario Hermann, Tobias Pentek, and Boris Otto from Dortmund University in Germany [2]. Figure 1 shows the design principles of I4.0. 'Interconnection' refers to machines, devices, and sensors connected by wireless communication through the Internet of Things (IoT). 'Information transparency' means the interconnection of objects, such that people quickly access all the information for the right people or objects, providing a virtual copy of the physical object. 'Decentralised decisions' make the interconnection of things and people more transparent in using multi-source information. 'Technical assistance' refers to virtual assistance to provide decision support systems to empower people to drive decision-making by transforming data into visualising information. Furthermore, technical assistance provides physical assistance, such as advanced robotic technology, to support a factory floor's operations.
Implicitly, these four design principles of I4.0 ( Figure 1) [26] The smart factory uses smart design, smart planning and process optimisation, smart material distribution and tracking, smart production, smart monitoring, smart maintenance, and smart quality [7].  The I4.0 readiness index is an instrument to measure the readiness level for adopting I4.0. This paper aimed to identify the dimensions and indicators for measuring I4.0 readiness to enhance INDI 4.0 by converting an I4.0 concept, with its dimensions and characteristics as a variable, to be an operational variable that could be measured by exploring its dimensions and indicators. In this research, 'variable' refers to a concept, person, place, thing, or phenomenon that can be measured [51] [52]. Dimensions and indicators of I4.0 were explored from documents and then discussed and explored again through interviews; so the readiness factors were based on the realities from interviews industry, practitioners, experts, professionals, and academics involved in implementing I4.0 in Indonesia. Furthermore, the I4.0 readiness index in Indonesia was also compared with the main I4.0 readiness index, using several literature reviews, to capture the reality of the I4.0 readiness dimensions and indicators.
Despite there being no literature, reference, or I4.0 index that contains a complete and perfect instrument model that meets the needs of I4.0, this study followed the recommendations and suggestions of previous research: a) Increasing the practicality of implementing I4.0 in the organisation [53]. b) Developing organisational factors or other important or specific aspects of the system in detail in I4.0 [49]. c) I4.0 initiators are generally from developed countries, so the companies that are studied are only those that have implemented and carried out I4.0 projects [49]. d) The research requires a different perspective -for example, from industries striving for the transformation of I4.0, especially from developing countries such as Indonesia [49]. e) Some research is too focused and oriented on technology [49]. f) Use different tools, taking into account a more diverse number of dimensions, kinds of support, and indicators to ensure that the integrity of the developed model is maintained [18].
Based on their dimensions, some I4.0 indices focus on technology and IT [42] [49] -for example, SIMMI 4.0 and RAMI 4.0, -while others consider organisational, management, and even cultural factors such as Acatech, the Singapore I4.0 smart index, and Dreamy, even when a portion of their version is familiar with innovation and new technology as a developed country. INDI 4.0 is unlike other major indices: all of its questions are designed for a single organisation's overall general scope. Some major indices, such as the Singapore smart industry readiness index, have questions designed for an organisation's functions: its facilities, shopfloor, and enterprise levels, while RAMI 4.0, DREAMY, Acatech, and so on have all of their questions designed for other functions of an organisation, such as production, quality, research and development, logistics, services, and marketing.  [54]: 1) Where is data stored? 2) What technologies are adopted in the logistics system? 3) What is the percentage of automation in the process? and 4) What advanced system is adopted in maintenance? It was confirmed by an initial survey of the industries involved in adopting I4.0 in this study. The survey result indicated an assessment of INDI 4.0 as capturing I4.0 readiness relating to the factory operations. Only 56.86% of the respondents agreed that the current INDI 4.0 assessment was accurate. Also, in the second survey, only 42.11% of the respondents answered that INDI 4.0 reported I4.0 readiness. These results indicated a low percentage and suggested that this concern required improvement, especially in the dimensions and indicators of I4.0 readiness.

METHODOLOGY
Pragmatism is the philosophical framework underpinning the mixed methods approach to provide a set of assumptions about knowledge and inquiry. It is distinguished from quantitative and qualitative approaches, which are respectively based on the philosophies of positivism and interpretivism [55] [56]. This study used a mixed methods approach to address its research objectives, so it is associated with the pragmatic paradigm and seeks to answer the research questions correctly using a multiphase research design. Table 1 shows that Phase 1 of the multiphase method adopted a qualitative approach through a literature and document review, interviews, and observations to explore the dimensions and indicators of I4.0 readiness. The goal of this phase was to ensure that all statements about the importance level of every indicator for each dimension were valid and reliable, besides being validated by experts. Phase 2 consisted of a quantitative approach, through a survey, to confirm the relevance of the indicators of the dimensions of I4.0 readiness.

respondents
Phase 1 is a Qualitative method for collecting data through interviews, focus group discussion, literature review, and document review (as noted in Table 1). Eleven interviewees had the necessary qualifications and a deep understanding, knowledge, experience, position, and involvement in I4.0 adoption projects in Indonesia. Phase 2 is a Quantitative method for collecting data through a survey. The 51 survey respondents represented industries, academics, and practitioners of I4.0. The survey was based on a non-probability and purposive sample for some reasons. The Government of Indonesia chose just twenty-five sample companies in its assessment of I4.0 readiness in April 2018 because, initially, the I4.0 project invited a few companies to participate [54]. From 2018 to 2022, only 903 companies in Indonesia assessed I4.0 readiness, with 8.52% of them at the level of maturity readiness and implementing I4.0. A self-assessment of I4.0 readiness was also part of the I4.0 project [57]. A limited number of companies in Indonesia are experienced, involved, and have deep knowledge of the I4.0 concept and its adoption.

RQ1: What are the dimensions and indicators of Industry 4.0?
The literature review was an early step in collecting related research and selecting the most relevant of it. Next, it was necessary to identify the research gap; understand the I4.0 dimensions, principles, indicators, and characteristics; explore related research; review backgrounds, concepts, and theories; and consider the most suitable research method. In achieving the research objective -to identify the dimensions and indicators of I4.0 readiness -the study explored existing models available in the literature, such as journal papers, books, internet articles, magazines, and government documents. Reviewing and analysing such kinds of literature was done in the initial steps of the research and in each of the steps. In the first research phase (the qualitative method), one of the crucial outcomes of the literature review was the development of the framework of semi-structured questions for interviews about the scope of I4.0's dimensions or pillars that were confirmed previously by experts and practitioners with qualifications in the digital transformation field and Industry 4.0. Table 2 shows that Phase 1 of the research consisted of semi-structured interviews to collect data to identify the dimensions and indicators of readiness for implementing I4.0 in the manufacturing industry and to classify the readiness levels. The study analysed the results of interviews about the essential indicators (Column 4 in Table 2) for adopting I4.0. The interview results were then recorded and analysed using NVivo 1.2 software. The concept of a smart factory has been running in Daihatsu for quite a while (RSP07) The purpose of the smart factory is first about the accuracy of the data to reduce or eliminate error or data bias to get accurate information (RSP02) The smart factory is to speed up a process to reduce the lead time and its outcome to finance, and the third is to produce new collaborations that previously could not be done, such as machine learning and AI (RSP02) In some areas, Honda Motor Indonesia partially implemented a smart factory as part of I4.0 adoption; but also, in fact, in some areas, it did not. Smart maintenance is a part of the smart factory in AHM (Astra Honda Motor) and is still at a level where existing tools and machines provide daily logs for review by maintenance technicians (RSP08) A smart factory is vital for customer-and productsensitive industries, especially when the market requires it quickly (RSP02) Smart factory in the electricity industry means that the smart grid is one of the dimensions of I4.0 (RSP06) Confirmed that the smart factory is an essential factor of I4.0. When talking about the smart factory, it is directly related to I4.0 technology (RSP 05) Toyota Manufacturing Indonesia is still not a smart factory, but the mother company of Toyota in Japan is a smart factory. Toyota plant in Indonesia fosters the ideal, as in Toyota Japan, having a roadmap towards I4.0 by referring to the mother

Summary of indicators References
Astra Honda Motor is ready for I4.0 implementation, even though some processes still use old technology, but gradually following the mother company in the I4. The median is the most informative measure of the central tendency for skewed distributions or distributions with outliers. For example, the median is often used to measure the central tendency for income distributions, which are generally highly skewed. Because the median only uses one or two values, it is unaffected by extreme outliers or non-symmetric distributions of scores. In contrast, the mean and mode can vary in skewed distributions. There are 27 leading indicators of I4.0 in four dimensions in Table 3. All indicators obtained a median from 5 to 6 (columns 4, 6, 8, 10) as results from the survey. It meant that most indicators were relevant because the median levels ranged from 4.51 to 5.50 = important and from 5.51 to 6.00 = very important. Also, Column 1 shows the approval level of the respondents for the 27 indicators as 'important', with a median of 5. Only indicator No. 21, real-time technology, with a median of 6, was 'very important' as an I4.0 readiness indicator. The instrument was also validated again through a statistical approach before being affirmed by a survey with a Pearson correlation > 0.361 and Cronbach's alpha = 0.987 > 0.6, which meant it was valid and reliable. The interviews revealed other dimensions, such as management, people, and smart maintenance. Smart maintenance has been included in the smart factory. At the same time, the dimensions of management and people, in general, are generally required by other concepts, apart from I4.0, because they are not unique to I4.0. In addition, this study only focused on I4.0 in the factory operation area. Further research would need to explore other dimensions to complement and improve the accuracy in measuring I4.0 readiness, especially those that are important for I4.0 readiness.