INDUSTRY 4.0 MATURITY LEVELS OF SUPPLIERS IN WHITE GOODS MANUFACTURING SECTOR

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 10, October 2018, pp , Article ID: IJMET_09_10_099 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed INDUSTRY 4.0 MATURITY LEVELS OF SUPPLIERS IN WHITE GOODS MANUFACTURING SECTOR Ezgi Turkyilmaz and Ufuk Cebeci Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey ABSTRACT Industry 4.0 and Internet of Things are becoming significant subjects in manufacturing sector. A survey based on Industry 4.0 was applied to the member companies of White Goods Suppliers Association. According to the study, one of the purposes is the measurement of factory maturity level and knowledge about Industry 4.0. Another aim is the analysis of core competencies and inadequate areas of company. Hypotheses were conducted and analysed. One of the results is; the prior area of companies with high application level of lean manufacturing techniques is assembly line for Industry4.0 transformation. The other result is; the companies that have medium and high ratio of engineers to white collars who have low application level of lean techniques. Company investment amount for transformation, application level of lean techniques in factory, and automation level in assembly line, were analysed. Keywords: Industry 4.0, White Goods Manufacturing, Lean Manufacturing, Statistical Analysis, Internet of Things. Cite this Article: Ezgi Turkyilmaz and Ufuk Cebeci, Industry 4.0 Maturity Levels of Suppliers in White Goods Manufacturing Sector, International Journal of Mechanical Engineering and Technology, 9(10), 2018, pp INTRODUCTION With the help of technology, companies improve and advance in various fields. These improvements and gains are limited. Moreover, they are isolated for efficiency of processes and quality of products. Therefore, being smart of manufacturing process becomes very popular for manufacturing companies. This innovation is implemented in horizontal level, which includes all players in the value chain system, and in vertical level that includes all layers of automation. Moreover, machines and products need minimum manual actions because of factories, which are integrated with advanced and intelligent technology. For instance, these technologies are Internet of Things, M2M (Machine to Machine communication), Industrial Internet, Cloud-based Manufacturing and Smart Manufacturing which are the main concept of Industry 4.0 [1] editor@iaeme.com

2 Industry 4.0 Maturity Levels of Suppliers in White Goods Manufacturing Sector Industry 4.0 combines automated technologies such as industrial technology with factory, service and products. That innovative way provides major chances for manufacturing with Information and Communication Technology (ICT) infrastructure. [2] Industry 4.0 investments can be more critical for SMEs, because of high amount. Sommer states that the smaller SMEs are, the higher the risk that they will become victims, instead of beneficiaries of industry 4.0 revolution [3]. 2. LITERATURE REVIEW One of the purposes is the measurement of mentality and maturity levels of Turkish companies in terms of Industry 4.0 and Internet of Things. Besides, the survey supports evaluation of the applications of Industry 4.0 and Internet of Things in production industry. The other purpose is the evaluation of susceptibility of investment for Industry 4.0. Moreover, the study answers the question whether companies have competence, capital or technology for digital transformation, according to correlation and analyses of the results by using SPSS (Statistical Package for Social Sciences). Oztemel and Gursev [4] reviewed the industry 4.0 related technologies in literature. They explain some leading countries investments and activities. Lee et al. [5] proposes a unified 5- level architecture for Industry 4.0-based manufacturing systems as a guideline for implementation of CPS (Cyber-Physical Systems). Ivanov et al. [6] presents a dynamic model and algorithm for short-term supply chain scheduling in smart factories Industry 4.0. Kaur and Singh [7] reviews the characteristics of some classical and agile methodologies that are widely used in software development, strengths and weakness between the two opposing methodologies. Bruzzone et al. [8] aims to discuss how impedance controlled parallel robots can effectively perform industrial assembly tasks of white goods manufacturing. Alexopoulos et al. [9] presents a context-aware information distribution system to support users in an industrial environment of white goods. 3. INDUSTRY 4.0 MATURITY LEVELS The research is carried out with the members of BEYSAD (Turkish White Goods Suppliers Association). Manufacturing, marketing and distribution of white goods are the scope of the member companies. Some global customers of BEYSAD s members are Bosch, Vaillant, Beko (Arcelik), Vestel, Indesit, etc. Other customers of BEYSAD s members are local manufacturers. The study is about the measurement of Industry 4.0 level of companies, which are dealing with production. The criterion of the thesis is a survey among those companies. According to the survey, maturity level of these companies was measured within their answers in terms of factories conditions. Seven hypotheses are defined that is related to the survey questions. Then, these hypotheses are examined according to the analyses of SPSS program. Finally, hypotheses are decided whether they are valid or not by using analyses and correlations. Hypothesis 1: The prior area of companies whose application level of lean manufacturing techniques is high level, for Industry 4.0 transformation is assembly line (machine tools). In the survey, lean manufacturing level is scaled from 1 (which means the companies have low level application) to 5 (which means they have high level application of lean manufacturing techniques in their assembly line). In addition, the question, which measures editor@iaeme.com

3 Ezgi Turkyilmaz and Ufuk Cebeci the prior areas of Industry 4.0 transformation, consists of assembly line (machine tools) that is indicated as smart factory in the research, logistics, and customer and supplier part. Before hypothesis is considered, the first inference is that mean of application level of lean manufacturing techniques is which denotes the companies in the survey have neither high nor low application level of lean manufacturing techniques. Firstly, level 4 and level 5 are chosen as a high level application of lean manufacturing techniques in order to evaluate Hypothesis 1. The primary area of Industry 4.0 transformation of companies is checked by whether it is a smart factory (assembly line) or not by using SPSS program. The companies with high level (level 4 or level 5) application of lean manufacturing techniques, the primary area of Industry 4.0 transformation is assembly line instead of logistics, customer and supplier part. Moreover, the mean of application level of lean manufacturing techniques is approximately 4.30, which is a high level of lean manufacturing technique. Hypothesis 2: The companies that completed Research and Development project in last three years tend to invest more on Industry 4.0 transformation. The number of completed research and development projects in last three years is divided into 4 ranges: One of them is 0, which matched 1.00 in SPSS program. The other range is between 1 and 5 matched 2.00, between 6 and 10 matched 3.00 and over 11 projects matched 4.00 in SPSS. Moreover, investment on Industry 4.0 transformation is based on 4 ranges as well which are less than or equal to 1 Million TL matched 1.00 in SPSS. Over 1 Million TL and less than or equal to 5 Million TL matched 2.00, over 5 Million TL and less than or equal to 10 Million TL matched 3.00, and over 10 Million TL matched 4.00 in SPSS. Although there is a positive correlation between those determinants, mean of the amount of investment for Industry 4.0 is , which means the companies invest between less than or equal to 1 Million TL. In addition, over 1 Million TL and less than or equal to 5 Million TL for R&D Projects which is not a high investment percentage of companies invest less than or equal to 1 Million TL for Industry 4.0 transformation which is not a high investment. Hypothesis 3: The companies that have medium and high ratio of engineers to whitecollar employees have high level of application of lean manufacturing techniques. Ratio of engineers to white collars is divided into 11 ranges in SPSS program. Between 0 and 10 is indicated 1.00, between 11 and 20 is indicated Between 21 and 30 is indicated 3.00, between 31 and 40 is indicated 4.00, between 41 and 50 is indicated 5.00, between 51 and 60 is indicated 6.00, between 61 and 70 is indicated 7.00, between 71 and 80 is indicated 8.00, between 81 and 90 is indicated 9.00, between 91 and 100 is indicated 10.00, and over 100 percentage is indicated in SPSS. Moreover, level of application of lean manufacturing techniques is scaled from 1 to 5 as can be seen from Hypothesis 1. When the ratio of engineers to white collars was selected more than 30% means chose from 4.00 to in SPSS, the percentage of the companies can be seen in terms of application level of lean manufacturing in Figure 1. Therefore, the companies who have medium and high ratio of engineers to white collars, apply medium level lean manufacturing techniques instead of high level that is stated in Hypothesis editor@iaeme.com

4 Industry 4.0 Maturity Levels of Suppliers in White Goods Manufacturing Sector Figure 1 The Percentage of Companies in terms of Application Level of Lean Manufacturing Techniques Hypothesis 4: The companies that have low ratio of white collar employees to total employees have low level of application of lean manufacturing techniques. Ratio of white-collar employees to total employees is divided into 10 ranges. Between 0 and 10 is denoted 1.00, between 11 and 20 is denoted 2.00, between 21 and 30 is denoted Between 31 and 40 is denoted 4.00, between 41 and 50 is denoted 5.00, between 51 and 60 is denoted 6.00, between 61 and 70 is denoted 7.00, between 71 and 80 is denoted 8.00, between 81 and 90 is denoted 9.00, between 91 and 100 is denoted in SPSS. Moreover, level of application of lean manufacturing techniques is scaled 1 to 5 as can be informed from Hypothesis 1. Whereas 1.00 and 2.00 can be called low level of application of lean manufacturing techniques, 3.00 is medium level and 4.00 and 5.00 can be interpreted as high level application. Therefore, the majority of companies have medium level of application of lean manufacturing techniques instead of low level of application of lean manufacturing among the companies, which have low ratio of white collar to total employees. Hypothesis 5: The companies that have low ratio of engineers to total employees and have low revenue, invest less in Industry 4.0 transformation. The distribution of companies according to revenue; Less than or equal to 5 Million TL, 1 company 5-15 Million TL (15 Million TL included), Million TL (30 Million TL included), Million TL (45 Million TL included), 1 More than 45 Million TL, 13 companies. 1 dollar is assumed as 4 Turkish Lira (TL) in this study. Consideration of the survey and SPSS program, ratio of engineers to total employees is divided into 10 ranges. Between 0 and 10 is indicated 1.00, between 11 and 20 is indicated 2.00, between 21 and 30 is indicated 3.00, between 31 and 40 is indicated Between 41 and 50 is indicated 5.00, between 51 and 60 is indicated 6.00, between 61 and 70 is indicated 7.00, between 71 and 80 is indicated 8.00, between 81 and 90 is indicated 9.00, between 91 and 100 is indicated in SPSS. Moreover, the company revenue is categorized in 5 ranges that are less than or equal to 5 Million TL displayed 1.00, over 5 Million TL and less than or equal to 15 Million TL displayed Over 15 Million TL and less than or equal to editor@iaeme.com

5 Ezgi Turkyilmaz and Ufuk Cebeci 30 Million TL displayed 3.00, over 30 Million TL and less than or equal to 45 Million TL displayed 4.00 and over 45 Million TL displayed 5.00 in SPSS. Furthermore, the amount of investment for Industry 4.0 is divided into 4 ranges which can be seen in Hypothesis 2 explanation part of this study. When the ratio of engineers to total employees is chosen just 1.00, 2.00, and 3.00 which are low level, and the company revenue is selected less than or equal to 5 Million TL and over 5 Million TL and less than or equal to 15 Million TL which are displayed 1.00 and 2.00 in SPSS. The companies that have low ratio of engineers and less company revenue, invest less in Industry 4.0 transformation and this result is supported by Hypothesis 5. Hypothesis 6: The companies, which have a plan of medium and high level of automation in assembly line next 3 years and have medium and high revenue, invest more in Industry 4.0 transformation. Automation level that companies want to create in their assembly line is divided into 6 ranges. Between 0 is demonstrated as 1.00, between 1 and 20 is demonstrated as 2.00, between 21 and 40 is demonstrated as 3.00, between 41 and 60 is demonstrated as 4.00, between 61 and 80 is demonstrated as 5.00 and between 81 and 100 is demonstrated as 6.00 in SPSS. Moreover, the company revenue is categorized in 5 ranges as can be seen from Hypothesis 5 explanation. As it was explained in Hypothesis 2, the amount of investment for Industry 4.0 is divided into 4 ranges. When the level of automation in assembly line next 3 years is selected medium and high level which is between 21% and 100% in the survey and between 3.00 and 6.00 in SPSS. And the company revenue is selected over 15 Million TL and less than or equal to 30 Million TL, over 30 Million TL and less than or equal to 45 Million TL and over 45 Million TL that are displayed 3.00, 4.00 and 5.00 in SPSS. The inference can be interpreted that 60% of companies who have medium and high automation level and revenue invest less for Industry 4.0 transformation, which refutes Hypothesis 6. Hypothesis 7: The companies that consider Industry 4.0 as a transformation of smart factory, have a plan of high level of automation in assembly line for the next 3 years. Answers to the question of what is the meaning of Industry 4.0 as a transformation according to companies includes Doing more digital transformation projects displayed 1.00, Creating a smart factory displayed Increasing materiality degree of information security displayed 3.00, Using more virtual reality technology displayed 4.00, Using more augmented reality technology displayed 5.00, and Using more robots displayed 6.00 in SPSS program. Moreover, automation level that companies want to create in their assembly line is divided into 6 ranges. When the answer of what is the meaning of Industry 4.0 as a transformation according to the companies was chosen Creating a smart factory, the analysis was established and shown in Figure 5 below. While the Figure 5 is examined, the companies which have an idea of Industry 4.0 transformation is Creating a smart factory, do not have a plan of high level automated assembly line for the next 3 years. In conclusion, this result refutes Hypothesis CONCLUSION Industry 4.0 and its transformation are major issues in order to improve for companies. The companies, who have a high level of techniques, prefer to invest in assembly line and machine tools in Industry 4.0 transformation firstly. Moreover, the idea that is the number of projects affects investment for Industry 4.0, can be shown with Hypothesis 2. The hypothesis explains the companies which completed 1 Research and Development project at least in the last 3 years, tend to invest less on Industry 4.0 transformation. Furthermore, the ratio of engineers to editor@iaeme.com

6 Industry 4.0 Maturity Levels of Suppliers in White Goods Manufacturing Sector white collar affects lean manufacturing techniques. Regarding Hypothesis 3, the majority of companies which have more than 30% of engineers to white collar, is medium level of application of lean manufacturing techniques instead of high level. Besides, majority of companies have low ratio of white collar employees to total employees has medium level of application of lean manufacturing techniques instead of low level due to the result of Hypothesis 4. After selecting factors, 81.8% of all companies who have low ratio of engineers to total employees and low revenue, invest less than or equal to 1 Million TL because of Hypothesis 5. When the companies which have medium and high automation level and medium and high company revenue that is more than 15 Million TL, the inference can be interpreted that 60% of companies who have medium and high automation level and revenue invest less for Industry 4.0 transformation due to Hypothesis 6. In addition, 71% of the companies, which consider Industry 4.0 as Creating a smart factory, have a low level of automation in assembly line for the next 3 years according to Hypothesis 7 result. The study can be repeated in the upcoming years to monitor the progress of companies. In addition, study can be applied to similar sectors and countries as further research. REFERENCES [1] Schumachera, A., Erol, S., & Sihna, W. (2016). A maturity model for assessing Industry 4.0 readines and maturity of manufacturing enterprises. Changeable, Agile, Reconfigurable & Virtual Production. [2] Stock, T., & Seliger, G. (2016). Opportunities of Sustainable Manufacturing in Industry 4.0. Procedia CIRP, 40, [3] Sommer, L. (2015). Industrial revolution-industry 4.0: Are German manufacturing SMEs the first victims of this revolution?. Journal of Industrial Engineering and Management, 8(5), [4] Oztemel, E., & Gursev, S. (2018). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, [5] Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, [6] Ivanov, D., Dolgui, A., Sokolov, B., Werner, F., & Ivanova, M. (2016). A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0. International Journal of Production Research, 54(2), [7] Kaur, N., & Singh, G. (2016). Critical Success Factors in Agile Software Development Projects: A Review. International Journal on Emerging Technologies, 7(1), 1. [8] Bruzzone, L. E., Molfino, R. M., & Zoppi, M. (2005). An impedance-controlled parallel robot for high-speed assembly of white goods. Industrial Robot: An International Journal, 32(3), [9] Alexopoulos, K., Makris, S., Xanthakis, V., Sipsas, K., & Chryssolouris, G. (2016). A concept for context-aware computing in manufacturing: the white goods case. International Journal of Computer Integrated Manufacturing, 29(8), editor@iaeme.com