Effects of industry 4.0 implementation on machine efficiency and an application on home appliance production sector

In this paper, Industry 4.0 implementations' efficiency effect which is main competitive tool of recent production systems was searched. Industry 4.0 concept which was proposed firstly at 2011 by H.Kagermann, was published as a manifesto at 2013 by ACATECH (German Academy of Science and Engineering). That way it has been strategical research tool on international stage. "New industry revolution and smart production systems technology road map" study which published by Tubitak (The Scince and Technological Research Council of Turkey), shows us that Turkey is an strategical shareholder. Tubitak road map includes Mckinsey's report that is about possible company achievements with new industry revolution. 1- Source and process : %3-5 efficiency increase 2- Active asset efficiency : %30-50 Machine off-time reduction 3- Workforce (labour) : %45-55 Technician efficiency increase 4- Stock level : %20-30 Inventory cost reduction 5- Quality : %10-20 quality cost reduction 6- Supply and demand equilibrium : %85 estimation sensivity increase 7- Time to market : %20-50 period reduction 8- After-Sell service : %10-40 service and maintenance cost reduction All predicted improvements are some factors in total productivity growth. Within the scope of the study, critical factors (variables) that were mentioned at the above, of improvement issues were determined and classical and fuzzy logic based efficiency analysis studies were conducted for Industry 4.0 transition applications. Fuzzy data have also been analyzed due to the need to take into account the uncertainty of industry 4.0 application effects and categorical considerations. Efficiency increase / decrease of Industry 4.0 applications was evaluated and compared with classical and fuzzy logic based efficiency analyzes. Studies have been carried out with classical and fuzzy data envelopment analyzes which allow the variables have different units of measurement and independent from the analytical production functions. As results of the study; 1-Efficiency effect of Industry 4.0 applications on machine productivity in home appliances manufacturing sector 2-The effect of the obscurity factor by comparing classical and fuzzy data envelopment analysis were found out. This paper, 1-The first quantitative academic study to measure the effects of Industry 4.0 applications on productivity. 2-The study of how fuzzy factors can influence decision making by comparing fuzzy and classical data envelopment analysis results 3-Due to the results of Industry 4.0 applications in home appliance sector. will make important contributions to the literature. The research space of this study is Industry 4.0 applications. The aim that is investigated, is the efficiency effect of these applications. Both the complexity of production systems which contain many parameters, the lack of Industry 4.0 application data and the state of not being specialized yet, create the unknowns. The results that are obtained from the DEA studies with crisp data have been widely accepted in the literature and the accuracy is accepted. Similarly, there are examples of FDEA (Fuzzy Data Envelopment Analysis) studies with fuzzy data and the application area is expanding with an increasing trend. As mentioned above, the fact that the subject has a wide range of obscurity makes it compulsory to work with "Fuzzy Numbers". Within the scope of the study, 39 machines which have similar quality and characteristic were investigated in a white goods manufacturing sector. Industry 4.0 applications are available in 3 of 39 machines. Efficiency analysis of production equipment or machines which is obtained with high investments and which is the most basic constraint in production capacities generally, is an obligation for companies. In this respect, the company that is investigated, makes new investments and allocates resources for the efficient use of existing machines. At the end of 2017, Industry 4.0 studies were implemented to the machines that were identified as bottleneck (production capacity constraints) In the study, machines (DMUs) are shown as M1, M2, ….. , M39. M6, M8 and M14 are machines with Industry 4.0 applications. The data of 2017 and 2018 are analyzed for the 39 machines that are examined and the two-year change is compared. For the 3 machines that are specified, this change is the effect of Industry 4.0. According to Thames and Schaefer (2016), Industry 4.0 will enable manufacturing ecosystems driven by smart systems that have autonomic self-properties, for examples self-configuration, self-monitoring, and self-healing. Industry 4.0 will allow us to achieve unprecedented levels of operational efficiencies and accelerated growth in productivity. New types of advanced manufacturing and industrial processes revolving around machine-to-human collaboration and symbiotic product realization will emerge. As a result of this study, Modified machines with Industry 4.0 applications (M6, M8 and M14 machines) productivity increase between from 2017 to 2018 was found minimum 19.7% regardless of the DEA models (Fuzzy-Classic, BCC-CCR- Input Oriented or Output Oriented). This data is in line with Tubitak and Mckinsey's Industry 4.0 report and also this data is in same way with above description. The other objective of this study is detection of effect of obscurity, For this investigation, after the analyses, M6 and M14 has been passed to the effective boundary. It was determined with Classical DEA study. However only the M14 has been passed to the effective boundary in Fuzzy DEA studies. Some other (M23,24,32,33) machines have similar results. This situation that take the decision with classical DEA in uncertain research, reveals the fault that the researchers can suppose to examined DMU is an efficient unit (such as M6) instead of being inactive DMU. In this respect, it can be said that FDEA studies have more clear results in the areas of uncertainty and categorical evaluations.