A mixed decision making model / methodology for digital transformation as a roadmap for automotive supplier industry

In recent years, digital transformation in manufacturing industry that is conceptualized as industry 4.0 and underlined as the Fourth Industrial Revolution have gained importance and popularity. Industry 4.0 namely digital transformation is defined as the integrating of complex machineries with smart sensors and software to predict, manipulate and plan for more sustainable business and societal outcomes. This transformation referred to the trends towards digitization of the manufacturing environments which takes the automation of processes and business systems to a next level by introducing intelligent usage of inter-communicating machines through the usage of technologies such as smart sensors to get reliable data for analysis, robotics technology to replace the labor for dangerous tasks as well as precise operations, data analytics to analyze what is happening and what will probably happen, cloud computing for a faster and reliable cooperation among companies with a performance, image processing to execute the operations with higher level of quality in a digitized environment, VR/AR to support labor and make robots more autonomous, 3D printing to shorten time to market and various state-of-art technologies with far too numerous benefits. New technologies arise uncertainty and unclarity for those organizations planning to transform their business models into digital. As the use of these technologies are newly emerging in manufacturing environments and stands as an uncertainty environment, identification and selection of the Industry 4.0 technologies and tools occurred as a challenge that is faced by manufacturing industry decision makers in the user side. Benefits and the barriers of the digital transformation is unclear, and selection of the best strategy is difficult requiring high level of knowledge and expertise competency. Theoretical and practical frameworks of roadmaps and decision making models that can be utilized for guiding manufacturing industry still remains insufficient or immature. Automotive supplier industry is chosen as the representing sector of manufacturing industries. Approaches on digital transformation are described and analyzed by the industry 4.0 technology providers, and the numbers of academic studies for users are limited in the literature. The literature is lack of studies especially for the dependent and follower countries such as Turkey that has limited resources and strict constraints. Although there are some reports and research studies on the digital transformation proposing an overall evaluation with generic expressions, it is needed to analyze manufacturing processes based on a detailed perspective for Turkish automotive industry. In this context, based on this emerging need, this study aims to present a hybrid method of decision making on prioritizing Industry 4.0 technologies and tools as a roadmap for technology selection and planning in manufacturing industry. This study proposes a multi-dimensional and hybrid methodology that covers technological issues, competitive competencies and managerial pillars. By combining the pillars of technological tools, benefits and challenges of their usage in manufacturing industry, the proposed model utilizes multi-criteria decision support model, namely AHP, a Needs Analysis framework based on Quality Function Deployment approach, and integer programming together in a stage based process. Data collected via interviews and survey, the qualified participant having manufacturing excellence are preferred. AHP (Analytical Hierarchy Process) is selected the best tool to assure relativity and prioritization as the technologies are pushed to be used all together, but the resources are limited. QFD (Quality Function Deployment) is selected as it is needed to choose the preferable strategy among the multiple dimensions and their interactions proposing an intervention matrix. At the final phase, a MIP model is used to collect and select the outputs from AHP and QFD tools to be able to reach the best strategy among the interactions between the dimensions. The proposed methodology suggests a flexible and adaptable structure which can be employed for various cases. As the inputs of MIP is derived from AHP and QFD, the MIP would select respective connections among the network based on the outputs of AHP and QFD. The proposed decision making model is expected to represent a solid practical and unique example that can guide the entrepreneur and the government in terms of the potential benefits, technologies and the barriers of industry 4.0 and enable them to better and systematically understand the potential impacts and benefits of digital transformation, and also to allocate resources more effectively. Keywords: industry 4.0, digital transformation, quality function deployment, analytical hierarchy process, mixed integer programming