Technology Selection for Digital Transformation: A Mixed Decision Making Model of AHP and QFD

Abstract

By the introduction of Industry 4.0 technologies and tools, manufacturing companies and especially SME (Small and medium sized companies) from supplier tiers of main manufacturing value chains faced the challenge of uncertain environment and lack of methods in making effective technology identification, prioritization and selection in accordance with their productivity and effectiveness improvement needs. Especially from a developing country perspective, being decision makers in the user side of Industry 4.0 value chain had been in a quest for decision making models that can be used in technology investments for Industry 4.0 technologies. However, due to the fact that, balancing potential benefits, barriers/challenges of Industry 4.0 tools with current or potential improvement needs in manufacturing systems require high level of knowledge and expertise both in manufacturing and Industry 4.0 technologies, most of the researches which focus on few pillars of the problem remained insufficient as they lack interdisciplinary and qualitative approaches. Especially for technology-dependent and follower countries such as Turkey that has limited resources and strict constraints, previous researches with proposed decision making models on digital transformation are very limited. In this context, this study proposes a multi-dimensional and hybrid technology evaluation methodology for technology selection on Industry 4.0 technologies that covers technological issues, competitive competencies and managerial pillars together. 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. Data collected via interviews and survey, the qualified participant having manufacturing excellence are preferred. Outputs from the model is expected to serve decision makers in SMEs of manufacturing industry for technology selection and use case scenario generation for the best and appropriate strategy.