Industry 4.0 transformation brings innovations that will radically change the existing operations and processes in the manufacturing industry. Expected changes in the Food and beverage manufacturing industry are increased efficiency; increased food security; higher food quality, and decreased waste. In order to maximise the benefits of Industry 4.0, implementations must be measurable and comparable. Industry 4.0 maturity models provide useful tools for standardisation and comparison in the transformation process. There are two types of Industry 4.0 maturity models: (1) Holistic approaches such as the studies by Lichtblau et al. (2015), Schumacher et al. (2016 and 2019), Santos and Martinho (2019; and (2) Specific approaches such as the models by Commuzzi (2016), Leyh et al. (2017), and Nemeth et al. (2018). In this study, a sector-specific Industry 4.0 maturity model with the Analytical Hierarchy Process is proposed. This model focuses on essential Industry 4.0 technologies and their usage.
2. DESIGN AND METHOD
The purpose of the study is to develop a technology-oriented, sector-specific Industry 4.0 maturity model for evaluating Industry 4.0 maturity levels of manufacturing companies. Tabular representation of the proposed model is given in Table 1.
Table 1. Proposed Industry 4.0 Maturity Model
Stage 1. Identifying maturity model criteria
Step 1.1 Literature survey
Step 1.2 Selecting essential Industry 4.0 technologies as criteria
Stage 2. Determining the weights of maturity model criteria
Step 2.1 Gathering sector-related Industry 4.0 data
Step 2.1 Defining weights of Industry 4.0 technologies via AHP
Stage 3. Development of Industry 4.0 maturity model
Step 3.1 Defining a scale for measurement
Step 3.2 A case application in food and beverage
The model is based on the usage of nine primary Industry 4.0 technologies which are: Augmented Reality, Cloud Computing, Big Data & Analytics, Additive Manufacturing, Internet of Things, Autonomous Robots, Cyber Security, Simulation and System Integration. A group of experts are asked the relative importance degrees of each technology in the food and beverage manufacturing industry. The weight of each technology is calculated using AHP. An Industry 4.0 maturity scale is constructed for evaluation.
3. FINDINGS AND DISCUSSION
The results of the study reveal that the most critical technologies for the food and beverage manufacturing industry are Autonomous Robots and Cyber Security. These two technologies are followed by Big Data & Analytics and Additive Manufacturing Systems, respectively. At the end of the study, Industry 4.0 maturity degree of a food manufacturing company is evaluated through the proposed model.
4. CONCLUSION, RECOMMENDATIONS AND LIMITATIONS
This model can be used as a useful self-assessment tool for Industry 4.0 maturity. It may be possible for companies to make comparisons and improvements by comparing themselves with acceptable practices in the industry. This scale also serves as a guide for small and medium-sized enterprises that have difficulty in accessing knowledge and in setting strategies for Industry 4.0 transformation. In future studies, the current maturity model can be adapted and applied to other manufacturing and service sectors.
- Alkan, M.A., 2020, Gıda Sektörü ve Endüstri 4.0, Endüstri 4.0 Platformu https://www.endustri40.com/gida-sektoru-ve-endustri-4-0/, erişim tarihi: 24.06.2020
- Akdil, K. Y., Ustundag, A., & Cevikcan, E. (2018). Maturity and readiness model for industry 4.0 strategy. In Industry 4.0: Managing the digital transformation (pp. 61-94). Springer, Cham.
- Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer networks, 54(15), 2787-2805.
- Bibby, L., & Dehe, B. (2018). Defining and assessing industry 4.0 maturity levels–case of the defence sector. Production Planning & Control, 29(12), 1030-1043.
- Boston Danışma Grubu (BCG), (2020). Embracing Industry 4.0 rediscovering growth, https://www.bcg.com/capabilities/operations/embracing-Industry-4.0-rediscovering-growth.aspx.
- Carmigniani, J., Furht, B., Anisetti, M., Ceravolo, P., Damiani, E., & Ivkovic, M. (2011). Augmented reality technologies, systems and applications. Multimedia tools and applications, 51(1), 341-377.
- Castelo-Branco, I., Cruz-Jesus, F., & Oliveira, T. (2019). Assessing Industry 4.0 readiness in manufacturing: Evidence for the European Union. Computers in Industry, 107, 22-32.
- Comuzzi, M., & Patel, A. (2016). How organisations leverage big data: A maturity model. Industrial Management & Data Systems, 116(8), 1468-1492.
- Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204, 383-394.
- Dilberoglu, U. M., Gharehpapagh, B., Yaman, U., & Dolen, M. (2017). The role of additive manufacturing in the era of industry 4.0. Procedia Manufacturing, 11, 545-554.
- Dillon, T., Wu, C., & Chang, E. (2010). Cloud computing: issues and challenges. In 2010 24th IEEE international conference on advanced information networking and applications (pp. 27-33). Ieee.
- Erbay, H., & Yıldırım, N. (2018, August). Technology Selection for Digital Transformation: A Mixed Decision Making Model of AHP and QFD. In The International Symposium for Production Research (pp. 480-493). Springer, Cham.
- Fırat, S., & Fırat, O. (2017). Gıda ve İçecek Sektöründe Endüstri 4.0 Devrimi: Otomasyon ve Robotlar. ST Robot Yatırımları, 216.
- Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144.
- Gökalp, E., Şener, U., & Eren, P. E. (2017, October). Development of an assessment model for industry 4.0: industry 4.0-MM. In International Conference on Software Process Improvement and Capability Determination (pp. 128-142). Springer, Cham.
- Gunal, M. M. (Ed.). (2019). Simulation for Industry 4.0: Past, Present, and Future. Springer.
- Huang, Y. L., & Sun, W. L. (2018, July). An ahp-based risk assessment for an industrial iot cloud. In 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) (pp. 637-638). IEEE.
- Jaganathan, S., Erinjeri, J. J., & Ker, J. I. (2007). Fuzzy analytic hierarchy process based group decision support system to select and evaluate new manufacturing technologies. The International Journal of Advanced Manufacturing Technology, 32(11-12), 1253-1262.
- Jazdi, N. (2014, May). Cyber physical systems in the context of Industry 4.0. In 2014 IEEE international conference on automation, quality and testing, robotics (pp. 1-4). IEEE.
- Klötzer, C., & Pflaum, A. (2017). Toward the development of a maturity model for digitalisation within the manufacturing industry’s supply chain.
- Lichtblau, K., Stıch, V., Bertenrath, R., Blum, R., Bleider, M., & Millack, A. (2017). IMPULS, Industry 4.0 readiness, VDMA.
- Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of industrial information integration, 6, 1-10.
- Luque, A., Peralta, M. E., De Las Heras, A., & Córdoba, A. (2017). State of the Industry 4.0 in the Andalusian food sector. Procedia Manufacturing, 13, 1199-1205.
- Luthra, S., & Mangla, S. K. (2018). Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117, 168-179.
- Ly, P. T. M., Lai, W. H., Hsu, C. W., & Shih, F. Y. (2018). Fuzzy AHP analysis of Internet of Things (IoT) in enterprises. Technological Forecasting and Social Change, 136, 1-13.
- Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). Journal of manufacturing systems, 49, 194-214.
- Nemeth, T., Ansari, F., Sihn, W., Haslhofer, B., & Schindler, A. (2018). PriMa-X: A reference model for realising prescriptive maintenance and assessing its maturity enhanced by machine learning. Procedia CIRP, 72, 1039-1044.
- Ötleş S. ve Özyurt, V.H. (2016). https://egeplm.ege.edu.tr/files/egeplm/icerik/endustri40_dunya_gida.pdf, Erişim tarihi: 24.06.2020.
- Özçelik, T. O., Erkollar, A., & Cebeci, H. I. (2019). Bir İmalat İşletmesi için Endüstri 4.0 (Dijital) Olgunluk Seviyesi Belirleme Uygulaması. 5th International Management Information Systems Conference, Ankara.
- Özdemir, Ö.ve Özdemir, E. G. Endüstri 4.0 ve yiyecek içecek işletmelerindeki yansımaları. (2019) Nevşehir HBV Üniversitesi Turizm Fakültesi, IV. International Gastronomy Tourism Studies Congress, 87-93.
- Özenir, İ., & Nakıboğlu, G. (2019). Sürdürülebilir üretimde Endüstri 4.0’ın yeri. Business & Management Studies: An International Journal, 7(5), 2248-2281.
- Pacchini, A. P. T., Lucato, W. C., Facchini, F., & Mummolo, G. (2019). The degree of readiness for the implementation of Industry 4.0. Computers in Industry, 113, 103125.
- Porter, K., Phipps, J., Szepkouski, A., Abidi, S.(2015). 3D opportunity serves it up: Additive manufacturing and food. Deloitte University Press. https://www2.deloitte.com/content/dam/insights/us/articles/3d-printing-in-the-food-industry/DUP_1147-3D-opportunity-food_MASTER1.pdf. Erişim tarihi: 26.06.2020
- Pricewaterhouse Coopers, P. (2016). Industry 4.0-Enabling Digital Operations Self Assessment. https://i40-self-assessment.pwc.de/i40/landing/, Erişim tarihi: 19.06.2020.
- Proença, D., & Borbinha, J. (2016). Maturity models for information systems-A state of the art. Procedia Computer Science, 100, 1042-1049.
- Saaty, R. W. (1987). The analytic hierarchy process—what it is and how it is used. Mathematical modelling, 9(3-5), 161-176.
- Santos, R. C., & Martinho, J. L. (2019). An Industry 4.0 maturity model proposal. Journal of Manufacturing Technology Management.
- Saucedo-Martínez, J. A., Pérez-Lara, M., Marmolejo-Saucedo, J. A., Salais-Fierro, T. E., & Vasant, P. (2018). Industry 4.0 framework for management and operations: a review. Journal of ambient intelligence and humanized computing, 9(3), 789-801.
- Schumacher, A., Erol, S., & Sihn, W. (2016). A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Procedia Cirp, 52(1), 161-166.
- Schumacher, A., Nemeth, T., & Sihn, W. (2019). Roadmapping towards industrial digitalisation based on an Industry 4.0 maturity model for manufacturing enterprises. Procedia Cirp, 79, 409-414.
- Sevinc, A., Gür, Ş., & Eren, T. (2018). Analysis of the difficulties of SMEs in industry 4.0 applications by analytical hierarchy process and analytical network process. Processes, 6(12), 264.
- Sony, M., & Naik, S. (2019). Key ingredients for evaluating Industry 4.0 readiness for organisations: a literature review. Benchmarking: An International Journal.
- Timor, M. (2011). Analitik Hiyerarşi Prosesi. Türkmen Kitabevi, Ankara.
- TÜSİAD, 2016, Türkiye’nin Sanayi 4.0 Dönüşümü, https://tusiad.org/tr/yayinlar/raporlar/item/8671-turkiyenin-sanayi-40-donusumu
- Yaşar, E., & Ulusoy, T. (2019). Industry 4.0 and Turkey. Business & Management Studies: An International Journal, 7(1), 24-41.