A Strategic Approach for Automation Technology Initiatives Selection (Thomassen et al., 2014Thomassen, M. K., Sjøbakk, B., & Alfnes, E. (2014). A strategic approach for automation technology initiatives selection. In IFIP International Conference on Advances in Production Management Systems (pp. 288-295). Heidelberg: Springer. http://dx.doi.org/10.1007/978-3-662-44733-8_36. http://dx.doi.org/10.1007/978-3-662-4473...
) |
Research - action |
Environmental and corporate policies |
No tools adopted |
Automation |
Size of the organization not specified (small, medium or large) |
Stage 1: technology strategy decisions. |
Process requirements |
Manufacturing Companies. |
Stage 2: process analysis. |
Technological maturity |
Country studies: Norway. |
Step 3: technology analysis. |
Economic feasibility |
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Stage 4: classification of the technology/process. |
Phase: Choice and Implementation |
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Step 5: investment and implementation. |
Modelling Technical and Economic Parameters in Selection of Manufacturing Devices (Daneshjo et al., 2017Daneshjo, N., Majerník, M., Krivosudska, J., & Danishjoo, E. (2017). Modelling technical and economic parameters in selection of manufacturing devices. TEM Journal, 6(4), 738.) |
None specified |
Technical parameters (level of automation, intelligence, machine model, etc.), |
No tools adopted |
No specific technology discussed |
Manufacturing Companies Identified the size of the organization (small, medium or large). |
No stages of a defined process; |
Economic parameters (costs involved and Return On Investment (ROI)) |
No type of industry specified |
Environmental parameters (connection to the line, interaction between machines, etc.) |
Country studied: not specified. |
Phase: Choice and Implementation |
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Analysis of the Difficulties of SMEs in Industry 4.0 Applications by Analytical Hierarchy Process and Analytical Network Process (Sevinç et al., 2018Sevinç, 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. http://dx.doi.org/10.3390/pr6120264. http://dx.doi.org/10.3390/pr6120264...
) |
Literature Review and Case Studies |
Innovation |
Analytic Hierarchy (AHP) and Analytic Network Process (ANP) |
No specific technology discussed |
Small and medium-sized enterprises. |
No stages of a defined process; |
Organization |
No type of sector specified |
Environment |
Country studied: Turkey. |
Cost |
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Phase: Choice and Implementation |
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A Technology Selection Framework for Manufacturing Companies in the Context of Industry 4.0 (Hamzeh et al., 2018Hamzeh, R., Zhong, R., Xu, X. W., Kajáti, E., & Zolotova, I. (2018, August). A technology selection framework for manufacturing companies in the context of Industry 4.0. In 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) (pp. 267-276). New York: IEEE.) |
Literature Review |
Cost choice, Lead Time, Quality, Flexibility, developing new products. Implementation: |
No tools adopted |
No specific technology discussed |
Size of the organization not specified (small, medium or large) |
Phase 1: Assessment of the current situation; |
(i) technical factors, including technology, complexity and interfaces, performance and quality; (II) project management, including finance, project dependencies, resources and pritization (sic); (III) organizational factors, including planning, control and communication; (IV) external factors, including suppliers, regulations, market and customers. |
Manufacturing Companies. |
Phase 2: Define the critical strategic factors for implementing Industry 4.0; |
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Country studied: not specified. |
Phase 3: establish the planning interval/time horizon; |
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Phase 4: identify the technology to be implemented; |
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Phase 5: Detailed assessment of the identified technology; |
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Phase 6: Risk assessment of the technology to be used; |
Selecting the best strategy for Industry 4.0 applications with a case study (Erdogan et al., 2018Erdogan, M., Ozkan, B., Karasan, A., & Kaya, I. (2018). Selecting the best strategy for Industry 4.0 applications with a case study. In F. Calisir & H. Camgoz Akdag (Eds.), Industrial engineering in the industry 4.0 era (pp. 109-119). Cham: Springer. http://dx.doi.org/10.1007/978-3-319-71225-3_10. http://dx.doi.org/10.1007/978-3-319-7122...
) |
Literature Review and Case Studies |
Leadership |
Analytic Hierarchy (AHP) and VIKOR Fuzzy |
No specific technology discussed |
Size of the organization not specified (small, medium or large) |
Stage 1: literature review; |
Customers |
No specific industry. |
Stage 2: Producing Questionnaires; |
Products |
Country studied: not specified. |
Stage 3: Applying AHP methods; |
Operations |
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Stage 4: Applying the Vikor method. |
Culture |
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People |
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governance |
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Technology |
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Quality |
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Organization |
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Phase: Choice and Implementation |
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What Drives the Implementation of Industry 4.0? The Role of Opportunities and Challenges in the Context of Sustainability (Müller et al., 2018Müller, J. M., Kiel, D., & Voigt, K. I. (2018). What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability, 10(1), 247. http://dx.doi.org/10.3390/su10010247. http://dx.doi.org/10.3390/su10010247...
) |
Literature Review and Survey |
Organizational strategy |
No tools adopted |
No specific technology discussed |
Covers different sizes of organizations (small, medium and large). |
No stages of a defined process; |
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Companies in different sectors. |
Operations |
Country studied: Germany |
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Environment and people |
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Competitiveness and future viability |
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Organizational change |
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Qualifications and employee acceptance |
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Drivers and Barriers in Using Industry 4.0: A Perspective of SMEs in Romania (Türkeș et al., 2019Türkeș, M. C., Oncioiu, I., Aslam, H. D., Marin-Pantelescu, A., Topor, D. I., & Căpușneanu, S. (2019). Drivers and barriers in using Industry 4.0: a perspective of SMEs in Romania. Processes, 7(3), 153. http://dx.doi.org/10.3390/pr7030153. http://dx.doi.org/10.3390/pr7030153...
) |
Survey |
Customer requirements |
No tools adopted |
Big Data and Big Data Analytics, Autonomous Robots, Simulation, Horizontal and Vertical Integration, Internet of Things (IoT), Cyber-security, 3D Printing, Augmented Reality, Cloud Computing, Artificial Intelligence, Radio Frequency Identification (RFID) and Real Time Location Systems (RTLS) |
Small and medium-sized enterprises. |
No stages of a defined process; |
Competitors are already using the 4.0 model |
Companies in different sectors. |
Cost reduction |
Country studied: Romania |
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Improved time to market |
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Due to legal requirements/changing legislation |
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Phase: Choice and Implementation |
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An Assessment Model for Organizational Adoption of Industry 4.0 Based on Multi-criteria Decision Techniques (Keskin et al., 2018Keskin, F. D., Kabasakal, İ., Kaymaz, Y., & Soyuer, H. (2018). An assessment model for organizational adoption of industry 4.0 based on multi-criteria decision techniques. In The International Symposium for Production Research (pp. 85-100). Cham: Springer.) |
Literature Review and Case Studies |
Products and services (degree of product customization, digital features for products augmented with intelligent services, etc.) Manufacturing and operations (data collection, systems integration, etc.) |
Analytic Hierarchy (AHP) and Topsis |
No specific technology discussed |
Size of the organization not specified (small, medium or large) |
Stage 1 - Criteria selected by a Working Group; |
Strategy and organization (level of innovation management, investment analysis of the 4.0 sector (financial, cost/benefit) |
Clothing Companies. |
Stage 2 - Weighting assigned to the relevant main and sub-criteria; |
Supply chain integration (use of intelligent inventory control, customer focus, etc.) |
Country studied: Turkey. |
Stage 3 - The level of organization was assessed and given an assessment score; |
Business model (automated and real-time programming, aligning strategies to customer needs, etc.) |
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Legal considerations (data sharing and data protection, protection of intellectual property etc.) |
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Phase: Choice and Implementation |
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Technology Selection for Digital Transformation: A Mixed Decision-Making Model of AHP and QFD (Erbay & Yıldırım, 2018Erbay, H., & Yıldırım, N. (2018). Technology selection for digital transformation: a mixed decision making model of AHP and QFD. In The International Symposium for Production Research (pp. 480-493). Cham: Springer.) |
Literature Review and Case Studies |
Improved process efficiency |
Analytic Hierarchy (AHP) and Quality Function Deployment (QFD) |
RFID, Big Data, Robotics, MES, ERP, Data Analytics, 3D Printing, Virtual Reality, Augmented Reality, Image Processing |
Size of the organization not specified (small, medium or large) |
Phase 1: Identifying I 4.0 technology; |
Improved quality performance |
Automotive Companies. |
Phase 2: Comparing technology using AHP; |
Improved delivery time |
Country studied: Turkey. |
Phase 3: Comparing technology using QFD; |
Efficient and robust production planning |
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Specialization |
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Reducing Stock |
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Increased productivity |
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Reduced Labor |
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Improved Maintenance |
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Phase: Choice and Implementation |
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Industry 4.0 on Demand A Value Driven Methodology to Implement Industry 4.0 (Leone & Barni, 2020Leone, D., & Barni, A. (2020). Industry 4.0 on demand: a value driven methodology to implement Industry 4.0. In IFIP International Conference on Advances in Production Management Systems (pp. 99-106). Cham: Springer.) |
Case Study |
Acquisition costs of Hardware and Software; |
Analytic Hierarchy (AHP) and Design thinking |
No specific technology discussed |
Size of the organization not specified (small, medium or large) |
Phase 1: Maturity assessment of Industry 4.0; |
Project Implementation Costs |
Pharmaceutical Company |
Phase 2: Analysis of the process |
Improving the Overall Equipment Effectiveness (OEE) |
Country studied: not specified. |
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Improving Quality |
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Phase 3: Design the Industry 4.0 Roadmap. |
Reduced wastage |
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Reducing Total Costs |
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Expanding the project to other companies in the industry. |
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A Multi-criteria Decision-Making Model for Digital Transformation in Manufacturing A Case Study from Automotive Supplier Industry (Beyaz & Yıldırım, 2020Beyaz, H. F., & Yıldırım, N. (2020). A multi-criteria decision-making model for digital transformation in manufacturing: a case study from automotive supplier industry. In Proceedings of the International Symposium for Production Research 2019 (pp. 217-232). Cham: Springer.) |
Case Study |
Financial feasibility; Organizational feasibility; |
TOPSIS |
Augmented Reality, |
Large business |
Stage 1: Identifying existing issues. |
Technology feasibility; |
(smart glasses), 3D Printing, GPS, RFID, RTLS, |
Component suppliers to the automotive industry. |
Phase 2: Financial, operational and technology feasibility study; |
Legal feasibility. |
|
Country studied: Turkey. |
Phase 3: Using the TOPSIS method. Select the most appropriate technology; |
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Creating a roadmap for Industry 4.0 by using an integrated fuzzy multicriteria decision-making methodology (Kaya et al., 2020Kaya, I., Erdogan, M., Karasan, A., & Ozkan, B. (2020). Creating a road map for Industry 4.0 by using an integrated fuzzy multicriteria decision-making methodology. Soft Computing, 24(23), 17931-17956. http://dx.doi.org/10.1007/s00500-020-05041-0. http://dx.doi.org/10.1007/s00500-020-050...
) |
Literature review |
Leadership, Customer, Product, Operation, Culture, People, Governance, Technology, Quality, Organization and Others. |
Analytic Hierarchy (AHP), TOPSIS and Interval-valued intuitionistic fuzzy sets (IVIFSs) |
No specific technology discussed |
Size of the organization not specified (small, medium or large) |
Stage 1: Decide the criteria; |
No specific industry |
Stage 2: Create criteria matrix; |
Country studied: not specified. |
Stage 3: Analyze the matrix for consistency; |
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Stage 4: Calculate the matrix of evaluation scores; |
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Phase 5: Multiply the matrices; |
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Phase 6: Decide the priority vectors; |
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Phase 7: Create matrices based on degree of possibility; |
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Phase 8: Standardize the weighting of the criteria. |
An integrated model of fuzzy multi-criteria decision making and stochastic programming for the evaluating and ranking of advanced manufacturing technologies (Olfati et al., 2020Olfati, M., Yuan, W., & Nasseri, S. H. (2020). An integrated model of fuzzy multi-criteria decision making and stochastic programming for the evaluating and ranking of advanced manufacturing technologies. Iranian Journal of Fuzzy Systems, 17(5), 183-196.) |
Not specified |
Economic (Payback Return investment, Discounted cash flow (NPW, IRR)) and Strategic (Technical importance, Business objectives, Competitive advantage, Research and development) |
Linear Programming and triangular fuzzy numbers. |
No specific technology discussed |
Size of the organization not specified (small, medium or large) |
Stage 1: Integrated decision making; |
No specific industry |
Phase 2: Establishing the strategic and economic criteria; |
Country studied: not specified. |
Phase 3: Establishing weighting for the criteria; |
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Phase 4: Deciding the better technology options; |
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Phase 5: Classifying each of the technology options and selecting the best investment. |
Evaluating strategies for implementing industry |
Literature Review and Survey |
Leadership |
Best Worst Model and TODIM-IVIF |
No specific technology discussed |
Size of the organization not specified (small, medium or large) |
Stage 1: literature review; |
4.0: a hybrid expert oriented approach of BWM |
Client |
No specific industry |
Stage 2: determining the requirements; |
and interval valued intuitionistic fuzzy TODIM (Mahdiraji et al., 2020Mahdiraji, H. A., Zavadskas, E. K., Skare, M., Kafshgar, F. Z. R., & Arab, A. (2020). Evaluating strategies for implementing Industry 4.0: a hybrid expert oriented approach of BWM and interval valued intuitionistic fuzzy TODIM. Economic Research-Ekonomska Istraživanja, 33(1), 1600-1620. http://dx.doi.org/10.1080/1331677X.2020.1753090. http://dx.doi.org/10.1080/1331677X.2020....
) |
Product |
Country studied: not specified. |
Stage 3: Consulting the specialists; |
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Operation |
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Stage 4: Applying the BWG (sic) method; |
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Culture |
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Stage 5: Applying the TODIM-IVIF method. |
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Teams |
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Technology |
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Organization |
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Quality |
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