IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i24p13777-d701780.html
   My bibliography  Save this article

Key Performance Indicators Selection through an Analytic Network Process Model for Tooling and Die Industry

Author

Listed:
  • Diogo Rodrigues

    (Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal)

  • Radu Godina

    (Research and Development Unit in Mechanical and Industrial Engineering (UNIDEMI), Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal)

  • Pedro Espadinha da Cruz

    (Research and Development Unit in Mechanical and Industrial Engineering (UNIDEMI), Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal)

Abstract

In the last few decades, the fast technological development has caused high competitiveness among companies, encouraging a pursuit for strategies that allow them to gain competitive advantage, such as the monitoring of performance by using key performance indicators (KPIs). However, its selection process is complex since there are several KPIs available to evaluate performance and different relationships between them. To overcome this challenge, the use of a multiple criteria decision-making model (MCDM) was proposed, namely the analytic network process (ANP) through which a reduced number of them are prioritized. To identify which KPIs are suitable for the press cast and die manufacturing industry, a literature review was made, and 58 unique KPIs were identified. Thus, to validate the proposed methodology, a case study was carried out in an automotive press molding industry. With the implementation of the proposed ANP model it was possible to identify 9 KPIs that ensure the correct molding process monitoring, while being aligned with the Balanced Scorecard criteria. The results show that the proposed model is suitable for selecting KPIs for the molding industry.

Suggested Citation

  • Diogo Rodrigues & Radu Godina & Pedro Espadinha da Cruz, 2021. "Key Performance Indicators Selection through an Analytic Network Process Model for Tooling and Die Industry," Sustainability, MDPI, vol. 13(24), pages 1-20, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13777-:d:701780
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/24/13777/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/24/13777/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jeongcheol Lee & Sungbum Jun & Tai-Woo Chang & Jinwoo Park, 2017. "A Smartness Assessment Framework for Smart Factories Using Analytic Network Process," Sustainability, MDPI, vol. 9(5), pages 1-15, May.
    2. Chemweno, Peter & Pintelon, Liliane & Van Horenbeek, Adriaan & Muchiri, Peter, 2015. "Development of a risk assessment selection methodology for asset maintenance decision making: An analytic network process (ANP) approach," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 663-676.
    3. Kannan Govindan & Aditi & Jyoti Dhingra Darbari & Arshia Kaul & PC Jha, 2021. "Structural model for analysis of key performance indicators for sustainable manufacturer–supplier collaboration: A grey‐decision‐making trial and evaluation laboratory‐based approach," Business Strategy and the Environment, Wiley Blackwell, vol. 30(4), pages 1702-1722, May.
    4. Bastian Verhaelen & Sina Peukert & Gisela Lanza, 2021. "Site Selection Processes in Global Production Networks," Management for Professionals, in: Thomas Friedli & Gisela Lanza & Dominik Remling (ed.), Global Manufacturing Management, chapter 7, pages 101-115, Springer.
    5. Nicole Stricker & Fabio Echsler Minguillon & Gisela Lanza, 2017. "Selecting key performance indicators for production with a linear programming approach," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5537-5549, October.
    6. Ha, Min-Ho & Yang, Zaili, 2017. "Comparative analysis of port performance indicators: Independency and interdependency," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 264-278.
    7. Thomas L. Saaty & Luis G. Vargas, 2013. "The Analytic Network Process," International Series in Operations Research & Management Science, in: Decision Making with the Analytic Network Process, edition 2, chapter 0, pages 1-40, Springer.
    8. Daniela Carlucci & Giovanni Schiuma, 2010. "Determining Key Performance Indicators: An Analytical Network Approach," World Scientific Book Chapters, in: Angappa Gunasekaran & Maqsood Sandhu (ed.), Handbook On Business Information Systems, chapter 21, pages 515-536, World Scientific Publishing Co. Pte. Ltd..
    9. Kheybari, Siamak & Rezaie, Fariba Mahdi & Farazmand, Hadis, 2020. "Analytic network process: An overview of applications," Applied Mathematics and Computation, Elsevier, vol. 367(C).
    10. Rainer Silbernagel & Tobias Arndt & Sina Peukert & Gisela Lanza, 2021. "Process Quality Improvements in Global Production Networks," Management for Professionals, in: Thomas Friedli & Gisela Lanza & Dominik Remling (ed.), Global Manufacturing Management, chapter 12, pages 167-177, Springer.
    11. Thomas L. Saaty & Luis G. Vargas, 2013. "Decision Making with the Analytic Network Process," International Series in Operations Research and Management Science, Springer, edition 2, number 978-1-4614-7279-7, March.
    12. Atanu Sengupta & Sanjoy De, 2020. "Review of Literature," India Studies in Business and Economics, in: Assessing Performance of Banks in India Fifty Years After Nationalization, chapter 0, pages 15-30, Springer.
    13. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
    14. Grodzicki, Maciej J. & Skrzypek, Jurand, 2020. "Cost-competitiveness and structural change in value chains – vertically-integrated analysis of the European automotive sector," Structural Change and Economic Dynamics, Elsevier, vol. 55(C), pages 276-287.
    15. Kourkoumpas, Dimitrios-Sotirios & Benekos, Georgios & Nikolopoulos, Nikolaos & Karellas, Sotirios & Grammelis, Panagiotis & Kakaras, Emmanouel, 2018. "A review of key environmental and energy performance indicators for the case of renewable energy systems when integrated with storage solutions," Applied Energy, Elsevier, vol. 231(C), pages 380-398.
    16. Brint, Andrew & Genovese, Andrea & Piccolo, Carmela & Taboada-Perez, Gerardo J., 2021. "Reducing data requirements when selecting key performance indicators for supply chain management: The case of a multinational automotive component manufacturer," International Journal of Production Economics, Elsevier, vol. 233(C).
    17. M. Ishaq Bhatti & H. Awan & Z. Razaq, 2014. "The key performance indicators (KPIs) and their impact on overall organizational performance," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3127-3143, November.
    18. Joshi, Deepika & Nepal, Bimal & Rathore, Ajay Pal Singh & Sharma, Dipti, 2013. "On supply chain competitiveness of Indian automotive component manufacturing industry," International Journal of Production Economics, Elsevier, vol. 143(1), pages 151-161.
    19. Jairo Ortega & Sarbast Moslem & János Tóth & Tamás Péter & Juan Palaguachi & Mario Paguay, 2020. "Using Best Worst Method for Sustainable Park and Ride Facility Location," Sustainability, MDPI, vol. 12(23), pages 1-18, December.
    20. Jairo Ortega & Sarbast Moslem & Juan Palaguachi & Martin Ortega & Tiziana Campisi & Vincenza Torrisi, 2021. "An Integrated Multi Criteria Decision Making Model for Evaluating Park-and-Ride Facility Location Issue: A Case Study for Cuenca City in Ecuador," Sustainability, MDPI, vol. 13(13), pages 1-16, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Puthearath Chan, 2022. "An Empirical Study on Data Validation Methods of Delphi and General Consensus," Data, MDPI, vol. 7(2), pages 1-16, January.
    2. Snežana Nestić & Ranka Gojković & Tijana Petrović & Danijela Tadić & Predrag Mimović, 2022. "Quality Performance Indicators Evaluation and Ranking by Using TOPSIS with the Interval-Intuitionistic Fuzzy Sets in Project-Oriented Manufacturing Companies," Mathematics, MDPI, vol. 10(22), pages 1-19, November.
    3. Jing-Wei Liu & Che-Wei Chang & Yao-Ji Wang & Yi-Hui Liu, 2022. "Constructing a Decision Model for Health Club Members to Purchase Coaching Programs during the COVID-19 Epidemic," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
    4. Tien-Hsiang Chang & Kuei-Ying Hsu & Hsin-Pin Fu & Ying-Hua Teng & Yi-Jhen Li, 2022. "Integrating FSE and AHP to Identify Valuable Customer Needs by Service Quality Analysis," Sustainability, MDPI, vol. 14(3), pages 1-15, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Clara Moreira Senne & Josiane Palma Lima & Fábio Favaretto, 2021. "An Index for the Sustainability of Integrated Urban Transport and Logistics: The Case Study of São Paulo," Sustainability, MDPI, vol. 13(21), pages 1-18, November.
    2. Rachele Corticelli & Margherita Pazzini & Cecilia Mazzoli & Claudio Lantieri & Annarita Ferrante & Valeria Vignali, 2022. "Urban Regeneration and Soft Mobility: The Case Study of the Rimini Canal Port in Italy," Sustainability, MDPI, vol. 14(21), pages 1-27, November.
    3. Hocine, Amine & Kouaissah, Noureddine, 2020. "XOR analytic hierarchy process and its application in the renewable energy sector," Omega, Elsevier, vol. 97(C).
    4. Abrahamsen, Eirik Bjorheim & Milazzo, Maria Francesca & Selvik, Jon T. & Asche, Frank & Abrahamsen, HÃ¥kon Bjorheim, 2020. "Prioritising investments in safety measures in the chemical industry by using the Analytic Hierarchy Process," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    5. Joram Schito & Joshu Jullier & Martin Raubal, 2019. "A framework for integrating stakeholder preferences when deciding on power transmission line corridors," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 7(3), pages 159-195, November.
    6. Melani, Arthur Henrique Andrade & Murad, Carlos Alberto & Caminada Netto, Adherbal & Souza, Gilberto Francisco Martha de & Nabeta, Silvio Ikuyo, 2018. "Criticality-based maintenance of a coal-fired power plant," Energy, Elsevier, vol. 147(C), pages 767-781.
    7. Wu, Chen-Fa & Wang, Hsiao-Hsuan & Chen, Szu-Hung & Trac, Luu Van Thong, 2024. "Assessing the efficiency of bird habitat conservation strategies in farmland ecosystems," Ecological Modelling, Elsevier, vol. 492(C).
    8. Wolfgang Ossadnik & Stefanie Schinke & Ralf H. Kaspar, 2016. "Group Aggregation Techniques for Analytic Hierarchy Process and Analytic Network Process: A Comparative Analysis," Group Decision and Negotiation, Springer, vol. 25(2), pages 421-457, March.
    9. A. M. Alamdari & Y. Jabarzadeh & B. Adams & D. Samson & S. Khanmohammadi, 2023. "An analytic network process model to prioritize supply chain risks in green residential megaprojects," Operations Management Research, Springer, vol. 16(1), pages 141-163, March.
    10. Amal Aldababseh & Marouane Temimi & Praveen Maghelal & Oliver Branch & Volker Wulfmeyer, 2018. "Multi-Criteria Evaluation of Irrigated Agriculture Suitability to Achieve Food Security in an Arid Environment," Sustainability, MDPI, vol. 10(3), pages 1-33, March.
    11. Stumpf, Lukas & Schöggl, Josef-Peter & Baumgartner, Rupert J., 2023. "Circular plastics packaging – Prioritizing resources and capabilities along the supply chain," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    12. Morteza Alaeddini & Masoud Mir-Amini, 2020. "Integrating COBIT with a hybrid group decision-making approach for a business-aligned IT roadmap formulation," Information Technology and Management, Springer, vol. 21(2), pages 63-94, June.
    13. Lin, Sheng-Hau & Zhao, Xiaofeng & Wu, Jiuxing & Liang, Fachao & Li, Jia-Hsuan & Lai, Ren-Ji & Hsieh, Jing-Chzi & Tzeng, Gwo-Hshiung, 2021. "An evaluation framework for developing green infrastructure by using a new hybrid multiple attribute decision-making model for promoting environmental sustainability," Socio-Economic Planning Sciences, Elsevier, vol. 75(C).
    14. David Staš & Radim Lenort & Pavel Wicher & David Holman, 2015. "Green Transport Balanced Scorecard Model with Analytic Network Process Support," Sustainability, MDPI, vol. 7(11), pages 1-19, November.
    15. Kik, M.C. & Claassen, G.D.H. & Meuwissen, M.P.M. & Smit, A.B. & Saatkamp, H.W., 2021. "Actor analysis for sustainable soil management – A case study from the Netherlands," Land Use Policy, Elsevier, vol. 107(C).
    16. Snežana Nestić & Ranka Gojković & Tijana Petrović & Danijela Tadić & Predrag Mimović, 2022. "Quality Performance Indicators Evaluation and Ranking by Using TOPSIS with the Interval-Intuitionistic Fuzzy Sets in Project-Oriented Manufacturing Companies," Mathematics, MDPI, vol. 10(22), pages 1-19, November.
    17. Nitin Panwar & Sanjeev Kumar, 2022. "Mathematical modelling and performance analysis of screening unit in paper plant," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2751-2763, October.
    18. Mohammed Ifkirne & Houssam El Bouhi & Siham Acharki & Quoc Bao Pham & Abdelouahed Farah & Nguyen Thi Thuy Linh, 2022. "Multi-Criteria GIS-Based Analysis for Mapping Suitable Sites for Onshore Wind Farms in Southeast France," Land, MDPI, vol. 11(10), pages 1-26, October.
    19. Lin, Sheng-Hau & Huang, Xianjin & Fu, Guole & Chen, Jia-Tsong & Zhao, Xiaofeng & Li, Jia-Hsuan & Tzeng, Gwo-Hshiung, 2021. "Evaluating the sustainability of urban renewal projects based on a model of hybrid multiple-attribute decision-making," Land Use Policy, Elsevier, vol. 108(C).
    20. Enrique Mu & Howard Stern, 2018. "A Contingent/Assimilation Framework for Public Interorganizational Systems Decisions: Should the City of Pittsburgh and Allegheny County Consolidate Information Technology Services?," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1611-1658, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13777-:d:701780. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.