IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v210y2019icp27-41.html
   My bibliography  Save this article

Performance cause and effect studies: Analyzing high performance manufacturing companies

Author

Listed:
  • Okoshi, Cleina Yayoe
  • Pinheiro de Lima, Edson
  • Gouvea Da Costa, Sergio Eduardo

Abstract

Manufacturing strategy has an important role in business competitive strategy, because it connects performance indicators to company goals. Operations strategy is organized in performance objectives and decision areas constructs that define its content. This paper aims to understand the cause and effect relationships between operations strategy constructs. Considering performance as a dependent variable qualified as the ‘effect’, and the independent variables as decision areas policies and resources, which play the role as ‘cause’. This paper intends to answer two hypotheses: Performance results are dependent on policies and capabilities that are addressed and managed by companies' decision areas; There are specific relationships that connect performance results to decision areas capabilities and policies in ‘High Performance Manufacturing Companies’. The study uses information obtained from the 4th round of the ‘High Performance Manufacturing Survey’. The dataset is formed by indicators from 304 manufacturing companies of three industries: automotive, electronics and machine tools; that operate in 13 countries. The techniques used for analyzing the available data are descriptive statistics, factor analysis, and multiple linear regression. Results show that innovation, flexibility, quality, cost, speed, and reliability performance have a systemic dependence on structural and infrastructural decision areas, which cover design and managerial perspectives. The study also presents a systemic view that maps the effects on performance by capabilities and policies that are managed by decision areas. The first hypothesis is confirmed by the structural elements that define performance objectives and decision areas, however the second derived hypothesis is partially confirmed, where two patterns are identified by two group of factors: innovation and flexibility, versus cost, quality and time. The understanding of the ‘performance function’ has an immediate effect on connect it to improvement actions.

Suggested Citation

  • Okoshi, Cleina Yayoe & Pinheiro de Lima, Edson & Gouvea Da Costa, Sergio Eduardo, 2019. "Performance cause and effect studies: Analyzing high performance manufacturing companies," International Journal of Production Economics, Elsevier, vol. 210(C), pages 27-41.
    • Handle: RePEc:eee:proeco:v:210:y:2019:i:c:p:27-41
    DOI: 10.1016/j.ijpe.2019.01.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527319300039
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2019.01.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ioannis Bellos & Mark Ferguson & L. Beril Toktay, 2017. "The Car Sharing Economy: Interaction of Business Model Choice and Product Line Design," Manufacturing & Service Operations Management, INFORMS, vol. 19(2), pages 185-201, May.
    2. Hallgren, Mattias & Olhager, Jan, 2006. "Quantification in manufacturing strategy: A methodology and illustration," International Journal of Production Economics, Elsevier, vol. 104(1), pages 113-124, November.
    3. Saif Benjaafar & David Chen & Rowan Wang, 2017. "Managing Production-Inventory Systems with Scarce Resources," Manufacturing & Service Operations Management, INFORMS, vol. 19(2), pages 216-229, May.
    4. Huang, Shi-Ming & Ou, Chin-Shyh & Chen, Chyi-Miaw & Lin, Binshan, 2006. "An empirical study of relationship between IT investment and firm performance: A resource-based perspective," European Journal of Operational Research, Elsevier, vol. 173(3), pages 984-999, September.
    5. Suwignjo, P. & Bititci, U. S & Carrie, A. S, 2000. "Quantitative models for performance measurement system," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 231-241, March.
    6. Sueyoshi, Toshiyuki & Goto, Mika, 2010. "Measurement of a linkage among environmental, operational, and financial performance in Japanese manufacturing firms: A use of Data Envelopment Analysis with strong complementary slackness condition," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1742-1753, December.
    7. Rebolledo, Claudia & Jobin, Marie-Hélène, 2013. "Manufacturing and supply alignment: Are different manufacturing strategies linked to different purchasing practices?," International Journal of Production Economics, Elsevier, vol. 146(1), pages 219-226.
    8. Che-Ha, Norbani & Mavondo, Felix T. & Mohd-Said, Saad, 2014. "Performance or learning goal orientation: Implications for business performance," Journal of Business Research, Elsevier, vol. 67(1), pages 2811-2820.
    9. Wong, Christina W.Y. & Lai, Kee-hung & Shang, Kuo-Chung & Lu, Chin-Shan & Leung, T.K.P., 2012. "Green operations and the moderating role of environmental management capability of suppliers on manufacturing firm performance," International Journal of Production Economics, Elsevier, vol. 140(1), pages 283-294.
    10. Yusuf, Yahaya Y. & Gunasekaran, Angappa & Musa, Ahmed & Dauda, Mohammed & El-Berishy, Nagham M. & Cang, Shuang, 2014. "A relational study of supply chain agility, competitiveness and business performance in the oil and gas industry," International Journal of Production Economics, Elsevier, vol. 147(PB), pages 531-543.
    11. Khanchanapong, Teerasak & Prajogo, Daniel & Sohal, Amrik S. & Cooper, Brian K. & Yeung, Andy C.L. & Cheng, T.C.E., 2014. "The unique and complementary effects of manufacturing technologies and lean practices on manufacturing operational performance," International Journal of Production Economics, Elsevier, vol. 153(C), pages 191-203.
    12. Bititci, Umit S., 1995. "Modelling of performance measurement systems in manufacturing enterprises," International Journal of Production Economics, Elsevier, vol. 42(2), pages 137-147, December.
    13. Prajogo, Daniel & Chowdhury, Mesbahuddin & Yeung, Andy C.L. & Cheng, T.C.E., 2012. "The relationship between supplier management and firm's operational performance: A multi-dimensional perspective," International Journal of Production Economics, Elsevier, vol. 136(1), pages 123-130.
    14. Hong, Paul & Doll, William J. & Revilla, Elena & Nahm, Abraham Y., 2011. "Knowledge sharing and strategic fit in integrated product development proejcts: An empirical study," International Journal of Production Economics, Elsevier, vol. 132(2), pages 186-196, August.
    15. Demirbag, Mehmet & Tatoglu, Ekrem & Glaister, Keith W. & Zaim, Selim, 2010. "Measuring strategic decision making efficiency in different country contexts: A comparison of British and Turkish firms," Omega, Elsevier, vol. 38(1-2), pages 95-104, February.
    16. Suresh Muthulingam & Anupam Agrawal, 2016. "Does Quality Knowledge Spillover at Shared Suppliers? An Empirical Investigation," Manufacturing & Service Operations Management, INFORMS, vol. 18(4), pages 525-544, October.
    17. Machuca, José A.D. & Ortega Jiménez, Cesar H. & Garrido-Vega, Pedro & de los Ríos, José Luis Pérez Diez, 2011. "Do technology and manufacturing strategy links enhance operational performance? Empirical research in the auto supplier sector," International Journal of Production Economics, Elsevier, vol. 133(2), pages 541-550, October.
    18. Pinheiro de Lima, Edson & Gouvea da Costa, Sergio E. & Angelis, Jannis Jan & Munik, Juliano, 2013. "Performance measurement systems: A consensual analysis of their roles," International Journal of Production Economics, Elsevier, vol. 146(2), pages 524-542.
    19. Yongbin Zhao & Yuan Li & Soo Hoon Lee & Long Bo Chen, 2011. "Entrepreneurial Orientation, Organizational Learning, and Performance: Evidence from China," Entrepreneurship Theory and Practice, , vol. 35(2), pages 293-317, March.
    20. Bongsug Kevin Chae & David L. Olson, 2013. "Business Analytics For Supply Chain: A Dynamic-Capabilities Framework," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 9-26.
    21. Ang, James S.K. & Shimada, Tomoaki & Quek, Ser-Aik & Lim, Eugene, 2015. "Manufacturing strategy and competitive performance – An ACE analysis," International Journal of Production Economics, Elsevier, vol. 169(C), pages 240-252.
    22. Leong, G. K. & Snyder, D. L. & Ward, P. T., 1990. "Research in the process and content of manufacturing strategy," Omega, Elsevier, vol. 18(2), pages 109-122.
    23. Onesun Steve Yoo & Charles J. Corbett & Guillaume Roels, 2016. "Optimal Time Allocation for Process Improvement for Growth-Focused Entrepreneurs," Manufacturing & Service Operations Management, INFORMS, vol. 18(3), pages 361-375, 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. Chin, Tachia & Wang, Wannan & Yang, Meng & Duan, Yunlong & Chen, Yantai, 2021. "The moderating effect of managerial discretion on blockchain technology and the firms’ innovation quality: Evidence from Chinese manufacturing firms," International Journal of Production Economics, Elsevier, vol. 240(C).
    2. Schaefer, Jones Luís & Mairesse Siluk, Julio Cezar & Stefan de Carvalho, Patrícia & Maria de Miranda Mota, Caroline & Pinheiro, José Renes & Nuno da Silva Faria, Pedro & Gouvea da Costa, Sergio Eduard, 2023. "A framework for diagnosis and management of development and implementation of cloud-based energy communities - Energy cloud communities," Energy, Elsevier, vol. 276(C).
    3. Yigit Kazancoglu & Muhittin Sagnak & Yasanur Kayikci & Sachin Kumar Mangla, 2020. "Operational excellence in a green supply chain for environmental management: A case study," Business Strategy and the Environment, Wiley Blackwell, vol. 29(3), pages 1532-1547, March.
    4. Kamandulienė Asta & Kulbokas Žilvinas, 2020. "The Assessment of Financial Performance of the Lithuanian Dairy Sector’s Listed Companies," Management of Organizations: Systematic Research, Sciendo, vol. 83(1), pages 73-92, June.
    5. Veiga, Gabriela Lobo & Pinheiro de Lima, Edson & Frega, José Roberto & Gouvea da Costa, Sérgio Eduardo, 2021. "A DEA-based approach to assess manufacturing performance through operations strategy lenses," International Journal of Production Economics, Elsevier, vol. 235(C).
    6. 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).
    7. Duan, Yunlong & Wang, Wannan & Zhou, Wei, 2020. "The multiple mediation effect of absorptive capacity on the organizational slack and innovation performance of high-tech manufacturing firms: Evidence from Chinese firms," International Journal of Production Economics, Elsevier, vol. 229(C).

    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. Arana-Solares, Iván Andrés & Ortega-Jiménez, César H. & Alfalla-Luque, Rafaela & Pérez-Díez de los Ríos, José Luis, 2019. "Contextual factors intervening in the manufacturing strategy and technology management-performance relationship," International Journal of Production Economics, Elsevier, vol. 207(C), pages 81-95.
    2. Dubey, Rameshwar & Gunasekaran, Angappa & Samar Ali, Sadia, 2015. "Exploring the relationship between leadership, operational practices, institutional pressures and environmental performance: A framework for green supply chain," International Journal of Production Economics, Elsevier, vol. 160(C), pages 120-132.
    3. Berrah, L. & Mauris, G. & Montmain, J., 2008. "Monitoring the improvement of an overall industrial performance based on a Choquet integral aggregation," Omega, Elsevier, vol. 36(3), pages 340-351, June.
    4. Geyi, Dan’Asabe Godwin & Yusuf, Yahaya & Menhat, Masha S. & Abubakar, Tijjani & Ogbuke, Nnamdi J., 2020. "Agile capabilities as necessary conditions for maximising sustainable supply chain performance: An empirical investigation," International Journal of Production Economics, Elsevier, vol. 222(C).
    5. Miltenburg, John, 2008. "Setting manufacturing strategy for a factory-within-a-factory," International Journal of Production Economics, Elsevier, vol. 113(1), pages 307-323, May.
    6. Gu, Qiannong & Jitpaipoon, Thawatchai & Yang, Jie, 2017. "The impact of information integration on financial performance: A knowledge-based view," International Journal of Production Economics, Elsevier, vol. 191(C), pages 221-232.
    7. El-Khalil, Raed & Mezher, Mohamad Ali, 2020. "The mediating impact of sustainability on the relationship between agility and operational performance," Operations Research Perspectives, Elsevier, vol. 7(C).
    8. Ortega Jimenez, Cesar H. & Machuca, Jose A.D. & Garrido-Vega, Pedro & Filippini, Roberto, 2015. "The pursuit of responsiveness in production environments: From flexibility to reconfigurability," International Journal of Production Economics, Elsevier, vol. 163(C), pages 157-172.
    9. Garrido-Vega, Pedro & Ortega Jimenez, Cesar H. & de los Ríos, José Luis Díez Pérez & Morita, Michiya, 2015. "Implementation of technology and production strategy practices: Relationship levels in different industries," International Journal of Production Economics, Elsevier, vol. 161(C), pages 201-216.
    10. Henao, Rafael & Sarache, William, 2022. "Sustainable performance in manufacturing operations: The cumulative approach vs. trade-offs approach," International Journal of Production Economics, Elsevier, vol. 244(C).
    11. Cliville, Vincent & Berrah, Lamia & Mauris, Gilles, 2007. "Quantitative expression and aggregation of performance measurements based on the MACBETH multi-criteria method," International Journal of Production Economics, Elsevier, vol. 105(1), pages 171-189, January.
    12. Iqbal, Tahir & Huq, Faizul & Bhutta, M. Khurrum S., 2018. "Agile manufacturing relationship building with TQM, JIT, and firm performance: An exploratory study in apparel export industry of Pakistan," International Journal of Production Economics, Elsevier, vol. 203(C), pages 24-37.
    13. Alfalla-Luque, Rafaela & Machuca, José A.D. & Marin-Garcia, Juan A., 2018. "Triple-A and competitive advantage in supply chains: Empirical research in developed countries," International Journal of Production Economics, Elsevier, vol. 203(C), pages 48-61.
    14. Lai, Kee-hung & Feng, Yunting & Zhu, Qinghua, 2023. "Digital transformation for green supply chain innovation in manufacturing operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    15. Franck Brulhart & Sandrine Gherra & Bertrand V. Quelin, 2019. "Do Stakeholder Orientation and Environmental Proactivity Impact Firm Profitability?," Journal of Business Ethics, Springer, vol. 158(1), pages 25-46, August.
    16. Scott, James & Ho, William & Dey, Prasanta K. & Talluri, Srinivas, 2015. "A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments," International Journal of Production Economics, Elsevier, vol. 166(C), pages 226-237.
    17. Henry M. H. Chan & Vincent W. S. Cho, 2022. "An Empirical Study: The Impact of Collaborative Communications on New Product Creativity That Contributes to New Product Performance," Sustainability, MDPI, vol. 14(8), pages 1-17, April.
    18. Sueyoshi, Toshiyuki & Goto, Mika, 2012. "DEA environmental assessment of coal fired power plants: Methodological comparison between radial and non-radial models," Energy Economics, Elsevier, vol. 34(6), pages 1854-1863.
    19. Sujan Piya & Ahm Shamsuzzoha & Mohammad Khadem & Nasr Al-Hinai, 2020. "Identification of Critical Factors and Their Interrelationships to Design Agile Supply Chain: Special Focus to Oil and Gas Industries," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 21(3), pages 263-281, September.
    20. Rayenda Khresna Brahmana & Maria Kontesa, 2021. "Does clean technology weaken the environmental impact on the financial performance? Insight from global oil and gas companies," Business Strategy and the Environment, Wiley Blackwell, vol. 30(7), pages 3411-3423, 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:eee:proeco:v:210:y:2019:i:c:p:27-41. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.