IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i7p4060-d782481.html
   My bibliography  Save this article

An Innovative Risk Matrix Model for Warehousing Productivity Performance

Author

Listed:
  • Rudiah Md Hanafiah

    (Faculty of Maritime Studies, University Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia)

  • Nur Hazwani Karim

    (Faculty of Maritime Studies, University Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia)

  • Noorul Shaiful Fitri Abdul Rahman

    (Faculty of Business, Higher Colleges of Technology, Abu Dhabi 25035, United Arab Emirates)

  • Saharuddin Abdul Hamid

    (Faculty of Maritime Studies, University Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia)

  • Ahmed Maher Mohammed

    (Faculty of Transport and Logistics, Muscat University, Muscat 113, Oman)

Abstract

In today’s era of industrial economics, warehousing is a complex process with many moving parts and is required to contribute productively to the success of supply chain management. Therefore, risk management in warehouses is a crucial point of contention to ensure sustainability with global supply chain processes to accommodate good productivity performance. Therefore, this study aims to analyse risks factors that affect warehouse productivity performance towards a systematic identification of critical factors that managers should target to sustain and grow warehouse productivity. This study utilised a traditional risk matrix framework, integrating it with the Borda method and Analytical Hierarchy Process (AHP) technique to produce an innovative risk matrix model. The results indicate that from the constructed ten warehouse operation risk categories and 32 risk factors, seven risk categories, namely operational, human, market, resource, financial, security and regulatory, including 13 risk factors were prioritised as the most critical risks impacting warehouse productivity performance. The developed risks analysis model guides warehouse managers in targeting critical risks factors that have a higher influence on warehouse productivity performance. This would be extremely helpful for companies with limited resources but seek productivity improvement and risks mitigation. Considering the increasing interest in sustainable development goals (economic, environmental, and social), arguably, this work support managers in boosting these goals within their organisation. This study is expected to benefit warehouse managers in understanding how to manage risk, handle unexpected disruptions, and improve performance in ever-changing uncertain business environments. It often has a profound effect on the productivity level of an organisation. This study proposes an innovative risks analysis model that aims to analyse risks, frame them, and rate them according to their importance, particularly for warehousing productivity performance.

Suggested Citation

  • Rudiah Md Hanafiah & Nur Hazwani Karim & Noorul Shaiful Fitri Abdul Rahman & Saharuddin Abdul Hamid & Ahmed Maher Mohammed, 2022. "An Innovative Risk Matrix Model for Warehousing Productivity Performance," Sustainability, MDPI, vol. 14(7), pages 1-21, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4060-:d:782481
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/7/4060/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/7/4060/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chand, Pushpendu & Thakkar, Jitesh J. & Ghosh, Kunal Kanti, 2018. "Analysis of supply chain complexity drivers for Indian mining equipment manufacturing companies combining SAP-LAP and AHP," Resources Policy, Elsevier, vol. 59(C), pages 389-410.
    2. Ahmed Mohammed & Morteza Yazdani & Amar Oukil & Ernesto D. R. Santibanez Gonzalez, 2021. "A Hybrid MCDM Approach towards Resilient Sourcing," Sustainability, MDPI, vol. 13(5), pages 1-30, March.
    3. Tang, Christopher S., 2006. "Perspectives in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 103(2), pages 451-488, October.
    4. Richard W. Monroe & Jay M. Teets & P. Richard Martin, 2014. "Supply chain risk management: an analysis of sources of risk and mitigation strategies," International Journal of Applied Management Science, Inderscience Enterprises Ltd, vol. 6(1), pages 4-21.
    5. C.R. Vishnu & R. Sridharan & P.N. Ram Kumar, 2019. "Supply chain risk inter-relationships and mitigation in Indian scenario: an ISM-AHP integrated approach," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 32(3/4), pages 548-578.
    6. Scott DuHadway & Steven Carnovale & Benjamin Hazen, 2019. "Understanding risk management for intentional supply chain disruptions: risk detection, risk mitigation, and risk recovery," Annals of Operations Research, Springer, vol. 283(1), pages 179-198, December.
    7. Alhawari, Samer & Karadsheh, Louay & Nehari Talet, Amine & Mansour, Ebrahim, 2012. "Knowledge-Based Risk Management framework for Information Technology project," International Journal of Information Management, Elsevier, vol. 32(1), pages 50-65.
    8. Hamed Jahani & Babak Abbasi & Zahra Hosseinifard & Masih Fadaki & James P. Minas, 2021. "Disruption risk management in service-level agreements," International Journal of Production Research, Taylor & Francis Journals, vol. 59(1), pages 226-244, January.
    9. Hao Fanghua & Chen Guanchun, 2010. "A Fuzzy Multi-Criteria Group Decision-Making Model Based on Weighted Borda Scoring Method for Watershed Ecological Risk Management: a Case Study of Three Gorges Reservoir Area of China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 2139-2165, August.
    10. Rocha, Phelipe Medeiros da & Barros, Alexandre Pinheiro de & Silva, Glauco Barbosa da & Costa, Helder Gomes, 2016. "Analysis of the operational performance of brazilian airport terminals: A multicriteria approach with De Borda-AHP integration," Journal of Air Transport Management, Elsevier, vol. 51(C), pages 19-26.
    11. Alexandre Dolgui & Dmitry Ivanov, 2021. "Ripple effect and supply chain disruption management: new trends and research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 59(1), pages 102-109, January.
    12. William Ho & Tian Zheng & Hakan Yildiz & Srinivas Talluri, 2015. "Supply chain risk management: a literature review," International Journal of Production Research, Taylor & Francis Journals, vol. 53(16), pages 5031-5069, August.
    13. Tang, Christopher & Tomlin, Brian, 2008. "The power of flexibility for mitigating supply chain risks," International Journal of Production Economics, Elsevier, vol. 116(1), pages 12-27, November.
    14. Lam, H.Y. & Choy, K.L. & Ho, G.T.S. & Cheng, Stephen W.Y. & Lee, C.K.M., 2015. "A knowledge-based logistics operations planning system for mitigating risk in warehouse order fulfillment," International Journal of Production Economics, Elsevier, vol. 170(PC), pages 763-779.
    15. Jyrki Wallenius & James S. Dyer & Peter C. Fishburn & Ralph E. Steuer & Stanley Zionts & Kalyanmoy Deb, 2008. "Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead," Management Science, INFORMS, vol. 54(7), pages 1336-1349, July.
    16. Ahmed Mohammed & Irina Harris & Anthony Soroka & Mohamed Naim & Tim Ramjaun & Morteza Yazdani, 2021. "Gresilient supplier assessment and order allocation planning," Annals of Operations Research, Springer, vol. 296(1), pages 335-362, January.
    17. Dmitry Ivanov & Ajay Das, 2020. "Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: a research note," International Journal of Integrated Supply Management, Inderscience Enterprises Ltd, vol. 13(1), pages 90-102.
    18. Hamed Taherdoost, 2016. "Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research," Post-Print hal-02546796, HAL.
    19. Peter Emerson, 2013. "The original Borda count and partial voting," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 40(2), pages 353-358, February.
    20. Jafar Namdar & Xueping Li & Rupy Sawhney & Ninad Pradhan, 2018. "Supply chain resilience for single and multiple sourcing in the presence of disruption risks," International Journal of Production Research, Taylor & Francis Journals, vol. 56(6), pages 2339-2360, March.
    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. Dalia Perkumienė & Kristina Ratautaitė & Rasa Pranskūnienė, 2022. "Innovative Solutions and Challenges for the Improvement of Storage Processes," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    2. Peter Bubenik & Juraj Capek & Miroslav Rakyta & Vladimira Binasova & Katarina Staffenova, 2022. "Impact of Strategy Change on Business Process Management," Sustainability, MDPI, vol. 14(17), pages 1-23, September.

    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. K. Katsaliaki & P. Galetsi & S. Kumar, 2022. "Supply chain disruptions and resilience: a major review and future research agenda," Annals of Operations Research, Springer, vol. 319(1), pages 965-1002, December.
    2. Antonio Zavala-Alcívar & María-José Verdecho & Juan-José Alfaro-Saiz, 2020. "A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain," Sustainability, MDPI, vol. 12(16), pages 1-38, August.
    3. Guoqing Zhao & Shaofeng Liu & Carmen Lopez & Yi Wang & Haiyan Lu & Jinhua Zhang, 2024. "Identification, establishment of connection, and clustering of social risks involved in the agri-food supply chains: a cross-country comparative study," Annals of Operations Research, Springer, vol. 338(2), pages 1241-1282, July.
    4. Lin, Yongjia & Fan, Di & Shi, Xuanyi & Fu, Maggie, 2021. "The effects of supply chain diversification during the COVID-19 crisis: Evidence from Chinese manufacturers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    5. Cheramin, Meysam & Saha, Apurba Kumar & Cheng, Jianqiang & Paul, Sanjoy Kumar & Jin, Hongyue, 2021. "Resilient NdFeB magnet recycling under the impacts of COVID-19 pandemic: Stochastic programming and Benders decomposition," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    6. Qazi, Abroon & Dickson, Alex & Quigley, John & Gaudenzi, Barbara, 2018. "Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks," International Journal of Production Economics, Elsevier, vol. 196(C), pages 24-42.
    7. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    8. Baghersad, Milad & Zobel, Christopher W., 2021. "Assessing the extended impacts of supply chain disruptions on firms: An empirical study," International Journal of Production Economics, Elsevier, vol. 231(C).
    9. Garvey, Myles D. & Carnovale, Steven, 2020. "The rippled newsvendor: A new inventory framework for modeling supply chain risk severity in the presence of risk propagation," International Journal of Production Economics, Elsevier, vol. 228(C).
    10. Jie Lu & Feng Li & Desheng Wu, 2024. "A Two-Stage Sustainable Supplier Selection Model Considering Disruption Risk," Sustainability, MDPI, vol. 16(9), pages 1-21, May.
    11. Shashi & Piera Centobelli & Roberto Cerchione & Myriam Ertz, 2020. "Managing supply chain resilience to pursue business and environmental strategies," Business Strategy and the Environment, Wiley Blackwell, vol. 29(3), pages 1215-1246, March.
    12. Shoufeng Ji & Pengyun Zhao & Tingting Ji, 2023. "A Hybrid Optimization Method for Sustainable and Flexible Design of Supply–Production–Distribution Network in the Physical Internet," Sustainability, MDPI, vol. 15(7), pages 1-34, April.
    13. Zhu, Xiaoyan & Cao, Yunzhi, 2021. "The optimal recovery-fund based strategy for uncertain supply chain disruptions: A risk-averse two-stage stochastic programming approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    14. Chowdhury, Priyabrata & Paul, Sanjoy Kumar & Kaisar, Shahriar & Moktadir, Md. Abdul, 2021. "COVID-19 pandemic related supply chain studies: A systematic review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    15. Dmitry Ivanov & Boris Sokolov, 2019. "Simultaneous structural–operational control of supply chain dynamics and resilience," Annals of Operations Research, Springer, vol. 283(1), pages 1191-1210, December.
    16. Muhammad Junaid & Ye Xue & Muzzammil Wasim Syed & Ji Zu Li & Muhammad Ziaullah, 2019. "A Neutrosophic AHP and TOPSIS Framework for Supply Chain Risk Assessment in Automotive Industry of Pakistan," Sustainability, MDPI, vol. 12(1), pages 1-26, December.
    17. Lapko, Yulia & Trucco, Paolo & Nuur, Cali, 2016. "The business perspective on materials criticality: Evidence from manufacturers," Resources Policy, Elsevier, vol. 50(C), pages 93-107.
    18. Kraude, Richard & Narayanan, Sriram & Talluri, Srinivas, 2022. "Evaluating the performance of supply chain risk mitigation strategies using network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1168-1182.
    19. Dong, Qingxing & Cooper, Orrin, 2016. "An orders-of-magnitude AHP supply chain risk assessment framework," International Journal of Production Economics, Elsevier, vol. 182(C), pages 144-156.
    20. Chowdhury, Nighat Afroz & Ali, Syed Mithun & Mahtab, Zuhayer & Rahman, Towfique & Kabir, Golam & Paul, Sanjoy Kumar, 2019. "A structural model for investigating the driving and dependence power of supply chain risks in the readymade garment industry," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 102-113.

    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:14:y:2022:i:7:p:4060-:d:782481. 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.