IDEAS home Printed from https://ideas.repec.org/a/igg/jssmet/v12y2021i1p142-181.html
   My bibliography  Save this article

The Business Transformation Framework and Enterprise Architecture Framework for Managers in Business Innovation: An Applied Holistic Mathematical Model

Author

Listed:
  • Antoine Trad

    (IBISTM, France)

Abstract

This journal article proposes a cross-business domain applied holistic mathematical model (AHMM) that is the result of a lifetime long research on business transformations, applied mathematics, software modelling, business engineering, financial analysis, and global enterprise architecture. This ultimate research is based on an authentic and proprietary mixed research method that is supported by an underlining mainly qualitative holistic reasoning model module. The proposed AHMM formalism attempts to mimic some functions of the human brain, which uses empirical processes that are mainly based on the beam-search, like heuristic decision-making process. The AHMM can be used to implement a decision-making system or an expert system that can integrate in the enterprise's business, information and communication technology environments. The AHMM uses a behaviour driven development environment or a natural language environment that can be easily adopted by the project's development teams. The AHMM offers a high level implementation environment that can be used by any team member without any prior computer sciences qualification. The AHMM can be used also to model enterprise architecture (EA) blueprints, business transformation projects, or knowledge management systems; it is supported by many real-life cases of various business domains. The uniqueness of this research is that the AHMM promotes a holistic unbundling process, the alignment of various EA standards and transformation strategies to support business transformation projects.

Suggested Citation

  • Antoine Trad, 2021. "The Business Transformation Framework and Enterprise Architecture Framework for Managers in Business Innovation: An Applied Holistic Mathematical Model," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 12(1), pages 142-181, January.
  • Handle: RePEc:igg:jssmet:v:12:y:2021:i:1:p:142-181
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSSMET.20210101.oa1
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Giulio D'Emilia & Antonella Gaspari & Diego Pascual Galar, 2018. "Improvement of Measurement Contribution for Asset Characterization in Complex Engineering Systems by an Iterative Methodology," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 9(2), pages 85-103, April.
    2. Houssem Felfel & Omar Ayadi & Faouzi Masmoudi, 2017. "Pareto Optimal Solution Selection for a Multi-Site Supply Chain Planning Problem Using the VIKOR and TOPSIS Methods," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 8(3), pages 21-39, July.
    3. Salimifard, Khodakaram & Wright, Mike, 2001. "Petri net-based modelling of workflow systems: An overview," European Journal of Operational Research, Elsevier, vol. 134(3), pages 664-676, November.
    4. Peterson, Stephen, 2011. "Why It Worked: Critical Success Factors of a Financial Reform Project in Africa," Working Paper Series 11-019, Harvard University, John F. Kennedy School of Government.
    5. Peterson, Stephen Bovard, 2011. "Why it Worked: Critical Success Factors of a Financial Reform Project in Africa," Scholarly Articles 4876869, Harvard Kennedy School of Government.
    6. Mohammad Azadfallah, 2018. "A New Entropy-Based Approach to Determine the Weights of Decision Makers for Each Criterion With Crisp and Interval Data in Group Decision Making Under Multiple Attribute," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 9(4), pages 37-56, October.
    7. Mohammad Azadfallah, 2018. "A Novel Method to Assign Weights to Decision Makers for each Criterion in Group Decision Making Under Multiple Criteria with Crisp and Interval Data," International Journal of Applied Management Sciences and Engineering (IJAMSE), IGI Global, vol. 5(2), pages 15-46, July.
    8. Salma Zaiane & Fatma Ben Moussa, 2018. "Cognitive Biases, Risk Perception, and Individual's Decision to Start a New Venture," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 9(3), pages 14-29, July.
    9. Biswajit Tripathy & Jibitesh Mishra, 2017. "A Generalized Framework for E-Contract," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 8(4), pages 1-18, October.
    10. F Della Croce & V T'kindt, 2002. "A Recovering Beam Search algorithm for the one-machine dynamic total completion time scheduling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(11), pages 1275-1280, November.
    11. Ho, William & Xu, Xiaowei & Dey, Prasanta K., 2010. "Multi-criteria decision making approaches for supplier evaluation and selection: A literature review," European Journal of Operational Research, Elsevier, vol. 202(1), pages 16-24, April.
    Full references (including those not matched with items on IDEAS)

    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. Irene Blanco-Gutiérrez & Consuelo Varela-Ortega & Rhys Manners, 2020. "Evaluating Animal-Based Foods and Plant-Based Alternatives Using Multi-Criteria and SWOT Analyses," IJERPH, MDPI, vol. 17(21), pages 1-26, October.
    2. Jianxiong Zhang & Lin Feng & Wansheng Tang, 2014. "Optimal Contract Design of Supplier-Led Outsourcing Based on Pontryagin Maximum Principle," Journal of Optimization Theory and Applications, Springer, vol. 161(2), pages 592-607, May.
    3. 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.
    4. Pishchulov, Grigory & Trautrims, Alexander & Chesney, Thomas & Gold, Stefan & Schwab, Leila, 2019. "The Voting Analytic Hierarchy Process revisited: A revised method with application to sustainable supplier selection," International Journal of Production Economics, Elsevier, vol. 211(C), pages 166-179.
    5. Ventura, José A. & Bunn, Kevin A. & Venegas, Bárbara B. & Duan, Lisha, 2021. "A coordination mechanism for supplier selection and order quantity allocation with price-sensitive demand and finite production rates," International Journal of Production Economics, Elsevier, vol. 233(C).
    6. Sushil, 2019. "Efficient interpretive ranking process incorporating implicit and transitive dominance relationships," Annals of Operations Research, Springer, vol. 283(1), pages 1489-1516, December.
    7. Alaa Alden Al Mohamed & Sobhi Al Mohamed, 2023. "Application of fuzzy group decision-making selecting green supplier: a case study of the manufacture of natural laurel soap," Future Business Journal, Springer, vol. 9(1), pages 1-20, December.
    8. E A Silver, 2004. "An overview of heuristic solution methods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(9), pages 936-956, September.
    9. Zhu, Bin & Xu, Zeshui, 2014. "Stochastic preference analysis in numerical preference relations," European Journal of Operational Research, Elsevier, vol. 237(2), pages 628-633.
    10. Imane Tronnebati & Manal El Yadari & Fouad Jawab, 2022. "A Review of Green Supplier Evaluation and Selection Issues Using MCDM, MP and AI Models," Sustainability, MDPI, vol. 14(24), pages 1-22, December.
    11. Hatami-Marbini, Adel & Emrouznejad, Ali & Tavana, Madjid, 2011. "A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making," European Journal of Operational Research, Elsevier, vol. 214(3), pages 457-472, November.
    12. Rego, César & Duarte, Renato, 2009. "A filter-and-fan approach to the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 194(3), pages 650-662, May.
    13. L. A. Shah & A. Etienne & A. Siadat & F. Vernadat, 2016. "Decision-making in the manufacturing environment using a value-risk graph," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 617-630, June.
    14. Chen, Jen-Yi & Baddam, Swathi R., 2015. "The effect of unethical behavior and learning on strategic supplier selection," International Journal of Production Economics, Elsevier, vol. 167(C), pages 74-87.
    15. Bo Yan & Zhuo Chen & Hongyuan Li, 2019. "Evaluation of agri-product supply chain competitiveness based on extension theory," Operational Research, Springer, vol. 19(2), pages 543-570, June.
    16. Jouglet, Antoine & Savourey, David & Carlier, Jacques & Baptiste, Philippe, 2008. "Dominance-based heuristics for one-machine total cost scheduling problems," European Journal of Operational Research, Elsevier, vol. 184(3), pages 879-899, February.
    17. Fecke, Wilm & Danne, Michael & Mußhoff, Oliver, 2018. "E-commerce in agriculture: The case of crop protection product purchases in a discrete choice experiment," DARE Discussion Papers 1803, Georg-August University of Göttingen, Department of Agricultural Economics and Rural Development (DARE).
    18. Ventura, José A. & Valdebenito, Victor A. & Golany, Boaz, 2013. "A dynamic inventory model with supplier selection in a serial supply chain structure," European Journal of Operational Research, Elsevier, vol. 230(2), pages 258-271.
    19. Jiang, R., 2013. "A tradeoff BX life and its applications," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 1-6.
    20. Kannan Govindan & R. Sivakumar, 2016. "Green supplier selection and order allocation in a low-carbon paper industry: integrated multi-criteria heterogeneous decision-making and multi-objective linear programming approaches," Annals of Operations Research, Springer, vol. 238(1), pages 243-276, March.

    More about this item

    Statistics

    Access and download statistics

    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:igg:jssmet:v:12:y:2021:i:1:p:142-181. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.