Mathematically Modeled Algorithm for Intelligently Customized Optimization of an Erp

An ERP is a bundle of software packages cascaded meshed in integration to replace and expedite the transaction governing systems in an organization, Institute or Industry. The selection of an ERP for an Industry today is an inference of an analog study with no concrete justification to requisites and desperations of the Industry. A lot of parameters like Industry's requirement definition, the Nature, complexity, ease of navigation, financial inputs sought by the ERP, implemetation methodology and pertaining hazards. The software features like aestheticism measure, GUI extent, User friendly nature, obviation of complexity, processing speed, memory utilization, interface designs, reports clarity and extendibility, robustness and security, simplicity and reliability, compactness of code, database paradigms, 00 or RDB philosophies, no db or Cloud frameworks, Web compatibility, integrity and consistency etc. are of vulnerable significance while opting an ERP. The hardware, platform Operating system, the front end used is yet some more deciding constraints. Organizational parameters, assets and inputs, sizes and volumes are some more roles playing add ons. The selection of an ERP is a complex decision and there is no thumb rule for selection of an ERP. Once an ERP is selected, based on the Volume of its implementation and the huge Human and tangible resource set working behind it in Industry, the most concerned but desperately ignored technical Optimization strategy articulation becomes a counting coin factor. The paper aims to provide a thorough comprehensive logic provision encompassed in an Algorithm that will help and assist the intelligent customized selection and post selection Optimization.

[1]  M. Yannakakis.  Expressing combinatorial optimization problems by linear programs , 1991, Symposium on the Theory of Computing.

[2]  E.Simos Theodore,et al.  Recent Advances in Computational and Applied Mathematics , 2011 .

[3]  Enterprise Resource Planning Systems –Theory and Practice , 2013 .

[4]  Craig C. Claybaugh,et al.  ENTERPRISE RESOURCE PLANNING , 2016 .

[5]  Canan G. Corlu,et al.  Subset selection for simulations accounting for input uncertainty , 2015, 2015 Winter Simulation Conference (WSC).

[6]  Simha R. Magal,et al.  Integrated Business Processes with ERP Systems , 2010 .

[7]  R. Bishop.  On Separating Predictability and Determinism , 2003 .

[8]  Ruoning Xu,et al.  Realization and Optimization of the Flow in ERP System for RCI , 2009, 2009 International Conference on Web Information Systems and Mining.

[9]  Gene H. Golub,et al.  An analysis of the total least squares problem , 1980, Milestones in Matrix Computation.

[10]  Trevor A. Spedding,et al.  Enterprise resource planning: An integrative review , 2004, Bus. Process. Manag. J..

[11]  L. Ljung,et al.  Asymptotic normality of prediction error estimators for approximate system models , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[12]  Hassan Artail,et al.  A Framework for Implementing Mobile Cloud Services in VANETs , 2013, 2013 IEEE Sixth International Conference on Cloud Computing.

[13]  Christiaan Heij,et al.  Approximate modelling of deterministic systems , 1987 .

[14]  He Peng,et al.  The optimization techniques for time synchronization based on NTP , 2010, 2010 2nd International Conference on Future Computer and Communication.

[15]  Torsten Munkelt,et al.  ERP systems: aspects of selection, implementation and sustainable operations , 2022, International Journal of Information Systems and Project Management.

[16]  Stefaan E. Cuypers,et al.  Determinism and the Paradox of Predictability , 2010 .

[17]  V. Pan,et al.  Polynomial and Matrix Computations , 1994, Progress in Theoretical Computer Science.

[18]  Michael W. Pelphrey.  Directing the ERP Implementation: A Best Practice Guide to Avoiding Program Failure Traps While Tuning System Performance , 2015 .