Procure to Pay
This book is about Data Science, Graph Analysis to understand, Procure to Pay occasionally referred as Buy to Pay | Source to Pay or Order to Cash, Supply Chain life cycle operations to effectively manage products, services & supplies in an organizations. In next few chapters, we will use data science technologies to understand, predict and perhaps prevent global supply chain shortages specially for those items which every person needs to survive.
about Author
Author: Amit Shukla
Bio: about me
Last Update Date: July 10 2022
Who should read this: small, medium, large ERP Consultants
Version: 0.22
Sponsorship: open for funding
how to use this book
This book first version is completely free(v1.2) and is published as website under GitHub gh-pages branch.
Most of the source code is MIT License, (except few ML/Deep Learning algorithms, which are proprietary and customer owned content).
Complete source code can be found here. https://github.com/AmitXShukla/P2P.ai
Platform: Oracle OCI, AWS, Google or Microsoft Azure data cloud.
Analytics: Jupyter | Pluto notebooks, Power BI, Tableau, Oracle Analytics Cloud
Programming/Framework: Julia, FluxML, Graph Analytics
Procure2Pay.ai
Procure to Pay Julia package provide a unified Analytics platform to support data analytical operations on all sort of Procurement, Accounts Payable, Procurement including Vendor, Use, Freight, Misc Tax Accruals data to address complete Buy to Pay data wrangling operations.
This package will provide a complete Analytic Software package, which can be deployed as a bolt-on or independent application for all data extract, load, transformation, ad-hoc reporting & Analytics, visualizations and tooling to support Data Science, AI, ML predictive Analytics.
This package is intended for small, medium, large and very Big Organizations who require a Big Data Tools which can ELT i.e. Extract very large amount of structured and unstructured data, load data into a uniform platform such as RDBMS, Hadoop Data Lake or non-SQL environment.
Further, advance data transformation wrangling techniques can be applied to prepare data for operations reporting, data analytic, advance data visualizations, data science operations including AI, ML for predictions.
This package also show case reporting, visualizations to support real time, live reporting on all mobile, web devices.
Table of Contents
- Define Problem Statement
- Business process
- Getting Facts data
- Graph Analysis
- Data insights
- Analytics
- Machine Learning
P2P.jl package supports these ERP systems data structures.
Oracle, PeopleSoft, SAP, Tally, Intuit, QuickBooks etc. I will cover examples from ERP Domains like GL (General Ledger), AP (Accounts Payable), AR (Account Receivables), B2P (Buy to Pay), Expense, Travel & Time, HCM Human Capital Management, CRM etc.