This document summarizes the key tables and steps involved in the procure-to-pay (P2P) process in Oracle Applications, including creating a requisition, purchase order, goods receipt, invoice, payment, and general ledger transfer. It provides descriptions of over 15 tables used to store header and line item information at each stage of the process, as well as important columns within each table.
The P2P cycle is a necessary process that helps ensure the accuracy of financial data in Oracle Applications including the accounting entries & tables. Learn More!
The P2P cycle is a necessary process that helps ensure the accuracy of financial data in Oracle Applications including the accounting entries & tables. Learn More!
Oracle EBS Order Management R12 Material along with screenshots and definitions
Visit us: https://www.techleadsit.com/oracle-r12-scm-online-training-course/
Youtube: https://www.youtube.com/channel/UCA3m9SKRKg3SSbyXb2Kf21A
Oracle EBS Order Management R12 Material along with screenshots and definitions
Visit us: https://www.techleadsit.com/oracle-r12-scm-online-training-course/
Youtube: https://www.youtube.com/channel/UCA3m9SKRKg3SSbyXb2Kf21A
3.2. Process Order Info (Change the name ComponentModule .docxgilbertkpeters11344
3.2. Process Order Info (Change the name “Component/Module
One to be the actual name of the module you are designing.
Repeat section 3.1 for ea. component)
3.2.1 Processing Description
Provide a Primitive DFD (SAD) or a Use Case Diagram (OOAD) and a brief
narrative
Customer
Sales Person
& Operations
Manager
Provide info
2.1
Create new
order
2.2
Update order
info
2.3
Search for
order
Order Data
Order Data
Search params
Order
Database
Invalid Data
Valid Order Info
Invalid search params
Search results
Valid search params
Search results
Valid Order Info
Invalid Data
Module 2.1 – Create new order
o Customer provides order information to Sales Person or Operations Manager (staff).
Staff enters data into system. If data is validated, system creates a new order in Order
Database and triggers Notification module. If data is invalidated, error message is
returned to Staff.
Module 2.2 – Update order info
o Customer provides order information to Staff. Staff enters data into system. If data is
validated, system updates order in Order Database. If data is invalidated, error message
is returned to Staff.
Module 2.3 – Search for order
o Staff enters order search parameters into system. If search parameters are validated,
system searches Order Database for order that fulfills search parameters and returns
search results to Staff. If system is unable to locate order that fulfills search parameters,
system will return failure message.
3.2.2 Interface Description
3.2.2.1 Module 2 – Process Order Info
There are several items that should be covered here:
If the component has a GUI, display the image of the GUI and define each
field, button, link on the GUI.
if the component receives data, describe the data coming in and where it
should go once it is processed (Input/Output)
If there is data to be passed to another component that should be listed
This section will have a detailed written description of what each button, link, data
entry field etc., does on the page, with an image of the proposed page layout, for
each page.
WSC – Order Processing
search
Job type
Media type
Catalog #
Printing/Engraving content
Order Processing
Create New Order Search Orders Update Order
Name of Customer (#cust_id)
Cancel
Order ID #
Type of Job:
Type of Media:
Media Catalog #:
Content to Print/Engrave:
W2
WSC - Message
Insufficient/Incorrect information
entered. Please verify data and try
again.
W2m
OK
Module 2 – GUI Control Info
Control Name
Control
Type Control Data Control Description
lbl_msg Label Name of
lbl_cust_name Label cust_fname + cust_lname
Display from Customer Database of customer first and last name
concatenated
lbl_cust_id Label cust_id Display from Customer Database of customer ID number
lbl_ord_id Label Order ID #
txt_ord_id Textbox Search
Enter Order ID # here to search for corresponding order from Order
.
This document shows the complete Pick,Pack and Ship Confirm Process in Oracle Apps using Public APIs(includes sample codes and table names from various sources)
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf
P2P table
1. P2P :( procure to pay)
Create Requisition
Create Purchase Order
Create InventoryReceipt
Enter AP Invoice
Make Payment
TransfertoGL
1. Create requisition:
Headers - po_requisition_headers_all
Lines - po_requisition_lines_all
Distribution- po_req_distribution_all
>> Po_requisition_headers_all:
Important columns:
Requisition_header_id: requisitionheaderunique identifier.
Segment1: requisitionnumber.
Authorization_status: authorizationtype. (Complete)
Type_lookup_code: requisitiontype. (Purchaseandinternal)
Description:
PO_REQUISITION_HEADERS_ALLstores information about requisitionheaders.
>>Po_requisition_lines_all:
Important columns:
Requisition_header_id: requisitionheaderunique identifier.
Line_type_id: line type (goods).
Quantity_number: quantityordered.
Categeory_id: itemcategory unique identifier.
Description:
PO_REQUISITION_lines stores informationabout requisition lines. Eachrowcontains theline number, item number, itemcategory,
item description, need-by date, deliver-tolocation, itemquantities, units, prices, requestor, notes, andsuggestedsupplier informationforthe
requisition line.
>>po_req_distribution_all:
Important columns:
Distribution_id: requisitiondistributionunique identifier.
Requisition_line_id: requisition lineunique identifier.
Code_combination_id: unique identifier for thegeneral ledger.
Distribution_num: distributionnumber.
Description:
Po_req_distribution_all stores information about the accountingdistributionassociatedwith each requisitionline.Eachrequisition line must
have at least one accountingdistribution.
Po_req_distribution_all is one of three tables storingyour requisitionnumber.
Accountingdistribution:
Accountingdistributions are usedto define howan amount will be accountedfor,such as howthe expense, tax, or charges will be
accountedforon a vendorinvoice. Everyamount that must be accountedfor when thevendor invoice is journalizedwill have oneor
more accountingdistributions
2. Create purchase order:
Header: po_headers_all
Line: po_lines_all
Distribution: po_distribution_all
2. >>po_headers_all:
Important columns:
Po_header_id
Segment1
Vendor_id
Vendor_site_id
Description:
Po_headers_all contains header information for all purchasingdocuments. There aresix types ofdocuments that use po_headers_all.
RFQs
Quotations
Standardpurchase orders
Plannedpurchase orders
Blanket purchase orders
Contracts
>>po_lines_all:
Important columns:
Po_line_id: document the unique identifier.
Po_header_id: document header unique identifier. (Reference po_headers_all ,po_header_id)
Item_id: itemunique identifier.
Line_num: line number.
Description:
Po_lines_all stores current informationabout eachorder purchase order line.there are fivedocument types that use line.
RFQs
Quotations
Standardpurchase orders
Blanket purchase orders
Plannedpurchase orders
>>po_distribution_all:
Important columns:
Po_distribution_id: document distributionunique identifier.Primarykeyforthis table.
Po_header_id: document header unique identifier. Reference po_headers_all.po_header_id.
Po_line_id: document the unique identifier. Reference po_lines_all.po_lines_id.
Line_location_id:
Description:
Po_distribution_all contains accountingdistributioninformationfor a purchase order shipment line.
3. Create the receipt:
Headers: RCV_SHIPMENT_HEADERS
Lines: RCV_SHIPMENT_LINES
Transaction: rcv_transactions
>>Rcv_shipment_headers:
Important columns:
Shipment_header_id: receipt header unique identifier.
Receipt_source_code: sourcetypeof the shipment – vendor,internal orderor customer.
Vendor_id: source supplier unique identifier.
Shipment_num: shipment number.
Receipt_num: receipt number.
Description:
Rcv_shipment_headers stores commoninformationabout thesource ofyour receipts or expectedreceipts.
There are two receipt source types, supplier andinternal order.
3. >>Rcv_shipment_lines:
Important columns:
Shipment_line_id: shipment line unique identifier.
Item_id: itemidentifier.
Vendor_item_num: suppliers item number.
Shipment_line_status_code: receipt status of the shipment line
Description:
Rcv_shipment_lines stores informationabout items that have been shipped.
>>Rcv_transaction:
Important columns:
Transaction_id: receivingtransactionunique identifier.
Request_id:
Transaction_type: receivingtransactiontype.
Transaction_date:
Quantity:
Description:
Rcv_transaction stores historical information about receivingtransactionthat youhave performed.
4. Invoices:
>>ap_invoices_all:
Important columns:
Invoice_id: generatedusinga database sequences.
Vendor_id:
Invoice_num:
Set_of_books_id:
Payment_currency_code:
Description:
This table corresponds to the Invoices header blockofInvoice workbench. AP_INVOICES_ALLholds information this table corresponds
to the Invoices header blockof Invoice workbench.AP_INVOICES_ALL holds information Oracle Projects,Supplier Portal, Refunds from
Oracle Receivables etc.This table holds all type ofinvoices, which includes Standard, Prepayments, Credit Memo,Debit Memo, Mixedinvoice,
Withholdinginvoice, Interest Invoice, Retainage invoices, Payment Requests etc.,
>>ap_invoice_distributions_all:
Important columns:
Invoice_id: Unique invoicedistributionidentifier.
SET_OF_BOOKS_ID: Ledger identifier ofthe invoice distribution.
AMOUNT: Amount on invoice distribution.
INVOICE_DISTRIBUTION_ID:
Description:
AP_INVOICE_DISTRIBUTIONS_ALL holds the distributioninformationthat is manuallyenteredorsystem-generated.
>>ap_checks_all:
Important columns:
Amount: payment amount
Bank_account_id: longer used
Bank_account_name:
Check_id_number:
Check_number: paymnt number
Vendor_name: suppliername
Vendor_site_code: supplier site code
Descriptions:
Ap_checks_all stores information about payment issuedto suppliers or refundreceivedfromsuppliers.
>>ap_invoice_payments_all:
Important columns:
Invoice_payment_id:
Invoice_id:
Check_id:
Payment_num:
4. Descriptions:
Ap_invoice_payments_all contains recordofinvoicepayments that youmade tosuppliers.
>>ap_payment_schedules_all:
Important columns:
Invoice_id:
Payment_num:
Amount_remaining:
Descriptions:
Ap_payment_schedules_all contain information about scheduledfor aninvoice.
5. GL Transfer:
>>gl_periods:
Important columns:
Period_set_name: name ofthe accountingcalendar.
Period_name: systemgeneratedaccountingperiodname.
Descriptions:
Gl_periods contains information about the accountingperiods that are definedusingthe accountingcalendarform.
>>gl_period_statuses:
Important columns:
Application_id: identifier associatedwith the application
Ledger_id: unique identifier of the ledger
Period_name:
Descriptions:
Gl_period_statuses contains the statuses of youraccountingperiods.
>>gl_set_of_books:
Important_columns: