After highlighting the key benefits of the AI Assistant for enterprise analytics in my previous blog post, I am sharing here a demo of what it looks like to use the AI Assistant. The video below demonstrates how a persona interested in understanding enterprise projects may quickly  find answers to their typical everyday questions. The information requested include profitability, project analysis, cost management, and timecard reporting.
A Perficient Demo of AI Assistant for Project Analytics

What to Watch Out For

With the right upfront configuration in place, the AI assistant, native to Oracle Analytics, can transform how various levels of the workforce find the insights they need to be successful in their tasks. Here are a few things that make a difference when configuring the AI Assistant.

• Multiple Subject Areas: When enterprise data consists of several subject areas, for example Projects, Receivables, Payables, Procurement, etc., performing Q&A with the AI Assistant across multiple subject areas simultaneously is not currently possible. What the AI Assistant does in this situation is prompt for the subject area to use for the response. That is not an issue when the information requested is from a single subject area. However, there are situations when we want to simultaneously gain insights across two or more subject areas. This can be handled by preparing a combined subject area that contains the key relevant information from other underlying subject areas. As a result, the AI Assistant interfaces with a single subject area that consists of all the transaction facts and conformed dimensions across the various transactional data sets. With a little semantic model adjustments this is an achievable solution.
• Be selective on what is included in AI prompts: Enterprise semantic models typically have a lot of information that may not be relevant for an AI chat interface. Therefore, excluding any fields from being included in an AI prompt improves performance, accuracy, and sometimes even reduces the processing cost incurred by AI when leveraging external LLMs. Dimension codes, identifiers, keys, and audit columns are some examples of things to exclude. The Oracle Analytics AI Assistant comes with a fine-grained configuration that enables selecting the fields to include in AI prompts.
• Metadata Enrichment with Synonyms: Use synonyms on ambiguous fields, for example to clarify what a date field represents (Is it the transaction creation date or the date it was invoiced on?). Another example of when synonyms are useful is when there is a need to enable proper interpretation of internal organization-specific terms. The AI Assistant enables setting up synonyms on individual columns to improve it’s level of understanding.
• Indexing Data: For an enhanced user experience, I recommend identifying which data elements are worth indexing. This means the AI LLM will be made aware of the information stored in these fields that you chose while setting up the AI Assistant. This is an upfront one-time activity. The more information you equip the AI Assistant with, the smarter it gets when interpreting and responding to questions.

For guidance on how to get started with enabling GenAI for your enterprise data analytics, reach out to mazen.manasseh@perficient.com.

In the third and final blog post of this three-part Solution Highlight series featuring a proven leader in defense-grade, high assurance cyber security solutions, the topic is Salesforce and Oracle Cloud integration. If you missed the two previous blog posts in the series, I encourage you to read the Solution Highlight – Oracle Revenue Management / SSP – Part 1, authored by my colleague Jeff Davis. He shared our client’s journey to automate its complex and manual revenue allocation processes. To round out the series, read Solution Highlight – Oracle Fusion Global SCM and Manufacturing – Part 2.

About Our Client: a trailblazer in the cyber security space, our client needed the ability to integrate Salesforce and Oracle Fusion to process Sales Orders and manage sellable items.

Challenge

• Item management without security
• Handling of additional product types and complicated attribution
• Sales Order Management for various business requirements
• Updating Salesforce with fulfillment information for better customer service

Solution

Improved Item Management integration by adding logic to Salesforce and expanding Product Management footprint

As a digital consultancy, we have the ability to work across multiple business units internally as one team to deliver a suitable solution for our client. In this solution, Perficient has engaged teams from three separate business units: Oracle, Salesforce and Cloud and API Consulting.

Oracle Fusion Product Management provides robust item management capabilities for item management.   In this engagement, we introduced a new family or items and improved attribution updates through the integration.  Key highlights include:

• Item Classes for Security: Item Classes improve Product Management security. By creating new item classes and custom roles, we implemented a secure item management capability for the client
• Item Classes for attribution: With the use of Extentisible Flexfield, we have added product family attribution to the item master
• Item Templates: We have improved the Item Management by including new Item Templates that requires separate attribution for revenue management and also for manufacturing and order fulfillment
• Additional Logic in Boomi Interface: Our technical team worked with our client and functional architects to create logic, and our Salesforce team assisted at the Salesforce end for item creation and synchronization, so that the items in Product Management drives proper Revenue Management functionality

Enhanced Order Management Fulfillment and Integration to Salesforce

Our client had implemented a simple Sales Order integration and out of the box Order Orchestration to process the sales orders.  With revenue in play, Order Fulfillment requirements have become for complicated to be supported with the current solution. Key highlights of our solution include:

• Improved Order Orchestration: Oracle Order Orchestration (DOO) is the key workflow engine processing the Sales Order Fulfillment process. Our Oracle team has worked internally to created multiple DOOs to handle different scenarios
• Improved Integration Logic: With the help of our Boomi development team and Salesforce consultants, we were able to improve the logic in creating the sales orders and writing back and forth between Salesforce and Oracle to update the Order Creation and Order Fulfilment activities

Benefits

• Secure Product Management – Ensuring only authorized users to update product information

• Flexible Order Fulfillment – Enablement of various order fulfillment scenarios as well as feeding Revenue Management

• Effective and Accurate Item Management – Using Item Templates, Item Classes makes item management more accurate and more efficient.
• Flexible Order Fulfillment – the solution can handle various fulfillment scenarios to support multiple business requirement

Oracle Fusion Order Management and Product Management can work together to fulfill customer Sales Orders that turns into profits for corporations, hence critical component of every business.

This solution is particularly effective for companies looking to user multiple technology stacks and still using Oracle Fusion applications as their backbone ERP and SCM platforms.

Let me know if you’d like a deeper dive into any of these features!

Asking questions about data has been part of Oracle Analytics through the homepage search bar for several years now. It did that with Natural Language Processing (NLP) to respond to questions with various automatically generated visualizations. What has been introduced since late 2024 is the capability to leverage Large Language Models (LLM) to respond to user questions and commands from within a Workbook. This brings a much-enhanced experience, thanks to the evolution of language processing from classic NLP models to LLMs. The newer feature is the AI Assistant, and while it was earlier only available to larger OAC deployments, with the May 2025 update, it has now been made available to all OAC instances!

If you’re considering a solution that leverages Gen AI for data analytics, the AI Assistant is a good fit for enterprise-wide deployments. I will explain why.

• Leverages an enterprise semantic layer: What I like most about how AI Assistant works is that it reuses the same data model and metadata that are already in place and caters for various types of reporting and analytical needs. AI Assistant adds another channel for user interaction with data, without the risks of data and metadata redundancy. As a result, no matter whether creating reports manually or leveraging AI, everyone across the organization remains consistent in using the same KPI definitions, the same entity relationships and the same dimensional rollup structures for reporting.
• Data Governance: This is along the same lines as my first point, but I want to stress the importance of controls when it comes to bringing the power of LLMs to data. There are many ways of leveraging Gen AI with data and some are native to the data management platforms themselves. However, implementing Gen AI data querying solutions directly within the data layer requires a closer look at security aspects of the implementation. Who will be able to get answers on certain topics? And if the topic is applicable to the one asking, how much information are they allowed to know?

The AI Assistant simply follows the same object and row level security controls that are enforced by the semantic data model.

• What about agility? Yes, governed analytics is very important. But how can people innovate and explore more effective solutions to business challenges without the ability to interact with the data that comes along with these challenges. The AI Assistant works not only with the common enterprise data model, but with individually prepared data sets as well. As a result, the same AI interface caters to questions asked about both enterprise data as well as departmental or individualized data sets.
• Tunability and Flexibility: Enabling the AI Assistant for organizational data, while relatively an easy task, does allow for a tailored setup. The purpose of tuning the setup is to increase the levels of reliability and accuracy. The flexibility comes into play when directing the LLM on what information to take into consideration when generating responses. And this can be done through a fine-tuning mechanism of designating which data entities and/or fields of data within these entities, can be considered.
• Support for data indexing, in addition to metadata: When tuning the AI Assistant setup, three options are available to pick from, down to the field level: Don’t Index, Index Metadata Only, and Index. With the Index option, we can include information about the actual data in a particular field so the AI Assistant is aware of that information. This can be useful, for example, for a Project Type field so the LLM is informed of the various possible values for Project Type. Consequently, the AI Assistant provides more relevant responses to questions that include specific project types as part of the prompt.
• Which LLM to use? LLMs continue to evolve, and it seems that there will always be a better, more efficient and more accurate LLM to switch to. Oracle has made the setup for the AI Assistant open, to an extent, in that it can accommodate external LLMs, besides the built-in LLM that is deployed and managed by Oracle. At this time, if not using the built-in LLM, we have the option of using an Open AI model via the Open AI API. Why may you want to use the built-in LLM vs an Open AI model?
  • The embedded LLM is focused on the analytical data that is part of your environment. So it’s more accurate in that it is less prone to hallucinations. However, this approach doesn’t provide flexibility in terms of access to external knowledge.
  • External LLMs include public knowledge (depending on what knowledge an LLM is trained on) in addition to the analytical data that is specific to your environment. This normally allows AI Assistant to have better responses when the questions asked are broad and require public knowledge to tie into the specific data elements housed in one system. Think for example about geographical facts, statistics, weather, business corporations’ information, etc. These are public information and can help in responding to analytical questions within the context of an organization’s data.
  • If the intent is to use an LLM but avoid the inclusion of external knowledge when generating responses, there is the option to restrict the LLM so it limits responses based on organizational data only. This approach leverages the reasoning capabilities of models without compromising the source of information for the responses.
• The Human Factor: AI Assistant factors in the human aspect of leveraging LLMs for analytics. Having a conversation with data through natural language is to the most part straight forward when dealing with less complex data sets. This is because, in the case, the responses are more deterministic. As the data model gets more complex, there will be more opportunities for misunderstanding and missed connections between what’s on one’s mind versus an AI generated response, let alone a visual one. This is why the AI Assistant has the capability for an end user to adjust the responses to better align with their preferences, without reiterating prompts and elongated back and forth conversations. These adjustments can be easily applied with button clicks, for example to change a visual appearance or change/add a filter or column, all within a chat window. And whatever visualizations the AI Assistant produces, can be added to a dashboard for further adjustments and future reference.

In the next post, I will mention a few things to watch out for when implementing AI Assistant. I will also demo what it looks like to use AI Assistant for project management.

When it comes to manufacturing, companies (OEM) require services from their manufacturing partners to help with the production of finished and semi-finished products.  There are two known solutions that Oracle Fusion SCM suite offers: Outside Processing (OSP) and Contract Manufacturing.  Both solutions involve a third-party vendor and a service component to either help complete a work order, fulfill a sales order, or fulfill subassembly demand. Both solutions serve a purpose and are quite powerful. Before I jump into the comparison, here’s a textbook definition of both solutions:

OSP:

OSP is the process of outsourcing a portion of the work order that is being done in house. For example, a steel shop that can cut and weld steel to manufacture frames may send the frames to a paint shop (vendor) to get painted. The steel shop then receives the painted frames in house and perhaps performs a few more value-added steps to complete the work order.  The paint portion of this work order is considered an outside job.  Companies may prefer outside processing for various reasons. The steel manufacturer may not be interested in installing a paint booth and employing painters, or the company may have a paint booth but it’s backlogged or is down. Specialization may be required. In all these scenarios, a vendor is needed to help.

Contract Manufacturing:

Contract Manufacturing is to (optionally) provide materials to a vendor and expect the vendor to produce assemblies and send them to external or internal customers.  Typically, the company ships raw materials and/or subassemblies to their vendor and manages stock in their warehouse.  With contract manufacturing, the vendor is in complete control of manufacturing process and is supposed to update and complete work orders or communicate back the process.  The OEM usually has owned stock at the vendor location and tracks in its books.

The one-million-dollar question; which one to pick?

In most cases the “textbook” response can be straightforward. In some cases, companies that are using production steps from a vendor as one of the operations in their in-house work orders use the OSP solution. It’s straightforward and quite easy to set up. Read this blog for OSP treatment advice in Cost Management.

Contract Manufacturing may be used directly to fulfill back-to-back sales orders, or to fulfill the Supply Planning generated subassembly transfer orders and work orders.  Two drawbacks to Contract Manufacturing (as of Release 25A), is that the second portion of the solution can only be accomplished by Supply Planning. Contract Manufacturing is a robust solution, but it requires vendors to actively participate providing feedback on inventory levels, or updating the production progress in Oracle Fusion.

There are use cases where companies may use a vendor to produce subassemblies, but would like to create manual work orders, manage the inventory, and want a less complicated solution.  In this case, the OSP solution can work beautifully simulating a Contract Manufacturing solution.

Based on the business requirements, the OEM may choose to go with OSP.  Let’s assume that the inventory sent to the vendor for Contract Manufacturing is only sent when new assemblies are required and there isn’t an opportunity to electronically integrate through webservices and communication is with emails or with other correspondence.

For this OEM, it is feasible to create a Work Center in Oracle Fusion Manufacturing for its vendor, automatically create service purchase orders, and have dedicated supply and completion subinventories.  Inventory Management could be a little challenging dedicating the raw material for contract manufacturer use only, but not unmanageable.  In this simple scenario, the OEM doesn’t have to go through a complex setup to use Contract Manufacturing, and they can go with the OSP solution.

There are various use cases and potential solutions using Oracle Supply Planning, Inventory Management, and min-max planning, Oracle Manufacturing, Inventory and Procurement, and the Supplier Portal to fulfill various manufacturing scenarios.

Contact Mehmet Erisen at Perficient for more introspection of this functionality, and how Perficient and Oracle Fusion Cloud can digitalize and modernize your ERP platform.

In the second blog post of this three-part Solution Highlight series featuring a proven leader in defense-grade, high assurance cyber security solutions, the topic is Salesforce and Oracle Cloud integration. If you missed the other blog posts in the series, I encourage you to read the Solution Highlight – Oracle Revenue Management / SSP – Part 1, authored by my colleague Jeff Davis. He shared our client’s journey to automate its complex and manual revenue allocation processes. To round out the series, read Solution Highlight – Oracle Fusion and Salesforce – Part 3.

About Our Client: a trailblazer in the cyber security space, our client needed the ability to integrate Salesforce and Oracle Fusion to process Sales Orders and manage sellable items.

Challenge

• Different Sales Order Fulfilment techniques based on the item type and country
• Global fulfillment with multiple countries (Business Units)
• Contract Manufacturing for Subassemblies

Customized Order Orchestration for various fulfillment scenarios

After analyzing the requirements, Perficient has customized the Order Orchestration to handle multiple fulfillment scenarios.

Key highlights include:

• Routed to different orchestration flows: Order Lines has been assigned to the requested orchestration trough the Salesforce Integration
• Wait Steps: Added wait steps based on business events for the Revenue Management requirements

Supply Chain Financial Orchestration for Intercompany Accounting

One of the requirements in the overall solution to create intercompany buy and sell transactions, in other words “internal drop ship” fulfillment.  We used Supply Chain Financial Orchestration and Cost and Receipt accounting custom mappings sets and accounting rules to fulfill this requirements.

Key Highlights include:

• Build the Intercompany Trading model with Internal Customers and Suppliers and build entity relationship
• Create the Supply Chain Orchestration Rules and Flows
• Use of Subledger accounting to drive intercompany accounting

Outside Processing for Contract Manufacturing

Our client used a contract manufacturer for the subassemblies, but they didn’t have the supply planning module. After analyzing their requirements and Oracle footprint, we successfully implemented OSP (Outside Process) flow for their contract manufacturing solution.

Please refer to the blog that explains the decision process for Outside Processing and Contract Manufacturing:

Outside Processing vs Contract Manufacturing

Benefits

• Flexible Order Fulfillment – the solution can handle various fulfillment scenarios to support multiple business requirement
• Flexible Intercompany Accounting transactions – Oracle Supply Chain Financial Orchestration can handle various rules and flows between different Business Units to give flexibility to corporations doing international business
• Fast, but thorough implementation – Modelling a simpler OSP approach enabled us to implement the solution in a very short timespan making it quite effective and efficient for the client
• This solution is particularly effective for companies looking to user multiple technology stacks and still using Oracle Fusion applications as their backbone ERP and SCM platforms.

Let me know if you’d like a deeper dive into any of these features!

In the first blog post of this three-part Solution Highlight series featuring a proven leader in defense-grade, high assurance cyber security solutions, I will cover Oracle Revenue Management.  My colleague, Mehmet Erisen will share his views on Global Supply Chain Management including Manufacturing with OSP and intercompany order fulfillment across business units featuring Oracle Supply Chain Management. We’ll round out the series with the third and final blog post focused on Salesforce to Order Cloud integration. 

About Our Client: a trailblazer in the cyber security space, our client needed the ability to automate its complex and manual revenue allocation processes. 

Challenge

• Manual revenue recognition processes leading to errors and delays 

• Difficulty in complying with ASC 606 / IFRS 15 standards 

• Lack of real-time visibility into revenue reporting 

Solution

Implemented Oracle Revenue Management – Managing Bundles and Stand-alone Selling Price (SSP)  

Oracle Fusion ERP provides robust functionality for managing and automating the implementation of product bundles and determining the SSP for revenue recognition under ASC 606 and IFRS 15 standards. Key highlights include: 

• Revenue Management: Automates revenue processing tasks, minimizing manual interventions, allowing organizations to comply efficiently and consistently with the ASC 606 and IFRS 15 core principles 

• Bundling Capabilities: Allows seamless configuration and management of product/service bundles with clear pricing structures 

• Automation and Scalability: Automates complex revenue allocation processes, improving efficiency and scalability 

• Real-time Analytics: Provides insights into sales trends and SSP analysis, enabling data-driven pricing strategies 

Benefits

• Reduced Manual Effort – Eliminated spreadsheet-based tracking 

• Improved Accuracy – Minimized revenue leakage and misreporting 

• Faster Close Cycles – Automated recognition speeds up month-end close 

• Regulatory Compliance – Ensured adherence to ASC 606 / IFRS 15 

• Enhanced Visibility – Real-time insights into revenue performance 

Oracle Revenue Management Cloud enables organizations to automate revenue recognition, reduce compliance risks, and gain real-time financial insights. This solution delivers value for companies with complex revenue streams, such as SaaS, manufacturing, and professional services. 

This solution is particularly effective for companies looking to streamline revenue recognition while maintaining compliance and operational efficiency.  

Let me know if you’d like a deeper dive into any of these features! 

When it comes to cycle counting, the first thing that comes to mind is ABC classifications of the items and then a cycle count program to count items throughout the year on a continuous basis.

There is nothing wrong with the ABC classifications if the organization is mature enough with its items and the classification criteria is going to be somewhat stable during the year. For example, the classification criteria is the on-hand quantities, that classification is only good at the time of creation. Three months later, the classification could be different. What if the company adds new items and starts transacting? There are a few challenges with the traditional ABC Classifications.

Category-based Cycle Counts

Oracle recently rolled out Category-based cycle counts in Oracle Fusion. In my opinion, it’s a powerful tool that could easily replace the ABC model.  I believe that the Category-based Cycle Counts has various advantages over ABC Classification:

• If all items are the same for cycle counting purposes, use an existing catalog, or see if any of the other catalogs can be used
• If none of the existing catalogs work, then create a Cycle Count Catalog
• Assign your items to the catalog: in our experience, this approach is more intuitive for most companies than performing the ABC classifications.
• Create a Category-based cycle count and let Oracle create counts based on the categories

The clear advantage with this approach is the flexibility of adding items and changing the item’s classification or category at any given time without cumbersome setups and IT work.

Another added benefit is that Oracle Fusion Cycle base can be used to perform highly accurate counts, if performing manual counts.

I will cover the newly improved Cycle Count pages with Redwood Experience in my next blog.  As a preview of that blog, Oracle is not improving direct manual classification and that may make the usage of ABC Classification attractive again.

This new approach to perform Cycle Counts in Oracle Fusion Supply Chain is a powerful tool worth exploring.

Contact Mehmet Erisen at Perficient for more introspection of this functionality, and how Perficient and Oracle Fusion Cloud can digitalize and modernize your ERP platform.

If you are a Game of Thrones fan, you are probably familiar with the “winter is coming” phrase.  When it comes to Oracle Fusion, the Redwood experience has been coming for years, but now it’s almost here.

Oracle is in the process of overhauling the whole fusion suite with what they call the “Redwood Experience.” The newly designed Redwood pages are not only responsive and more powerful than their ancestors, but they bring great capability to the table.

• Redwood pages are built for the future. They are all AI-ready and some come with pre-built AI capabilities.
• They are geared toward a “Journey Guide” concept, so enterprise-level software implementations are no longer full of “technical jargon.”
• The new AI Studio and the Visual Studio give Oracle Fusion clients the ability to modify the application for their business needs.

How to Move Forward with the Redwood Experience

Adopting to Redwood is not a straightforward task.  Every quarterly release, Oracle will add more and more pages with the Redwood design, but how do you adopt and take on to the Redwood experience and explore AI opportunities?

1. First, deploy the setup screens where Redwood experience is available.
2. Second, review quarterly updates and decide what screens are mature enough to be deployed.
3. Third, review is the new design is beginning new functionality or lacking any functionality. For instance, Oracle Work Definition Redwood pages are bringing new functionality, whereas the newly designed Order Management pages won’t support certain flows.  Having said that, Order Management screens brings so much when in comes to AI capabilities, if the “not yet available” features is not a business requirement, moving to the Redwood experience will bring efficiency in customer service and much better user experience.
4. Fourth, have a game plan to roll out with your pace. With the cloud, you are in total control of how and when you roll out the SCM pages. According to Oracle, there isn’t yet a definitive timeframe that Oracle will make the Redwood pages mandatory (04/2025).  Please note that some of the pages are already in play and some made have been mandatory.

User acceptance and adoption comes with time, so the sooner the transition begins, the more successful the implementations will go. Perficient can help you with your transition from traditional Fusion or legacy on-prem applications to the SCM Redwood experience. When you are ready to take the first step and you’re looking for some advice, contact us. Our strategy is to craft a path for our clients that will make the transition as seamless as possible to the user community and their support staff.

New modern looking newly designed Manage Manufacturers Redwood Experience with built-in AI Assist

Below are the Supply Chain features Oracle has released from release 24D to 25B. (2024 Q3- 2025 Q2) only for Inventory Management and yet it is an overwhelming list.  Please stay tuned for our Redwood series that will be talking about select features.

Isn’t SFO an airport?  The airport one would travel if the destination is Oracle’s Redwood Shores campus.  Widely known as the initialism for the San Francisco International Airport, the answer would be correct if this question were posed in that context.  However, in Oracle Fusion, SFO stands for the Supply Chain Financial Orchestration. Based on what it does, we cannot call it an airport, but it sure is a control tower for financial transactions.

As companies are expanding their presence across countries and continents through mergers and acquisitions or natural growth, it becomes inevitable for the companies to transact across the borders and produce intercompany financial transactions.

Supply Chain Financial Orchestration (SFO), is the place where Oracle Fusion handles those transactions. The material may move one way, but for legal or financial reasons the financial flow could be following a different path.

A Typical Scenario

A Germany-based company sells to its EU customers from its Berlin office, but ships from its warehouses in New Delhi and Beijing.

Oracle Fusion SFO takes care of all those transactions and as transactions are processed in Cost Management, financial trade transactions are created, and corporations can see their internal margins, intercompany accounting, and intercompany invoices.

Oh wait, the financial orchestration doesn’t have to be across countries only.  What if a corporation wants to measure its manufacturing and sales operations profitability?  Supply Chain Financial Orchestration is there for you.

In short, SFO is a tool that is part of the Supply Chain management offering that helps create intercompany trade transactions for various business cases.

Contact Mehmet Erisen at Perficient for more introspection of this functionality, and how Perficient and Oracle Fusion Cloud can digitalize and modernize your ERP platform.

www.oracle.com

www.perficient.com

Roeslein & Associates 

Business Challenge + Opportunity 

Replaced disparate and outdated legacy systems with Oracle Fusion Cloud Manufacturing at a well-established manufacturing company.  We implemented a scalable Fusion solution, including Project Driven Supply Chain (PDSC), and full Financial and Supply Chain Management Suites to enable Roeslein to execute and extend their business processes globally. 

The challenge in manufacturing was to set standard manufacturing processes to fulfill highly customized demand originating from their customers. In addition, Perficient designed a Supply Chain Data Architecture to support the functionality of the solution. 

Achievements

• Created Global Solution Template to be used globally 

• Redesigned Enterprise Structure to enable Roeslein to track profits in different business units. 

• Defined processes to execute standard manufacturing processes for custom and highly flexible manufacturing demand 

• Implemented Project Driven Supply Chain including Inventory, Manufacturing, Order Management, Procurement and Cost Management 

• Implemented Solutions to support aftermarket part orders in addition to Manufacturing Orders 

• Designed two Integration between Fusion and UKG to support labor capture in Manufacturing and Projects 

• Built Integration between Roeslein’s  eCommerce Platform and Fusion to support of their Aftermarket Business 

Contact Mehmet Erisen at Perficient for more introspection of this phenomenal achievement.  Congratulations to Roeslein & Associates and their entire staff! 

If you’re trying to integrate your Oracle Fusion Cloud Apps data into Snowflake, you may have realized it’s going to be a different approach than with traditional type ERP systems. One challenge is that Oracle Fusion Cloud Applications and Snowflake are hosted on different clouds. Oracle Fusion apps (ERP, SCM, HCM, CX) run on Oracle Cloud, whereas Snowflake operates on AWS, Azure, or Google Cloud. There are, however, several approaches to making this integration happen for bulk data. (If interested in a similar analysis for Databricks, reference the Databricks blog.)

Although there are various ways to perform this integration, this post will concentrate on two main criteria:

1. Native Capabilities: These rely solely on Oracle and Snowflake Cloud-based technologies without the need for third-party tools.
2. Scalable Methods for High Data Volume: The emphasis is on techniques capable of handling a large number of objects and massive data volumes. While alternatives like REST APIs, OTBI, or BI Publisher exist for Fusion data extraction, they are not recommended for substantial bulk extractions from Oracle Fusion. However, these methods can still be used alongside the primary approaches if necessary.

The following diagrams summarize four different approaches on how to replicate Oracle Fusion Apps data in Snowflake. Each diagram highlights the data flow, and the technologies applied.

• Approach A: Leverages Oracle Autonomous Data Warehouse and an Oracle GoldenGate Replication Deployment
• Approach B: Leverages Oracle Autonomous Data Warehouse and Oracle Data Transforms to load data into Snowflake via a JDBC connection
• Approach C: Leverages Oracle Data Integrator to load Fusion data into Snowflake via a JDBC connection
• Approach D: Leverages AWS S3 compatibility API to load Fusion data into Snowflake

Choosing the right approach for your use case is dependent on the objective of performing this integration and the ecosystem of cloud platforms that are applicable to your organization. For guidance on this, you may reach me at mazen.manasseh@perficient.com. Perficient’s Oracle and Snowflake specialists will connect with you and provide recommendations.

Following up on my previous post which highlights different approaches of accessing Oracle Fusion Cloud Apps Data from Databricks, I present in this post details of Approach D, which leverages the Perficient accelerator solution. And this accelerator applies to all Oracle Fusion Cloud applications: ERP, SCM, HCM and CX.

As demonstrated in the previous post, the Perficient accelerator differs from the other approaches in that it has minimal requirements for additional cloud platform services. The other approaches of extracting data efficiently and in a scalable manner require the deployment of additional cloud services such as data integration/replication services and an intermediary data warehouse. With the Perficient accelerator, however, replication is driven by techniques that are solely reliant on native Oracle Fusion and Databricks. The accelerator consists of a Databricks workflow with configurable tasks to handle the end-to-end process of managing data replication from Oracle Fusion into the silver layer of Databricks tables. When deploying the solution, you get access to all underlying python/SQL notebooks that can be further customized based on your needs.

Why consider deploying the Perficient Accelerator?

There are several benefits to deploying this accelerator as opposed to building data replications from Oracle Fusion from the ground up. Built with automation, the solution is future-proof and enables scalability to accommodate evolving data requirements with ease. The diagram below highlights key considerations.

A Closer Look at How Its Done

In the Oracle Cloud: The Perficient solution leverages Oracle BI Cloud Connector (BICC) which is the preferred method of extracting data in bulk from Oracle Fusion while minimizing the impact to the Fusion application itself. Extracted data and metadata is temporarily made available in the OCI Object Storage buckets for downstream processing. Archival of exported data on the OCI (Oracle Cloud Infrastructure) side is also automatically handled, if required, with purging rules.

In the Databricks hosting cloud:

• Hosted in one of: AWS, Azure or GCP, the accelerator’s workflow job and notebooks are deployed in the Databricks workspace. The Databricks delta tables schema, configuration and log files are all hosted within the Databricks Unity Catalog.
• Notebooks leverage parametrized code to programmatically determine which Fusion view objects get replicated through the silver tables.
• The Databricks workflow triggers the data extraction from Oracle Fusion BICC based on a predefined Fusion BICC job. The BICC job determines which objects get extracted.
• Files are then transferred over from OCI to a landing zone object store in the cloud that hosts Databricks.
• Databricks AutoLoader handles the ingestion of data into bronze Live Tables which store historical insert, update and delete operations relevant to the extracted objects.
• Databricks silver Live Tables are then loaded from bronze via a Databricks managed DLT Pipeline. The silver tables are de-duped and represent the same level of data granularity for each Fusion view object as it exists in Fusion.
• Incremental table refreshes are set up automatically leveraging Oracle Fusion object metadata that enables incremental data merges within Databricks. This includes inferring any data deletion from Oracle Fusion and processing deletions through to the silver tables.

Whether starting small with a few tables or looking to easily scale to hundreds and thousands of tables, the Perficient Databricks accelerator for Oracle Fusion data handles the end-to-end workflow orchestration. As a result, you end up spending less time with data integration and focus efforts on business facing analytical data models.

For assistance with enabling data integration between Oracle Fusion Applications and Databricks, reach out to mazen.manasseh@perficient.com.