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sudhir
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AI- Artificial Intelligence and ML- Machine Learning are often used interchangeably, but machine learning is a subset of the broader category of AI.

AI refers to the general ability of computers to emulate human thought and perform tasks in real-world environments, while machine learning refers to the technologies and algorithms that enable systems to identify patterns, make decisions, and improve themselves through experience and data.

Machine learning is a pathway to artificial intelligence and uses algorithms to automatically learn insights and recognize patterns from data, applying that learning to make increasingly better decisions.

Organizations in every industry must be able to transform their data into actionable insight giving organizations the advantage of automating a variety of manual processes involving data and decision making. Incorporating AI and machine learning into their strategic plans and systems, business leaders can understand and act on data-driven insights with greater speed and efficiency.

 

Transformative Intelligence of SAP AI

Artificial Intelligence is built to transform every aspect of the business.


Bar for the business has risen and so have expectations, demands and competition. Transformative intelligence and adaptable innovations equip you rise to the challenge and optimize every aspect of business.

Artificial Intelligence and machine learning are often associated with enterprise-level applications.

  • Automating purchase orders – More and more companies have been automating purchase orders using AI for quite some time. This minimizes human errors and frees employees from manual and repetitive work.



  • Predicting the better supplier –Finding the most suitable suppliers is probably one of the biggest challenges faced by companies in the procurement and supply niche. To solve this issue, machine learning techniques can be used to create prediction models in selecting a supplier.

  • AI chatbot support – Chatbot support has plenty of applications in the procurement and supply chain business. For example, it can be used to:



  • Automate goods pick-up process in warehouses.

  • Answer customer queries

  • Track shipment coming from suppliers.

  • Integrate GPS functionalities in delivery vehicles so managers can keep track of progress and send in new dispatch orders.


However, the possibilities of AI and ML usage are infinite and still evolving.

Below are some great examples of operations-level applications of AI:-

  • Driverless trucks can add to the safety of workers, especially for underground mines.

  • AI-driven crop monitoring, soil monitoring and greenhouse management.

  • Predictive equipment maintenance.

  • Prediction of stocks in transit – With predictive analysis, companies delivering and receiving stocks get to address crucial issues before they happen. It can help minimize expenses and schedule disruptions.


 

AI in business processes supported by SAP software help organizations to become intelligent enterprise and enable the delivery of the following business outcomes.

  • Accelerate time to value.

  • Deliver agile and flexible processes.

  • Enable data-driven decisions.

  • Future proof IT solutions.

  • Comply with regulatory requirements.

  • Reduce IT costs.


 

Machine Learning with S/4 HANA has delivered several released use cases to help the digitization enablers for all process streams i.e.

  • Lead to Cash

  • Design to Operate

  • Source to Pay

  • Recruit to Retire


 

However, in this blog, we will outline on Source to Pay use cases for ML/AI and iRPA. Details for each scenario can be further searched on SAP portal.

Use of Artificial Intelligence turns sourcing into competitive advantage. Use of SAP AI transforms Source to Pay processes and takes complexity out of finding, negotiating, and procuring supplies. AI usage enables procurement professionals to simplify sourcing, discover quality suppliers with intelligent filtering, optimizing purchasing with automated assistance and handle invoices with automatic processing.

Intelligent Technologies help the operational purchaser.


Source to Pay use cases:-





















































Best Practice Scope ID Process Scenario Process Overview
1QR Contract Consumption The Purchaser can analyze a high-level overview of important information, such as expiring contracts, overdue purchase orders or urgent purchase requisitions, as well as an overview of different procurement KPIs. The Buyer can use this information to predict full consumption of a contract based on factors such as a historical data, other available influencing parameters, and so on.
2XW Proposed Creation of New Catalog Items During purchase requisition creation, free text item entry can be interpreted as a catalog item, due to recommendations from the Machine Learning algorithm.
2XX Payment Block- Cash Discount at Risk When a supplier has a quantity blocking reason at the item level, the cash discount may be at risk. Utilizing the predictive modeling integration, you can predict the goods receipt delay and thus meet the cash discounts.
2XV Propose Material Group for Free Text Items During the purchase requisition process using the shopping cart scenario, the free text material item is filled automatically by the proposal of the material group based on, among other things, the historical purchase order data, utilizing the Machine learning recommendation.
30W Proposal of Options for Materials without Purchase Contract This scope items deals with providing options for the purchaser to create an RFQ where the materials don't have a purchase contract.
3FY Supplier Delivery Prediction Materials required for production are supplied by multiple suppliers. A delay in delivery can impact on-time production at the plant and cause rescheduling of assembly lines, which is very costly. Indirect material delay also causes interruption in supporting employees with required products or services. The Machine Learning algorithms identify the supplier delays based on the multiple situation and predicts the chances of delay. During creation of purchase orders or purchase requisitions, the lead time from the material master sometimes doesn't consider the processing and approval time.
43E Intelligent Workflow Approval Analyze the approval pattern history for the purchase requisitions based on price, source of supply, material group, approvers, attachments, and so on, and provide recommendations for a mass automated approval.
3UH Image Based Ordering This scope item covers the process to order and procure an item based on an image. If the image is available already in the cross catalog, recommendations can be provided based on similar images and patterns. Similar images can be detected by normalizing images to increase the likelihood of finding similarities. Images can be added into the shopping cart from a laptop. Alternately, a dedicated image-based app can create a draft shopping cart and, subsequently, a purchase requisition.
2ZS Goods Receipt/ Invoice Receipt Monitor ML Status Proposal A machine learning feature of the GR/IR application makes a prediction of the next status based on the history of processing goods and invoices received. The predictions are provided by a Machine Learning (ML) service that runs on the SAP Cloud. This communication scenario is related to the connection of the SAP S/4HANA backend and the ML service.  This scope item is deployed in conjunction with the Goods Receipt / Invoice Receipt monitor.

 

Source to Pay - SAP Best Practice in for iRPA- intelligent Robotic Process Automation

RPA automates processes and high volume/repetitive tasks steps by mimicking the activity of a human operator or end user allowing humans to focus on more complex and/or value-added operations.

The following iRPA use cases are available in S/4HANA.

 

































Scope Option Source Systems Type of Automation BOT Skill Description
Simple Purchase Requisition Creation from Excel S/4HANA Cloud/ On- Prem API

The bot reads data from a set template format and then create a Purchase Requisition in S/4HANA using the standard whitelisted API. This skill supports scope items:

§  Requisitioning 18J

 
Purchase Order Confirmations S/4HANA Cloud/ On- Prem UI

Bot calls service to extract the PO number and line-item confirmation detail from incoming email response. Bot performs manual steps to update PO with information via UI. This skill supports scope items:

§  Requisitioning (‏18J‏)

§  Consumable Purchasing (‏BNX‏)

§  Procurement of Direct Materials (‏J45‏)

§  Central Requisitioning (1XI)
Supplier Invoice Status Checks S/4HANA Cloud/ On- Prem UI & API Bot automates reply to standard requests by providing the answer for one or many invoices in a user-configurable spreadsheet template.
Automatic Upload of Supplier Invoices S/4HANA Cloud API

Post the receipt of materials/products, the supplier sends the invoice of item procured referencing the purchase order. This invoice can be sent to the buyer either physically or electronically as attachment through emails.

Through bots, the processing of such emails and reading both structured and unstructured data from email attachments can be automated thereby streamlining the entire process with minimal or no manual effort.

 

Conclusion and Key Takeaway

In the procurement and supply chain industry, companies continue to move forward, seeking better and more efficient ways to do business. One of the key adaptations used in the industry is the increased and improved use of AI and machine learning. With AI and machine learning in place, businesses get to streamline work processes, automate repetitive work, and boost employee efficiency.

Solutions such as artificial intelligence and machine learning are amongst the biggest enablers of digitization and tech advancement, regardless of the industry.

Procurement and Supply Chain professionals have been early adopters of AI, ML and iRPA. The industry is highly data-driven, so algorithms in AI and machine learning come in quite handy. These provide companies with extensive computational power to process massive datasets. Today, with the help of technology, decision-makers get to analyze large chunks of data and have that plugged into a supply chain model to get better insights. And over time, the processes improve because machine learning allows for the recognition of patterns or trends.  SAP has built AI to transform every aspect of business and the trend is here to stay and grow.
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