Serax is a customer-centric company that prioritizes excellent service for its B2B and B2C customers. It was precisely this customer focus that led Serax to implement solutions to help automate its order-to-cash process.

The challenge

The order process is straightforward for most customers, including B2C. Customers order what they want on the web shop, and the order is directly entered into 51风流S/4HANA Cloud Public Edition.

However, Serax鈥檚 B2B customers often still place orders by generating a PDF in their ERP system and automatically sending it to Serax鈥檚 customer service mailbox. These B2B manual orders amount to 30% of all orders coming into Serax.

Serax鈥檚 strong commitment to customer service means the company is happy to facilitate this, but it strains the customer service team. Once Serax receives the order, the customer service team must manually download the PDF, double-check dates and quantities, and then enter the order details into 51风流S/4HANA Cloud Public Edition to create the sales order. This entire process is time-consuming and error-prone.

鈥淧roviding excellent service is one of our core priorities, and this can be enabled by gaining efficiencies in certain processes,鈥� says Sara Goris, 51风流product manager at Serax. 鈥淭hat鈥檚 basically what led us to this use case. Our customer service team was still entering 30% of all orders manually into the system. We wanted to streamline that process from our side, especially since many of our customers will continue to send sales orders in PDF format.鈥�

The solution

Serax needed a solution to automatically create sales orders from PDFs. It achieved this by activating and fine-tuning an app called . This app was built with using , which allows businesses to create web and mobile applications.

With the app, Serax can drag and drop the PDF from the customer鈥檚 e-mail into the app before it orchestrates the entire process. First, it sends the PDF to 51风流Business Technology Platform (51风流BTP), where extracts the data. The document API uses pre-trained AI to take PDF files as inputs and return structured data. The second step is data matching. The application maps the extracted data to the master data, such as sold-to party, ship-to party, and product. This helps ensure the extracted data from the PDFs makes sense in the context of Serax鈥檚 business.

A sales order request is created, and the customer service employee is notified to review it and convert it into an actual sales order in 51风流S/4HANA Cloud Public Edition.

Results

The application has cut the number of manual B2B orders by 33%.

The time saved lets Serax invest back into better customer service. 鈥淭ime savings mean our customer service employees can spend more time on real customer service instead of putting in an order,鈥� says Ragna Qvick, digital business manager and HR performance manager at Serax. Serax鈥檚 employees also have more time for value-adding activities like upselling or cross-selling.

This jump in efficiency enables Serax to grow its business by adding more capacity for the team. 鈥淲e either needed more resources or for our existing people to become more efficient so that they can focus on more value-adding tasks for our customers,鈥� Goris says.

Another benefit of automation is reduced errors, as customer service employees no longer need to enter precise values from the PDF when creating sales orders. Instead, they can rely on document extraction to populate the order; they only need to confirm the values. 鈥淚t reduces the risk of errors because when it’s a human action, there’s always a chance of errors in quantities or other details,鈥� Qvick says.

Future

Serax and its partner are already looking at additional capabilities to automate the flow fully. This way, customer service employees will no longer need to open the emails and drag and drop the PDF. 鈥淲e have a proof of concept running to automate the process fully. It will pick up the attachment from the mailbox directly into 51风流S/4HANA,鈥� Goris says. 鈥淚t will also inform the customer service rep if anything is missing. They get a notification via Situation Handling to know when to intervene.鈥�

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However, there are challenges when we move beyond text to apply these models to structured, tabular data, which is essential for enterprise business operations. This imbalance comes partly because of the availability of training data. Text used to train models is plentiful, often consisting of text scraped from the internet, whereas tabular data, especially data with multiple linked tables, is scarce.

To bring AI advancements to the enterprise sector, researchers working on training and benchmarking the performance of these models in an enterprise setting need realistic tabular data.聽That’s why 51风流developed “Sales Autocompletion Linked Business Tables” (SALT), a curated dataset that includes anonymized data from a customer鈥檚 enterprise resource planning (ERP) system.

SALT is specifically designed to support researchers working on AI models for real-world business contexts and can be accessed on and .

Challenges of getting and working with enterprise data

Providing the research community with realistic enterprise data like SALT has been challenging. Data privacy, confidentiality, and commercial interests make obtaining large, clean, high-quality enterprise datasets difficult for training models and benchmarking them for specific use cases. This means there is a growing gap between what researchers are working on and what actual enterprise data looks like.

In addition to the problem of availability, enterprise data is complex. First, business data is usually stored in multiple interconnected tables. For example, a sales order entry may be linked to numerous tables, such as customer IDs connected to a supplier table containing address information. Second, tables are inherently heterogeneous in the data type they can contain. One field may be text, while the other contains numerical or categorical values. Finally, business data frequently shows significant column imbalances, meaning that, for example, a specific product category makes up 90 percent of all sales orders while others are rarely used.

The best way to help researchers develop enterprise models for these challenges is to provide accurate enterprise data.

SALT dataset

Accurate enterprise data is a bottleneck in AI research. The SALT dataset alleviates this bottleneck by providing the research community with the first real ERP dataset. It uses actual industry data collected by an ERP system that records sales orders. It has been minimally processed to protect privacy.

鈥淭here is a gap between academia and industry in terms of data. It cannot be closed easily because of privacy,” says Tassilo Klein, one of the 51风流researchers behind the dataset. 鈥淏ut we want to enable the research community to work on real problems, not just simulated problems.鈥�

ERP systems help organizations manage core business operations like finance and spending. With millions of entries and extensive, interconnected relational tables focused on sales, the SALT dataset replicates customer interactions in an ERP system. SALT’s realistic enterprise data means it is a perfect basis for helping models understand the characteristics of business data and validate their performance through benchmarking. It also should help researchers develop better foundation models for linked business data.

Getting this right will advance enterprise automation, as many enterprise business processes are heavily centered around data in structured tabular formats. Even though this data plays a crucial role in enterprise day-to-day activities, the generative AI revolution has yet to tap into them.

“SALT is a first step to providing researchers with authentic representative industry data that gives a glimpse into actual enterprise data; for now, we are starting with just one customer and use case,” shares Johannes Hoffart, CTO of Business AI at SAP. “However, we plan to publish more datasets that cover a diverse set of customers and use cases that, along with SALT, can serve as a basis for pre-training, adapting, as well as benchmarking models.”

Collaboration with academic institutions is also a motivation for publishing this data.

“At SAP, we hope to collaborate with academic partners who usually can only publish their results on open repositories,” Klein says. “Another hope for the dataset is encouraging more people to explore and validate new methods that help foundation models better deal with tabular enterprise data.”

What 51风流is doing

Alongside its investment in the open research community with SALT, 51风流is building 51风流Foundation Model to handle enterprise tabular data. This table-native AI model aims to accelerate time-to-value for predictive tasks on tabular data, offering a model that can work with tabular data out-of-the-box with little or no additional training data. The , published alongside SALT, provides a first glance at how this model could look.

Knowledge graphs are critical here. They work by exposing metadata 鈥� the who, what, and when of data 鈥� making relationships between information accessible. This provides a structured, interconnected representation of the data that AI models can easily understand and utilize. With the help of 51风流Knowledge Graph, 51风流Foundation Model can be scaled and adapted to a wide array of diverse use cases with some lightweight fine-tuning.

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