{"id":44341,"date":"2026-06-27T06:38:08","date_gmt":"2026-06-27T06:38:08","guid":{"rendered":"https:\/\/www.europesays.com\/germany\/44341\/"},"modified":"2026-06-27T06:38:08","modified_gmt":"2026-06-27T06:38:08","slug":"lemvigh-muller-ai-automates-order-confirmations","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/germany\/44341\/","title":{"rendered":"Lemvigh\u2011M\u00fcller: AI Automates Order Confirmations"},"content":{"rendered":"

The Danish wholesaler Lemvigh\u2011M\u00fcller has deployed artificial intelligence<\/a> to automate one of the most time\u2011consuming tasks in procurement: processing supplier order confirmations. The solution consists of multiple AI agents, each responsible for a clearly defined task, orchestrated into a single automated workflow built on SAP Business AI<\/a>. The outcomes are faster processing, improved data quality, and more accurate delivery information for customers.<\/p>\n

When suppliers send order confirmations as PDF files, even minor discrepancies in price, quantity, or delivery dates can trigger significant manual effort within procurement. For Lemvigh\u2011M\u00fcller, one of Denmark\u2019s largest wholesalers within steel, plumbing, heating and electrical products, this has long been a familiar challenge, consuming substantial time and resources.<\/p>\n

The company has now tackled the very point where earlier automation initiatives often stalled. With a new solution based on several specialized AI agents, developed on SAP technology and implemented in close collaboration with NTT DATA Business Solutions, supplier PDF order confirmations can now be read, interpreted, compared, and processed automatically\u2014directly against SAP systems.<\/p>\n

Capture business-wide AI value with speed and confidence<\/p>\n

\u201cWe have previously tried both RPA and traditional automation approaches without really achieving the desired effect. The key difference this time is that we broke the task down into multiple independent AI agents, each responsible for a specific part of the process. Together, they now handle what previously required manual review,\u201d says Frederik Aakerlund, IT director at Lemvigh\u2011M\u00fcller.<\/p>\n

10 weeks from idea to AI agents in production<\/p>\n

The project originated with an e-mail from Jess Frederiksen, an AI\u2011savvy project manager in Lemvigh\u2011M\u00fcller\u2019s Market and Procurement organization. After successfully matching an order confirmation with a purchase order using ChatGPT as an experiment, he approached the IT director to explore whether this could be turned into a fully integrated system solution.<\/p>\n

From the initial tests to production deployment, the entire project took just 10 weeks. According to Lemvigh\u2011M\u00fcller, this short implementation timeline was critical in allowing the solution to demonstrate tangible business value quickly and build internal support.<\/p>\n

\u201cThis was not a long-running project. In 10 weeks, we moved from idea to AI agents in production, already delivering measurable value to our procurement officers,\u201d Aakerlund says.<\/p>\n

Over time, Lemvigh\u2011M\u00fcller expects the solution to free up resources equivalent to three to four full-time employees. These resources will instead be redeployed to higher-value activities, including handling the most complex and exception\u2011driven orders.<\/p>\n

\u201cThe objective is not to reduce headcount, but to use our expertise more effectively. The AI agents take care of routine tasks, enabling procurement officers to focus on cases where their experience genuinely matters,\u201d Aakerlund adds.<\/p>\n

More than 100,000 order confirmations automated<\/p>\n

Each year, Lemvigh\u2011M\u00fcller sends approximately 175,000 purchase orders to more than 2,000 suppliers. While part of this volume is handled in a structured manner via EDI, around 60% of supplier order confirmations are still received as unstructured documents.<\/p>\n

With the coordinated AI agents in place, the company can now automatically identify delays, quantity changes, and price discrepancies\u2014and respond significantly faster.<\/p>\n

\u201cPreviously, when order confirmations were handled manually, it could take hours or even days before changes were reflected across the organization. Today, the AI agents update the data almost immediately, allowing customers to receive a much more accurate picture of deliveries far sooner,\u201d says Klaus Heinemann, head of SAP ERP at Lemvigh\u2011M\u00fcller, who led the development together with the project team. \u201cIn addition, we now identify price discrepancies before the final invoice is issued, saving time both for us and for our suppliers.\u201d<\/p>\n

\"\"<\/a><\/p>\n

Multiple AI agents orchestrated in a single workflow<\/p>\n

The solution is built around three cooperating AI agents, each with a clearly defined role in the process. One agent handles incoming e-mails and attachments, a second extracts and structures data from PDF documents, and a third compares the extracted information against purchase orders in SAP to determine whether there is a match or a deviation.<\/p>\n

As a result, complex and unstructured supplier data can be processed in a unified, automated workflow without requiring procurement officers to open and manually review lengthy PDF files.<\/p>\n

\u201cWhat makes this solution robust is the interaction between the agents. Each agent is highly specialized, but they are orchestrated in a way that ensures the process flows seamlessly from start to finish,\u201d Heinemann explains.<\/p>\n

Three AI agents working together at Lemvigh\u2011M\u00fcller<\/p>\n

Lemvigh\u2011M\u00fcller\u2019s solution is built around three specialized AI agents, each responsible for a clearly defined task within the procurement process. Together, they form a single, end\u2011to\u2011end, automated workflow:<\/p>\n

1. The e-mail agent receives and sorts incoming e-mails from suppliers. The agent identifies relevant order confirmations and attached documents and routes them to the next step in the process.<\/p>\n

2. The data extraction agent extracts key information such as prices, quantities, and delivery dates from PDF documents and structures the data so it can be compared directly with purchase orders in SAP.<\/p>\n

3. The matching agent compares the extracted data with existing purchase orders in SAP and determines whether there is a match or a deviation. In case of a match, the process continues automatically, while deviations are flagged for further handling.<\/p>\n

During the project, the importance of master data quality also became increasingly clear.<\/p>\n

\u201cIn areas such as Incoterms and other master data, we identified improvements that need to be addressed. This has been an important learning not just for this initiative, but for our broader work with AI,\u201d he says.<\/p>\n

While it is still too early to measure the full impact on customer experience, error rates, or claims, expectations are that faster and more precise handling of supplier confirmations will, over time, lead to fewer surprises and significantly improved delivery transparency. Internally, the solution has been met with strong interest and curiosity among employees.<\/p>\n

\u201cProcurement officers clearly recognize the value of being relieved from the most tedious routine work. This has sparked a constructive dialogue about how technology can best support their day\u2011to\u2011day responsibilities,\u201d Heinemann says.<\/p>\n

\"\"<\/a>The interaction between the three AI agents makes it possible to automate a task that previously required manual review of unstructured documents.<\/p>\n

Business AI with a clear business outcome<\/p>\n

According to Lemvigh\u2011M\u00fcller, the investment is expected to deliver a return within a relatively short timeframe.<\/p>\n

\u201cWe are talking about quarters rather than years when it comes to ROI. That is why it was essential for us to get the solution into production quickly and focus on processes with a clear and measurable impact,\u201d Aakerlund says.<\/p>\n

For SAP, the project serves as a concrete example of how artificial intelligence can be embedded directly into core business processes rather than remaining a disconnected experiment.<\/p>\n

\u201cMany companies talk about AI agents primarily in terms of automation. Lemvigh\u2011M\u00fcller demonstrates that the real challenge\u2014and the real opportunity\u2014lies in coordination,\u201d says David Pontoppidan, head of AI at SAP for the Nordics and Baltics. \u201cIt is the orchestration of three specialized agents directly within the core process that makes this solution robust. This is also where many multi\u2011agent initiatives fail, not due to limitations of individual agents but because of insufficient coordination. Lemvigh\u2011M\u00fcller has succeeded by anchoring the solution in its SAP landscape, where data, business rules, and governance frameworks are already firmly established.\u201d<\/p>\n

He continues: \u201cInnovation is not about company size. Lemvigh\u2011M\u00fcller shows that a Danish organization with short decision paths and a pragmatic approach to technology can move faster than many large global enterprises that are still in the planning stage. Ten weeks from idea to production is far from the norm, but perhaps it should be.\u201d<\/p>\n

Designed for operations and scalability<\/p>\n

The solution was implemented in close collaboration with NTT DATA Business Solutions, which was responsible for making the solution production\u2011ready and fully integrated into Lemvigh\u2011M\u00fcller\u2019s SAP landscape.<\/p>\n

\u201cBy distributing responsibilities across multiple AI agents, Lemvigh\u2011M\u00fcller has been able to automate a complex process without losing transparency or control. This has enabled a fast and secure transition from pilot to production and ensures a more robust solution that can easily be expanded as new requirements emerge,\u201d says Kristian Dahl, SAP UX manager at NTT DATA Business Solutions.<\/p>\n

According to Dahl, the modular, agent\u2011based architecture was a key enabler in moving efficiently from proof of concept to live operation.<\/p>\n

First step in a broader AI agent strategy<\/p>\n

Initially, the AI agents have been deployed for selected supplier inboxes and business areas. However, Lemvigh\u2011M\u00fcller already sees significant potential in applying the same agent\u2011based approach across additional administrative processes.<\/p>\n

\u201cThis is the first AI agent solution we have put into production. The experience has given us the confidence to consider similar approaches across other areas, including invoice processing and order management,\u201d Aakerlund concludes.<\/p>\n

Ellen Vig Nelausen is a Nordic Integrated Communications Expert at SAP.<\/p>\n

Subscribe to the SAP News Center for the latest SAP news each week<\/p>\n","protected":false},"excerpt":{"rendered":"The Danish wholesaler Lemvigh\u2011M\u00fcller has deployed artificial intelligence to automate one of the most time\u2011consuming tasks in procurement:…\n","protected":false},"author":2,"featured_media":44342,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[21036],"tags":[9693,6409,25493,424,9695,21972,37967],"class_list":["post-44341","post","type-post","status-publish","format-standard","has-post-thumbnail","category-sap","tag-agentic-ai","tag-ai","tag-customer-story","tag-procurement","tag-sap","tag-sap-business-ai","tag-wholesale-distribution"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/posts\/44341","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/comments?post=44341"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/posts\/44341\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/media\/44342"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/media?parent=44341"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/categories?post=44341"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/tags?post=44341"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}