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Valio introduces intelligent automation to improve their business processes

Valio and Sogeti introduce intelligent automation and RPA to enable a more effective approach to knowledge work activities.

Getting the most out of skilled labor

Ever since Valio was founded in 1905, the company has worked to produce dairy goods in a responsible, ethical, and sustainable manner. These efforts have been rewarded, as Valio is now the largest dairy goods provider and food exporter in Finland and an international industry leader. Success has taken the form of ~1.8 billion EUR in annual sales and a presence through its subsidiaries in a variety of countries around the world, including Russia, Sweden, the Baltics, the United States, and China.

A key element to achieving and maintaining success of this kind has been to constantly ensure that employees can effectively apply their talent to essential tasks. However, an organization as large as Valio is guaranteed to have numerous business processes and supporting tasks that are time consuming, repetitive, and do not represent the best use of valuable time and effort. To more effectively utilize its highly skilled employees, Valio decided to introduce knowledge work automation to business processes and identified HR processes as a prime target for the pilot project.

However, modern knowledge automation technologies continue to develop rapidly and efforts to keep up with the latest innovations require a significant time investment. Before pursuing the project, Valio decided to host an RPA hackathon to test out a variety of different partnership candidates. By the end of this event, the organization selected Sogeti to help develop its RPA solution.

While moving to the next phase, automation was introduced to business areas such as marketing and finance and accounting. Some highlights of the developed automations are the Valio Akatemia® stipend application pre-processing and purchase invoice handling processes.

Supporting the stipend application evaluators with a more effective pre-evaluation process

Valio Akatemia® is a sponsorship program that encourages children and young people across Finland to exercise and make healthy lifestyle choices. As part of this program, Valio receives thousands of applications every year, each of which needs to be given thorough consideration.

As the application program became increasingly popular, Valio faced a positive challenge due to the annual increase in the number of applicants. The organization wanted to create a better way of organizing and categorizing applications so that they were shared with the most relevant evaluator. To do so, Valio and Sogeti implemented the Valtteri robot, which reads, sorts and categorizes thousands of stipend applications in order to simplify evaluator review by ensuring that they only read correctly allocated applications.

Enhancing finance processes with machine learning and RPA

Valio wanted to better understand how to automate the preposting and circulation of purchase invoices that do not contain a purchase order number as a reference. This woud significantly decrease the amount of time needed for inspectors and acceptors to review, validate, and perform such postings for received purchase invoices.

The partners implemented a solution in which a software robot now collects and sorts invoices using historical data to predict posting details. Meanwhile, a machine learning algorithm coordinates with an RPA robot to enter the predicted information into the invoice workflow system. By doing so, the solution provides inspectors and acceptors with pre-filled invoice summaries that they only need to check for accuracy. In addition, the solution is built to self-learn from prediction errors in order to become more effective. This way, the solution requires less human oversight over time and can function increasingly independently.

In addition, the Valio finance teams have been able to eliminate a substantial amount of manual work by transforming General Ledger (GL) memo posting and archiving into a digital process with which the robot can work. This has enabled the usage of GL memos and their attachments for reporting purposes by providing easy access to the required documents throughout the organization without the need to search for the correct
physical folder.

Efficient intelligent automation as a service model to tackle business process automation challenges

Sogeti’s Intelligent Automation as a Service model meant that Valio did not need to find and evaluate different technologies on its own. Instead, the organization simply provided Sogeti with a summary of its existing manual processes. With this knowledge, Sogeti then helped to design and implement RPA solutions in addition to managing continuous operations, all without forcing Valio to increase its own RPA developer resources.

While Valio’s employees continue to provide manual oversight, the organization has experienced improvements in the processes that have been transformed by RPA solutions. This includes gains in efficiency and a reduction in throughput time. Through this partnership and platform, the partners have addressed potential future issues proactively, assuring that Valio remains constantly ahead of the curve and at the forefront of innovation.

Download reference paper (pdf). 

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Contact
  • Jaakko Lehtinen
    Jaakko Lehtinen
    Director of Automation, AI and Analytics
    +358 415013917
About Valio

Valio, offering the taste of Nordic nature since 1905, is a brand leader in Finland and a major player in the international dairy ingredients market. Their product development follows in the footsteps of Nobel Prize winner A. I. Virtanen, and the company holds over 300 patents in 50 countries.

Valio has net sales of EUR 1.7 billion and is Finland’s biggest food exporter. Their products are found in some 60 countries and account for 25% of Finland’s total food exports. Valio has subsidiaries in Russia, Sweden, the Baltics, USA and China. Valio employs a total of 25,000 people at dairy farms and 4,000 professionals at Valio.

 

Tiedosto:VALIO logo RGB53mm.png – Wikipedia