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SAP Data Hub opens predictive possibilities at Paul Hartmann
When medical supply firm Paul Hartmann AG tested a supply chain analysis system built on SAP Data Hub, it found that it could predict demand better than the company's sales team.
Organizations have access to more data than they've ever had, and the number of data sources and volume of data just keeps growing.
But how do companies deal with all the data and can they derive real business use from it? Paul Hartmann AG, a medical supply company, is trying to answer those questions by using SAP Data Hub to integrate data from different sources and use the data to improve supply chain operations. The technology is part of the company's push toward a data-based digital transformation, where some existing processes are digitized and new analytics-based models are being developed.
The early results have been promising, said Sinanudin Omerhodzic, Paul Hartmann's CIO and chief data officer.
Sinanudin Omerhodzic
Paul Hartmann is a 200-year-old firm in Heidenheim, Germany that supplies medical and personal hygiene products to customers such as hospitals, nursing homes, pharmacies and retail outlets. The main product groups include wound management, incontinence management and infection management.
Paul Hartmann is active in 35 countries and turns over around $2.2 billion in sales a year. Omerhodzic described the company as a pioneer in digitizing its supply chain operations, running SAP ERP systems for 40 years. However, changes in the healthcare industry have led to questions about how to use technology to address new challenges.
For example, an aging population increases demand for certain medical products and services, as people live longer and consume more products than before.
One prime area for digitization was in Paul Hartmann's supply chain, as hospitals demand lower costs to order and receive medical products. Around 60% of Paul Hartmann's orders are still handled by email, phone calls or fax, which means that per-order costs are high, so the company wanted to begin to automate these processes to reduce costs, Omerhodzic said.
One method was to install boxes stocked with products and equipped with sensors in hospital warehouses that automatically re-order products when stock reaches certain levels. This process reduced costs by not requiring any human intervention on the customer side. Paul Hartmann installed 9,000 replenishment boxes in about 100 hospitals in Spain, which proved adept at replacing stock when needed. But it then began to consider the next step: how to predict with greater accuracy what products will be needed when and where to further reduce the wait time on restocking supplies.
Getting predictive needs new data sources
This new level of supply chain predictive analytics requires accessing and analyzing vast amounts of data from a variety of new sources, Omerhodzic said. For example, weather data could show that a storm may hit a particular area, which could result in more accidents, leading hospitals to stock more bandages in preparation. Data from social media sources that refer to health events such as flu epidemics could lead to calculations on the number of people who could get sick in particular regions and the number of products needed to fight the infections.
"All those external data sources -- the population data, weather data, the epidemic data -- combined with our sales history data, allow us to predict and forecast for the future how many products will be required in the hospitals and for all our customers," Omerhodzic said.
Paul Hartmann worked with SAP to implement a predictive system based on SAP Data Hub, a software service that enables organizations to orchestrate data from different sources without having to extract the data from the source. AI and machine learning are used to analyze the data, including the entire history of the company's sales data, and after just a few months of the pilot project was making better predictions than the sales staff, Omerhodzic said.
"We have 200 years selling our product, so the sales force has a huge wealth of information and experience, but the new system could predict even better than they could," he said. "This was a huge wake up for us and we said we need to learn more about our data, we need to pull more data inside and see how that could improve or maybe create new business models. So we are now in the process of implementing that."
Innovation on the edge less disruptive
The use of SAP Data Hub as an innovation center is one example of how SAP can foster digital transformation without directly changing core ERP systems, said Joshua Greenbaum, principal analyst at Enterprise Applications Consulting. This can result in new processes that aren't as costly or disruptive as a major ERP upgrade.
Joshua Greenbaum
"Eventually this touches your ERP because you're going to be making and distributing more bandages, but you can build the innovation layer without it being directly inside the ERP system," Greenbaum said. "When I discuss digital transformation with companies, the easy wins don't start with the statement, 'Let's replace our ERP system.' That's the road to complexity and high costs -- although, ultimately, that may have to happen."
For most organizations, Greenbaum said, change management -- not technology -- is still the biggest challenge of any digital transformation effort.
Change management challenges
At Paul Hartmann, change management has been a pain point. The company is addressing the technical issues of the SAP Data Hub initiative through education and training programs that enhance IT skills, Omerhodzic said, but getting the company to work with data is another matter.
"The biggest change in our organization is to think more from the data perspective side and the projects that we have today," he said. "To have this mindset and understanding of what can be done with the data requires a completely different approach and different skills in the business and IT. We are still in the process of learning and establishing the appropriate organization."
Although the sales organization at Paul Hartmann may feel threatened by the predictive abilities of the new system, change is inevitable and affects the entire organization, and the change must be managed from the top, according to Omerhodzic.
"Whenever you have a change there's always fear from all people that are affected by it," he said. "We will still need our sales force in the future -- but maybe to sell customer solutions, not the products. You have to explain it to people and you have to explain to them where their future could be."
