The Schaeffler Group has been driving groundbreaking inventions and developments in the field of motion technology over the last 75 years. From bearing solutions and linear guidance systems to repair and monitoring services, the manufacturer has innovated a range of critical technologies, products, and services for electric mobility, CO₂-efficient drives, chassis solutions, and renewable energies.

All this hard work and ingenuity has turned Schaeffler into a reliable partner in making motion more efficient, intelligent, and sustainable across the entire life cycle.

“We see sustainability and digitalization as key topics for our future success,” said Christof Heurung, head of Strategic Digitalization at Schaeffler. “By combining the right digital technologies, such as AI innovations, with our pioneering spirit, we can take sustainability to the next level and create an even more sustainable future for Schaeffler, as well as for our customers and suppliers.”

Partnering for Greener Supply Chain Digitalization

Shipping diverse offerings to customers worldwide requires a wide array of containers — all of which must be managed efficiently and transparently across internal operations, suppliers, and customers. However, to be successful, this approach demands the integration of numerous business systems and applications to access and exchange information securely, streamline processes strategically, and react to new challenges and opportunities with precision.

Unfortunately, most companies fall short of achieving this advantage. Shipping containers are often managed with a fragmented application landscape. This setup challenges the ability to respond quickly to packaging trends and new business requirements. It also limits the transparency needed to reconcile customers’ returnable-packaging accounts accurately and outsource delivery services to the right shared-service center. Suppliers using the same containers operate on a separate third-party system, leading to disjointed operations.

“After using SAP solutions for around 40 years now, during the past five years, we have established a strategic partnership with SAP to develop and introduce new IT application systems while forging ahead in our desire to run a more sustainable operation,” said Gerhard Stoessel, IT program lead for SAP S/4HANA at the Schaeffler Group. “Such a relationship is allowing Schaeffler and SAP to identify unmet industry needs and launch numerous innovations.”

Introducing Industry-Wide Circular Logistic Flows

One of those innovations is the  SAP Returnable Packaging Management solution. The industry-specific solution supports circular logistic flows for returnable and reusable packaging material such as containers, boxes, and pallets — from supplier and customer deliveries to intra-company movements and the journey back to the company.

When Schaeffler first used SAP Returnable Packaging Management, it covered only 30% of container management requirements. Sven Proschek, product owner of SAP S/4HANA Cloud for logistics at Schaeffler, notes that the company’s close collaboration with SAP was key to turning this trend around.

“Our partnership with SAP enabled us to enrich the solution with the industry-relevant capabilities that companies like ours need, as well as near-seamless, end-to-end integration of the container management cycle.”

Schaeffler is currently rolling out the solution across over 80 manufacturing plants in its worldwide ecosystem. This approach is streamlining the supply chain into a more efficient and robust network, resulting in fewer delivery failures and deeper transparency in the company’s packaging material inventory.

The overall workload is decreasing even though the company ships products to more customers — thanks to AI capabilities such as container reconciliation automation and self-services for reordering packaging materials and checking inventory.

“We can now react quickly to new trends, support an end-to-end collaborative solution for suppliers and customers, and save time and costs through automation,” Proschek reports. “Efficient container management also helps us avoid delivery failures due to shortages.”

Making Sustainability More Cost-Efficient

Schaeffler is also exploring new AI-enabled capabilities for SAP Returnable Packaging Management that can help the business, as well as its peers, further bridge the gap between sustainability and cost efficiency.

One idea that is gaining traction is machine learning-powered returnable packaging account matching. It aims to reduce the manual effort needed to analyze unmatched items and determine whether the underlying issue is a logistical exception or a discrepancy in matching attributes between incoming statements and open line items.

With this capability, Schaeffler can perform returnable account matching with the help of machine learning models and automate the matching process by activating line-item matching. Doing so streamlines the reconciliation process and enhances efficiency, empowering packaging planners to focus on strategic tasks. Additionally, it minimizes errors and increases the accuracy of the returnable packaging material inventory across the supply chain.

Expanding the Possibilities with a Future-Minded Vision

Schaeffler’s commitment to innovation and sustainability is evident in its adoption of AI-powered solutions for sustainable packaging and supply chain management. By using technologies such as the line-item matching functionality available in SAP Returnable Packaging Management, Schaeffler is not only streamlining its operations but also reducing costs, minimizing errors, and increasing efficiency.

As Schaeffler continues to lead the way in sustainable packaging practices, it sets a compelling example for the industry, showcasing the transformative potential of cloud innovations and AI in driving sustainable business practices.

Join our virtual event on February 14 to hear more about the newest breakthrough solutions from SAP that will enable all your business processes, data and AI to work as one. Register now.

In 2023 during a particularly demanding day on the Texas electrical grid, a bitcoin mining operation in Rockdale was told to shut down in exchange for $31.7 million. 

At first glance this may not appear to be pertinent to the subject matter of this newsletter, but this incident is an attribute of the 4^th industrial era. In this era of big data and server farms hosting applications requiring intensive processing, it is easy to assume there are no limits. The hosting and energy costs seem cheap until they are scarce.

Artificial Intelligence may be the biggest buzzword in industry. Data centers are filling up with AI applications for a myriad of objectives. The very nature of AI gives the impression that you can load it, and it will automatically make things better.

In the same way bitcoin miners demand computing resources and energy, AI applications can eat up demand. Beyond the resource impact, there are other reasons to be judicious in deploying AI in industry.

We need to begin with what I have stated so many times that you may be tired of hearing it, but a castle built on a faulty foundation will crumble. Many facilities are so far behind in their base infrastructure and maintenance capacity that any AI that sits on top of it will fail. Instrumentation will produce garbage data without sufficient maintenance, which will render any AI application useless or dangerous. Systems relying on eBay for spare parts, multiple layers of gateway bottlenecks, and insufficient network security are vulnerable to failures that will take AI applications offline. A system needs to be solid under 3^rd industrial era criteria before it is ready for the 4^th era.

Assuming you have a solid foundation, before investing in AI we need to know what it is. It is a term like many others that can be so broadly and randomly applied that it can be hard to know what we are talking about. Some consider AI to be anything that replaces or behaves like a human. A human can watch a flow gauge on a pipe and manually throttle a hand valve to set flow at the desired rate. Does that mean every PID loop is AI?  Perhaps a better definition is an application that can adapt its model (learn) based on continuous realtime data. That definition often applies to Machine Learning, which is a subset of AI. There is ambiguity in defining AI, so it is important to know how an application works before investing in it.

Assuming there is clarity on the AI attributes, it needs to be acknowledged that AI is in its infancy in industry. While applications like ChatGPT are mind blowing, they are not applicable for deterministic industrial automation. Some industrial AI applications do not require determinism, such as an open loop machine learning prediction model. The point is that first principles and well-established algorithms should not be dismissed with the notion AI can do anything. In the same way a baby can’t drive a car, AI is not mature enough for every industrial application.

Now we come to the bitcoin mining analogy. While a lot of AI applications can and should be locally deployed with an on-premises server, it is very enticing to deploy in the cloud. Especially for big data applications, the cloud is the ideal architecture for many reasons. However, the growth in server farms can outstrip the growth in energy capacity. The 4^th industrial era coincides with an era of mandates to cut fossil fuel usage. While AI applications may not have the resource demand of bitcoin mining, there can come a time where cloud resources can become scarce for any application.

Some have fears of AI and others enthusiastically embrace it. For those that are bold there is a high ceiling of unknown potential. Even for the bold, caution will serve you well by considering the risks described above.

When we talk about returnable packaging, an old timer like me thinks about pop bottles when we were kids. We could get a penny for an empty bottle. The crown lined metal cap could be wedged into the spokes on our bicycle to add a cool decoration. And then there were the milk bottles, delivered to our homes and put in an insulated container by your front door. You put your empties there and the milkman picked them up, replacing them with full ones.

We got away from this convenience because it wasn't fungible--the return bottles had to go back to their point of refill, no where else. All of this was done with human brainpower--simply what bottle goes where?

Today, with AI, barcodes, GPS and central planned systems, keeping track of packaging and efficiently and economically returning it to the right place to be reused can be an attractive alternative to once and done.

Powered by AI, 5G and the drive for sustainability, the Internet of Things (IoT) has reshaped the world we know, connecting everything from homes to factories, unlocking real-time insights and unmatched efficiency.

Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.

From primary packaging through end-of-line and distribution, diverse packaging automation solutions optimize efficiency and reduce costs for packagers of all types and sizes.

It seems everywhere we turn, AI is making headlines. When it comes to the Internet of Things (IoT)/the Industrial Internet of Things (IIoT), all signs point to us being able to continue unlocking astonishing capabilities in predictive and prescriptive maintenance from AI in the near (and far) future.