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Software to the Rescue: Solving Supply Chain Challenges

By Karen Haywood Queen Contributing Editor, SME Media
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En route to Industry 4.0, manufacturers face obstacles on a variety of fronts—from frayed and fraught supply chains outside their plants to lack of visibility inside their plants, and data silos inside and out.

Despite the divergent issues, there could be a common solution: advanced software systems. A host of technologies and apps are already available—and you can bet even more are in the works—to help manufacturers solve these problems and improve their operations.

Making Sense of Logistics

The need to dynamically manage supply chains has become increasingly critical, but manufacturers often still rely on highly manual processes such as phone calls and paper records, says Dominik Metzger, head of digital supply chain development at SAP.

“Many companies have not been able yet to connect the silos of many supply chain functions,” Metzger says. “This problem existed long before the pandemic. There are still process breaks, especially in domains that by nature require a high degree of collaboration. There are disconnects in logistics from teams providing raw materials. Supply chain domains are not truly integrated. Even the best purchasing conditions, best production rates and quality, don’t help me achieve my objective if supplies simply stop coming.”

Siemens’ Supply Chain Suite and AX4 software can help manufacturers plan logistics. For example, when a ship with supplies is expected to reach port at 8 a.m. on Monday, the manufacturer needs to make sure its carrier is at the port ready to pick up goods by 10 a.m. or 11 a.m., reasons Rahul Garg, vice president of industrial machinery and SMB (small- and medium-sized business) at Siemens Digital Industries Software.

“Manufacturers can get a full view of their entire logistics supply chain,” Garg says.

Another key step is translating a manufacturer’s demand plan for materials first to the supply plan and then to the fulfillment of orders, Metzger adds. “How do I collaborate efficiently with my suppliers long before I create purchase orders to find out if there is a constraint? Close collaboration with suppliers and internal logistics teams is integral to success.

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Dominik Metzger is head of digital supply chain development at SAP.

“SAP has been betting many years on AI,” Metzger continues. “Since traditional AI helped improve processes, especially in supply chain planning, we have been infusing AI in the form of heuristics but also optimization engines for many years. Now, with the rise of more advanced artificial intelligence, we have brought machine learning capabilities into our applications.”

Metzger cites a use case designed to increase demand forecasting accuracy, so manufacturers can keep less inventory and still achieve the same production demands and service levels. “Considering the massive leaps in technologies around large language models we have seen in the past months,” he explains, “we will further embed these new capabilities into our applications to bring completely new use cases and with that business value to manufacturers.”

Better Vision, Better Business

To anticipate disruptions earlier in the process, manufacturers need visibility deep into their supply chains.

“Even the best integrated supply chains end at your supplier,” Metzger says. “I know when my supplier is late. But what if my supplier’s supplier is late? How do I get visibility into more than just my direct supplier, but across all tiers of the supply chain, to my suppliers’ suppliers? You solve that by using business networks.”

SAP Business Network connects manufacturers with materials and components suppliers. “It’s one of our most successful applications,” Metzger points out. “We already have a huge base of suppliers onboarded. We sit on a treasure trove of data. We already know about many of these relationships. We have an understanding of who delivers what to whom.”

Although AI helps, even more important is accurate data, he says, including information about global events and suppliers through all tiers. “The key is the integrity of the data,” Metzger says.

For example, he notes, the conflict in Ukraine initially led to a shortage of automotive cable wiring, which uses components produced in Ukraine.

“It took the automotive industry weeks to identify they were going to have a shortage of wiring,” Metzger explains. “They did not know where their suppliers sourced their materials.”

Data and insight are available through SAP’s different industry consortia, such as Catena-X and Manufacturing-X, where individual suppliers and manufacturers report information that is then anonymized. The automotive industry is the most involved, according to Metzger, who notes that, in addition to supply issues, automakers collaborate through their consortia on parts quality.

“Traditionally, if I had a callback because of a faulty part, I’d try to blame the supplier who gave me the faulty part,” Metzger says. “Now, collaborative quality management can find not just what component of the part, but what subcomponent of the component affected quality. Finding the root-cause in a sub-component, allows (the manufacturer) to reduce the recall rates of vehicles from thousands to only a few hundred or perhaps even a few dozen cars.”

Interest and traction is increasing in manufacturing, including semiconductors (no surprise given recent shortages), pharmaceuticals and medical device manufacturing.

“More industries are realizing that optimization of their supply chains is hinging on the fact of sharing data,” Metzger notes.

Key questions include:

  • What suppliers and sub-suppliers form a chain until the finished component reaches my warehouse?
  • What ingredients form the raw materials?
  • How does that assemble into the final batch?

“I can take the statistical demand forecast, but now I have weather, trade promotions, demand signals of orders,” Metzger says. “(And) also unstructured risk data, such as the impact of armed conflicts and natural disasters that I can integrate into my supply chain planning application.”

With the right data and insight, managers can make more informed decisions. For example, Metzger asks, what if the container won’t arrive in time? “What does that mean and what is my next best option? That leads to more scenario modeling—what if I bump up orders two, three and five and drop down orders one and four?”

Improving the process further, down to Tier 5 suppliers for example, requires a greater willingness of companies to share data in industry consortia and to trust that their individual identifying factors will remain confidential, he says.

“In some areas, ROI has been tough to achieve, not because of the value but because the scale was lacking,” Metzger asserts.

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It Takes a Village

Another key success metric is collaboration with the factory ecosystem, notably improving orchestrations between shop floor factory workers and internal logistics teams.

One SAP customer—a medical device manufacturer—worked on short-notice, one-week plans to manufacture devices, Metzger says, thus regularly faced situations where a key part was missing. In such cases, the customer struggled to shift production to other devices that could be made with components it had on hand.

“They simply could not complete the plan and execute orders because of short-term supply shortages,” he explains. “Shift managers worked with the plant manager to re-orchestrate production. Then the entire material floor had to follow and completely different raw materials had to be staged to production. That created an absolute nightmare on the shop floor.”

That disconnect—the lack of a tight integration between tactical operations and planning­—was predominantly driven by high-level plans that did not integrate production at dozens of factories, he adds.

SAP provided more vertical integration into processes with weekly feedback, instead of monthly, between the top floor and the shop floor, Metzger says. “They realized deeper integration from factory managers to shift managers across the factories.”

An upcoming SAP software release, now in the pilot stage, will simplify unloading trucks, according to Metzger. Most companies reconcile the Advance Shipping Notification (ASN) to the load list—what is actually on the truck before the truck is unloaded. The paperwork can be 40 to 50 pages long and completing the process can take 10 to 40 minutes, he adds.

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Rahul Garg is VP of industrial machinery and SMB for Siemens Digital Industries Software.

The software uses optical character recognition, large language models and tokenization, and it can either receive pages electronically as a digital file or scan pages.

“We have invested in a better way to reconcile inbound freight,” Metzger asserts. “This saves at least 10 minutes a truck. With thousands of trucks a day and dozens or hundreds of warehouses, that’s a huge savings. And it can easily scale.”

SAP’s new software is scheduled for a March 2024 release. This will be followed by an updated version in October with enhanced capabilities.

Insights in an Instant

Inside the factory, obtaining real-time data and updates of manufacturing processes is critical, Garg says. “Real-time data connectivity allows manufacturers to get live production data, visualize manufacturing operations, make real-time improvements of manufacturing processes, and take corrective action on manufacturing processes and perform predictive maintenance using IoT and lifecycle performance predictions.”

Manufacturers can compare factory to factory, and within a factory compare line to line, shift to shift and even operator to operator, Garg says. This type of comparison, in some cases, may uncover problems related to the amount of torque an individual operator or robot uses to adjust a bolt or nut, he adds.

An End to Silos

Manufacturers still face the ongoing challenge of silos of automation as opposed to a completely digitally integrated plant, according to Vik Vedantham, senior director, Fusion strategy at Autodesk.

“The challenge with these automation technologies is that, while they are rich, they have grown over the years as vertical specialized tools, packed in silos,” Vedantham says. “The process is mature. But data is being trapped. Whatever automation is put in place is vertical automation and is not connected end to end. How then do we supercharge the factory with automation and AI?

“At Autodesk, (silos are) not a new problem,” Vedantham continues. “Late in 2010, we started addressing some of these silo issues. Data has to stay connected and technology has to converge to truly drive automation.”

To this end, he says, Autodesk’s Fusion offers a fully integrated cloud—including computer-aided design, engineering and manufacturing —product development platform.

Today, Fusion helps companies at the design and engineering level, Vedantham says. Autodesk’s next-generation Fusion Industry Cloud introduced in November, he adds, will cover the full end-to-end from design to production.

Despite an increasingly complex environment, manufacturers are under pressure to shrink time to market. In the past, companies budgeted money for long-range research and development; now the focus is shifting to controlling costs and incremental improvements.

“Can they innovate fast? Produce the product fast?” Vedantham asks. “That demand is outpacing the reality of how these manufacturers can design.”

The Role of Advanced Simulation

At one plant in Brazil, appliance OEM Electrolux Group’s simulation using Siemens’ Tecnomatix portfolio uncovered 16 unexpected costs that would have resulted in six more months of production time and more than $500,000 in extra costs, Garg says.

“Many manufacturers do not have the capacity for high-level production planning and scheduling,” Garg adds. “Our very robust portfolio gives the ability to do that high-level planning with full visibility into production and the ability to make continuous improvements. This leads to a more efficient production process.”

In October 2023, Autodesk acquired FlexSim, a provider of simulation technology that allows manufacturers to test a factory to identify problems before they occur and optimize processes before production begins.

Optimized simulation helps manufacturers avoid the expensive process of dealing with problems once production begins, Vedantham explains. “That’s very expensive,” he says. “Taking down a manufacturing line costs tens of thousands of dollars. You have to stop the lines, move material and manpower around, deal with scrap.”

Using digital twins/simulation technology enables manufacturers to try out designs, processes and other scenarios before and after starting production, he says.

With FlexSim’s capabilities, factory and logistics center planners can better understand the downstream effects and unintended consequences of changes in a production facility and more accurately predict operational performance—before purchasing expensive equipment, Autodesk stated in an announcement about FlexSim.

“The factory planning process is very cumbersome,” Vedantham notes. “What if we gave you the tools to simulate those plans, try out a few scenarios prior to retooling or reconfiguring existing factories, or constructing new factories? Being able to design for those changes preemptively means manufacturers immediately see cost savings before costly capital-asset decisions are locked in. Before we even start building the factory, we have a simulation of the factory, effectively connecting the manufacturing value chain with a digital twin that captures building and operational data. They also get the right design for future scaling.”

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