Supply Chain Technology Adoption

PepsiCo is testing AI and digital twin technologies to assess their effectiveness in simulating, validating and optimizing plant and warehouse facilities before making any physical changes.

The branded consumer packaged goods company has started an early-stage pilot of the technology with AI chipmaker Nvidia and global industrial technology company Siemens, according to a Jan. 6 press release. The companies aim to remake PepsiCo's plant and supply chain operations through a multi-year collaboration.

"The scale and complexity of PepsiCo’s business, from farm to shelf, is massive—and we are embedding AI throughout our operations to better meet the increasing demands of our consumers and customers,” PepsiCo Chairman and CEO Ramon Laguarta said in the release.

As part of its digital-first planning strategy, PepsiCo is using digital twins and AI agents as co-designers of facility layouts, specifically utilizing Siemens Digital Twin Composer, built on Nvidia's Omniverse AI toolkits.

A digital twin is a real-time virtual model of a physical asset, system or process. Companies, such as Amazon and Walmart, use this technology to simulate the effects of operational changes or additions. PepsiCo plans to first simulate upgrades to its U.S. facilities, then expand globally, starting with its biggest markets in 2026 and 2027, the company said in an email to Supply Chain Dive.

“Working with NVIDIA and Siemens, PepsiCo is re-architecting its operations—using physically accurate digital twins and AI to reinvent how it designs, optimizes, and runs its global operations," Nvidia founder and CEO Jensen Huang said in the release.

PepsiCo and Siemens have converted selected U.S. manufacturing and warehouse facilities into high-fidelity 3D digital replicas, per the release. The conversion helps establish a performance baseline for plant operations and the end-to-end supply chain. Teams use the virtual twin to validate and optimize new configurations meant to boost capacity and factory line throughput.

The Siemens technology enables PepsiCo to recreate every machine, conveyor, pallet route and operator path with physics-level accuracy, according to the release. The snacks manufacturer can use AI agents to simulate, test and refine system changes, helping it identify up to 90% of potential issues before making any physical changes, per the companies.

"With a unified, AI-powered digital foundation, PepsiCo is building toward a world where every plant and warehouse operates as part of a single, intelligent ecosystem," Athina Kanioura, CEO of Latin America and global chief strategy and transformation officer at PepsiCo, said in the release. "In this future, our facilities don’t just respond to demand, they anticipate and then adapt to it."

Dive Brief:

• Walmart has deployed inventory-tracking sensors across its supply chain through a collaboration with Wiliot, providing more data for the retailer's artificial intelligence push.
• Data from Wiliot's ambient Internet of Things sensors aim to help Walmart improve its supply chain efficiency, inventory accuracy and cold chain compliance via real-time insights into stock levels and locations, according to an Oct. 2 news release.
• The sensors provide automated alerts and reduce manual tasks for employees, per the release. They are currently deployed across 500 Walmart stores, with a national expansion covering 4,600 retail locations and 40-plus distribution centers planned for 2026.

Dive Insight:

Millions of Wiliot sensors, called Pixels, are already deployed on pallets moving from distribution centers to stores throughout Walmart's supply chain, said Julien Bellanger, Wiliot's president, in an interview with Supply Chain Dive. The company eventually aims to enable tagging at the case level for Walmart, rather than the pallet level, so the retailer can track inventory in an even more granular fashion, he added.

The sensors are particularly helpful in cold chain applications, such as making sure produce is quickly moved to a cooler once it reaches a store, according to Bellanger. They also provide automatic signals that make it easier to know where a pallet is in real time.

"Positive confirmation of receipts in supply chain, as you know, is very important," he said. "No manual scanning, no paper trail, just a signal that is automatically created based on real data, and that both the supplier and Walmart will capture eventually."

Wiliot's sensors feed inventory data into Walmart's AI systems. The collaboration comes as Walmart scales up its use of AI in its supply chain, with inventory management being a key focus area for the technology.

"With Wiliot's ambient IoT technology, coupled with our AI systems, we're not only optimizing our supply chain to make faster, smarter inventory decisions, but we're also tackling one of the hardest problems in retail—knowing exactly what we own and where it is at any given moment," Greg Cathey, Walmart SVP of transformation and innovation, said in the release.

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By Zeigo by Schneider Electric

In recent years, supply chain decarbonization has gained significant attention as a critical component of corporate climate action. While many organizations have already taken steps to reduce carbon emissions from their own operations, the emissions embedded in supply chains often represent the largest and most complex part of the companies’ environmental footprint. Understanding how these emissions are classified, how they occur, and how they can be measured and addressed is essential for building credible strategies for dealing with them.

The Challenges of Supply Chain Decarbonization

1. Fragmented Data

One of the key difficulties in measuring Scope 3 emissions lies in the fragmented nature of the underlying data. Much of the information required to calculate these emissions accurately resides with third parties, such as raw material providers, logistics partners and external service suppliers. These stakeholders often do not monitor their own emissions, or they may be unwilling or unable to share the necessary data. This results in gaps or inconsistencies, especially when trying to aggregate emissions across multiple categories or suppliers. Additionally, in the absence of consistent primary data, companies are frequently forced to rely on estimates, industry averages, or third-party databases. While these sources can provide a general sense of emissions, they are not tailored to the specific circumstances of a company’s supply chain, which limits both accuracy and comparability.

2. Complexity of Regulations

The complexity of tracking Scope 3 emissions has also been recognized by regulators. In the United States, for instance, recent climate disclosure rules acknowledge that these emissions are harder to measure, as they often come from third-party activities throughout a company’s value chain. Because of this, companies would only be expected to report Scope 3 emissions if they are considered material or if their reduction targets already include them. The rules align closely with the GHG Protocol and suggest using the same organizational boundaries as financial reporting, making it easier for companies to present consistent and reliable information.

3. Lack of Transparency

Another common challenge is the lack of transparency beyond direct suppliers. In complex, global supply chains, companies often have little or no visibility into second or third tier suppliers, even though these may represent a significant portion of the overall carbon footprint. Furthermore, measuring Scope 3 emissions is rarely a one-off task. Supply chains evolve over time – new suppliers are introduced, products change, logistics networks shift, meaning emissions data must be regularly updated and reassessed to remain relevant and useful.

Leveraging Software as a Solution

Because of the numerous challenges companies have had to face regarding decarbonization, supply chain digitization has become a hot topic globally across industries. Software solutions have emerged as a crucial enabler of supply chain decarbonization by helping businesses track, optimize and reduce their carbon footprints through data-driven insights, especially as more and more granular data is being obtained. Sustainability software platforms encourage supplier engagement and provide transparency and collaboration opportunities to ensure decarbonization efforts are aligned across the supply chain. They help companies track supplier emissions, sustainability certifications and progress on reducing their environmental impact. Additionally, some platforms offer tools to manage carbon offset purchases by investing in verified offset projects.

How Zeigo Hub Can Help You

Supply chain decarbonization can be complex, but sustainability software like Schneider Electric’s Zeigo Hub helps make the process more manageable. The platform addresses one of the toughest challenges in corporate sustainability-measuring and reducing value chain emissions, by simplifying data collection and supplier engagement across increasingly complex supply chains. The software also enables organizations to collect emissions data from suppliers, identify emissions hotspots, and track progress over time. Its data management tools help companies focus on the most impactful reduction opportunities, while built-in guidance supports compliance with local and global reporting standards. Zeigo Hub also encourages collaboration by giving suppliers practical tools and resources to participate in decarbonization efforts. The software solution also supports industry-specific programs that accelerate the transition to clean energy. In the pharmaceutical sector, the Energize program brings together companies such as AstraZeneca, Novartis, and GSK to aggregate renewable energy purchases, making clean power more accessible and affordable for smaller suppliers. Suppliers can also use the platform to procure, track, and measure energy use and emissions.

By digitizing supplier engagement and emissions tracking, Zeigo Hub helps companies identify cost-effective reductions and make measurable progress toward net-zero goals. To learn more about decarbonizing your supply chain, click here.

GE Appliances is betting big on reshoring and automation technology to power a more resilient supply chain. 

The company, which manufactures household appliances such as refrigerators and washing machines, has been scaling stateside production over the last decade, investing $3.5 billion in its operations throughout the country since 2016, with plans to spend another $3 billion in the next five years. 

The investments support the company’s vision — what VP of Supply Chain Bill Good calls a “zero distance mentality” for the supply chain. 

“We believe that is [an] advantage to manufacture in the market that you're serving next to our consumers,” Good said in an interview. He added that making products closer to customers allows the company to build a nimble and flexible supply chain that can respond more effectively to shifts in demand while maintaining shorter lead times. 

Technology is at the heart of GE Appliances’ “zero distance” approach. Of the $3 billion it plans to spend in the U.S. over the next half-decade, a large portion will go toward installing new automation and capital equipment at its 11 manufacturing facilities and microfactories. 

Here are three ways the company is currently using automation and other technologies within its plants, along with the benefits the applications are providing to its operations and supply chain.

1. Improving production efficiency

GE Appliances has fully automated the process by which it builds many of the components for appliances, such as ovens, refrigerators and washers. For example, when constructing a refrigerator, the company employs a highly automated process to shape coiled steel into the outer case and doors of the product.

“All of those processes, for the most part, are automated,” Good said. “Same way with the refrigerator door or whether it is an oven door or whatever. All of the cavities for your ovens, that's all automated.” 

The company also uses robots within its production processes, including for tasks such as welding, gluing adhesives and inserting screws. 

By leveraging automation technology, the company can now produce many products in less than two labor hours, which is a reduction of at least 50% compared to output 25 years ago, according to Good. 

2. Boosting end-to-end visibility

Over the last 10 years, GE Appliances has established a “digital thread” throughout its manufacturing systems, according to Good. 

As the leader of the company’s supply chain, Good has access to complete visibility of every piece of equipment within a given plant and the ability to configure data to communicate to the right people on the floor to solve problems. Along with equipment issues, within these “digital factories,” Good can also automatically receive alerts to inventory or component shortages. 

By building a system with a real-time data stream, the company has been able to reduce downtime to less than 5% in most of its facilities, according to Good. Previously, that mark hovered between 15% and 20%. 

GE Appliances has further expanded its digital visibility capabilities by extending track-and-trace technology to its customer orders, as well as its procurement systems. 

“You name the country where we get a component, whether it's China, Italy or wherever, we have complete visibility to those components [in] real time and shipment and can plan accordingly,” Good said.

3. Speeding up material movement

In pursuit of more efficient operations, GE Appliances is also reducing logistical roadblocks, both within and between its facilities. 

“In the old days, everything got moved by these forklifts and things like that, and you came in very large bulk quantities,” Good said. “You put this huge band with hundreds of parts in the bin, and you had the operator bending over in the bin. And every movement of an operator makes them more inefficient.”

To accelerate the movement of goods, the company has automated many of its processes, including using autonomous vehicles to transport components within its plants. Additionally, the company is piloting an automated box truck that can deliver products from a manufacturing facility to a warehouse without human intervention, Good said. 

Scotts Miracle-Gro is leveraging machine learning and artificial intelligence to improve inventory management through more accurate demand forecasting.

The effort was spurred by an unsustainable inventory buildup in the wake of the COVID-19 pandemic. Demand during the crisis spiked, leading the lawn and garden care brand to exponentially grow its production and distribution footprint. At its post-pandemic peak, the company operated 18 distribution centers, President and COO Nate Baxter told Supply Chain Dive.

However, once demand normalized in 2023, Scotts Miracle-Gro needed to strategically right-size its inventory operations, Baxter said. At the time, Scotts Miracle-Gro was carrying $1.3 billion worth of inventory.

“When the demand dried up, we were left with a balance sheet that wasn’t healthy from a capital allocation standpoint,” the COO said. “So, our first effort was how do we reduce the inventory we keep on hand?”

With the help of data analytics and predictive modeling, the company reduced its inventory to $627 million in value by October 2025. Baxter said the company would likely finish under $500 million by the end of that year. 

The company has also shrunk its distribution center footprint to five sites, but such a reduction was no easy feat. To execute effectively, the company needed the planning side of the business to get smarter, Baxter said.

“And the other thing I realized anecdotally is with such a vast distribution network, it's very easy for your supply chain people just to build inventory and stuff it into the network,” he said. “And the problem is if you don't have a good planning tool, you don't necessarily know where the demand is.”

Previously, Scotts Miracle-Gro based its inventory planning strategy by comparing against a national average. However, because different regions have different demand levels, that method didn’t work, according to Baxter.

By applying machine learning algorithms to already-visible inventory levels at retail partners, Scotts Miracle-Gro obtained individual store data down to the SKU level to drive better planning capabilities, Baxter said. From there, the company set out to build a machine learning model that can predict where inventory is needed and when.

“Without the implementation of machine learning — and now we’re layering AI on top of that to help make better predictive decisions about inventory — it would have been really hard to get where we needed to be on the inventory piece,” Baxter said.

When it began constructing the model, Scotts Miracle-Gro was “still absolutely managing things on spreadsheets,” creating a need to leverage this information with the new technology, Baxter said. To build the model, Scotts Miracle-Gro also had to verify data sources into a single data pool.

The next step was inputting the verified data with a planning software provider, Baxter said. Scotts Miracle-Gro also signed an agreement with a third party to integrate its machine learning model into a planning tool. Baxter did not provide specifics about the partner company. 

Scotts Miracle-Gro plans to further integrate the tool into a new ERP system that can accommodate AI components, Baxter said. 

While Scotts Miracle-Gro plans to continue developing in-house tools, the brand is now starting a larger transformation to "complete this journey" with software partners such as Kinaxis and Sierra.AI, Baxter said.

The company also plans to continue ramping up and piloting the use of new technology in its supply chain. 

In 2024, Baxter challenged his supply chain team to take $150 million out of its supply chain over a three-year period through technology projects. Inventory management has been a huge part of the effort, with the company also exploring the use of inventory drones and automated forklifts. The team achieved cost savings of $75 million by July 2025, with the company projecting it will hit the $150 million goal by fiscal year 2027.

Dive Brief:

• Nestlé has completed the first phase of a cloud-based SAP S/4HANA deployment, rolling out the enterprise resource planning software upgrade across 112 countries and 50,000 employees, according to an Oct. 23 press release.
• The installation, which will take two more years to complete, will embed SAP's AI-powered assistant into Nestlé's core business systems, per the release. The company expects the added intelligence to improve procurement, supply chain management and order fulfillment efficiency.
• "With the upgrade, we gain more flexibility, capabilities and insights that will help us roll out new products globally faster to meet the needs of our customers and consumers," Chris Wright, CIO and head of Nestlé IT, said in the release.

Dive Insight:

With the deployment of a more AI-focused ERP system, Nestlé expects more advanced order fulfillment that matches supply and demand in real time for in-store and online retailers. Other upgrade advantages include consistency across reporting and planning processes, along with automated and standardized procurement procedures, according to the release.

“Nestlé has not been the most efficient company in the past," CEO Philipp Navratil told investors in October, adding that the organization wants to speed decision-making through more digitization and automation.

Nestlé’s upgrade comes as the company aims to reduce costs by cutting 16,000 jobs, or 6% of the global workforce, over the next two years. Three-quarters of the cuts will include corporate workers, with the remainder coming from manufacturing and supply chain staffing reductions.

The food and beverage maker is currently upgrading from SAP S/4HANA Finance, the legacy Enterprise Central Component ERP system on SAP HANA, an SAP spokesperson said in an email. SAP introduced ECC in 2004 and the finance component on the in-memory database management system in 2014. SAP plans to end mainstream support for ECC in 2027.

Nestlé migrated its entire SAP ERP from its data centers to the SAP Private Cloud in 2022, the spokesperson said.

UPS' Happy Returns is using artificial intelligence to stymie a particular strain of returns fraud in the thick of the post-holiday sprint for reverse logistics providers.

The company announced in November that it is piloting two new capabilities with select shippers, including apparel seller Everlane, to tackle the issue.

The first capability provides a risk assessment and score for specific returns based on shopper behavior and patterns. Returns deemed to be high risk — based on timing, frequency or other factors — are flagged for review before the shopper can receive a refund. The second, called Return Vision, audits the high-risk returns by using AI to compare the returned products with images from the retailer's online catalog to see if they match.

The process aims to provide an extra layer of protection against "decoy return" attempts, in which a shopper aims to get a refund by returning a similar-looking but cheaper product instead of the original item, Happy Returns co-founder and CEO David Sobie said in an interview with Supply Chain Dive.

"Maybe I bought a very expensive little black dress, and I'm returning a very cheap little black dress," Sobie said in November. "And it's hard for a human, especially a busy human, in taking in a return to necessarily say, 'Oh, this one has a different stitching on it.'"

An estimated 9% of all returns are fraudulent, and decoy returns are among the tactics retailers are seeing grow in prevalence, per a 2025 report from Happy Returns and the National Retail Federation.

Happy Returns' Return Vision software, used at company hubs, looks for discrepancies in returns that are difficult for humans to catch. This could include a strap being too wide, a missing logo or a mislabeled tag. The audit helps determine whether a product refund should be given or withheld.

"We do this because we also have busy humans in our warehouses, and so you need to make sure that you take the images, you validate the human decision with AI, and then these images will help the retailer down the line if they need to explain to a shopper why a refund wasn't approved," COO Juan Hernandez-Campos said in an interview.

Happy Returns is making sure the process happens quickly to avoid clogging its warehouses with items waiting to be audited. When an item is flagged at a Return Bar location, the company will complete the audit within a day of it arriving at a hub, Hernandez-Campos said.

Nearly all returns from Return Bar locations are verified as legitimate, with less than 1% being flagged for review, according to Happy Returns’ announcement. Retailers see a per-return average of $218 in prevented loss on flagged items.

Happy Returns is planning to roll out Return Vision more broadly in 2026 after the post-holiday returns peak, with a goal to expand the service to more retailers, Sobie said.

"The wonderful thing about AI and machine learning is that the bigger your dataset gets, the more accurate it is," he said.

Asos is leveraging traceability and compliance platform TrusTrace to gain real-time visibility into its supply chain, down to Tier 5, or the farm level, according to a Sept. 2 press release.

The artificial intelligence-powered platform aims to streamline risk, compliance and impact management operations, using data to prompt proactive decision-making and due diligence, per the release.

Asos will use TrusTrace’s technology for:

• Supply chain mapping to promote end-to-end visibility from Tier 1 to Tier 5
• Product-level traceability to track verified material data from source to final product
• Centralized compliance documentation to ensure operations adhere to evolving global regulations
• Configurable analytics and reporting to aid with supplier collaboration and ESG disclosures

“TrusTrace enables us to improve product traceability, helping us meet compliance standards, understand and address risks, and boost resilience in our supply chain by supporting our suppliers to implement improvements,” Asos EVP of Product Elena Martínez Ortiz said in a statement.

Asos is upgrading its technology stack as the fashion industry contends with numerous challenges, including a dynamic trade and tariff policy landscape and a push for labor transparency and emissions reductions. 

Earlier in 2025, Asos also tapped the Celonis Process Intelligence platform to better connect its inbound supply chain, logistics and outbound delivery and returns operations to provide a holistic view of its supply chain. Asos plans to use the metrics to help streamline and enhance order fulfillment processes.

Last June, Asos said it was also considering using Celonis across other business operations, such as order and warehouse management.

Retail brand Mango is now using Inspectorio’s Lab Test Management tool to gain supplier, material and product insights to optimize sourcing decision-making, according to a Nov. 12 press release.

The artificial intelligence-powered platform streamlines lab testing protocols across Mango’s product categories and digitizes the information into a standardized format, Antonio Leon de la Barra Rocha, VP of strategy, growth and innovation at Inspectorio, told Supply Chain Dive. Mango can now access the Lab Testing analytics, allowing it to drive supply chain improvements and support its long-term strategic sustainability plan.

“The platform helps Mango have a centralized view of ALL of their different testing operations,” he said in an email. “The platform serves to centralize different types of lab tests, sometimes executed by different labs, into a single system so that Mango can have greater control over one of the most important parts of their overall Quality and Compliance program.”

Implementation began in 2025, and thousands of Lab Tests have already been done, Leon de la Barra Rocha said. He further noted that while the first part of the rollout is complete, there is still “more work to be done” on exploring and expanding use-cases.

Previously, Mango had limited visibility over its end-to-end lab testing operations, essentially just receiving a completed report, Leon de la Barra Rocha said. The report would then be manually processed while Mango closed out any non-conformities with its suppliers.

“Now, they have visibility into the end-to-end process – from the test request creation through execution, report generation, and corrective action plan collaboration with suppliers,” he said.

Mango has been leveraging Inspectorio’s inspection capabilities — consolidating lab test requests, results and audit-ready compliance documents — for six years across hundreds of suppliers, per Leon de la Barra Rocha. Currently, Mango manages 100% of its inspection programs with the software company, including third-party inspections and factory self-inspections. Mango also uses Inspectorio’s performance analytics to reduce supplier quality defects and costs.

“We see this latest announcement as just the beginning of our expanded collaboration with Mango,” Leon de la Barra Rocha said. “Now that key product compliance information is on the platform, we're looking into seeing how we can further support Mango across other areas like supplier and factory onboarding and management, WIP monitoring, strategic sourcing and beyond.”