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Lenovo Excels in Supply Chain Planning with a Hybrid Approach

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2 ans ago

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Lenovo Excels In Supply Chain Planning With A Hybrid Approach

Jack Fiedler, the vice president for digital transformation of the global supply chain at Lenovo

Lenovo is ranked tenth by one leading analyst firm among a list of global companies with exceptional supply chains. Based on an interview with Jack Fiedler, the vice president for digital transformation of the global supply chain at Lenovo, the word “exceptional” certainly applies. I’ve not seen a company that does a better job of agile planning across an end-to-end, multi-tier supply chain.

Lenovo is a multinational company listed on the Hong Kong Stock Exchange. The company, which achieved $ 57 billion in revenues in its last fiscal year, is the leading global supplier of PCs. The high-tech firm is more than a manufacturer of PCs, tablets, smartphones, and servers. In their last quarter, the division selling personal devices accounted for only a bit more than half of global revenues.

The company has more than 2000 suppliers and operates over 30 manufacturing sites. Factories serve local markets. During COVID, this more agile and resilient model allowed the firm to grow their market share.

The following interview was edited for conciseness.

Steve Banker: Maybe you could start by talking a little bit about the Lenovo supply chain and what makes it distinctive.

Jack Fiedler: We’re unique in the technology industry. We run one of the few truly hybrid supply chain networks. We own a significant portion of our network, but we also work extensively with partners. A lot of our competition has largely outsourced their supply chain.

We decided 8 or 9 years ago that to create a competitive advantage, we really needed to control much of our supply chain. That has worked out well for us.

We’ve taken the same hybrid approach from a supply chain technology perspective. We have a lot of in-house solutions that we’ve built for our digital transformation, which is my area of expertise. I’m responsible for the overall digital transformation, including technology. But then we also partner with Blue Yonder and others.

We put a huge amount of focus on digitalization, as many companies have. But I think we’ve taken it to a more extreme level.

We invest a huge amount of time and resources into our people and making sure that we have the best digital talent in the industry and that we’re doing the most innovative things in the supply chain.

Banker: You mentioned an approach to digitalization that’s both in-house as well as being reliant on external software partners. Could you talk more about that?

Fiedler: I’ll start with Blue Yonder, because Blue Yonder truly is the foundational building block for our supply chain intelligence.

We’ve been using Blue Yonder for many years. We have evolved Blue Yonder from being a traditional demand and supply planning solution. We’ve done a lot of customization, with Blue Yonder’s help, to create a digital twin of our entire supply network. We’ve moved from weekly supply collaboration with suppliers to daily.

We have all our factories, both in-house and outsourced, all of our distribution centers, and our transportation network on the Blue Yonder foundational system. We now have complete visibility of our supply chain. And then we’ve layered our own AI on top of that, which allows us to simulate the entire supply chain.

We can run a plan simulation to maximize revenue, maximize shipments, maximize the customer experience, or minimize transportation costs. We’ve now built this AI capability to simulate the entire supply chain. This was a huge effort, but it has been very valuable.

We have continued to build on that foundation. Just last year we finished integrating the sales process into the end-to-end supply chain process. A seller, from the moment they engage with a customer, and all the way through the sales process, can get whatever information they need to communicate with a buyer on what we have available, and what the lead times are, and other similar information. This starts with the initial discussion about what kind of products are available and how the product will be configured. Then a simulation is run and we get an estimated date for delivery. Once a contract is in place, we have real-time visibility on the delivery status. This includes visibility to emerging supply chain constraints. During COVID constraints were popping up all over the place. An iGPU (integrated graphic processing unit) is a current example. Everybody wants to know what the lead time on iGPUs are, and what the alternatives are if they are not available.

We’ve used the Blue Yonder technology, and that digital twin of the network, and the simulations we run, to give the sales team full visibility as to what is available and when it can be delivered. The sales team can go have those conversations, with real-time lead times and even the factory the product will ship from, with customers.

This has been a real game changer for sales. It’s really reduced a lot of sales friction. It used to be that a customer would ask questions, then sales would have to go to the supply chain organization, and we’d have to get that information, and back and forth it would go.

We’ve connected the supply chain end-to-end and made it intelligent. That is what everybody’s trying to do, but we’ve done it from the beginning of a sales opportunity all the way to the delivery of a shipment.

That was made possible because of this investment we made with Blue Yonder and our investment in building our simulation capabilities in the digital twin.

The second thing we’ve done, and this is our most valuable asset, is that 7 years ago we made a big investment in our own intelligence platform we call supply chain intelligence. It started out as a traditional control tower. But then it very quickly evolved into a full intelligence platform.

We have benchmarked our SCI (Supply Chain Intelligence) solution; we looked at all the solutions in the industry. We don’t believe anybody has anything as comprehensive as what we built in-house. We run the entire supply chain from this intelligence platform.

We have full visibility. We have all the connected planning data we get from blue Yonder, all of the product data we get from the product systems, all of the shipment information that’s coming in from the carriers, as well as risk information from Everstream and other sources.

We have complete visibility of the performance of the entire supply chain in one tool. But it’s not just a visibility platform. It provides risk alerts, decision-making, and automation. As an example, if we have congested lanes, the system will automatically flag that we have a potential risk of delay based.

The platform will look at all the potential alternatives and the cost of those alternatives, and it will make a recommendation for a supply chain person to go in and look at the event. That planner can choose to reroute a shipment so that it doesn’t get delayed.

To build this took seven years and a significant investment . This was meant to be an internal tool for Lenovo. But we’ve now got customers that are starting to lease this technology from us.

We are continuing to invest in the solution. We are working to make the platform more autonomous. For example, we’re working on telling the solution that it has a budget. “You have a budget of $5,000,000 and here are some other parameters. Show us the best way to fix the freight delays!”

The AI looks at the potential alternatives and the trade-offs and then spits out an answer. That frees up the logistics team to go work on even more difficult problems.

When the chief supply chain officer wants to review the performance of the supply chain, we start with the KPI dashboards. Then, the tool drills down and looks at real-time performance on late orders or parts. It might highlight logistics jams, manufacturing capacity, quality issues, or procurement cost trends. Really, everything you need to manage the supply chain. This is so much more than a control tower.

Banker: Can you speak in a bit more detail about what you are doing around artificial intelligence?

Fiedler: We have built a number of AI use cases over the last four years that we’ve embedded into the tool. The first wave of those was made possible because of the foundational digital twin capability work that we did with Blue Yonder.

Advanced demand forecasting based on machine learning, for example, is a classic example of the use of AI in supply chain management. But we have taken machine learning further than this.

During COVID there were so many part shortages. We struggled to figure out what we could build and, then, beyond this, what should be built based on optimizing for either cash or revenue or customer satisfaction or other things as well.

Banker: Was this based on a series of Bill of Materials explosions?

Fiedler: Yes, that is exactly what it does. It takes the demand that we have, it takes the orders that we have, it takes the BOMs on those orders and then compares it against the digital twin of the supply chain and says, “What do we have right now? What can we make? And based on our objectives, what should we make?”

And this is not just a solve done at the plant level. A lot of companies can do that. This is a network solution based on the centralized supply visibility and management. It can involve moving parts from plant A to plant B, for example.

We are also using AI to help with customer allocation issues. When critical components are in short supply, it can end up being whichever customer screams the loudest that gets prioritized. That’s not a sustainable way to manage supply issues.

We used AI to create smart allocation. Basically, this allows us to say, “OK, if we want to reprioritize our order stack, what are the impacts if we move customer C from slot 8 up to slot 2? How will that impact other customers? How will it impact our supply chain?

It took a very chaotic area and helped create order. We can now have really good data-driven conversations. The sales team can now make better trade-off decisions involving their customers.

We also recently created a machine learning capability that helps us better predict when suppliers will make deliveries to us. During COVID, all of our suppliers got very conservative because all their suppliers got very conservative. The accuracy of the delivery dates we were getting went way down. Basically, we use AI to go say, “Who’s hedging?”

There was unpredictability during COVID from key suppliers on shipment dates due to the dynamics everyone was trying to navigate. Using AI, we were able to predict when the delivery would occur. This allowed us to plan our manufacturing capacity more effectively.

Banker: You know, if you were to talk to Blue Yonder, they would say they’re investing in the same sort of things that you’ve invested in. So why do it yourself?

Fiedler: A couple of reasons.

Lenovo is a large global business and with that comes some complexity for supply chain software solutions companies. We have many products, many different bill of material structures, and many different business models. We operate in many countries. Supporting the complexity of the business in somebody else’s tool is difficult.

And while supply chain solution vendors are building some of these capabilities, they can’t match all the things that we could do ourselves, or the speed at which we can do them.

We do use their technology and where it makes sense, where they’ve invested in it, we will leverage their capabilities. We practice a hybrid model – we use what the supply chain vendors are really good at, and then we add to it.

Lenovo has a huge research team. Thousands of AI data scientists work for us. Some of these data scientists are among the best in the world. We’ve got the ability to build this stuff very quickly with our own skills.

I’ll give you an example. If we want to change the machine learning algorithm three times a day, based on new information we’re getting from the sales team or suppliers, we can go do that. When you’re working with a partner, and using their technology, that’s much, much more difficult to do.

And so, I think just the bottom line is that the size and scale of our company allows us to make choices surrounding AI other companies can’t make. To be as responsive, as agile, and as innovative as we want to be requires us to use a hybrid model.

Banker: Jack, thank you so much. This is fascinating! I could talk to you for hours.

Fiedler: Thank you.

The post Lenovo Excels in Supply Chain Planning with a Hybrid Approach appeared first on Logistics Viewpoints.

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Last Chance: Join the Webinar on AI, Component Sourcing, and the Future of Procurement

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1 jour ago

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22 juin 2026

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Electronic component sourcing is becoming one of the most important cost and risk challenges facing manufacturers.

Pricing remains opaque. Supplier quotes do not always reflect true market pricing. Internal purchase history may show what a company paid, but not whether that price was competitive.

At the same time, chips and components are increasingly tied to geopolitics, tariffs, AI infrastructure, defense demand, electrification, industrial automation, and supply chain resilience.

The webinar is tomorrow at 11 AM ET. Register now to join ARC Advisory Group’s discussion, The Hidden Cost of Component Sourcing — and How AI Is Fixing It, featuring Jim Frazer in conversation with Lytica CEO Martin Sendyk.

This is a practical conversation for procurement, supply chain, engineering, operations, and executive leaders who are trying to understand how component sourcing is changing.

Manufacturers need to control cost, protect supply, support product launches, and manage risk in a market where visibility is often limited. Overpayment can remain hidden. Component risk can appear too late. Engineering and procurement decisions can become locked in before teams have enough market intelligence to make the best sourcing choices.

Tomorrow’s webinar will examine why traditional approaches to component sourcing are under pressure and how manufacturers can use better intelligence to identify hidden cost, improve benchmarking, and manage sourcing risk more effectively.

Attendees will learn:

Why electronic component pricing remains difficult to benchmark

How hidden overpayment can persist inside normal procurement activity

Why supplier quotes, list prices, and internal history are not enough

How real transactional data can improve pricing visibility

Why geopolitics, AI demand, tariffs, electrification, and defense demand are changing the sourcing risk equation

How AI and sourcing intelligence can help procurement teams make better cost and risk decisions

The issue is no longer only whether a company can secure supply.

The issue is whether it can secure the right components, at the right price, with the right risk profile, early enough to influence the business outcome.

For many manufacturers, that requires a more transparent, data-driven, and intelligence-led sourcing model.

Register now for the ARC Advisory Group webinar with Jim Frazer and Lytica CEO Martin Sendyk before the session begins tomorrow at 11 AM ET.

Register for the Webinar

The Hidden Cost of Component Sourcing — and How AI Is Fixing It
Date: June 23, 2026
Time: 11:00 AM ET
Location: Online
Speakers: Jim Frazer, Vice President, ARC Advisory Group, and Martin Sendyk, CEO, Lytica

If your organization manages a significant electronic component spend, this webinar will help you understand how AI and transactional market data can expose hidden sourcing costs and turn procurement into a more proactive system of intelligence.

Register now to reserve your spot.

The post Last Chance: Join the Webinar on AI, Component Sourcing, and the Future of Procurement appeared first on Logistics Viewpoints.

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Weekly Supply Chain and Logsitics News Round Up (June 15th-18th 2026)

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5 jours ago

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19 juin 2026

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Weekly Supply Chain And Logsitics News Round Up (june 15th 18th 2026)

This week in logistics, the industry faces a pivotal shift as Transportation Management Systems evolve into ‘decision intelligence’ hubs, moving beyond basic routing to become the core operating brain of the supply chain. Meanwhile, operational complexity reaches new heights with the massive logistical undertaking of the 2026 FIFA World Cup, even as trade tensions show signs of cooling following the European Parliament’s approval of a landmark EU-US tariff relief deal. From record-breaking automation at Nestlé’s new California hub to the fluctuating volatility of global air freight rates, these developments underscore a sector increasingly defined by high-tech integration and rapid adaptation to global market forces.

The Leading Supply Chain and Logistics Stories of the Week:

TMS Is Becoming Less of a Routing Tool and More of a Decision Intelligence Layer Beyond Execution

The role of the Transportation Management System (TMS) is undergoing a major paradigm shift. While traditional evaluations still focus heavily on execution-level metrics—like route optimization, automated tendering, and freight audit capabilities—these features have essentially become table stakes. Moving forward, the true strategic value of a TMS lies in its evolution from execution software to “transportation decision infrastructure.” Rather than just completing transactions, next-generation platforms serve as the continuous decision-making layer of the supply chain. By drawing data from across the entire network, integrating external market signals, and resolving multi-functional bottlenecks, modern TMS solutions are transitioning into the core operating brain that synchronizes movement, cost, and service levels in real time.

The Logistics Issue: The Supply Chains Behind the World Cup

While most fans focus entirely on the action on the pitch, supply chain professionals are watching what might be the most complex logistical undertaking in sporting history: the 2026 FIFA World Cup. Spanning three host nations—the United States, Canada, and Mexico—the sheer scale of the tournament requires moving more than twenty million pounds of equipment, coordinated across 5,000 vehicles and millions of square feet of warehouse space. The challenge isn’t just massive volume; it’s the absolute lack of tolerance for delay or error across highly regulated international borders. Industry experts point out that success hinges on establishing a unified ecosystem in which freight forwarders, customs officials, and vendors collaborate in real time. Crucial to this effort are standardized product identification and cloud-based labeling networks, which ensure that every critical piece of equipment, food shipment, and medical supply is fully traceable and compliant with differing regional mandates—proving that at this scale, elite collaboration is the only way to avoid catastrophic bottlenecks.

Transatlantic Trade Relief: European Parliament Greenlights EU-US Tariff

In a major relief to transatlantic supply chain operators, the European Parliament has officially voted to implement the long-awaited trade agreement with the United States. Under the newly approved legislation, the EU will eliminate tariffs on all American industrial goods and grant preferential market access to key U.S. agricultural and seafood shipments. In return, the U.S. has agreed to cap import tariffs on European products at 15%—effectively averting threatened 25% tariff hikes on European-built vehicles. Importantly for logistics planners, the deal incorporates a “defensive toolbox” to mitigate long-term trade volatility, including a sunset clause set for late 2029, a safeguard mechanism to protect EU markets from disruptive import surges, and strict conditions that allow the EU to suspend tariff preferences by the end of 2026 if the U.S. fails to lower existing duties on European steel and aluminum derivatives.

Nestlé Opens Its Largest and Most Technologically Advanced Distribution Center in the U.S.

Nestlé USA has officially unveiled its new 700,000-square-foot distribution hub in Arvin, California. Equipped with a $330 million price tag, the state-of-the-art facility represents a critical step in the company’s broader $25 billion U.S. infrastructure upgrade, emphasizing a pivot toward leaner, automation-first supply chain workflows. The Arvin facility houses the largest Automated Storage and Retrieval System (ASRS) in Nestlé’s global network, operating alongside laser-guided vehicles, automated crane systems, and layer-picking robotics. This build marks a major shift from retrofitting existing spaces to intentionally designing high-tech capabilities directly into greenfield logistics layouts from day one. Designed to mitigate peak-season labor bottlenecks, upskill the frontline workforce, and run on 100% renewable electricity as a zero-waste site, the facility showcases how global leaders are leveraging heavy automation to establish flexible, resilient distribution networks that protect margins against ongoing labor and capacity constraints.

Air Freight Spot Rates Spike 41% YoY in May, but Relief Is Expected Soon

Global air cargo spot rates surged by 41% year-over-year in May, averaging $3.40 per kilogram, driven by persistent geopolitical disruptions, carrier fuel surcharges, and localized demand booms like semiconductor and data center equipment shipments. According to Xeneta data, spot rates from Northeast and Southeast Asia to North America jumped nearly 40% compared to earlier this year. However, the pricing pressure isn’t uniform; transatlantic lanes from Europe to North America actually saw a 26% decline over the same period. For procurement teams battling these elevated costs, there is a glimmer of light on the horizon. Long-term contract rates appear to have peaked in April, and as carriers restore capacity and the market enters its traditional summer lull, analysts predict that year-over-year spot rate comparisons will finally begin to cool down, offering much-needed breathing room for shippers who have been relying on short-term contract extensions.

Song of the week:

The post Weekly Supply Chain and Logsitics News Round Up (June 15th-18th 2026) appeared first on Logistics Viewpoints.

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Why Octave’s Austin Event Matters: From Asset Lifecycle Software to Intelligence at Scale

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7 jours ago

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17 juin 2026

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Octave Live OnTour Austin takes place at a consequential point in the evolution of the industrial software market. Asset-intensive organizations are under sustained pressure to improve capital project execution, asset reliability, operational resilience, safety, quality, cybersecurity, and workforce productivity. At the same time, they are being asked to make better use of data and apply AI in ways that are practical, governed, and operationally relevant.

This is the context in which Octave’s Austin event should be evaluated.

Octave, the software spin-off from Hexagon AB, brings together software assets across engineering, construction, geospatial intelligence, asset operations, quality, public safety, physical security, and industrial cybersecurity. Its Design, Build, Operate, and Protect framework provides a clear structure for organizing those capabilities around the industrial asset lifecycle.

However, the strategic significance of the event is not limited to Octave’s portfolio structure. The more important issue is what Octave’s positioning indicates about the broader direction of industrial software.

The market is shifting from digitized workflows toward intelligence at scale.

Industrial Software Is Moving Beyond Functional Digitization

For much of the past two decades, industrial software investment has centered on functional digitization. Engineering teams adopted design, modeling, analysis, and engineering information management tools. Construction teams deployed project controls and field execution systems. Operations teams invested in EAM, APM, optimization, and reliability applications. Quality, safety, physical security, and cybersecurity functions developed their own specialized technology environments.

These investments created meaningful value within individual domains. But they also reinforced a long-standing structural problem: industrial work is highly interconnected, while the supporting software environment often remains fragmented.

A design change can alter construction cost and schedule. Construction execution quality can affect commissioning performance. Poor handoff from construction to operations can increase maintenance burden. Maintenance backlog can elevate safety and compliance risk. A cybersecurity incident can become an operational disruption. A public safety event may require geospatial, security, asset, and operational context at the same time.

This is the gap that lifecycle intelligence seeks to address.

Lifecycle Intelligence Requires Context Across the Asset Lifecycle

Octave’s Design, Build, Operate, and Protect framework is meaningful because it reflects how industrial assets are planned, built, used, maintained, protected, and improved over time.

In the Design domain, Octave can address engineering, modeling, analysis, information management, and geospatial intelligence. In Build, the portfolio extends into construction, supply chain management, and project performance. In Operate, the focus expands to operations optimization, asset performance, enterprise asset management, quality, compliance, and risk. In Protect, Octave’s positioning includes public safety, physical security, and industrial cybersecurity.

Individually, these are established industrial software categories. Collectively, they suggest a broader strategic direction: the use of software to preserve, connect, and operationalize context across the asset lifecycle.

That is where the Austin event becomes important. Customers and partners should look for evidence that Octave is moving beyond portfolio aggregation toward a more integrated model of lifecycle intelligence.

Intelligence at Scale Depends on Integration, Data, and Workflow Relevance

The phrase “intelligence at scale” should be interpreted operationally, not rhetorically. In industrial environments, intelligence at scale means that software can connect relevant data, apply domain context, and support better decisions across complex workflows.

This requires more than analytics dashboards. It requires software that can help users understand the implications of decisions across functions. It also requires a data foundation that connects engineering data, project execution status, asset histories, maintenance records, geospatial information, quality events, safety incidents, and cybersecurity signals.

AI increases the importance of this foundation. AI capabilities will have limited enterprise value if they are disconnected from operational systems and industrial context. The more material opportunity is AI that is embedded in real workflows and supported by trusted domain data.

For Octave, the strategic question is whether its portfolio can support AI-enabled decision-making across the asset lifecycle, rather than isolated AI features within individual applications.

The Event Should Be Assessed as a Roadmap Signal

Buyers should treat Octave Live OnTour Austin as a roadmap signal.

The first area to assess is integration. Octave’s portfolio breadth creates potential value, but customers will need clarity on how the company intends to connect products and workflows over time. Important indicators include shared data models, workflow orchestration, user experience consistency, API strategy, and cross-domain analytics.

The second area is AI. Customers should listen for specific use cases, not general AI messaging. Relevant examples could include project risk identification, asset performance optimization, maintenance prioritization, quality exception management, safety response, cyber risk monitoring, or engineering decision support. The key issue is whether AI is being tied to operational outcomes.

The third area is ecosystem fit. Industrial organizations rarely standardize on a single vendor across the full technology landscape. Octave will need to clarify how its offerings interact with ERP, EAM, APM, MES, PLM, project controls, cybersecurity, and analytics environments. The value proposition must be additive without increasing architectural complexity.

The fourth area is sequencing. Broad portfolios require disciplined execution. A credible roadmap should identify where Octave will focus first, what integration steps matter most, and how customers should think about value realization over time.

Broader Market Implications

Octave’s Austin event matters because it reflects a larger shift in industrial software.

The next stage of the market will not be defined solely by applications that digitize individual workflows. It will be defined by platforms and architectures that connect operational context across functions. This does not mean every customer will consolidate around a single software suite. Industrial technology environments will remain heterogeneous. But the strategic requirement for connected data, workflow continuity, and decision support will continue to intensify.

AI will accelerate this trend. Effective AI depends on relevant context. If industrial data remains trapped in disconnected systems, AI will be limited to narrow productivity assistance. If data and workflows are connected, AI can support higher-value decisions involving risk, reliability, performance, safety, and resilience.

That is why lifecycle intelligence is becoming an important industrial software concept. It reflects the need to move from systems that record activity to systems that help organizations understand and act on operational complexity.

ARC Advisory Group Perspective

Octave has a credible opportunity to participate in this market transition. The company has meaningful software assets across multiple industrial domains, and its Design, Build, Operate, and Protect framework provides a practical way to organize the portfolio.

The central question is execution. Octave will need to demonstrate that its portfolio can become more than a set of adjacent capabilities. Customers will expect integration clarity, practical AI use cases, ecosystem openness, and a roadmap that connects near-term value to a longer-term lifecycle intelligence strategy.

For buyers, the Austin event should be used to evaluate roadmap direction and strategic fit. For partners, it should clarify Octave’s intended role in the industrial software ecosystem. For the broader market, it is another indication that industrial software is moving toward connected intelligence at scale.

The companies that define this next phase will not simply digitize industrial work. They will connect context across the asset lifecycle and convert that context into better decisions.

The post Why Octave’s Austin Event Matters: From Asset Lifecycle Software to Intelligence at Scale appeared first on Logistics Viewpoints.

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