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Jack Fiedler, the vice president for digital transformation of the global supplychain at Lenovo Lenovo is ranked tenth by one leading analyst firm among a list of global companies with exceptional supplychains. Jack Fiedler : We’re unique in the technology industry. That has worked out well for us.
The adoption of AI in supplychainautomation is enabling companies to make more accurate decisions, reduce cycle times, and better manage complexity. These capabilities are now being integrated into mainstream TMS, WMS, and ERP platforms.
The industrial sectorparticularly supplychain management, is facing unprecedented complexity. Volatile markets, global disruptions, and the need for real-time insights are pushing traditional systems to their limits. Typically, these systems consist of standard-task agents (e.g.,
I laugh when business leaders tell me that they are going to replace their current supplychain planning technologies with “AI.” Each supplychain planning technology at the end of 2024, went through disruption–change in CEO, business model shift, layoffs, re-platforming and acquisitions.
Why Transformation Is a Boardroom Priority Supplychain management is now a core strategic concern for business leaders. Companies that fail to modernize face supply shortages, revenue loss, and regulatory risks. A data-driven, technology-enabled approach is required to build resilience and efficiency.
The logistics and supplychain industry is a critical component of global trade, responsible for moving goods and materials efficiently to meet consumer and business demands. Proactively adopting cleaner energy sources ensures alignment with these evolving regulations.
When one thinks of supplychainsoftware vendors, the name InterSystems may not spring to mind. A supplychain data fabric can help companies augment their supplychain processes. They aim to achieve the same success in supplychain management that they have achieved in the healthcare sector.
At ToolsGroup, we’ve long championed probabilistic demand forecasting (also known as stochastic forecasting) as the cornerstone of effective supplychain management software. However, most modern product portfolios aren’t this predictable.
It has led supplychain vendors to discuss how they currently use artificial intelligence. Further, virtually every supplier of supplychain solutions is eager to explain the ongoing investments they are making in artificial intelligence. The forecast can be compared to what actually shipped or sold.
The supplychain industry is no stranger to uncertainty. Unexpected challenges like shifts in global markets, economic upheaval, commodity shortages, advancements in technology, or environmental changes can send shockwaves through operations in unexpected ways.
Supplychain disruptions have become a persistent operational risk. Traditional supplychain planning, which relies on historical data and reactive adjustments, is no longer adequate for managing these challenges. Amazon is a leader in AI-driven supplychain management.
I find that most companies’ understanding of supplychain planning is immature, and that next week, at the Gartner SupplyChain Summit in Orlando, that many will don their Mickey ears to discuss what I consider outdated supplychain planning models. How can I improve the process of software selection?
Enterprise procurement leaders are under more pressure than ever—juggling cost control, compliance, supplier risk, and internal complexity, all while trying to modernize outdated systems. AI, automation, and generative tools are redefining efficiency, allowing procurement teams to move from reactive to proactive decision-making.
Harvard Business Review recently published an article, “ To Build Resilience, CEOs Need to Become Supply-Chain Experts ”. In this article, we wanted to discuss one aspect of supplychain that is often not given enough attention – building strategic relationships and shared value with direct spend suppliers.
Many large organizations have multiple systems for order, warehouse, or transportation management that are barely integrated frequently not at all. Optimizing fulfillment requires a series of steps to get a shipment from its source to the end customer.
Historically, supplychain leaders managed supplychains in a world of abundance. There are many factors: war, supply shortages, climate change, labor (knowledge and availability), and shifts in governmental regulation. The waste included: Negative Forecast Value Added (FVA) in demand planning.
They are focusing on how Infor creates value through insights, automation, and process. Industry-specific content is available for processes like Source to Settle, Procure to Pay, Order to Cash, and more. Automate: utilizes technologies such as RPA, IDP, and IPaaS. RPA automates manual and repetitive tasks.
Balancing forecast accuracy with inventory management gets more challenging every day. Artificial intelligence (AI) and rapidly developing generative AI tools provide complex, real-time, and in-depth insights specific to supplychain management. This makes it hard to reach agreement.
In this type of environment, traditional procurement software and manual processes are insufficient – and many procurement teams are looking to artificial intelligence (AI) for answers. Without the right tools, it’s difficult to properly vet vendors or catch the early warning signs of potential fraud or other issues.
Procurement and supplychain management are often used interchangeably—but in practice, the lines between them can blur in ways that create real friction. Misaligned priorities, siloed systems, and unclear ownership can directly impact key performance indicators like cost savings percentage and procurement cycle time.
The global supplychain landscape is undergoing significant transformations, influenced by rapid technological advancements, shifting consumer expectations, and the intricacies of international commerce. Developing Analytical Skills Data analysis is at the heart of effective supplychain management.
Demand forecasting has evolved dramatically in recent years. Businesses have shifted from supply-focused approaches to demand-driven models, yet many still struggle to balance accuracy with agility. Traditional forecasting methods often fail under high variability, leading to excess costs, stockouts, and obsolescence.
Gartner predicts that by 2026, 95% of data-driven decisions will be at least partially automated. In fact, Gartner also found that only 10% of CEOs say their business uses AI strategically, and just 9% of technology leaders report having a clearly defined AI vision statement. Yet, many companies struggle to harness AIs full potential.
Safety Stock: Navigating SupplyChain Volatility Through Strategic Inventory Planning Demand volatility represents a critical challenge for supplychain executives today, with safety stock emerging as a key strategic tool to mitigate market uncertainties.
For years, supplychains were engineered to be lean. Reducing cost was the primary objective, and most operational decisionsfrom sourcing to fulfillmentreflected that mindset. Recent years have brought a series of disruptions that exposed vulnerabilities in how supplychains are designed.
Machine learning (ML)a specialized field within artificial intelligence (AI)is revolutionizing demand planning and supplychain management. According to McKinsey , organizations implementing AI-driven demand forecasting solutions can reduce forecast errors by 30% to 50%.
The pace of technological evolution is pushing organizations to the brink. This is more evident in supplychain, where time-tested methods are being replaced with new ones. The groundbreaking technology is transforming how companies manage sales and operations planning (S&OP). Enter Generative AI (GenAI).
Technology can change or even improve work. Companies today making a fundamental mistake: they are attempting to automate current processes with AI versus challenging and redefining work. Today, in supplychain planning, this could not be further from reality. Most companies forecast a single stream with a focus on error.
SAP is embedding its generative Joule across the SAP Ariba source-to-pay solution portfolio to make it easier for their customers to manage routine inquiries, such as status updates, summarization, and frequently asked questions. It is a brilliant tool.” SAP’s Business Network is a supplychain collaboration network.
AI is reshaping the way organizations source, manage suppliers, and drive value today. As supplychains become more interconnected and risks more dynamic, traditional procurement tools fall short. AI agents offer a smarter, faster way to manage sourcing, risk, and spend across the entire procurement lifecycle.
AI is reshaping the way organizations source, manage suppliers, and drive value today. As supplychains become more interconnected and risks more dynamic, traditional procurement tools fall short. AI agents offer a smarter, faster way to manage sourcing, risk, and spend across the entire procurement lifecycle.
Adding to this already uphill battle, we don’t have trustworthy new product forecasting methods because forecasting new products with no sales data is very hit-and-miss. Machine learning (ML) provides an effective weapon for your new product forecasting arsenal. Why is new product forecasting important?
Global supplychains have been tested repeatedly by a series of disruptive events, including the COVID-19 pandemic, U.S.-China Companies that previously prioritized cost-cutting and centralized sourcing quickly found themselves exposed to serious production and distribution risks. China trade disputes, and natural disasters.
The rise of AI technology combined with Source-to-Pay (S2P) digitization are becoming key allies for leading procurement teams in their quest for ever smarter workflows, improved insights, and data-based decision-making. Analysts see a growing shift toward broader, integrated platforms and suites.
When my fiance heard about the price, he advised that I find a local hairdresser and set up a frequent-shopper account with them for a few months until the tool is back in stock. When the pandemic started in 2020, no one could foresee the impacts of the global supplychain disturbances would last this long.
As a supplychain executive, picture beginning your day with a cup of coffee when a news alert notifies you of newly imposed tariffs affecting your primary suppliers in China. Companies leaning heavily on global sourcing? Theyre feeling the heat most, as sudden trade policy curveballs throw procurement plans into chaos.
Unfortunately, outdated tools and fragmented processes make it difficult to maintain visibility across the supplychain and adapt at the pace of business. Digital procurement streamlines workflows and unifies data, enabling faster sourcing, better collaboration, and improved accuracy.
If you’re evaluating procurement technology or exploring ways to drive more value from existing systems, chances are you’re looking beyond tactical fixes – you want a smarter, scalable strategy. But when spend is scattered across systems, business units, and suppliers, finding those insights is easier said than done.
Over the past few years, supplychain and procurement pros have faced the challenge of navigating their enterprises through one disruption after another. Despite their best efforts, current events and market dynamics caught up with them, leading to issues managing their suppliers and sourcing the materials needed for their products.
“May you live in interesting times,” widely attributed as a Chinese curse, some claim this as a blessing; whichever side you take, this is exactly what’s happening in the supplychain world since 2020, and it looks like we will be living with this blessing/curse for a while longer. Emerging Themes for SupplyChain Planning.
In this article, we break down the differences between procurement and purchasing, explain why the distinction matters, and explore how technology can align both functions to drive efficiency, compliance, and long-term value. Technology unifies procurement and purchasing, improving visibility, compliance, and workflow automation.
During his tenure in the industry, he built innovative pricing and forecasting models, leveraging internal and external data sources to improve internal decision-making and increase profitability. Prior to joining DAT, Adamo led the pricing and decision science teams at FedEx.
Manufacturing ERP (Enterprise Resource Planning) software integrates all your core business processes into one powerful platform. Think of it as the central nervous system of your operation, connecting everything from production planning and inventory control to supplychain management and financial reporting.
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