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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.
The adoption of AI in supplychainautomation is enabling companies to make more accurate decisions, reduce cycle times, and better manage complexity. AI in supplychainautomation is gradually reshaping how core functions operate, particularly in procurement, warehousing, and logistics.
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.
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 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.
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.
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%.
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.
The industrial sectorparticularly supplychain management, is facing unprecedented complexity. While Generative AI (GenAI) has shown promise, its limitations in planning, workflow automation, and dynamic adaptation necessitate a more sophisticated approach. Colin Masson, ARC Advisory Groups expert on Industrial AI.
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.
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.
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?
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.
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.
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.
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.
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?
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.
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.
Optimizing fulfillment requires a series of steps to get a shipment from its source to the end customer. These steps include sourcing and receiving inventory, storing inventory, order processing, picking and packing an order, shipping the order, and returns management.
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.
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).
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.
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.
When it comes to running a company, when things break down executives have traditionally said “we need to improve our forecasting!” Would better forecasting accuracy be a good thing? Unfortunately, most companies cannot, and will never be able to, consistently rely on highly accurate forecasts. Absolutely!
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. In this blog, we cover the key differences between procurement and supplychain management, and explain where the biggest disconnects typically occur.
If you’re exploring procurement technology, chances are you’re not just looking for a better tool – rather, you’re looking for a smarter, scalable strategy. AI, automation, and generative tools are redefining efficiency, allowing procurement teams to move from reactive to proactive decision-making.
Increasing concerns over mass supplychain disruptions. Its a rollercoaster for logistics and supplychain leaders operating in global markets. Businesses are facing greater volatility as tariff changes wreak havoc on supplychains, operational costs, and overall profitability. Extreme tariff volatility.
Consequently, everyone asserts they are utilizing AI, and those in the supplychain world are no exception. How will it address “the world hunger problem” in supplychains, particularly in the context of supplychain planning? How can AI contribute to end-to-end decision automation?
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.
For the past few years, the news has been filled with stories about supplychain disruptions, supplychain fragility, and the need for supplychain resilience. A term once prominent in supply discussions optimization isn’t heard quite as often as it used to be.
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.
Supply management. Supplychain management. Supplychain planning. The lack of interoperability between decision support platforms is a problem for companies attempting to improve decisions from the channel to supplier bi-directionally through technology. Are these terms the same? The answer is no.
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. Accurate, centralized data is the foundation of AI readiness and automation.
Your Aftermarket SupplyChain is More Complex Than You Think: Stop Guessing, Start Optimizing Lets be honest: managing spare parts inventory requires specialized strategies unlike any other inventory management process. Your distribution network spans multiple locations. Your parts portfolio includes thousandssometimes millionsof SKUs.
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.
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.
In follow-up qualitative interviews, one of the largest issues with organizational alignment was metric definition and a clear definition of supplychain excellence. In my post Mea Culpa, I reference my work with the Gartner SupplyChain Hierarchy of Metrics. They do not excel in planning or forecasting.
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