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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. Probabilistic DemandForecasting represents a paradigm shift in supply chain planning.
Jack Fiedler : We’re unique in the technology industry. We’ve taken the same hybrid approach from a supply chain technology perspective. I’m responsible for the overall digital transformation, including technology. We have evolved Blue Yonder from being a traditional demand and supply planning solution.
Volatile markets, global disruptions, and the need for real-time insights are pushing traditional systems to their limits. Understanding AI Agents At its core, an AI Agent is a reasoning engine capable of understanding context, planning workflows, connecting to external tools and data, and executing actions to achieve a defined goal.
At ToolsGroup, we’ve long championed probabilistic demandforecasting (also known as stochastic forecasting) as the cornerstone of effective supply chain management software. Like betting that a champion racehorse will win a specific race, this “single-number” forecast assumes one definitive result.
Machine learning (ML)a specialized field within artificial intelligence (AI)is revolutionizing demand planning and supply chain management. According to McKinsey , organizations implementing AI-driven demandforecasting solutions can reduce forecast errors by 30% to 50%.
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than ever—juggling cost control, compliance, supplier risk, and internal complexity, all while trying to modernize outdated systems.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.
When one thinks of supply chain software vendors, the name InterSystems may not spring to mind. They offer softwaresystems and technology for complex integration, rapid application development, and advanced analytics and sell those solutions to companies that need to accelerate optimized business outcomes.
Access to Unique Process and Asset Capabilities: Some suppliers offer unique skills, technologies, or processes that are not available in-house or through other sources. Long term forecast collaboration becomes a critical requirement for manufacturers and their direct suppliers to focus on to de-risk their supply chains.
The logistics and supply chain industry is a critical component of global trade, responsible for moving goods and materials efficiently to meet consumer and business demands. Regulatory Demands: Governments worldwide are enforcing stricter emissions standards and introducing carbon taxation schemes, pressuring companies to adapt.
Many large organizations have multiple systems for order, warehouse, or transportation management that are barely integrated frequently not at all. Sudden and significant changes in demand, especially in consumer markets, stack up more challenges, requiring order revision and reallocation.
I laugh when business leaders tell me that they are going to replace their current supply chain planning technologies with “AI.” Each supply chain planning technology at the end of 2024, went through disruption–change in CEO, business model shift, layoffs, re-platforming and acquisitions. You are right.
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 supply chain management. Traditional approaches often divide departments like sales, marketing, and production.
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. Automate: utilizes technologies such as RPA, IDP, and IPaaS. iPaaS provides a comprehensive set of tools for connecting applications.
AI is not a new technology in the supply chain realm; it has been used in some cases for decades. Demand planning engines have natural feedback loops that allow the forecast engine to learn. The forecast can be compared to what actually shipped or sold. More recently, many other cases have emerged.
The global supply chain landscape is undergoing significant transformations, influenced by rapid technological advancements, shifting consumer expectations, and the intricacies of international commerce. Preparing the next generation to excel in this dynamic field requires more than traditional education methods.
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.” Those types of disagreements disappear in a SCCN platform.
Business leaders see the open sharing of feedback on software as too risky. How can I improve the process of software selection? How can I improve the process of software selection? Buying supply chain planning software is hard. Many technologies (I count six) are missing, and most of the ratings are just wrong.
From sourcing and bid evaluation to warehouse slotting and dynamic routing, AI tools support faster and more consistent outcomes by processing large volumes of operational data and identifying patterns that human decision-makers may overlook. These capabilities are now being integrated into mainstream TMS, WMS, and ERP platforms.
Geopolitical instability, extreme weather, labor shortages, and fluctuating consumer demand regularly impact global logistics. They integrate AI into demandforecasting, inventory optimization, and logistics operations to improve efficiency, reduce costs, and mitigate risks.
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.
Recent disruptions have exposed significant vulnerabilities in traditional models, driven by geopolitical instability, fluctuating demand, and operational inefficiencies. A data-driven, technology-enabled approach is required to build resilience and efficiency. GPT-4 is being used to improve inventory allocation and demandforecasting.
Demandforecasting 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.
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. And pretty much everyone realized that the old technologies used in planning are not going to cut it anymore when there are so many moving parts in the game.
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.
AI is reshaping the way organizations source, manage suppliers, and drive value today. As supply chains 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 supply chains 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?
Unfortunately, outdated tools and fragmented processes make it difficult to maintain visibility across the supply chain and adapt at the pace of business. Digital procurement streamlines workflows and unifies data, enabling faster sourcing, better collaboration, and improved accuracy.
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.
The pace of technological evolution is pushing organizations to the brink. The groundbreaking technology is transforming how companies manage sales and operations planning (S&OP). This eliminates the need for lengthy back-and-forth communications and manual data entry by delivering a single source of truth.
They need visibility across multiple internal systemslike ERP, CRM, and financial platformsand even external sources shared with suppliers, partners, and customers. Thats why modern BI systems are quickly becoming the go-to solution for data-driven enterprises. But lets be clear: not all BI platforms are created equal.
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 supply chain management and financial reporting.
Proprietary warehouse, transportation , and labor management systems bolted onto legacy ERP systems, all “enriched” with off-the-shelf and bespoke software solutions, are a recipe for disaster. Yet, the money was spent, and the technology is now in place. Every system in your network collects it and stores it somewhere.
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.
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. If nothing else, the last few years highlighted the importance of sourcing strategically. in advanced economies — causing a potential slump in demand. [2].
In the corridors of Unilever, a team of dedicated supply chain planners from demand to supply to transportation embarks on a daily journey. End-to-End Supply Chain Planning Platform The end-to-end process begins with data. The result is an end-to-end planning process operating on the highest quality data possible.
She wrote, “I have been working in the supply chain for 35 years, and we are still trying to solve the “demand” issue. Given your expertise, I’d love to hear what alternatives you recommend for better demandforecasting and real-time visibility beyond what’s commonly adopted today.” The reason?
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. AI-powered demandforecastingsoftware can significantly improve predictive accuracy, making it a crucial component of modern supply chain planning software.
How 3PLs Can Gain Visibility and a Competitive Advantage Offering Automated Billing and a Self-Service Interactive Customer Portal It’s hard to imagine a third-party logistics (3PL) business today operating without some form of a warehouse management system ( WMS ) connecting the digital dots. But can technology do more?
We need planning platforms to keep up with all the changes. This means we need more agile, flexible, and scalable planning platforms to process and consolidate new data sources, drive insights using advanced analytics such as AI/ML to drive autonomous decisions, and expand collaboration within and outside our organizations.
And demand patterns are highly unpredictable. With multi-echelon networks, supplier uncertainty, multiyear product lifecycles, and reverse logistics channels , aftermarket supply chains exceed the capabilities of traditional planning tools. Your parts portfolio includes thousandssometimes millionsof SKUs. The outcome?
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. He was named a Pro to Know in 2021 by Supply and Demand Chain Executive.
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