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Those that also leverage scenarios in IBP are even better prepared to deal with the supply chain shocks caused by COVID-19. Our second webinar delved deeper into the technology aspect, focusing on analytical capabilities and scenariomodeling. Let’s explore them briefly in this blog post.
India’s growth story can continue if it streamlines and effectively manages its supply chain like the iconic dairy brand Amul that recently entered the US market. Amul’s model supports small producers by integrating large-scale economics, cutting out intermediaries, and connecting producers directly with consumers.
For years, supply chains were engineered to be lean. Reducing cost was the primary objective, and most operational decisionsfrom sourcing to fulfillmentreflected that mindset. Lean models alone are no longer sufficient. Why Current Supply Chains Struggle The common failure points are not surprising.
Supply chain disruptions have become a persistent operational risk. Geopolitical instability, extreme weather, labor shortages, and fluctuating consumer demand regularly impact global logistics. Amazon is a leader in AI-driven supply chain management. Executives are left making high-stakes decisions with incomplete information.
One policy shift and your sourcing strategy is upside down. How Market-Leading Companies Are Navigating Tariff Chaos with GEP’s AI-Powered Procurement Platform breaks down how procurement teams are staying ahead of the chaos with real-time insights, smarter sourcing moves and AI-powered agility.
But many supply chain practitioners dont realize that the most common approach to supply chain planningusing a demand-driven forecast as the primary input to future planningis just as outdated. Companies that rely solely on deterministic models are struggling to keep up with demand fluctuations.
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. Businesses face heightened uncertainty in managing costs and securing stable energy supplies.
Global supply chains 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.
Artificial intelligence (AI) and rapidly developing generative AI tools provide complex, real-time, and in-depth insights specific to supply chain management. Further, AI-driven demand sensing allows businesses to combine scattered data which is essential for better forecast accuracy.
Why do some embedded analytics projects succeed while others fail? We surveyed 500+ application teams embedding analytics to find out which analytics features actually move the needle. Read the 6th annual State of Embedded Analytics Report to discover new best practices. Brought to you by Logi Analytics.
The modern supply chain is a complex network of suppliers, manufacturers, distributors, and customers, all interconnected and reliant on a shared ecosystem of trust and accountability. As industries evolve and global markets expand, ethical considerations have become central to supply chain compliance.
Trade policies are constantly evolving, forcing companies to assess how these changes impact customer demand, supply networks, fulfillment strategies, and cost to serve. Supply chains need to be more agile than ever, yet much of the advice circulating in the industry remains high-level or less than ideal.
Ted Krantz, CEO of Interos Interos , a company providing supply chain resilience and risk management software, emailed me to say that there was a supply chain risk everyone seemed to be ignoring – AI-related risks. The AI-related risks include data poisoning and model corruption.
Supply chain practitioners seeking the best way to speed decision intelligence, unify supply chain data, and increase operational efficiency can benefit from a supply chain data gateway. Here are 10 ways a supply chain data gateway can improve your performance across the end-to-end supply chain.
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When one thinks of supply chain software vendors, the name InterSystems may not spring to mind. A supply chain data fabric can help companies augment their supply chain processes. They aim to achieve the same success in supply chain management that they have achieved in the healthcare sector. Who is InterSystems?
At ToolsGroup, we’ve long championed probabilistic demand forecasting (also known as stochastic forecasting) as the cornerstone of effective supply chain management software. In conventional supply chain planning , planners using basic tools (typically spreadsheets or legacy systems) forecast just one number for each item.
Improving demand forecast accuracy is crucial for supply chain success. Traditional demand forecasting methods often fall short, resulting in inefficiencies, excess inventory, and lost revenue. Unlike static demand prediction models, AI-driven forecasting adapts over time, leading to improved demand forecast accuracy.
As a supply chain 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. This isnt a hypothetical scenario; its the daily grind for many businesses in 2025, where global trade rules shift faster than you can update your spreadsheets.
Explore the most common use cases for network design and optimization software. This eBook shares how supply chain leaders leverage their supply chain design software to tackle a variety of challenges and questions. Scenario analysis and optimization defined. Modeling your base case. Modeling carbon costs.
Sales and Operations Planning has become the preferred method to facilitate clear and formal communication between the demand and supply sides of a business. To address this, Gartner advises organizations to adopt scenario planning for S&OP. Read on to explore the benefits and how you can apply this in your organization.
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. What is Demand Forecasting in Supply Chain Management? What is Demand Forecasting in Supply Chain Management?
In today’s interconnected global economy, sustainability within supply chains and logistics has become a necessity rather than an option. Regulatory demands, rising consumer expectations, and global challenges such as climate change and social inequality have made sustainable practices a strategic priority.
Safety Stock: Navigating Supply Chain Volatility Through Strategic Inventory Planning Demand volatility represents a critical challenge for supply chain executives today, with safety stock emerging as a key strategic tool to mitigate market uncertainties.
Think your customers will pay more for data visualizations in your application? Five years ago they may have. But today, dashboards and visualizations have become table stakes. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics.
Supply chain practitioners seeking the best way to speed decision intelligence, unify supply chain data, and increase operational efficiency can benefit from a supply chain data gateway. Here are 10 ways a supply chain data gateway can improve your performance across the end-to-end supply chain.
The industrial sectorparticularly supply chain management, is facing unprecedented complexity. Lets delve into the core concepts of AI Agents and multi-agent workflows, their relevance to what ARC Advisory Group calls Industrial AI , and their potential to revolutionize supply chain management.
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 demand forecasting solutions can reduce forecast errors by 30% to 50%.
For the past few years, the news has been filled with stories about supply chain disruptions, supply chain fragility, and the need for supply chain resilience. A term once prominent in supply discussions optimization isn’t heard quite as often as it used to be. ” What is Supply Chain Optimization? .”[1]
It creates a single source of truth for your rate management, automating RFQs and streamlining the entire procurement process. billion rate data points monthly to provide the most comprehensive view of the market, helping you identify savings opportunities and make data-driven decisions.
Expanded health insurance coverage led to increases in the demand for care. As vice president of supply chain and procurement, Mr. Wengert was brought in to drive change. In healthcare, large, powerful distributors sell hospitals medical supplies and deliver those supplies right to individual medical facilities.
All supply chain vendors seek to position themselves as leaders in supply chain AI. Datacenter Hardware: The demand for powerful computing to train ever larger and more accurate AI models is insatiable. AWS has custom AI chips Trainium and Inferentia , for training and running large AI models.
The adoption of AI in supply chain automation is enabling companies to make more accurate decisions, reduce cycle times, and better manage complexity. AI in supply chain automation is gradually reshaping how core functions operate, particularly in procurement, warehousing, and logistics.
Increasing concerns over mass supply chain disruptions. Its a rollercoaster for logistics and supply chain leaders operating in global markets. Businesses are facing greater volatility as tariff changes wreak havoc on supply chains, operational costs, and overall profitability. Intensifying geopolitical unrest.
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. I’ve not seen a company that does a better job of agile planning across an end-to-end, multi-tier supply chain.
Today’s supply chains are fraught with uncertainties across demand and supply yet are tasked with adding incremental value to their organizations while also meeting commercial, working capital and sustainability goals. The challenge for supply chain teams lies in increasing knowledge to create value amid this complexity.
She wrote, “I have been working in the supply chain for 35 years, and we are still trying to solve the “demand” issue. Solving from a supply side seems to work for many companies I work with. Over the last two years, I actively engaged technologists and business leaders to redefine demand planning.
The supply chain industry is no stranger to uncertainty. While businesses cant predict every challenge, they can take proactive steps to anticipate disruptions and strengthen their supply chain management systems with advanced demand planning tools.
Supply chain network design (SCND) is a powerful tool for improving business operations. Optimization and simulation are the two main branches of SCND. Optimization accounts for over 90% of all work that is being done by SCND teams. It can be used to solve a wide variety of supply chain problems. But it has gaps.
Supply Chain & Logistics News Round-Up (October 7th – 11th) This past week, I’ve been playing tourist in London, one of my favorite cities in Europe. In the context of supply chains, I found the exhibit fascinating and saw that the obsession with foreign objects has persisted throughout history.
The Connected Supply Chain. Drip Digital Supply Chain. Autonomous Supply Chain Planning. Self-Healing Supply Chains. Touchless Supply Chains. Small companies outperform large companies, and the marquee customers of major supply chain planning technology providers underperform. Industry 4.0. Drip Big Data.
This disconnect between AIs potential and real-world adoption presents a significant opportunity for companies to gain a competitive edge, especially in supply chain management where uncertainty is the norm. However, its important to recognize that AI and machine learning are not magic fixes for supply chain challenges. The secret?
Supply chains, which facilitate the movement of products from manufacturers to consumers, have historically encountered issues such as inefficiency, fraud, and a lack of transparency. Companies find it difficult to fully trust the data from suppliers, complicating efforts to ensure product authenticity, safety, and ethical sourcing.
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