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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.
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.
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.
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.
Just by embedding analytics, application owners can charge 24% more for their product. How much value could you add? This framework explains how application enhancements can extend your product offerings. Brought to you by Logi Analytics.
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.
In follow-up qualitative interviews, one of the largest issues with organizational alignment was metric definition and a clear definition of supply chain excellence. In my post Mea Culpa, I reference my work with the Gartner Supply Chain Hierarchy of Metrics. ” Let’s face it all supply chains have error.
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%.
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.
Historically, supply chain leaders managed supply chains 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. Build the governance model.
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.
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.
Ever feel like your supply chain is a tangled mess of spreadsheets, frantic phone calls, and last-minute scrambles? It’s the key to transforming your supply chain from a source of frustration into a well-oiled, profit-generating machine. You’re not alone. That’s where data analytics comes in.
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.
The global supply chain is built on three assumptions: rational government policy, availability of reasonably priced logistics, and low variability. In March 2023, the Global Supply Chain Pressure Index fell to the lowest level since November 2008. Over the past three years, supply chain cycles shifted. Higher variability.
Digital twins are emerging as digital transformation accelerators for supply chain and logistics organizations seeking enterprise-level visibility, real-time scenariomodeling, and operational agility under disruption. Introduction: Why Digital Twins Matter Now The pressure on supply chains has never been greater.
Five years ago, we all thought the COVID-19 pandemic resulted in the most disrupted supply chain landscape we would ever see. Since then, supply chain disruptions and volatility have only increased. The pace and scope of supply chain disruption are beyond human cognition, manual analysis, and consumer-grade spreadsheet tools.
Supply chain efficiency is the cornerstone of success and involves the effective management of processes, resources, and technologies from procurement to production, transportation to warehousing. As companies across industries have discovered, a well-optimizedsupply chain can drive significant improvements throughout their operations.
This model simplifies the world of RtM into a series of three steps that any RtM practitioner can execute. Here are the Top 5 Do’s and Don’ts to help you build a high-performing RTM model and distributor network: ✅Top 5 Do’s Do Align RTM Strategy with Consumer Behaviour : Design your RTM based on where, how, and why your consumers shop.
Consumers are ever more conscious of value, sensitive to health and environmental issues – especially after the COVID pandemic, each demanding more options for their money. End-to-end supply chain visibility, planning, and execution support software are critical in agile supply chain performance.
But between rising costs, complex logistics, and the constant struggle to optimize space and labor, staying ahead can feel like an uphill battle. That’s where warehouse optimization comes in. Here’s what you can expect: A clear definition of warehouse optimization and its core components. Ready to get started?
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.
Digital commerce efficiently requires the digitalization of many customer-facing operations and sourcing and procurement. For businesses of all sizes, the digital transformation of supply chain planning became the most important initiative. . These data sources are often spread across multiple platforms and come in various formats.
Gartner measures supply chain analytics maturity across seven different dimensions. There are supply chain and demand analytics models that describe the type of analytics being deployed (e.g., Gartner reports a strong correlation between supply chain organizations that use analytics and improved business performance.
Strategic sourcing and innovative solutions are often viewed as two distinct procurement tools, but they should not be seen in isolation. Strategic Sourcing: The Foundation of Effective Procurement Strategic sourcing is far more than simply choosing suppliers. Done well, it can become a key driver of competitive advantage.
The global supply chain 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 supply chain management.
Instead of relying solely on a single, monolithic AI model (based on a massive large language model), a company can orchestrate a team of specialized agents, each leveraging the best AI or mathematical technique for its specific task. The chemical industry has a complex supply chain. Agentic allows for much greater flexibility.
Supply chain resiliency and sustainability are top priorities for CEOs today. To achieve these goals, corporate leadership must focus on two key areas: shift from internally focused supply chains to collaborative supply networks and actively design their supply chains.
A new report from Nucleus Research, Value Drivers of Single Model S&OP , concludes that the historical disconnect between planning and execution in S&OP is best bridged by a single unified data model that allows companies to continuously synchronize their strategic, tactical and execution plans.
Note: Today’s post is part of our “Editor’s Choice” series where we highlight recent posts published by our sponsors that provide supply chain insights and advice. This article comes from Greg Quirk, Product Marketing Manager at Kinaxis and looks at AI in supply chains. Data is the lifeblood of AI in the supply chain.
Two months before COVID made headlines in the US, nobody was forecasting the dramatic downturn in demand. If a recession hits, demand will decrease. How much will demand be impacted? When companies talk about improving their forecasting, they are most often referring to demand forecasting. When will the recession hit?
Anyone who has done demand planning knows it is extremely complex, with forecasting challenges and rapidly shifting consumer demand, often exacerbated by seasonality, new product introductions, promotions, and myriad causal factors (e.g. weather, social media). Set Specific Business Objectives at the Start.
In general, S&OP consists of a New product review, Demand Review, Supply Review, Management Review and some level of reconciliation between these steps. The process as is implemented in many companies focuses on balancing demand and supply based on preestablished rules and policies that serve as guardrails for the planning process.
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.
Supply chain and logistics teams today face a pivotal moment in their evolution. The traditional metrics of excellence cost efficiency, on-time delivery while still important, are no longer sufficient in an era defined by volatility, complexity and political changes. Third, decision-making is evolving from human-led to AI-augmented.
A large multinational is undergoing an impressive supply chain transformation that will run through 2023. A Complex Supply Chain. Not surprisingly a company this big, delivering different solutions to a variety of industries, has a complex supply chain. The company has been on a supply chain transformation journey since 2005.
Can you describe the outside-in model? Based on the work with Georgia Tech, we are getting clear on which metrics matter by industry. As companies adopt a balanced scorecard, the functional metrics shift to a focus on reliability. (For Their goal is clear–stabilize the blood supply in the United States.
When reviewing strategy decks for supply chain teams, I often see statements like “move from a functional-silo’d focus to a drive a more holistic response.” Companies became less clear on the definition of supply chain excellence and how to implement decision support technologies. Functional Metrics.
This year, a recurring theme that I saw was about using supply chain data to improve the customer experience across the entire value chain. Here are the ones that stood out to me, especially as it relates to supply chain data. This new dashboard gives retailers a lot of supply chain data to see where they stand.
Supply chain excellence is easier to say than to explain. Executive teams strive to drive improvement in supply chain results; yet, sadly, only four percent of public companies succeed. The supply chain is a complex non-linear system. Understanding this relationship requires modeling. The reason? A Case Study.
This is my 440th blog post and the tenth anniversary Supply Chain Shaman. It is useful to analyze demand data to understand “forecastability” and randomness. Not all data is forecastable, and not all demandoptimization engines are equal. Companies do not have one supply chain, they have many.
In today’s dynamic and unpredictable business environment, companies face various challenges such as changing consumer demands, global uncertainty, and the impact of natural and man-made events. The purchasing of products at companies can be looked at in terms of two major dimensions: supply risk and impact on production (Figure 1).
Digital Twin from Infor Nexus Drives Supply Chain Agility. When it comes to driving supply chain agility there are several solutions that are important, but the key solution is a Multi-enterprise Supply Chain Network (MSCN). But even in more normal times, a supply plan usually can’t be fully executed. Stuff happens.
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