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Each supply chain planning technology at the end of 2024, went through disruption–change in CEO, business model shift, layoffs, re-platforming and acquisitions. To build an outside-in model, and use new forms of analytics, we must start the discussion with the question of, “what drives value?” My advice?
This uncertainty makes dynamic inventory replenishment optimization essential for business success. Effective inventoryoptimization directly impacts customer satisfaction, loyalty, operational costs, and waste reduction making it a critical business function in todays volatile market.
Lean models alone are no longer sufficient. Sudden tariff increases can quickly make a cost-optimized procurement strategy untenable, leaving companies scrambling to adjust. AI is helping companies better detect risk, model alternatives, and make faster decisions with more confidence. AI also helps with scenario modeling.
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. Error is error, but is it the most important metric? My answer is no.
Venture capitalists are high on Artificial Intelligence (AI), and over-exuberant professors with shiny new models are jockeying into position to get rich. Most of the answers will fall into categories: Engines: The improvement of the math in models to improve decisions. Building a software company is hard work. Ask for use cases.
Delays, excess inventory, missed handoffs, and reactive decision-making are all signs of a supply chain that lacks coordination. These models allow planners to test different responses in advance and choose the most practical option if a disruption occurs. This doesnt eliminate those systems, it organizes the data they produce.
The issue is that when companies optimize functional metrics, they throw the supply chain out of balance and sub-optimize value. Traditional approaches built optimization on top of relational databases. This shift improves modeling options and the use of disparate data. Today, the bright and shiny object is AI.
Meanwhile, advances in AI-driven route optimization reduce unnecessary mileage, cutting emissions and costs. Smart energy management systems further enhance efficiency by tracking and optimizing energy use in real-time. Reducing carbon emissions is a cornerstone of this effort.
By harnessing the power of data science and analytics, you can gain end-to-end visibility across your entire network, breaking down information silos and optimizing every stage of your operations. Data analytics also offers actionable insights for: Inventory Management: See stock levels across multiple locations in real-time.
Green Logistics: Optimizing transportation routes, consolidating shipments, and employing energy-efficient vehicles to reduce emissions. Advanced route optimization tools further support these goals. AI-powered warehouse management improves inventory flow and reduces waste.
Three months into 2025, we have seen a barrage of on-again, off-again tariffs that have supply chain and logistics teams reeling, as they must rethink everything from next weeks shipping route to their foundational network models. The Ukraine-Russia conflict is ongoing. Tensions flare in the Middle East without warning. billion to $23.07
Reason #4 Making key decisions by modelling the supply chain in Excel. Reason #6 Not effectively managing inventory. Reason #9 Relentless pursuit of one supply chain metric at the expense of other metrics. Reason #9 Relentless pursuit of one supply chain metric at the expense of other metrics. Don’t care.
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?
It is clear that the past is not a good model for the future. Recognize that most supply chain leaders are structurally encased in their mental models with schema-on-write architectures. Take classes on the use of Large Language models, Deep Learning, and Agentic AI. Form and socialize your own hierarchy of metrics.
Picture this: You’re a warehouse manager, and with a few taps on your smartphone, you instantly know the exact location and quantity of every item in your inventory. That’s not science fiction—it’s the power of mobile inventory management. Ready to turn your inventory from a headache into a strategic asset?
According to Gartner , early stages of S&OP maturity often lack formal processes, metrics, and cross-functional participation. However, many overlook the need to leverage supply planning capabilities effectivelymissing a vital opportunity to align operational plans and optimize business outcomes.
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.
Supply chain optimization has also improved in significant ways that can address these trade-offs better than before. Analytical techniques like linear programming can create the mathematically “optimal” plan, but these methods must be implemented well to avoid creating other challenges. Supply chain optimization for today’s realities.
Continuous network optimization recognizes that supply chains are complex organisms. Continuous network optimization creates an environment where supply chain planning operates at the next level. World class organizations can sustain living models of their networks and keep them tuned to small, frequent changes.
trillion distortion inventory problem. Trillion Inventory Distortion Problem In this podcast, Karl Swensen, CEO and Co-founder of Pull Logic, discusses how their AI-enabled technology helps retailers, brands, and manufacturers reduce lost sales by addressing supply chain and selling process failure points. Summary: Solving the $1.8
As companies across industries have discovered, a well-optimized supply chain can drive significant improvements throughout their operations. In the automotive sector, manufacturers are simultaneously reducing inventory costs and delivery times. This post delves into the core drivers of supply chain efficiency.
Continuous network optimization recognizes that supply chains are complex organisms. Continuous network optimization creates an environment where supply chain planning operates at the next level. World class organizations can sustain living models of their networks and keep them tuned to small, frequent changes.
Returns Management and Integration With 35% of online purchases being returned, predominantly to physical stores, retailers are grappling with the ripple effects on inventory management. Early adopters of these integrated platforms report significant improvements in inventory turnover and reduction in stockouts.
Digital twins are emerging as digital transformation accelerators for supply chain and logistics organizations seeking enterprise-level visibility, real-time scenario modeling, and operational agility under disruption. These are not static dashboards or simple visualizationstheyre living, data-rich models of real-world operations.
Even more impressive, lost sales due to stockouts can decrease by up to 65%, while inventory reductions of 20% to 50% are possible. This advanced analysis allows businesses to predict promotional lift with unprecedented accuracy, ensuring optimized production schedules and inventory positioning through sophisticated supply planning.
The primary payback for demand and supply solutions comes in the form of reducing the amount of raw material, work-in-process, and finished goods inventory a company needs to carry. A network design model figures out where factories and warehouses should be located. Each time horizon usually has its own model associated with it.
Supply chain resilience refers to planning for things that could go wrong and then creating inventory buffers or contingency plans. SCP solutions provide a solid ROI based on hitting targeted service levels with less raw material, work-in-process, or finished goods inventory. Supply planning engines “optimize” the schedule.
” As I write, I think about the ironies: We talk about the bullwhip, but we do not measure it or use it in driving optimization. We talk about the move from functional metrics to a balanced scorecard, but we don’t use a balanced scorecard as an objective function. My question is “Will the work make a difference?”
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. Based on market conditions, the meat packer will shift to optimize the demand opportunity.
There are supply chain and demand analytics models that describe the type of analytics being deployed (e.g., Now Gartner has created a different look at the issue by creating a five-stage maturity model for assessing the overall maturity level of your organization in using supply chain analytics. descriptive, prescriptive, etc.).
The promise was the delivery of a decision support system that would allow the organization to optimize the relationships between cash, cost and customer service against the strategy. It was also the preference of the consulting partners because the projects were longer, more costly and better aligned with the consulting model.
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.
Use tools like network design optimization and simulation modeling to help people model trade-offs. Force finance and sales teams off of spreadsheets that cannot model the complex relationships of trade-offs. Advance their thinking to use more advanced supply chain modeling tools. The value proposition still holds.
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. The convergence of artificial intelligence and digital networking technologies is fundamentally reshaping our operating models.
What is the Perfect Delivery Metric? Improving on this metric will always involve a focus on people and processes, but often also includes implementing new, more robust, supply chain applications. The wrong metrics drive suboptimal behaviors and metrics can often be manipulated.
The world of NoSQL unified data models is inconceivable to most. Software built on graph technology can model flow, but the transactional paradigms of historic practices hold development team’s hostage. The larger the global corporation, the more that the use of functional goals sub-optimizes growth, margin and inventory levels.
It’s a natural fit for an environment built on orchestration across vendors, partners, inventory, and data. Immediately, the agent reoptimizes inventory routes in North America and updates the customer in Europe all without human involvement. ” What makes supply chains an ideal proving ground for this evolution?
To keep customers like my dad satisfied, RGD and Quick-commerce companies need to invest in new technologies to optimize the supply chain and logistics operations. InventoryOptimization. InventoryOptimization involves decisions about the inventory level, the location, and the mix of products.
Closing the gaps happens when there are aligned metrics, clarity of vision and aligned planning processes. This includes optimization and discrete event simulation. Metrics Alignment. Most companies operate well within functions, but struggle to build strong horizontal processes. They lack cohesion.
How are companies rethinking their liquidity management strategies in response to the recent degradation across major working capital metrics? In the wake of economic uncertainty, many companies have experienced a degradation in key working capital metrics.
How wrong and how biased depends on the inputs and the refinement of the model. The problem is helping models sort through inaccuracy and bias. The general AI models like ChatGPT are the buzz, but the greatest lift for the supply chain is happening in the world of narrow AI driven by deep learning. Relationship Management.
Recent examples included the rollout of a new inventory planning application and the introduction of a hybrid AI-powered demand forecasting engine. Clark noted that these developments were designed to meet emerging customer needs, particularly in markets experiencing unpredictable demand and inventory constraints.
Without sufficient data, AI models can’t uncover meaningful patterns, make accurate predictions, or provide valuable insights for informed decision-making in complex and dynamic environments. At the same time, feeding your AI models too much data can also be a problem. Data is the lifeblood of AI in the supply chain.
The award, based on beating the industry peer group on rate of improvement on the key metrics of growth, operating margin, inventory turns, and Return on Invested Capital (ROIC) while outperforming their peer group, is tough to achieve. The orbit chart below illustrates L’Oréal’s performance at the intersection of two metrics.
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