This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
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.
This uncertainty makes dynamic inventory replenishment optimization essential for business success. Effective inventory optimization directly impacts customer satisfaction, loyalty, operational costs, and waste reduction making it a critical business function in todays volatile market.
Developing Models : Building and scaling AI models in a manner that ensures they are reliable and understandable. These new data fabrics will need to go beyond traditional enterprise data fabrics, which are optimized for cloud environments, to be able to embrace complex supply chain data.
billion rate data points monthly to provide the most comprehensive view of the market, helping you identify savings opportunities and make data-driven decisions. It handles everything from rating and booking to shipment management, invoice auditing, and beyond.
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.
Public Reporting: Publishing sustainability reports and ethical compliance metrics to highlight progress and areas of improvement. For example, using AI-powered tools to optimize logistics can reduce energy consumption and enhance sustainability. The energy sector provides a compelling example of CSR-driven compliance.
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.
Green Logistics: Optimizing transportation routes, consolidating shipments, and employing energy-efficient vehicles to reduce emissions. Advanced route optimization tools further support these goals. Internet of Things (IoT): IoT devices monitor vehicle performance and energy usage, enabling real-time optimization.
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 leads a team of market experts who study every facet of the logistics industry to bring the best available insight to customers.
Similarly, UPS uses its ORION system, which integrates real-time and historical data to optimize delivery routes, saving fuel and enhancing delivery reliability. Real-time route optimization allows fleets to adapt to dynamic conditions such as traffic and weather, minimizing fuel consumption and delivery delays.
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. Route Optimization: Calculate the most efficient delivery routes based on several factors. Ready to get started? Let’s dive in.
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. What can we do now?
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.
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?
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.
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
billion rate data points monthly to provide the most comprehensive view of the market, helping you identify savings opportunities and make data-driven decisions. It handles everything from rating and booking to shipment management, invoice auditing, and beyond.
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.
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.
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. We needed to model the data in a way that we can do simple searching.
For organizations layered in functional metrics and driving a cost agenda, this is a tough nut to crack. During the pandemic, companies struggled with planning systems turning off the optimizers, and using the technology as a system of record. Steps to Take Here are three steps to take: Adaptive Modeling. Higher variability.
Bosch + Port of Hamburg: V2X for Urban Freight Flow Optimization At one of Europes busiest ports, Bosch has deployed infrastructure that connects trucks to dynamic signage, smart signals, and route suggestion engines. This is a working model for connected urban freight corridors.
As companies across industries have discovered, a well-optimized supply chain can drive significant improvements throughout their operations. We’ll examine the key components of efficient supply chains, explore essential performance metrics, and uncover the fundamental drivers that influence efficiency.
These models allow planners to test different responses in advance and choose the most practical option if a disruption occurs. These models provide teams with visibility into how changes in demand or equipment availability might affect production. This reduces delays and improves coordination between operations and planning teams.
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.).
In this final blog on agility and why you should consider becoming an agilist to survive the new completion (of the continuous mention) of the application of enterprise decision management systems (EDMS) from Taylor and Raden cited in the first blog, I turn to the metric of agility and a new ROI metric of decision yield. The Takeaway.
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.
Today’s most effective forecasting tools incorporate advanced demand sensing capabilities, collaborative features, probabilistic modeling, and comprehensive tracking mechanisms for increased accuracy and accountability. Agent AI is emerging as a game-changing tool for understanding and responding to customer behavior in real-time.
A network design model figures out where factories and warehouses should be located. The key solutions are demand forecasting/inventory optimization, supply planning, and network design. Each time horizon usually has its own model associated with it. Supply and network design models are constraint-based models.
This advanced analysis allows businesses to predict promotional lift with unprecedented accuracy, ensuring optimized production schedules and inventory positioning through sophisticated supply planning. Today’s advanced demand planning systems treat weather conditions as causal factors alongside pricing, promotions, and store traffic.
ARC defines supply chain planning (SCP) products as including supply planning, demand planning/inventory optimization, and network planning. Supply Planning Supply planning systems create models that allow a company to understand capacity and other constraints it has in producing goods or fulfilling orders.
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.
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.
This means routinely bringing together the C-suite, finance, supply chain, manufacturing, sales and marketing teams so everyone is seeing, working from and agreeing to an aligned plan that achieves optimal business outcomes. The SCOR model contains more than 150 key indicators that measure the performance of supply chain operations.
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.
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.
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.
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.
” 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?”
But what seems like a simple case requires multiple agents and AI tools interpreting a disruption, assessing impact, optimizing multiple alternative scenarios, selecting the best, communicating rerouting instructions, updating customers, and so on. The true breakthrough lies in whats next: multi-agent orchestration.
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. Inventory Optimization. Inventory Optimization involves decisions about the inventory level, the location, and the mix of products. Route Optimization.
Not all data is forecastable, and not all demand optimization engines are equal. The more forecastable the data set, the easier it is to find an optimizer. In addition, there is often a seasonal, new product launch and service supply chain logical model. To be successful, each logical supply chain model needs different tactics.
Ultimately, what KPIs, as metrics and indicators derived from the set of plans are taken into account and prepared for each scenario. Here, planning solutions with optimization fit very well with this concept. Technology for Effective Planning. The best decision here takes into account the most viable option among all possible options.
We organize all of the trending information in your field so you don't have to. Join 102,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content