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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. Prior to joining DAT, Adamo led the pricing and decision science teams at FedEx.
When it comes to running a company, when things break down executives have traditionally said “we need to improve our forecasting!” Would better forecasting accuracy be a good thing? Unfortunately, most companies cannot, and will never be able to, consistently rely on highly accurate forecasts. Absolutely!
Supply chain was defined in 1982 as interoperability between source, make and deliver. Each organization has multiple demand streams with different characteristics–forecastability, demand latency, and bias. Most companies forecast a single stream with a focus on error. Why is a reinvention needed?
Proactively adopting cleaner energy sources ensures alignment with these evolving regulations. The industry’s dependency on traditional energy sources necessitates an urgent shift toward cleaner alternatives. Companies like DHL and Amazon are already setting benchmarks by integrating EVs into their logistics operations.
I’ve always maintained that improving demand forecast accuracy, as helpful as it can be, shouldn’t be the end goal itself, but simply a means to the end. A recent report from Gartner agrees, focusing specifically on the challenge of building a better business case for improved forecast accuracy.
Optimizing fulfillment requires a series of steps to get a shipment from its source to the end customer. These steps include sourcing and receiving inventory, storing inventory, order processing, picking and packing an order, shipping the order, and returns management.
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. Unlike inflation, which has historical benchmarks, the newly announced U.S.
This week I interviewed Robert Byrne, Founder of Terra Technology , on the results of their fourth benchmarking study on forecasting excellence. The work done by Terra Technology, in my opinion, is one of two accurate sources of benchmark data on forecasting in the industry. Background on the Study.
Benchmarking is a measurement of the quality of an organization’s policies, products, programs or strategies against standard measurements. Today, I am going to share five insights that I have gleaned from our work on Supply Chain Planning Benchmarking. Benchmarking is not Benchmarking. Business Dictionary.com.
Example 1: Retail Example 2: Food Ingredients Example 3: Medical Device After mapping the demand flows and identifying market data, latency, and forecastability, the class then designs bi-directional orchestration activities. The one-size-fits-all, tight integration of APS to ERP degrades the forecast and accelerates the bullwhip effect.
Segmentation enables differentiated supplier management strategies , such as focusing risk mitigation efforts on sole-source vendors or negotiating long-term value with strategic suppliers. Centralizing data enables better sourcing decisions and improves collaboration across procurement, legal, and business other units.
Segmentation enables differentiated supplier management strategies , such as focusing risk mitigation efforts on sole-source vendors or negotiating long-term value with strategic suppliers. Centralizing data enables better sourcing decisions and improves collaboration across procurement, legal, and business other units.
Digital procurement streamlines workflows and unifies data, enabling faster sourcing, better collaboration, and improved accuracy. AI and automation boost procurement’s strategic impact, helping teams reduce risk, ensure compliance, and forecast spend.
We have benchmarked our SCI (Supply Chain Intelligence) solution; we looked at all the solutions in the industry. Advanced demand forecasting based on machine learning, for example, is a classic example of the use of AI in supply chain management. It started out as a traditional control tower.
Mike is the Head of Intermodal Solutions at SONAR, the leading freight market analytics tool and dashboard, aggregating billions of data points from hundreds of sources to provide the fastest data in the transportation and logistics sector. Mike Baudendistel and Joe Lynch discuss the CPG supply chain.
Most CPG companies have hit a demand forecasting ceiling. And complexity creates a challenge of how to forecast accurately when faced with new items, new channels and demand shaping. Recent evidence strongly suggests that traditional forecasting techniques in this environment have reached their limits and hit a ceiling.
Machine Learning, a Form of Artifical Intelligence, Has Feedback Loops that Improve Forecasting. A supply chain planning model learns when the planning application takes an output, like a forecast, observes the accuracy of the output, and then updates its own model so that better outputs will occur in the future.
Digital commerce efficiently requires the digitalization of many customer-facing operations and sourcing and procurement. Supply chain planning involves interaction with different types of information based on internal and external data sources. These data sources are often spread across multiple platforms and come in various formats.
This blog offers a clear, practical overview of what spend analysis is, how it supports strategic sourcing, and why it matters for both direct and indirect procurement. When done right, spend analysis enables cost savings, supplier optimization, risk reduction, and more strategic sourcing decisions.
AI is reshaping the way organizations source, manage suppliers, and drive value today. AI agents offer a smarter, faster way to manage sourcing, risk, and spend across the entire procurement lifecycle. For example, agentic AI can analyze supplier data, evaluate contracts, manage purchase orders, or recommend sourcing strategies.
AI is reshaping the way organizations source, manage suppliers, and drive value today. AI agents offer a smarter, faster way to manage sourcing, risk, and spend across the entire procurement lifecycle. For example, agentic AI can analyze supplier data, evaluate contracts, manage purchase orders, or recommend sourcing strategies.
Building optionality in the supply chain through collaborative sourcing: Supply chain teams can proactively identify choke points within the existing network by leveraging emerging technologies such as digital twins and advanced analytics, and modeling their end-to-end supply chains.
Definition: Financial forecasting is a projection of the company's future financial performance based on historical data, market research, and business needs. The forecasts act as a guide, which you can use to make strategic decisions on resource allocation and define clear, attainable goals.
These include alternative sourcing strategies, backup transportation routes, and emergency inventory reserves. Businesses that depend on a single supplier or a limited number of vendors are at higher risk if production delays, raw material shortages, or geopolitical issues impact their primary source.
The company’s focus on item proliferation resulted in 40% of items moving into a non-forecastable category. Since the company does not measure forecastability, the team continued to use traditional methods for forecasting, increasing the bullwhip effect and driving internal supply chain disruption.
” Or alternatively, “Is there data that could be sourced to help?” Determine Forecastability and Forecast Value Added (FVA). I am often asked to benchmark demand. Executives will ask, “What is a good target for forecast error?” Then analyze Forecast Value Added (FVA).
This guide breaks down the key procurement technologies in use today and the trends reshaping the future, such as AI-driven sourcing, predictive risk management, and deeper integration across the supply chain. What Is Procurement Technology? Here are some of the most important tools teams are already using.
The network senses, translates, and orchestrates market changes (buy- and sell-side markets) bidirectionally with near real-time data to align sell, deliver, make and sourcing organizations outside-in. It is about much, much more than Vendor Managed Inventory (VMI ) or Collaborative Forecasting and Replenishment. Use of Channel Data.
There is a significant amount of angst in the transportation marketplace lately in regards to freight spend forecasts. In other words, if a planner receives multiple spot quotes for a load, the best he or she can do is a comparative analysis between the quotes without a benchmark. Technology vs. Manual Benchmarking Approach.
At last year’s Gartner Supply Chain Executive Conference analyst Tom Enright summarized the essence of successful demand forecasting with the following statement: “ Demand forecast accuracy depends on your ability to recreate the environment in which historical demand occurred.”.
Their services can be categorized into three main areas: Assessment and Analysis Freight Planning and Forecasting: Loadsmart helps businesses predict future freight needs. Rate Analysis and Benchmarking: Loadsmart helps clients compare freight rates. FreightIntel AI: Their AI-powered platform provides real-time data and insights.
FeaturedCustomers 2023 Market Leader in Demand Forecasting Software MAY 2023 – FeaturedCustomers has named ToolsGroup a Market Leader in the Demand Forecasting Software category in its Spring 2023 Customer Success Report. ToolsGroup has also been named a FeaturedCustomers 2023 Top Rated Software and Hot 100 Vendor.
Cloud has evolved to provide the ideal infrastructure and platform for hosting multiple supply chain partners to collaboratively offer optimized services including planning, logistics, sourcing, procurement and service parts management. Check out our primer on probabilistic forecasting here. Who can provide them? Not at all!
Inconsistency Across Sources Carriers may use different naming conventions, billing formats, or rate structures. However, this data often suffers from: 1. When done right, auditing isn’t just about catching errors — it’s about transforming raw carrier billing into clean, enriched, and structured data.
Planting the Seeds of Resilience Most companies understand that accurate forecasts are critical to minimizing inventory, maximizing production efficiency, streamlining purchasing, optimizing distribution, minimizing waste, and projecting future performance confidently.
Having that SKU-level inventory visibility across the entire supply chain is critical to the ability to more accurately forecast supply and demand, and compress the time needed for all parties to make any necessary adjustments. Is it sitting at the warehouse yard, or has it reached the store? Integration with other key systems.
Last week, I interviewed Robert Byrne, founder of Terra Technology on his demand planning benchmarking study. I enjoy creating the podcast series, and Rob’s findings in his benchmark study are always thought-provoking. Traditional approaches like consensus forecasting can make this bias even worse. Need for Demand Sensing.
Otherwise, it provides only a historical view but does not enable firms to forecast future performance or adapt to an evolving regulatory framework. Understanding the data, metrics and benchmarks can help the organization collaborate with suppliers to set meaningful, actionable and quantifiable goals.
Whether you''re a manufacturing company in China, a sourcing agent in London or a world''s leading company in Silicon Valley, we''re all in a global supply chain networks. Research from APQC’s Open Standards Benchmarking in procurement shows that organizations without formal SRM programs report 121 percent longer supplier lead times.
What does best-in-class forecasting and inventory management look like? Now in its ninth year, E2open’s 2019 Forecasting and Inventory Benchmark Study is the most consistent, comprehensive and useful study of its kind.
As the markets plummet, it is time to remind ourselves that demand is not a forecast. Traditional forecasting approaches are not adequate in a time of market volatility. In the real world, companies operate with a Mean Absolute Forecast Error of 24-60%, and have a bias of 9-40%. Markets drive supply chains.
I am currently working on an in-depth study on supply chain planning benchmarking (publishes in August), and working with the team to analyze supply chain planning adoption. In my analysis of the supply chain planning benchmarking data, I can see it. As a result, we can push and push on forecasting processes and not drive improvement.
For instance, a Gartner industry benchmarking shows that a 1% increase in forecast accuracy typically achieves: 2-7% reduction in the value of total inventory as a percentage of sales (working capital). 4-9% decrease in the value of obsolete inventory as a percentage of total stocked goods (product costs).
As both sourcing and procurement are related to obtaining supplies for the organization, confusing these two terms is easy. Understanding their distinction can help you ensure the coordination between the responsible teams and adopt the right strategies for improving both sourcing and procurement. Procurement. Contains fewer steps.
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