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Demand forecasting is a critical strategy for supply chain management that can dramatically improve business decision-making and financial performance. However, securing leadership buy-in for demand forecasting technology requires a strategic approach that clearly demonstrates value.
Open Sky Group, a global leader in supply chain execution solutions, has announced a strategic partnership with Easy Metrics , a premier provider of labor management and warehouse performance management solutions.
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.
Solvoyo has a metric they call the user acceptance rate. This metric measures the percentage of time the planners accept replenishment, transportation, or inventory plans as they are without any change in the timing of the delivery or the quantity to be delivered. Forecasting is not an actionable item.” That’s an action.
In today’s architectures and functional metrics, value optimization does not exist. And, when procurement and tactical planning operate in isolation, there is no decision support framework to guide the trade-offs especially when the functions are tethered to different and conflicting metrics. You are right.
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!
CPFR: Collaborative Planning, Forecasting and Replenishment garnered great fanfare late in the 1990s. The biggest issue with CPFR was the quality of the customer forecast. We are stuck with old models Without new thinking, we wont get unstuck. _ The building of collaborative processes remains an unfulfilled goal.
If “the forecast is always wrong,” is improving forecast accuracy even the solution to our demand planning woes? Artificial intelligence and machine learning ( AI/ML ) can improve forecast accuracy, but a bigger problem is the failure to set accurate expectations around forecasting models, not the accuracy of the models themselves.
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.
For instance, advanced factory scheduling solutions use predictive maintenance inputs, which rely on sensor data to forecast equipment failures. Short-term forecasting relies on POS and other forms of downstream data. Not all the transactional data, just the data required to calculate a metric or make a decision.
However, as carbon taxes and emissions reporting requirements continue increasing, supply chain professionals face mounting pressures from inside and outside their organizations to measure and improve performance against new, nebulous sustainability metrics. Freight transportation makes up over 10% of total global carbon emissions.
Machine Learning for demand forecasting has matured to a level of accuracy, transparency and replicability that translates into transformative results, including in these five areas: Accuracy, transparency, thoroughness of analytical options and results. Let’s take a closer look at each one. Accuracy and transparency.
Form and socialize your own hierarchy of metrics. For example, don’t focus on forecast error. Instead, analyze demand flow characteristics by demand stream to evaluate Forecast Value Added (FVA), forecastability, and bullwhip impact. Design your supply chain with a focus on the form and function of inventory.
This means going beyond high-level forecasts to embrace tools and practiceslike Demand Collaboration, Scenario Planning, and detailed modelingthat make Sales & Operations Planning actionable, dynamic, and performance-driven. As Gartner highlights, companies tend to prioritize improving forecast accuracy to strengthen S&OP results.
That’s precisely what demand forecasting feels like for many businesses today. Enter causal forecasting. Unfortunately, many companies hesitate to use causal forecasting, thinking it’s too complicated or resource-hungry. What is Causal Forecasting? That’s where causal forecasting comes into play.
A shift from functional metrics to a balanced scorecard. I like the use of growth, margin, inventory turns, Return on Invested Capital, customer service and ESG metrics. The focus on functional metrics sub-optimizes balance sheet results. Improved Forecast Value Added (FVA). A Focus on ‘One-Number Forecasting.’
The SAS forecasting system implemented in 2019 was not tested for model accuracy. An example for this client would be to use 2017 and 2018 history to forecast 2019. So, I asked the questions, “Is your data forecastable? Data at this level of variability is complicated to forecast.) The reason? The answer?
The system also contributes to better forecasting accuracy. Built-In Sustainability Reporting Some orchestration tools are adding carbon tracking or energy use metrics alongside cost and delivery performance data. The factory uses this information to make scheduling and inventory decisions more efficiently.
Component 1: AI-Powered Probabilistic Forecasting for Inventory Optimization Effective forecasting enables businesses to navigate uncertainty and respond rapidly to disruptions. Multi-scenario prediction : Generates diverse forecast possibilities with precise probability assessments for informed decision-making.
Samuel Parker and Joe Lynch discuss DAT iQ: the metrics that matter. Key Takeaways: DAT iQ: The Metrics that Matter In the podcast interview, Samuel Parker gave a freight market overview based on DAT’s database of $150 billion in annual market transactions.
To address these return-driven challenges, the industry is moving away from siloed solutions toward integrated systems that seamlessly connect Merchandise Financial Planning , Assortment Planning , Allocation , and Demand Forecasting.
When a new tariff is proposed, companies using AI-based forecasting tools are often able to adjust their sourcing or logistics strategies well before the policy takes effect. Rather than planning based on a single forecast, supply chain teams can evaluate multiple options in parallel: What happens if tariffs increase by 15%?
Using balance sheet data from 2011 to 2019, we chart companies’ progress by peer group on rate of improvement and performance in the metrics of growth, operating margin, inventory turns, and Return on Invested Capital (ROIC). Let me share some and see what you think: Does forecast ownership make a difference in outcomes?
beef from 1,000 to 13,000 metric tons , removing the 20% tariff within that limit. Cost Forecasting : The 10% tariff baseline increases landed costs and may affect margin forecasts across multiple sectors. Further details on derivative product eligibility and quota volumes have not been published.
Functional Metrics and the Lack of Alignment to Strategy. Few companies are clear on the number of supply chains they operate, design the rhythms and cycles of each, and align metrics to the strategy. The industry is not clear on desired outcomes. Clarity on Value. Guess what? It doesn’t. These two reports are coming soon.
AI-driven predictive maintenance can also forecast potential issues before they occur, reducing downtime and improving product reliability. This integration includes tracking individual components and collecting data on environmental impact, including sustainability metrics such as carbon footprint and recyclability.
With freight transport accounting for a significant share of global emissions, efforts to improve logistics now extend beyond operational metrics to include resilience, regulatory compliance, and climate performance. CEVA Logistics, a CMA CGM subsidiary, uses Googles AI tools for warehouse management and demand forecasting.
Despite knowing all this, too many retailers ignore the impact of weather and this adds error to plans and demand forecasts. And even though meteorology has come a long way, weather is a notoriously fickle and uncontrollable factor, and no forecaster can reliably predict it beyond the next few weeks. It all evens out in the end.
Forecasting projections is one of the toughest things to get right. Whether your brand is experiencing gradual sales or is in high-growth mode , we’ll walk you through some tips to improve your ability to forecast demand. Jump to section: What is demand forecasting? Jump to section: What is demand forecasting? Conclusion.
Innovative tools provide actionable insights and improve operational efficiency Artificial Intelligence (AI): AI systems optimize routing and demand forecasting, reducing energy consumption and empty miles. Set Measurable Goals: Establish clear targets for emissions reduction, energy efficiency, and sustainability metrics.
A planner could ask the SCP engine to achieve 95% service, with CO2 emissions under a million metric tons at a given factory in the coming month. These forecasts occur in three different time horizons: Long-term planning. Often called strategic planning, this is a forecast spanning 1 – 5 years. Medium-term planning.
In my first classes, I taught the group how to speak the language of demand—forecastability, Forecast Value Added (FVA), backcasting, demand and market latency, and market drivers. 40-50% of items are not forecastable at an item/location level. Lack of aligned metrics. Instead, we need to Jump. The So What?
Supply chain optimization is crucial for enhancing efficiency and cost-effectiveness by providing end-to-end visibility, aligning with demand forecasts, and continuously improving processes through technology and analytics. Demand Forecasting: Analyze past data to predict future needs.
Protecting sensitive data—such as vehicle locations, driver information, and operational metrics—requires rigorous cybersecurity measures. Predictive analytics offers the added benefit of forecasting maintenance needs and planning routes based on historical data, allowing for proactive resource allocation.
We speak about the need to move from a functional understanding to a global, holistic capabilities, but the traditional supply chain leader defines bonus incentives and process performance goals based on functional metrics. I often laugh when companies ask me to define a good forecast. Measurement.
A study by E2open – the 2021 Forecasting and Inventory Benchmark Study: Supply Chain Performance During the Covid-19 Pandemic – provides the answers. Benchmarking the forecasting process is difficult. Forecasting Accuracy Was Terrible . No matter what kind of demand planning solution was used, forecasting accuracy dropped.
It is useful to analyze demand data to understand “forecastability” and randomness. 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. When I delve into the data, I find: Forecasting Solution Signal Efficacy.
In the intricate world of supply chain management, the accuracy of demand forecasting often serves as the cornerstone of business growth. Yet, despite its significance, demand forecasting continues to be a thorny issue for many businesses, particularly manufacturers. What is Demand Forecasting Accuracy? Let’s get started!
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.
We’ll examine the key components of efficient supply chains, explore essential performance metrics, and uncover the fundamental drivers that influence efficiency. Efficient supply chains strengthen collaborative relationships through automated communication systems and shared performance metrics.
Top 3 Demand Forecasting Mistakes —How To Avoid Them with Demand planning software Demand forecasting is a critical facet of successful business operations, acting as the helm guiding companies through the rocks hiding beneath the water of market demands. What is Demand Forecasting?
The formula for OTIF is: Measuring a supply chain against OTIF metrics is a key strategy that helps decision makers attach a tangible value to the success of their fulfillment and allows them to determine key strategies. The ability to meet fulfillment goals is impeded by several issues.
This company leverages AI and automation across multiple layers of its supply chain, including forecasting, replenishment, and transportation logistics. Minimize Manual Forecasting Adjustments : For this manufacturer, planners dont touch the forecast, focusing instead on strategic oversight.
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