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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.
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!
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.
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.
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.
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.
Editor’s Note: Two years ago we posted a blog about how to set an annual forecast accuracy target and it was one of our most popular topics. It seems as if everyone is looking to improve their forecasting performance. How much can you realistically expect to improve your forecast accuracy each year?
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.
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.
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?
I have been blogging and advocating for the past 15 years on probabilistic approaches to planning and forecasting, and am happy to see in the last few years it has finally started gaining traction and attention. A symptom of a probabilistic plan or forecast is that its results are generally also expressed as probability distributions.
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.
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?
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.
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.’
Over my 25+ year supply chain career I have worked for several distribution-intensive companies and every single one of them had a focus on improving forecast accuracy. Achieving a high SKU level forecast accuracy is a top goal for supply chain planning teams regardless of industry, size, location, etc.
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.
The organization had little energy to test forecasting models. I then asked about his success with Forecast Value Added (FVA) analysis. Companies speak about moving from a functional metric focus to managing corporate metrics, but this does not happen. I asked if he had tested the models by backcasting.
A large consumer products manufacturer with nine Enterprise Resource Planning (ERP) instances and several divisions wanted to discuss forecasting. The team was not calibrated on the role of forecasting and the basics around process excellence. What Is a Forecast Anyway? A forecast is not a forecast. Bear with me.
Implementation of Sales Forecasting. The focus on sales forecasting started shortly after Y2k. Few companies measured the impact on error and bias through the rigor of Forecast Value Added (FVA) analysis. While the input from sales on market trends is invaluable, sales should never be asked to forecast. The reason?
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.
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.
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?
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!
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.
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.
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.
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?
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.
build alignment (driving goal congruence), building reward systems (defining a cross-functional set of metrics to align to the strategy) and defining the right reporting relationships (the S&OP process needs to report to the profit center manager). Most retail forecasts were just not good enough to drive success. The problem?
Manufactures are continuously faced with the challenge of forecasting how much (raw material) to purchase and how much (finished goods) to produce. To manage this delicate balance of demand and supply, manufacturers often use statistical forecasting techniques to predict future demand by looking at historical sales data.
This included one-number forecasting, Integrated Business Planning (with tight integration to the budget), labor arbitrage strategies (chasing low-cost labor with extended supply chains), and tax-efficient supply chain strategies. Focus on functional metrics without alignment to a balanced scorecard to drive value. Mistake #3.
Gartner says that the most common outsourced SCP processes are inventory management, statistical forecasting and service parts planning. Companies moving to BPO in these practice areas are experiencing supply chain improvements in metrics such as inventory turnover and customer service. Driven by improvements in performance and cost.
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.
For example, an SOP may define how forecasting is done in the organization; therefore, the RFI focuses on features that enable that approach. There may be a better way to approach forecasting, but it’s not captured in the RFI because no one has any experience with it. #6 6 Focus on the wrong metric. 7 Poor use of resources.
MTSS platforms facilitate hands-on projects where learners can apply statistical methods to identify trends, forecast demand, and optimize inventory levels. Through interactive tutorials and practical exercises, learners can become adept at using software for inventory management, transportation planning, and demand forecasting.
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.
Review Supplier Performance Performance Measurement: S&OP provides a framework for tracking supplier performance against agreed-upon metrics, such as delivery time, quality, and cost. Help Forecast Upstream Supply Constraints Early Warning Signs: S&OP can identify potential demand increases.
PO Collaboration focuses on maintaining accurate demand forecasts, timely communication with suppliers, and efficient replenishment processes to ensure optimal stock levels and minimize stockouts. Consequences of Lack of PO Collaboration Capabilities Failure to prioritize PO collaboration can lead to severe consequences for companies.
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