Insight from Applied Statisticians for Forecasting: Is It Worth the Effort and the Mirage of Random Variation

Arkieva

Insight from Applied Statisticians for Forecasting: Is It Worth the Effort and the Mirage of Random Variation was first posted on October 14, 2020 at 8:09 am. ©2017

Turn a Good Demand Forecast into the Optimum Inventory Plan

ToolsGroup

How Do You Turn a Good Demand Forecast into the Optimum Inventory Plan? Your customers don’t care if you have a great demand forecast. How do you turn a good forecast into a great inventory plan? About 15 years ago, many forecasting solutions turned to multi-echelon optimization.

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Get Started Using Machine Learning for New Product Forecasting

ToolsGroup

Adding to this already uphill battle, we don’t have trustworthy new product forecasting methods because forecasting new products with no sales data is very hit-and-miss. Machine learning (ML) provides an effective weapon for your new product forecasting arsenal. In this blog we will share machine learning techniques that can produce fully-automated forecasts for new products. Why is new product forecasting important? Overall reasonably accurate forecasts.

Probabilistic Forecasting - a Primer

ToolsGroup

An experienced gambler might hedge their bet on multiple outcomes, rather than just the most likely "single number forecast". At ToolsGroup we have been big advocates of probabilistic forecasting (sometimes also known as stochastic forecasting). Understanding and employing this relatively simple principle can take your forecasting and supply chain planning from ‘good to great’. In this case, forecasting 100 units is a pretty safe bet.

How is Demand Forecasting changing? How do you compare to your peers?

AIMMS conducted research to assess how supply chain teams perceive the accuracy of their forecast and discover the tools and techniques they are using to upgrade the forecasting process. In this report, you will find helpful benchmarks and insights offered by your peers on the latest demand forecasting techniques, forecast granularity and periodicity, and expectations for the future.

Machine Learning and AI: Profiling Demand History – A Wiser Forecasting

Arkieva

In the simple version of supply chain management (SCM) the goal for demand forecasting in the tactical decision tier is prediction accuracy. Machine Learning and AI: Profiling Demand History – A Wiser Forecasting was first posted on August 5, 2020 at 8:11 am. ©2017

What Technology are Teams Using to Support Their Demand Forecasting Process?

AIMMS

Part II in our series on assessing your Demand Forecasting process . In Part I of this series , we looked at supply chain professionals’ perception of forecast accuracy and how they see their forecast evolving in the future. With supply chain complexity on the rise, can new technologies help to improve forecast accuracy and achieve benefits like an optimized inventory and better customer service? The use of technology for demand forecasting is widespread .

Dreaming about accurate forecasts?

DELMIA Quintiq

There is a saying among forecast users: “ If you forecast, you may be wrong; but you will always be wrong if you do not forecast.”. But how much faith are companies willing to place in inaccurate forecasts? Many companies use demand forecasts that are based solely on historical data. However, these forecasts are less effective in a volatile global market facing sociopolitical challenges, such as trade wars and Brexit.

Four Steps to Better Demand Forecasting

Logility

Forecasting is an “inexact science” that relies on the data available to you, the math you use, and how you implement the forecast. And your forecasting success is fundamentally impacted by your understanding of that data, its strengths and limits.

How do supply chain professionals rate their demand forecast’s accuracy?

AIMMS

Part I in our series on assessing your Demand Forecasting process. Is your demand forecasting process evolving with the times? Are you satisfied with your level of forecast accuracy? Which function is responsible for managing the forecast process and who is accountable for accuracy? . 6 0 % of respondents stated that the Supply Chain function is primarily responsible for managing the forecast process at their organizations. How are demand forecasts evolving?

5 Perceptions on Demand Forecasting and How it's Changing

Speaker: Brian Dooley, Director SC Navigator, AIMMS

It’s no secret that demand is getting more difficult to predict. Is your demand forecasting process evolving with the times? How does your process stack up against others? Are you satisfied with your level of forecast accuracy? This webinar shares research findings from a recent survey among supply chain planning professionals to help you answer these questions.

Probabilistic Forecasting: Right Fit for Your Business?

Supply Chain Shaman

A Closer Look at Forecasting. Traditionally companies forecast using history (shipments or orders) and applying linear regression to understand the patterns of historical demand using these to estimate future requirements in a time-series format. Traditional forecasting methods, as a result, do not work very well to predict future forecasts. Spairliners: A Case Study in Probabilistic Forecasting. Improving Value Through the Testing of Forecasting Techniques.

ToolsGroup Supply Chain Forecast | Inventory Now Available ?in the Microsoft Azure Marketplace

ToolsGroup

Supply Chain Forecast | Inventory to improve forecast accuracy and customer service levels, while reducing investment in stock. The tool is designed to help businesses grow by moving beyond spreadsheet-based forecasting and trial-and-error inventory management.

Calculating Forecast Accuracy & Forecast Error

EazyStock

The Importance of Demand Forecasting Accuracy. In supply chain management it’s important to be able to measure the accuracy of your demand forecasts. Inaccurate demand forecasting can lead to the accumulation of excess stock or the reverse: issues with product availability. Ensuring demand forecasting accuracy should be a key responsibility for any conscientious inventory planner. What is Forecast Error? Forecast Accuracy/Forecast Error Calculations.

Demand Planning & Forecasting During COVID-19 [Webinar]

CHAINalytics

Last month, Chainalytics’ Neelesh Asati moderated a panel discussion on demand planning and forecasting during COVID-19 hosted by Logistics Insider.

The Best Sales Forecasting Models for Weathering Your Goals

Every sales forecasting model has a different strength and predictability method. It’s recommended to test out which one is best for your team. This way, you’ll be able to further enhance – and optimize – your newly-developed pipeline. Your future sales forecast? Sunny skies (and success) are just ahead!

Machine Learning: Optimization and Community Intelligence – A Wiser Forecasting

Arkieva

Machine Learning: Optimization and Community Intelligence – A Wiser Forecasting was first posted on August 12, 2020 at 7:41 am. ©2017

Probabilistic Forecasting and Confidence Intervals

Arkieva

Probabilistic Forecasting and Confidence Intervals was first posted on September 27, 2019 at 11:18 am. ©2017 Historically, most of the key planning and computational activities (models, time series, machine learning, and other analytics) that support extended supply chain management (SCM) are “deterministic models”. ©2017 " Supply Chain Link Blog - Arkieva " Use of this feed is for personal non-commercial use only.

Untangling the Complexities of Demand Forecasting

ModusLink Corporation

Whether facing a major product launch or compiling standard monthly forecasts, the accuracy of demand forecasts is crucial. The post Untangling the Complexities of Demand Forecasting appeared first on ModusLink Global Solutions. Manufacturing to Market logistics manufacturing supply chain forecasting Demand Planning Demand ForcastNo one understands this as intimately as Apple, who is fresh off of its much-anticipated iPhone X, 8 and 8 Plus product announcements.

What’s Your Forecast Accuracy Target for 2019?

ToolsGroup

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. Many companies have already started their 2019 planning and budgeting cycles, so if you are in charge of demand forecasting for your company, it’s about time to thinking about your organization's 2019 goals. It seems as if everyone is looking to improve their forecasting performance. Forecasting Demand and Analytics

Machine Learning: A Quantum Leap in Forecast Accuracy for the Modern Supply Chain

Whether you realize it or not, Machine Learning is having a profound impact on your everyday life

“Optimized” Inventory Forecasting a Co-Product of Optimized Central Planning

Arkieva

In a recent blog on Inventory Forecasting the core challenges and business importance of estimating inventory are outlined. Optimized” Inventory Forecasting a Co-Product of Optimized Central Planning was first posted on February 18, 2020 at 8:08 am. ©2017 A projected inventory position across time (plan) is a natural co-product of most central or master planning models that match assets with the demand to create a projected supply line linked to demand.

Inventory Forecasting Overview Part 1

Valogix

This blog post provides some basic information about viable inventory forecasting techniques. Forecasting is one of the key steps in inventory planning but not the only one. inventory forecasting

How to Measure Forecast Errors in Intermittent Demand Forecasting

Arkieva

Stop using traditional forecast accuracy metrics to measure forecast for sporadic demand patterns. How to Measure Forecast Errors in Intermittent Demand Forecasting was first posted on March 19, 2019 at 12:19 pm. ©2017 Use this method instead. ©2017 " Supply Chain Link Blog - Arkieva " Use of this feed is for personal non-commercial use only. If you are not reading this article in your feed reader, then the site is guilty of copyright infringement.

What’s Your Forecast Accuracy Target for 2017?

ToolsGroup

In 2016, it seemed as if everyone in supply chain was looking to improve their forecasting performance. Unsurprisingly, the highest percentage of respondents in a recent Gartner/Supply Chain Digest survey selected forecast accuracy and demand variability as the top barriers preventing them from reaching their broader supply chain goals. How much can you realistically expect to improve your forecast accuracy each year? Forecasting Demand and Analytics

Key Questions for a Successful Distribution Network

Speaker: Irina Rosca, Director of Supply Chain Operations, Helix

As we plan for the world of eCommerce and the customer expectation of quick, free shipping, our ability to forecast is turned on its head. How many distribution centers do we even need, and is that number feasible? Can we use historical data to plan for demand and design our networks, or is there a better way? If we're going to offer the speed of shipping and variety of inventory that today's customers have come to expect, there are a lot of different questions that need to be asked.

Business Forecasting Lessons From Hurricanes

Arkieva

With each storm, there comes a bevy of forecasts put out by different computer models. These forecasts begin about 10 days out and change as the storm gets closer and closer. This blog tries to extract some learnings from this process of forecasting. Business Forecasting Lessons From Hurricanes was first posted on September 11, 2019 at 10:09 am. ©2017 ©2017 " Supply Chain Link Blog - Arkieva " Use of this feed is for personal non-commercial use only.

Common Forecasting Myths Debunked – Part 1 – One Forecast is Enough

Logility

This is the first of a series of four posts that will explore common forecasting myths. In supply chain the impact of a single error can quickly derail an entire forecast. The first step is to recognize there are several myths surrounding the forecasting process that can lead you down the wrong path. Many companies believe that once a forecast is created success will follow throughout the product life cycle. What forecasting methods does your company employ?

Revisiting Transportation Forecasting

Talking Logistics

Many companies have collaborative planning and forecasting processes with suppliers and manufacturing partners, but very few companies translate demand and production forecasts into transportation capacity requirements. In this episode, Adrian discusses the key challenges and opportunities associated with transportation forecasting. Watch as Adrian discusses: Two key reasons why transportation forecasting has been a challenge to implement.

Improve Forecast Quality and Reliability with Value-add Forecasting (Part 1)

Logility

The ability to effectively forecast demand is essential for supply chain management decisions. In fact, demand forecasts are used throughout the supply chain including supply chain design, purchasing, operations, inventory, and sales and marketing. In large part due to computer processing power, new advances in forecasting and the abundance of new data sources have helped to increase forecast reliability. Most forecast errors increase as the time horizon lengthens.

Automated Order Processing and Proactive Inventory Management

Speaker: Irina Rosca, Director of Supply Chain Operations, Helix

Organizations need to focus on demand driven supply planning, utilizing real time information on customer orders from all marketplaces (e-commence, Amazon - or other online retailers, and point of sale data from brick and mortar). Focusing on this information once per month during the S&OP meeting is too late for all business units to align. Companies should have seamless integration between order entry, inventory management, forecasting and supply planning models and purchase order status to sense risk, pull levers to mitigate potential risk, and communicate within and outside the organization. This is especially important for new product releases, in store programs or promotions (sales, end caps, PDQ. etc) or online promotions (company run or 3rd party). Depending on total supply chain lead time, not having real time visibility and analysis of this information can significantly affect sales and the bottom line.

Brochure: Promotion Forecasting

Cognira

Brochure: Promotion Forecasting. With decades of retail and data science experience, intelligent retail forecasting is Cognira’s core expertise. Our highly accurate, quality omnichannel forecasts represent true customer demand – not just sales history.

Promotion Forecasting

Cognira

Brochure: Promotion Forecasting. With decades of retail and data science experience, intelligent retail forecasting is Cognira’s core expertise. Our highly accurate, quality omnichannel forecasts represent true customer demand – not just sales history. The post Promotion Forecasting appeared first on Cognira.

How Not to use Machine Learning for Demand Forecasting

ToolsGroup

Eight years ago ToolsGroup was one of the first supply chain planning software vendors to employ machine learning to improve demand forecasting. Even in 2014 when Gartner wrote a case study about Danone using our machine learning to help forecast promotions, it was barely a blip on the horizon. Statistical forecasting approaches have others. Each does better in specific circumstances, so machine learning should be used as a complement to statistical forecasting.

How To Do Proper Retail Demand & Sales Forecasting

Demand Solutions

If you’re a retailer, then you’re likely selling thousands of SKUs, making it hard just to collect data on them let alone analyze it in order to forecast demand ! There are things retail businesses can do to make sure that their demand is adequately forecasted. What Is Demand Forecasting? Demand forecasting is how a business predicts how many of which produce or SKU customers will buy during a specified time period. So how is demand forecasting done?

Overcoming the Accuracy vs Agility Trade-Off in Demand Planning & Forecasting

Agility and accuracy don’t necessarily need to be at logger heads. In-fact, anticipatory agility enabled by AI and Machine Learning can move the accuracy frontier forward in terms of validity and consistency driving significant business value.