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Karin Bursa
Executive Vice President
Logility


Supply Chain Comment

With more than 25 years of experience in the development, support and marketing of enterprise software solutions, Karin is able to provide provoking perspectives including market-shaping events, end-user perspectives and technical reviews. She is a widely quoted source on the evolution of the supply chain, frequent author to many leading publications, and can be found speaking at many of the industry’s leading conferences.


October 11, 2018

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

The First of a Series of Four Posts That Will Explore Common Forecasting Myths

 

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. This creates a ripple effect across the entire value chain which can result in lost sales or worse, a lost customer. How can you avoid a disaster like this? The first step is to recognize there are several myths surrounding the forecasting process that can lead you down the wrong path.


Bursa Says...

Companies must combine available historical data, forward-looking market knowledge and multiple forecasting techniques to build the foundation of a forecast the business can trust.

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Myth #1: Companies can obtain an accurate forecast with the same forecasting method at all stages of a product’s life cycle.

Many companies believe that once a forecast is created success will follow throughout the product life cycle. However, this is not the case.

Reality: To create the most accurate forecasts, planners must employ the forecasting methods that best serve the unique dynamics of their business at a specific moment in time. Forecasting needs to be flexible and change with shifts in demand patterns.

Top performing organizations employ multiple forecasting methods that best serve the unique dynamics of their business at a specific moment in time. Advanced demand planning and forecasting solutions automate many of the functions required to select, model and generate forecasts. This removes the burden of manually switching models and accelerates sensitivity as market conditions evolve. For example, a modified Holt-Winters decomposition model with best-fit analysis can generate forecasts based on demand history that incorporates trends and seasonal information which may later be refined based on demand sensing and POS data. This capability to automatically change forecasting methods is an essential feature of a comprehensive forecasting solution.

No company or their supply chain is the same. Spreadsheets and homegrown solutions are the way of the past and cannot scale to compete with best-in-class forecasting solutions. Today, companies must combine available historical data, forward-looking market knowledge and multiple forecasting techniques to build the foundation of a forecast the business can trust. Through artificial intelligence, we can automatically adjust methods during a product’s life cycle to maintain maximum accuracy from product launch through end-of-life. A single forecast is unable to keep up with these rapid changes and will often lead to either too little or too much inventory.

Companies must move beyond simple forecasts for all items in their portfolio and draw from a range of methods to create a demand plan that best fits each forecasted item at every stage of its life cycle.

What forecasting methods does your company employ? How do they improve your forecast accuracy? Stay tuned to discover more truths around common forecasting myths.


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