Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
    data driven insights
    How Data-Driven Insights Are Addressing Gaps in Patient Communication and Equity
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Don’t Fine Tune Your Forecast!
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Don’t Fine Tune Your Forecast!
Big Data

Don’t Fine Tune Your Forecast!

mvgilliland
mvgilliland
5 Min Read
SHARE

Does your forecast look like a radio? No? Then don’t treat it like one.

Contents
Two Things Can Happen — And One of Them is BadCan’t Small Adjustments Make a Big Improvement in Accuracy?

Image of RadioA radio’s tuning knob serves a valid purpose. It lets you make fine adjustments, improving reception of the incoming signal, resulting in a clearer and more enjoyable listening experience.

Does your forecast look like a radio? No? Then don’t treat it like one.

Image of RadioA radio’s tuning knob serves a valid purpose. It lets you make fine adjustments, improving reception of the incoming signal, resulting in a clearer and more enjoyable listening experience.

More Read

Image
Music App Predicting the 2014 Top Artists with Big Data
How Businesses Are Using Big Data For Social Media Marketing?
Scrooge Didn’t Believe in Sentiment Analysis Either
How Big Data Boosts Recognition of Remote Employees
How Businesses Use Their Target Audience Data

But just because you can make fine adjustments to your forecast, doesn’t mean you should. In fact, you shouldn’t.

Two Things Can Happen — And One of Them is Bad

Famed college football coach Woody Hayes (fired unceremoniously in 1978 for punching an opposing player) was know for powerful teams that ran the ball, eschewing the forward pass. Of the latter, he is credited with saying “When you pass the ball three things can happen, and two of them are bad.” [For those unfamiliar with American football, the good thing is a pass completion, and the bad things are an incompletion or an interception by the opposing team.]

Whenever you adjust a forecast two things can happen — you can improve the accuracy of the forecast, or make it worse.

Obviously, if you make the adjustment in the wrong direction (e.g., lowering the forecast when actuals turn out to be higher), a bad thing has happened — you’ve made the forecast worse. But you can also make overly aggressive adjustments in the right direction and overshoot, making the forecast worse. (For example, initial forecast of 100, adjusted forecast of 110, actual turns out to be 104.)

When you make just a small adjustment, there is little chance of overshooting. So as long as you are directionally correct, you have improved the forecast. But even if we assume every small adjustment is directionally correct, is that reason enough to spend time making small adjustments?

No. And here’s why not:

First recognize that “small adjustment” means small as a percentage of the original forecast. So changing a forecast from 100 to 101 is a “small” adjustment, just 1%. Likewise, changing 1,256,315 to 1,250,000 would be considered a small adjustment (0.5%) even though the change is over 6300 units.

Another way to characterize adjustments is their relevance — whether they are significant enough to cause changes in decisions and plans.

On this criterion, small adjustments are mostly irrelevant. An organization is probably not going to grind to a halt, scuttle existing plans, and suddenly change direction just because of a 1% adjustment in a forecast.

[Note that even “large” forecast adjustments may be irrelevant, when they don’t require any change in plans. This could happen for very low value items, such as 1/4″ galvanized washers sold at a hardware store. Such items are usually managed via simple replenishment rules, like a two-bin inventory control system. Unless the forecast change is so large that current bin sizes are deemed inappropriate, no action will be taken.]

Can’t Small Adjustments Make a Big Improvement in Accuracy?

It’s true that even a small adjustment can make a big improvement in forecast accuracy. Changing the forecast from 100 to 101, when actuals turn out to be 102, means you cut the error in half! (On the other hand, if actuals turned out to be 200, then you only reduced forecast error by 1%.)

But the purpose of forecasting is to help managers make better decisions, devise better plans, and run a more effective and profitable organization. Improved forecast accuracy, in itself, has no value unless it results in improved organizational performance.

So if a small forecast adjustment does not change any of the behavior (or resulting outcomes) of the organization — why bother??? Making small adjustments takes effort and resources, but is simply a waste of time.

TAGGED:forecasting
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

data analytics and truck accident claims
How Data Analytics Reduces Truck Accidents and Speeds Up Claims
Analytics Big Data Exclusive
predictive analytics for interior designers
Interior Designers Boost Profits with Predictive Analytics
Analytics Exclusive Predictive Analytics
big data and cybercrime
Stopping Lateral Movement in a Data-Heavy, Edge-First World
Big Data Exclusive
AI and data mining
What the Rise of AI Web Scrapers Means for Data Teams
Artificial Intelligence Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

forecasting stock market
AnalyticsBig DataBusiness IntelligenceDecision ManagementPredictive Analytics

Forecasting the Stock Market: Lessons Learned

5 Min Read

Data Mining Book Review: Future Ready

2 Min Read

One-Number Forecasting: A New Worst Practice?

3 Min Read

Don’t rely on your staff’s ability to do math

5 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai chatbot
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
Chatbots
ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?