21UTB13
What are the features of a good forecast?
How will present the same to the management?
Demand forecasting is the process used to predict future customer demand based on historical
sales data. An accurate forecast can make the difference between a business thriving or
potentially not surviving the fiscal year. The results of demand forecasting are used for a wide
variety of business processes ranging from capacity planning to market research initiatives.
The criteria for good demand forecasting include1. Accuracy
A demand forecast that is not accurate is detrimental to a business's short term and long term
success. A common cause of inaccurate forecasting occurs when the data available and used for
demand forecasting is inaccurate or incomplete.
2. Replicability
Significant benefits of using a good forecasting method are the replicability and adaptability
potential they offer. The likelihood of generating an accurate forecast is greatly improved when
the same forecasting model is used long term.
Business professionals can learn from any mistakes that occurred during a previous demand
forecast in order to increase the accuracy of upcoming forecasts. When business professionals
need to constantly acclimate to different forecasting methods a lot of time, money, and energy
are used.
3. Flexibility
In the ever changing global market, good forecasting must be flexible and revisable. Both short
term and long term rigidity can deter businesses from important decision making and necessary
corrective actions.
4. Economical
Demand forecasting is a time consuming and labor intensive process. Labor costs can quickly
add up, especially when heavier statistical methods that require intensive data analysis are used.
Keep a resource optimization mindset when deciding which forecasting method or methods to
use. The benefits that demand forecasting will provide should always outweigh the initial
investment that a business incurs.
5. Accessibility
Complex statistical methods of demand forecasting may be intimidating to interpret. However,
for a forecasting technique to be sustainable long term it must be accessible to a business's staff
members.
6. Time Horizon
The length of time over which a decision is being made has a bearing on the appropriate
technique to use. The probability of forecasting error generally decreases with an increase in the
length of the time horizon.
7. Level of Detail
The level of detail needed should match the focus of the decision-making unit in the forecast.
8. Pattern of Data
Data required to use the underlying-relationships should be available on a timely basis. Each
forecasting method is based on an underlying assumption about the data.
9. Type of Model
Other assumptions are also made in each forecasting technique that must fit the situation under
consideration. The technique used should be easily comprehended by the management to give
quick meaningful results.
10. Plausibility
The management should have good understanding of the technique chose and they should have
confidence in the technique adopted. Then only proper interpretation will be made.
11. Availability
Immediate availability of data is a vital requirement in forecasting method. The technique should
yield quick and meaningful result. Delay in result will adversely affect the managerial decision.
Presenting to the Management:
With predictive analytics, our efforts will be fruitless unless our insight is effectively presented
to people who can use it.
1) Nobody Likes Boring Facts & Figures, So Tell A Story
It is important that you just don’t present numbers and a forecast but present a story that people
can follow. To do this we need to present forecasts and data in a way that is simple and easy to
understand.When telling a data story, it is not unlike storytelling in general. All stories have
conflict which, in our case, is business questions like balancing supply and demand. It is the
demand planner’s task to weave a narrative to enlighten the audience about the problems the
company is facing and how they can be solved
2) Find The Right Format To Present Your Insight
They say a picture speaks a thousand words. We live in a visual age – whenever possible ditch
the text and visualize the story. Your data is only as powerful as your visual presentation of it.
Graphs are often easier to understand than tables and have a more meaningful impact on the
audience. A chart that takes 30 seconds to understand, compared to visual representation of the
forecast or data that takes only 2 seconds, could mean the difference between accepting or
rejecting your analysis.
3) Know Your Audience
Too often when we present our data, it will make sense to those who do the analysis but not to
those who might actually use it. Determine what is most important to your audience – it is easy
to summarize all the data you’re working with, but some pieces of data are more important to
your audience than others.
It is important that you also calibrate data altitude optimally. Present forecasts and data in
measurements that are meaningful to the decision makers. You can never assume the audience
fully understands what you are saying. Simplicity is critical to getting your message home. Keep
your story clear, simple, and impactful.
4) Consolidate Information To Get To The Point As Quick As Possible
Good data stories include enough information to state a case, but not so much information that
the audience struggles to understand the point. A common criticism of ineffective data stories is
that they fail to get to the point fast enough. The demand planner needs to avoid clouding up
their story with information and data that do not directly add to the narrative of the analysis and
help answer the question at hand.