The market today is more challenging than ever. The customers have more power to demand, there are more competitors now and the competition is fierce. With the advent of modern technology, the internet, and better transport system, customers have more options to choose from so manufacturers, distributors, retailers cannot slack off in today´s economic climate.
The situation today has forced a lot of companies to lower profit margin in order to stay ahead of the game at the same time the traditional practice of having an oversupply of materials or products in warehouses is not anymore acceptable. Everything invested in must in one way or another return that investment at the soonest possible time.
So today several companies are at a crossroad where they are demanded to provide high quality products or services with a minimal investment on their side to stay profitable. One of the best ways to achieve this is by improving the accuracy of their forecasting practices.
The Traditional Forecast method
Surprisingly, several companies still use the traditional way of forecasting the demand of products or services through averaging based on sale or consumption from the preceding months.
For example, if we are selling running shoes and we want to forecast how many shows we will need to have ready by April 2017, we will then need to look at the sales of this type of running shoes over the last 6 months.
November – 300
December – 400
January – 200
February – 250
March – 200
April – ??
Therefore, if we use the traditional method of forecasting we will calculate as follows:
300 + 400 + 200 + 250 + 200 = 1350 / 5 months
Then we say for April there will be approximately, 270 shoes needed.
By simply looking at the historical sales of this type of shoes, 270 does not seem to far fletched.
The Problem with traditional forecast method
The traditional means of forecasting works in many instances however, there are scenarios where this type of formula does not work. For example, if in December a certain store chain run a “Christmas Campaign” + “Boxing Day” Campaigns to bring the sales up, this will skew the results.
It will then look like this:
November – 300
December – 2300
January – 200
February – 250
March – 200
April – ??
In this scenario, 650 will be the estimate for April. It is a number much higher than the previous months only because there were campaigns that were successfully run in the month of December. In another scenario, what if there were no sales in February, will the forecast of the demand for the product still be the same? Of course not.
In this scenario, there must be adjustments based on the campaigns or promotions that are run that may alter the usual count of products on a month to month basis.
Tips on Improving the Forecasting Accuracy
To improve the forecast accuracy there are a few things that must be remembered:
- Examine a possible scenario of unusual usage/sales count
- Coordinate with Marketing or Sales departments regarding planned activities such as promotions or seasonal campaigns that can potentially greatly affect the figures for a certain month
- Monitor and examine closely figures of certain months that exceed the usual usage percentage
- Identify possible non-recurring activities that can affect the count
- Continuously monitor the sales trend on a month to month basis
- Use several sources of information to be able to forecast such as including results of forecasting using the traditional averaging, planned promos or market specific campaigns, trend percentage and its effects, information sourced from customers or sales agents and many others applicable for your organization.
- Identify products that are hard to forecast due to very irregular usage/sales so that instead of forecasting demand, there will only be maintenance of a target level of the product.
Automation of Forecast Process
Forecasting is one of the more complex and tedious tasks laid upon the shoulders of the finance department or inventory managers. Having a tool that can help these people monitor and track the figures on a month to month or year on year basis will aid a lot in improving the accuracy of forecasting.
Forecasting that is done manually through Excel is not only laborious and time-consuming, it is also very prone to errors and mistakes that are more often than not hard to notice. By automating the forecasting process, business rules can be put in place to make analysis of trends and customer behavioral patterns easier to track and keep notice of.
Forecasting tools such Performance Canvas Financials saves time and reduces human made errors through automation. To know more about Performance Canvas Financials (pcFinancials), visit www.performancecanvas.com or email info@dspanel.com.
DSPanel offers cutting edge technology platform for business analytics, planning, and visualization. DSPanel designs, builds, and operates with the end users in mind. Performance Canvas was created by DSPanel to answer the unarticulated needs of the market not addressed by previous available solutions. With Performance Canvas, information is transformed into valuable business insights for the business executives to utilize in their decision-making process. DSPanel currently has over 2500 organizations deploying their solutions.
