Forecasting provides some core and crucial benefits and many businesses partake in it. However, it also happens that some companies don’t get the process of their forecasting correct and it hinders some parts of their operations.
All complex functions of Supply Chain Management have one common goal, and it is customer satisfaction. A customer being unsatisfied with a service may lead to loss for the company. So in order to avoid that, there needs to be necessary measures put into place to ensure supply meets demands. The one way to do that is by utilizing a forecasting technique. Forecasting is the process of predicting the future based on past and present data. It is a great and certain way of making sure you are carrying the right amount of inventory for the months approaching. Various supply chain forecasting software can be used to predict weeks to months of future demand.
There are two known and used methods of forecasting and they are qualitative and quantitative methods. In instances where historic data is absent, the qualitative method is found useful and normally consists of Delphi techniques, market research, expert judgement, reviewing sales force estimates and panel consensus. This method, however, is not very reliable nor is it accurate. A consistent and accurate method rather, is the quantitative one. Quantitative forecasting is not only reliable and accurate, but it also allows for customer satisfaction and keeps vendors happy without having to deal with rush orders. What quantitative technique does is that it utilizes previously available data and shows a pattern in trend analysis. Quantitative methods commonly used are Moving Average Range and Exponential Smoothing.
Why is Forecasting Accuracy Important?
Research has revealed that Business Analysts use supply chain software with forecasting tools to predict demand in order to always have supply on hand. If it happens that demand is underestimated, the company find itself in a position where it cannot have enough stock to satisfy all of its customers. Loyal customers may decide to wait until the product is available on the market while new customers on the other hand, will instead choose to return to your competitors. To solve this, companies will often resort to rush orders – a solution which only worsens the situation and doubles the amount paid to suppliers, leading to reduction in profit and net income.
Many may think having extra inventory ready to avoid last minute orders is the solution, but overestimation of demand has dire consequences too. Carrying extra stock becomes more of a liability than an asset. Excess inventory means waste and hinders growth and blocks the distribution network. Therefore it is best to avoid having excess stock as a solution and instead carry enough stock to meet demands in order to achieve and improve forecasting accuracy.
5 Tips To Improve Forecasting Accuracy:
Set your forecasting level closer to the customer
Supply chain consists of a large ecosystem of people and processes along with various levels between suppliers and end-consumers. Information and data in such cases may be distorted because there’s a large number of people involved, with internal and external inventory drives and policies too. This means there will be inefficiency in forecasting and inventory at supply chain levels. This is the very reason that makes it a point to ensure that forecasts are made at customer level to improve forecasting accuracy and communication.
Filter the forecasting down to exceptions
Forecasting software has previous demand data which at times has items that are no longer available or are one time variables. Instead of going through all previous data, companies should learn how to filter data relevant and important.
Focus on demand forecast, not sales forecast
In order to improve forecasting, businesses should learn to treat supply and demand as separate variables. When a company is exhausted on a product line, it means that there have been no sales for that particular product for as long as they were on stock out. The demand may still be available and may not be met until the product is restocked. This is the truth of the demand side. But on the sales side, history shows there have been no sales as there was no demand. This difference in data types can be very crucial in forecasting and hence demand data should be utilized.
Identify distorted demand data
Companies at times find that they have to set a minimum requirement on orders so they can reach desired sales point. Having parameters in place such as batch size, minimum order quantity, sales incentives, etc., distorts the demand data because customers then order the minimum quantity just to fulfill their orders. This is not accurate as it does not indicate the actual demand. This can falsify forecasting accuracy and needs to be identified and eliminated.
Evaluate the forecast accuracy
For forecasting accuracy evaluation, there are tools that can be used and help monitor the progress and report the outcome to sponsors. Analyzing the forecast will present the forecasting team with an overview of their performance over a period of time. Once a process has been established, it can be recorded and used for future references. Evaluating forecast accuracy is one concrete way and step to improve forecasting accuracy.
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