Know how Inventory forecasting using time-series analysis helps e-commerce players keep up with the demand

Algoscale

Algoscale

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To explain e-commerce in very simple words, it is about providing customers what they want before when they want it. So to meet up these expectations you must understand the need of the market. 

 

At the same time, you have to be aware of what will customers need in the nearby future. To know about these things Inventory forecasting comes into the picture. So without any further ado let us know about inventory forecasting and its uses in e-commerce.

 

What is Inventory forecasting?

What-is-inventory-forecasting

Practically Inventory forecasting is like a detailed analysis of the market for predicting future demands. That is why inventory forecasting is also known as demand forecasting. So initially you have to study the history of the market, know the current situation, and by observing the patterns predict the upcoming trends.

 

Well, this is only a piece of superficial knowledge about Inventory or demand forecasting. As you start knowing about it you will fold many layers of it and get better at it day by day. However for now we have clear knowledge to move forward with e-commerce.

Types of Inventory forecasting models

Inventory forecasting models can be broadly classified into 4 types, and they are:

  1. Time series model
  2. Econometric model
  3. Judgemental forecasting model
  4. The Delphi method

If you want you can study all of the above methods. every model has its own way of giving desired results. Right now we are more concerned about the time series model that is related to all the historical statistics or data to date. The Time series model is quite a reliable and effective way in e-commerce.

 

The economic method as the name suggests is related to economical statistics. The economical method has more data related to demand and supply. On the other hand, the judgmental model is more of predicting the market based on limited information and assumptions. Delphi’s method is more of an opinion of what experts have to say about the market.

 

How does Inventory forecasting help e-commerce players keep up with the demand?

Here are some key points related to the benefits of the Times series Inventory forecasting in e-commerce.

 

Times series Inventory forecasting

1. You can make reliable predictions

Predicting things on basis of gut feelings and emotions can work sometimes. The chances of getting it right are however very low, so you can go with another effective way. Don’t worry, you don’t have to visit any astrologer for predicting the future, a time-series inventory forecast can make it for you. The Time series model gives you clear historical data so that you can get the ups and downs. By observing the recurring patterns you can predict things and make moves accordingly.

 

2. You can notice clear patterns

At times it is really difficult to remember things and for normal humans, you will definitely not remember things when you need them the most. So the best way is to keep track of a record. In a time series model, you can create a record and draw a chart out of it. This will help to give clear patterns and make life easy for you.

 

3. You can find the missing data

Having said that keeping records can make it easy, but keeping a record is a tough task within itself. So don’t worry if you cannot find the data of any particular month you can still find that value. Using the time-series method and some calculations you can fill up the missing data with almost an accurate answer.

 

How exactly does the Time series model work?

The time series model works on the study of events and their effect on the market. We can say that during a particular period of time certain things get in demand. For every product, its demand is high during certain events. So if you know when a product is going to be on-demand you can be prepared for it beforehand.

 

How does the Time series inventory forecasting model work

Now the most common question that arises is, how can we know about it? This is how the Time series model will help you in e-commerce. Let’s take an example, every year in December, Christmas is celebrated worldwide. So during that period, we can see products like Christmas trees, bells, or cakes get hiked in their sales. So this data will be completely visible in the time series model. This way you can be ready for the events well in advance.

 

You can also be ready for the events that occur after a particular duration. For example, sports events like Worldcups or Olympics occur after every 4 years. So now you can start creating relative products or start advertising your services based on that theme. This is how you can smartly be related and updated with the help of the Time series model in Inventory forecasting.

 

The time series model can also predict the downfall

In the above example, we found out how we can predict upcoming events. In the same way, we can also predict when can our product is least in demand. By observing the patterns in the time series model we can find out the lowest point of the sale. This is important to understand when is an offseason. During that time you can either start preparing yourself for the next season or observe which things are in demand during that particular time.

 

For example, if you are running a clothes shop by studying the time series you can find out particular clothes are sold based on seasons. Like no one will buy woolen sweaters in the summer so you can start using more beach attire, as people visit the beaches more often in the summer season. You can even take your time off during that time and invest that time in preparing more woolen clothes for the upcoming winter season.

 

Final words

So this was all you need to know about the Time series inventory forecasting. Your gut feelings and emotions can help you but not all the time. It is always better to use the available resources and proper analysis to predict anything. So instead of simply writing the predictions, it is better to use the time series inventory forecasting model and get the most probable predictions.