Sales forecasting

We utilize a method of estimating future sales and demand to get precise catalog and predefined output. A sales forecast helps to show the plane of future events and drive to get the production on a monthly or yearly bases. 

Apart from that, forecasting methods facilitate by providing a wealth of business insights to improve customer satisfaction and overall sales. Business management is requisite to arrange deals and demand forecasts based on industry and market trends. So, it is a method of estimation of the sales potential for a future event to be safe and predictable. 

What is a forecasting/time series?

From predicting the sales of a product to estimating the usage of any machinery, time series forecasting is one of the core skills our data scientists and are familiar with it. 

Any forecast is known as an indicator of what is likely to happen in a specified future time frame in a particular field. Accurate sales forecasting is essential for a business house to enable it to produce the required quantity at the right time. 

• Is there any preference to choose sales forecasting?
• Is the information given is correct?
• Should I go for testing different models?
• Which model best fits my business process?

This forecast helps the management in calculating the revenue and helps to know how much to manufacture in upcoming months. You can also decide the total investment for products and resources based on sales forecasting. For the same, the client has several questions that are mandatory before choosing any sales forecast.

The significant types of forecasting we carry are: 

• Demand forecasting
• Supply forecasting
• Price forecasting

Technical bits and pieces:

Prediction for future sales and inventory is mandatory these days with changing market trends and customers’ needs. The forecasting system ensures safe functions for organizing and monitoring all your orders by single go based on the available data, real-time information, and customer’s behavior.

• Database- MySQL
• Programming language- Python
• ElasticSearch for logging file
• AWS for data rules
• S3 Bucket for data storage

How does sales forecasting work?

We provided with a dataset several times for sales and demand forecast to a particular period where the object increased/decreased harshly for some time. We undergo data research initially to know the data and analyze it to use records by applying some algorithms to get an accurate result.  

Sales can of two types: Seasonal & Nonseasonal

Seasonal business sale refers to the fluctuations in business that corresponds to changes in season. The nonseasonal sale is something that works steadily over the year, where there can never be high sales all of a sudden.

The season can be unspoken in this circumstance that consists of a) seasons of the year, b) festivals, and c) summer holidays, and more. These seasonal events affect industries and businesses differently that aid in improving sales.

Forecasting Flowchart

Methods for time series forecasting

There are several methods for forecasting functions based on business needs. We are using one of the best that applies to almost all industries. Your sales forecast is also your guide to how much you should be spending.

• Naive Approach 
• SA-Simple Average
• MA- Moving Average
• Holt’s Linear Trend method
• ARIMA- Autoregressive Integrated Moving Average
• SARIMA- Seasonal Autoregressive Integrated Moving Average

SARIMA- Seasonal Autoregressive Integrated Moving Average

Seasonal ARIMA, known by Seasonal Autoregressive Integrated Moving Average, is a step ahead of ARIMA by assisting seasonal forecasting, and you can have an accurate forecast of sales over months or a year. It supports time-series data with a seasonal element. 

Our data experts use SARIMA and add value to the business with a sales forecast result, including three hyperparameters. It helps to specify the seasonal components such as autoregression (AR), moving average (MA), and differencing (I) that adds the factor for the period of the seasonality.

Four seasonal elements are not part of ARIMA that is configuring are as below:

D: Seasonal difference order.
P: Seasonal autoregressive order.
M: Several time steps for a seasonal period.
Q: Seasonal moving average order.

As shown in the image, the cast is a step ahead to provide accurate results for your business sales. It will show the exact picture as per the data and help to get the right amount of production. The blue line in the graph shows the accuracy for the year 2018 from past records and helps to get over the dead stock.

We follow these three steps to exploit forecasting:

1. Define the model.
2. Fit the defined model.
3. Prediction with the fit model.
4. Optimization of the model to have better prediction.

Challenges:

• There was a lack of inventory management.
• Deadstock.
• Inaccurate numbers of engaged customers have adverse effects on sales volume. 
• People fail to schedule production effectively.
• Product obsolescence cost was high.
• Inaccurate pricing and lack of promotion management lose your upcoming sales.

Benefits:

• The sales forecast is achieving a prediction of obligation on the part of the production for the sales department.
• Demand and sales forecasts help out to determine the life cycle of the products.
• Sales forecast helps to prepare and maintain purchasing schedules based on data science.
• Aid value by allocating resources and set overhead levels within a business.
• It helps to determine production volumes considering facilities such as capital, equipment, human resource, location, and more.
• Accurate sales forecasting is based on the method and correct way of doing forecasting for better decision making.
• It helps in guiding the marketing team and business activities for achieving the seasonal and nonseasonal targets.

There is more advanced forecasting for business functions, to maintain inventory and prevent production loss. Contact us for more detail: 

Sales: sales@sigmadatasys.com