Forecasting Better with Data Analytics

Like all businesses, you indeed perform predictive analytics too. But how often do you go wrong? How do you check your accuracy? How much does it cost you???

Predictive analytics describes how the outcomes in the future are in the given circumstances. Predictive analytics is a powerful tool that can help businesses improve their decision-making processes and increase their profitability.

With the rapid advances in technology and the increasing availability of data, businesses can now use predictive analytics to gain insights into customer behavior, market trends, and other critical factors that impact their bottom line. Predictive analytics involves using statistical algorithms and machine learning models to analyze historical data and make predictions about future events.

Some common things that you as a business person surely perform;

1. Sales forecast

2. Production Forecast

3. Buying Raw material forecasting

4. Day sales outstanding

5. Stockout days

Before going into the details of it, while predictive analytics can be incredibly valuable, it can also be challenging to get it right. Many businesses still rely on guesswork and outdated forecasting methods, which can lead to inaccurate predictions and costly mistakes. Not doubting your experience but it’s about time that you use your available data effectively.

If you use growth rate and simply multiply it, well, I believe it will still work on a macro level just to see how on average you’d do. But doing average won’t cut it, at least not anymore!

With the rapid changes in technology, you can quickly achieve the target of knowing when to produce? What to produce? How to produce? How much to make? Who will buy it? When will they buy it? Why will they buy? For how much they will buy? When will they pay? When to order which raw material? How much to order?

But the main question that stays still, is how you calculate the accuracy of the model you devise.

You must specify your loss function!

By defining a loss function and selecting the right predictive analytics model, businesses can improve their forecasting accuracy and make more informed decisions about how to allocate resources, manage inventory, and respond to changing market conditions. This can lead to significant cost savings, increased sales, and improved customer satisfaction.

A loss function is how you calculate the loss when you predict inaccurately, for example, I predicted there will be a sale of 20MTon of rebars or 20pizza in the coming week but the orders came of 40units, I ran short of supplies hence, the customer wanted a specific SKU/product type, they canceled. Now your loss is the storage costs + supplies cost + opportunity lost cost x 12 (studies prove that a bad customer experience is shared with 12 others by the customer).

Do you realize how much loss have you incurred up till now?

Let’s change this!