Does Data Science Help In Inventory Management?

What is the status of your inventory optimization? At best, you might improve it. And at the worst, it can turn out to be a predicament for you. 

In either case, you may be interested in the most current advancements in the industry. That is what data science can demonstrate in this overview of several methods to inventory optimization. 

Data science is only effective when used for a specific goal, and it can provide spectacular outcomes.

It is difficult to overestimate the value of proper inventory management. After all, management costs might account for up to 40% of inventory costs.

That figure alone demonstrates why you should invest in inventory management. Controlling such costs might result in thousands of saved revenue for your company.

It is where data analytics and inventory management tools come into play.

What are the different approaches related to inventory optimization?

1. Including a safety cushion in the expected demand statistics

It is the simplest method, but it is also the least efficient. 

Let’s assume you are using the most cutting-edge data science approach to anticipate demand. Your most precise projections for a specific SKU will predict that you’ll sell 3,286 bottles of milk X at a particular retailer Y tomorrow. 

However, bear in mind that demand is continuously variable, and you may be able to sell more or less. So you decide to cope with the volatility in demand by allowing your category managers to add a safety buffer to the predicted statistics.

The category manager chooses to increase the expected quantity by 15%. Consequently, they ordered 3,779 bottles without considering holding expenses against out-of-stock charges. 

Later, the amount of milk you can sell happens to be close to the initial projection, resulting in an overstock situation for the business. 

Furthermore, the keeping expenses for this milk are substantial, and this does not seem like an inventory management success story. 

Naturally, such safety buffers aren’t the way to go because they rely on the gut instincts of category managers.

2. Calculating optimum inventory as per demand’s known probability distribution

Unlike the previous strategy, this one considers the expenses of stock ownership and stock scarcity. 

However, demand uncertainty remains a concern, which businesses attempt to address by computing demand probability distributions.

Calculating optimal inventory as per deep learning

This machine learning-based solution, which includes a deep neural network or DNN at its heart, turns out to be the most promising since it solves an inventory optimization challenge more successfully than other systems. 

This method skips the demand forecasting stage and calculates inventory directly, and it implies you won’t have to make educated predictions about the probability distribution of demand. 

Instead, a DNN will examine your most comprehensive historical sales data and take into account various factors ranging from product promotions to shop locations, weather conditions, and so on. 

It will then apply a loss function to the expected inventory number; weighting holding costs against shortfall costs to return the ideal one.

You can learn more regarding the intricacies of the growing field of data science technology if you pursue a data science online course from Great Learning. 

What is the mechanism of Deep Learning?

To understand deep learning, the most updated and efficient machine learning methodology, which outperforms all other methods, let’s first understand how a deep neural network functions.

DNN Structure

DNNs have a complicated design with several layers of neurons (or ‘nodes’). The neurons of one layer are linked to the neurons of the next layer. 

Specific coefficients (or ‘weights’) are assigned to the values obtained by the neurons of the preceding layer at each layer. So, to provide accurate forecasts, the weights must be appropriately calibrated.

What are a few advantages of Deep Learning?

Prediction based on a wide range of data

DNNs can digest both numerical and categorical variables when predicting based on different data. 

It is to say that they can successfully ‘ingest’ sales figures, days of the week, product categories, shop kinds, and so on.

Detection of complicated non-linear relationships

DNNs may capture connections where the output does not fluctuate in proportion to the input. 

As a result, because dependencies are seldom linear in a corporate setting, this technique produces a more accurate image of the real world.

Providing an unbiased outcome

In contrast to the previous two approaches mentioned, the deep learning-based strategy does not rely on either safety buffers or dangerous probability distribution assumptions. 

If you appropriately chose all of the DNN settings, you might be confident that a DNN will offer an exact, objective, and accurate forecast.

Drawbacks of Deep Learning that one should be aware of

Inability to incorporate elements on its own

If you do not advise your DNN to study a factor, the network will not be aware that this element impacts the outcome. 

For example, if a fashion shop fails to include weather as one of the DNN inputs, it may have a surplus of warm clothing.

Depending on the volume of data

There aren’t enough resources for a DNN to learn from if there isn’t enough data. The more elements you wish to consider, the more inputs and weights you’ll need, and the more data you’ll require.

Reliance on data quality

A DNN will not be able to translate several uncommon or erroneous observations into exact predictions if your data is excessively noisy. 

After all, DNNs don’t perform any magic; they recognize existing patterns that impact current data and forecast future data based on them.

Conclusion

Deep learning approaches to inventory optimization appear to be very promising and have a lot of practical applications, especially for organizations that sell perishable commodities like food. 

The approach’s main benefit is DNN’s capacity to recognize non-linear correlations between parameters, which is difficult to do without deep learning. 

DNN is an essential part of data science, and this is how it helps the functioning of inventory management. Great Learning’s MTech in data science eligibility is necessary for this field, and you can gather more information about it, which is available on Great Learning.