Today, businesses are realizing the critical importance of investing in robust data science workflows. Data-driven decision making has been well documented in numerous industry reports and case studies to deliver a transformative impact on business processes and help create winning strategies even in an uncertain market.
Investing in data science processes can add value in several ways – influencing critical decisions in recruitment, marketing, sales, supply chain, operations and many more. Let us take a closer look at a popular ML use case and how decision making can be improved by applying ML and deriving value from it.
Forecasting Daily Demand By Store
By capturing and analyzing store data, AI and ML models can be used to forecast the actual demand levels at retail stores. This is very useful in predicting overstock and understock situations – thus helping retail organizations to fine-tune their supply chain and working capital.
Retail chains often find it challenging in ensuring that the right product is available at the right time and the right place. This optimization can provide immense cost savings and also help to build effective customer relationships. If the retail organization is holding too much inventory, then the store may find several challenges in disposing of it. It may have to resort to tactics such as deep discounting (lowering the profit margin), or in many cases even having to sell off the inventory at lower than the purchase price. Purchasing more inventory than needed also reflects a poor allocation of working capital. Conversely, if the store holds less inventory than needed, then it will experience loss of sales, customer frustration and consequently a negative impact on the customer relationship metrics.
For retail organizations, optimizing the inventory and stock level is a priority. Traditionally, they do this through lengthy spreadsheet-based and statistical analysis models such as ARIMA. Such solutions, which can be very tedious and time-consuming, often fail to provide granular and actionable insights in a short time-frame to provide valuable recommendations to optimize stock levels.
Machine Learning, Deep Learning and Artificial Intelligence models can provide solutions to this. By churning through volumes of in-store data, such models can provide nuanced answers to the desired stock levels at each store – helping solve overstock and understock issues, by doing accurate forecasting at massive scale. There are a number of variables which impact the output value – such as seasonality, historical sales, demography, current trends. Machine learning models can be used to take these into account in order to come up with a working and accurate model.
The Emergence of AutoML
However, a strong word of caution here. Implementing traditional machine learning models using popular open-source frameworks on R and Python can be challenging. Currently, running data science and machine learning projects are strictly the prerogatives of data scientists who need to work with cleaning the data and performing exploratory analysis before identifying different models, running them on by one, performing parameter tuning and dynamically updating the prediction log based on what the various models are telling.
Not only is the process time taking and tedious, but it is so technically challenging that domain, and functional experts find it very difficult to integrate it into their workflows for effective decision making. Additionally, with the shortage of data scientists in the industry, and even more, lack of data science experts who carry strong functional and industry understanding, it results in data science projects often failing to integrate the desired levels of functional knowledge to make accurate and powerful predictions.
AutoML is changing the data science workflows in significant ways. Automated machine learning can be by anyone – from data scientists, software engineers, business analysts and functional leaders to run several different machine learning models at the same time on the data set to perform both unsupervised clustering exercises as well as supervised predictive models, in order to dynamically come up with the best performing model.
It does not eliminate the role of the data scientist, however, but it significantly boosts productivity by automating several different tasks in the data science process. This way, it not only allows functional experts to set up and run different models by themselves, but it also frees up the data scientist’s time to work on several projects by aligning with the various functional teams. It democratizes analytics in the organization by creating a new class of citizen data scientists who can create advanced ML models all without having to learn to code.
Investing in AutoML platforms is serving a clear competitive advantage for businesses. It is helping them to become more innovative, do more customer-centric use cases, collaborate with partners, create a business impact and give the power to run advanced machine learning applications to anyone within the organization who would benefit from it. AutoML platforms automates decision making, helping key stakeholders collaborate and extract business value from their raw data. AutoML gives the power and effectiveness of executing advanced models to everyone – from data scientists, business analysts, analytics professionals as well as functional leaders and managers, software engineers, operations managers.