Gala Groceries is a technology-led grocery store chain based in the USA. They rely heavily on new technologies, such as IoT to give them a competitive edge over other grocery stores.
They pride themselves on providing the best quality, fresh produce from locally sourced suppliers. However, this comes with many challenges to consistently deliver on this objective year-round.
Gala Groceries approached Cognizant to help them with a supply chain issue. Groceries are highly perishable items. If you overstock, you are wasting money on excessive storage and waste, but if you understock, then you risk losing customers. They want to know how to better stock the items that they sell.
This is a high-level business problem and will require to dive into the data in order to formulate some questions and recommendations to the client about what else we need in order to answer that question.
Once you’re done with your analysis, we need you to summarize your findings and provide some suggestions as to what else we need in order to fulfill their business problem.
Based on your recommendations, they want to focus on the following problem statement:
“Can we accurately predict the stock levels of products based on sales data and sensor data on an hourly basis in order to more intelligently procure products from our suppliers?”
The client has agreed to share more data in the form of sensor data. They use sensors to measure temperature storage facilities where products are stored in the warehouse, and they also use stock levels within the refrigerators and freezers in store.
It is your task to look at the data model diagram that has been provided by the Data Engineering team and to decide on what data you’re going to use from the data available. In addition, we need you to create a strategic plan as to how you’ll use this data to complete the work to answer the problem statement.
It is now time to get started with some machine learning!
The client has provided 3 datasets, it is now your job to combine, transform and model these datasets in a suitable way to answer the problem statement that the business has requested.
Most importantly, once the modeling process is complete, we need you to communicate your work and analysis in the form of a single PowerPoint slide, so that we can present the results back to the business. The key here is to use business-friendly language and to explain your results in a way that the business will understand. For example, ensure that when you’re summarizing the performance of the results you don’t use technical metrics, but rather convert it into numbers that they’ll understand.
Gala Groceries saw the results of the machine learning model as promising and believe that with more data and time, it can add real value to the business.
To build the foundation for this machine learning use case, they want to implement a first version of the algorithm into production. In the current state, as a Python notebook, this is not suitable to productionize a machine learning model.
Therefore, as the Data Scientist that created this algorithm, it is your job to prepare a Python module that contains code to train a model and output the performance metrics when the file is run.
The ML engineering team has taken your Python module and deployed the algorithm into production along with the DevOps, which is great!
Before it goes live, the DevOps team has been collecting some predictions from the algorithm and has provided these to the ML engineering team, who have performed some testing of the predictions against the actual results for ‘estimated_stock_pct’. The ML engineering team were testing the predictions vs actual results to see how well the algorithm is performing on “live” data.
After performing the tests, the ML engineering team wants to discuss with you some questions about the algorithm in order to further improve the model before the DevOps team integrates it with Gala Groceries’ live system.