2024.08.08
Panasonic Group People
Helping Blue Yonder Customers Stay Competitive
How Repeated Fine-Tuning of Demand Forecasting Boosts Inventory Accuracy and Lower Overstock: Nico Bartmann

At the heart of Blue Yonder’s latest SaaS^*2 solution, Cognitive Demand Planning, is its forecasting capabilities. This technology enables companies using these solutions to use artificial intelligence (AI) and machine learning (ML) to forecast demand for the products they handle. Incorporating hundreds of variables, the technology produces the relevant data while also calculating business impacts and risks. This enables customers to quickly manage orders and purchases based on highly accurate and reliable data forecasting. In addition to increasing sales, optimizing inventory, and reducing waste, the technology is also expected to help build a more resilient^*3 supply chain.
^＊2 Software as a Service. Cloud server-based software services available to users via internet networks.
^＊3 Supply chains that can flexibly respond to fluctuations, etc.

Working on what is “at the heart” of Blue Yonder solutions is very exciting. I believe that retail customers who use our solutions will be able to get and stay ahead of the competition. Recently, Sainsbury’s, a major UK-based retailer, has acknowledged Blue Yonder as a significant part of its success story. It was very inspiring to see the CEO of Sainsbury’s speak during their full-year results call about how the partnership with Blue Yonder and a solution I worked on was a key reason for their success. Sainsbury’s real-time forecasting with Blue Yonder is optimizing sales, reducing waste, and distributing stock when and where it is needed.

Thorough Pursuit of Hidden Data Patterns
But With No Predetermined Methods To Do So

I work with other data scientists^*4/planners and users on the customer side to check and verify the precision of the data that is produced in order to fine tune demand forecasting capabilities. I also explain the reasoning behind the predictions and why the model is behaving like it does, tuning the forecasting capabilities by incorporating feedback from customers.
^＊4 Scientists who use big data analytics to create new products and services and improve operational processes.

I’m a data enthusiast and so enjoy digging through data to find new patterns. I use my findings to add to and enhance the current ML models, ultimately ensuring the forecasting capabilities will work within the solution and provide accurate predictions.

In recent years, however, market conditions and customer behavior has been constantly changing. For example, even at a store level, patterns are changing constantly. Against this backdrop, there really isn’t a predefined way to find those patterns, so the work is incredibly challenging. But once you do find those patterns there is a sense of excitement that comes with it. We need to constantly keep pushing the model and adding new features to meet our customers’ needs.

In 2022, Blue Yonder formed a partnership with Snowflake, a company based in Silicon Valley in the U.S., and I have huge expectations for the Data Cloud^*5 service that it provides. The partnership with Snowflake allows Blue Yonder to integrate Snowflake’s data capabilities into its Blue Yonder Platform. This enables retailers, manufacturers, and logistics service providers to leverage Blue Yonder’s data cloud offerings to access, share and consume live governed data, industry-specific datasets, and data services at scale. Moreover, use of the Data Cloud will allow more reliable creation and fine-tuning of the machine learning models that I work on. This is a game changer^*6 for both Blue Yonder and its customers! Making full use of these benefits, we will do everything we can to contribute to the creation of even better solutions.
^＊5 A technology that stores huge amounts of data across multiple servers, making the data easier to access when necessary.
^＊6 Revolutionary business development that exceeds industry boundaries and smashes conventional processes and rules to bring change to society.

Automating Manual Tasks and Making Demand Forecasting Capabilities Faster and More Agile

While fine-tuning is very important to ensure accurate forecasting, to optimize the system the fine-tuning process must be repeated dozens of times (sometimes more than a hundred times). Simplifying the task is a major challenge, but we have recently made a significant step toward finding a solution.