To drive productivity and enhance customer service, we continue to identify ways to use artificial intelligence (AI) and machine learning systems across our business, especially in supply chain.
Given the scale of our supply chain network — and the massive amounts of operational data it generates — our technology teams, in partnership with our data scientists, developed flexible computing solutions to meet the real-time demands of business needs. These solutions quickly process the large sets of data in real time, which allows us to run more complex algorithms simultaneously.
Through the use of AI, we have been able to develop better supply chain solutions for greater operational productivity and, in turn, greater customer service to our stores. In particular, machine learning shows the potential to help us to use historical operational data to more quickly predict targeted outcomes with greater accuracy from across various unstructured data sets.
Additionally, moving the appropriate sets of data to cloud solutions provides benefits of more quickly gaining broader insights from a single model.
Our efforts here have already generated numerous learnings — two of them being key for any company pursuing these technologies. First, training machine learning models is not a simple or fast process; this is an investment, not a quick win. Second, a statistically significant amount of good data is needed to conduct meaningful machine learning modeling, which in our case merited a significant project on its own.
As AI and machine learning becomes more integrated in our business processes, we are encouraged by what we’ve learned so far, and will continue to make inroads in 2019.