The Role of Data and AI in Supply Chain Management

Data plays a crucial role in supply chain management, particularly when dealing with physical goods. The logistics involved can be mind-boggling, making it essential to utilize data and AI to optimize the process. One area where data and AI are used effectively is in inventory planning and management. This involves using various signals to understand internal and external factors and make informed decisions about how to manage inventory levels and placement.

For instance, demand forecasting is a critical component of supply chain management. By analyzing different kinds of signals, businesses can gain a better understanding of their customers' needs and adjust their inventory accordingly. Data and AI can help analyze historical sales data, seasonal trends, and market fluctuations to predict future demand. This enables businesses to make informed decisions about how much stock to hold in warehouses, where to store it, and when to replenish supplies.

Another area where data and AI are used is in optimizing supply chain routes. Businesses can use data analytics to determine the most efficient routes for their trucks, reducing fuel consumption and lowering emissions. This not only benefits the environment but also saves costs for businesses. Walmart has recently launched a product called Load Optimizer, which uses AI to optimize middle-mile logistics by finding the best route planning solutions for businesses.

In addition to optimizing supply chain routes, data and AI are also used in warehouse management. By analyzing data on how boxes are packed into trucks, businesses can identify opportunities to improve efficiency. This involves optimizing the order of boxes in the truck to maximize space and reduce waste. While it may seem like a small detail, this can have a significant impact on supply chain operations.

However, managing multiple data sources and integrating them into a cohesive system presents a challenge. Businesses operating internationally must contend with varying market conditions, data capture methods, and generation processes across different regions. This can make data integration more complex, particularly when supporting multiple markets like Canada and Mexico. Nevertheless, companies are developing robust data lake strategies to bring together most of the data from these diverse sources.

These data lakes serve as a foundation for building AI solutions that can provide insights into customer behavior, market trends, and supply chain performance. By ensuring data integrity and following policies around data management, businesses can maintain transparency and explainability throughout the supply chain process. This is particularly important in international operations where stakeholder expectations are high.

Ultimately, the effective use of data and AI in supply chain management requires a deep understanding of the intricacies involved. By leveraging these technologies to optimize inventory planning, demand forecasting, supply chain routes, and warehouse management, businesses can improve their overall efficiency, reduce costs, and enhance customer satisfaction.

"WEBVTTKind: captionsLanguage: enthe other thing you mentioned right at the start was that um you make use of data for the supply chain I have to say obvious you're dealing with lot of physical goods and the logistics got to be pretty mindboggling here um yeah can you talk me through how data and AI are used on the supply chain side yes um I think supply chain is one area where we are really using Ai and data in the best way possible to for a couple of use cases U to give an example like of course inventory planning and management is a important one and demand forecasting is a very important component so how do we use different kinds of signals to kind of understand the internal and external factors to then uh make plans on how how our inventory should look like where it should be placed um and and the entire supply chain that goes around uh you know enabling that right from last mile to Middle mile to First mile so all that is definitely a place where data and you know how it flows uh through uh an item level at geographical level plays a very important role um the other thing I would say is also on and I um uh where Walmart is playing a very important role is how we are helping um to optimize um the supply chain um through uh how we are packing our trailers how we are using the most optimal route to get from place one to B um Etc and uh actually Walmart just uh few days back again launched a product called load optimiz load Optimizer which is basically an assas based product uh which any business can use now to uh optimize their middle mile um for best route planning so I think those are some of the use cases where Walmart is really doubling down to ensure again to have the right product at the right time but then also do it in a way which is um optimal uh in terms of our supply chain strategies that's really interesting suppose I was thinking of Supply chains mostly about like well how do you do warehouses right but actually just figuring out what order do you put the boxes in the truck in order to pack more of them in that's going to have a huge impact so yeah L lots of subtleties there and house is also important one I mean that's like like I said there are many different use cases where uh everything is sort of part of the puzzle like everything needs to be done right uh for to to get the right experience for the customers okay so if you've got lots of different components here um I imagine you got like lots of different data sources all over the place different parts of this um does that give you like a data integration challenge like how do you manage all the different steps of this together um yes uh def definitely data is challenging and I think working for international uh business where we are not only uh supporting one market but many different markets such as Canada and Mexico um there are challenges around you know how data resides in different sources how it's captured how it's generated and then how it's captured uh but then uh over time we are building robust uh uh Del Lake strategies where we are able to bring um most of the data if not all into our um data lakes and we are able to then build AIC Solutions on top of it now this is all done in a very um uh very scientific way uh in in terms of not just ensuring the data is the Integrity of the data but then how we follow all the uh all the policies around data how we keep it very transparent and explainable at all timesthe other thing you mentioned right at the start was that um you make use of data for the supply chain I have to say obvious you're dealing with lot of physical goods and the logistics got to be pretty mindboggling here um yeah can you talk me through how data and AI are used on the supply chain side yes um I think supply chain is one area where we are really using Ai and data in the best way possible to for a couple of use cases U to give an example like of course inventory planning and management is a important one and demand forecasting is a very important component so how do we use different kinds of signals to kind of understand the internal and external factors to then uh make plans on how how our inventory should look like where it should be placed um and and the entire supply chain that goes around uh you know enabling that right from last mile to Middle mile to First mile so all that is definitely a place where data and you know how it flows uh through uh an item level at geographical level plays a very important role um the other thing I would say is also on and I um uh where Walmart is playing a very important role is how we are helping um to optimize um the supply chain um through uh how we are packing our trailers how we are using the most optimal route to get from place one to B um Etc and uh actually Walmart just uh few days back again launched a product called load optimiz load Optimizer which is basically an assas based product uh which any business can use now to uh optimize their middle mile um for best route planning so I think those are some of the use cases where Walmart is really doubling down to ensure again to have the right product at the right time but then also do it in a way which is um optimal uh in terms of our supply chain strategies that's really interesting suppose I was thinking of Supply chains mostly about like well how do you do warehouses right but actually just figuring out what order do you put the boxes in the truck in order to pack more of them in that's going to have a huge impact so yeah L lots of subtleties there and house is also important one I mean that's like like I said there are many different use cases where uh everything is sort of part of the puzzle like everything needs to be done right uh for to to get the right experience for the customers okay so if you've got lots of different components here um I imagine you got like lots of different data sources all over the place different parts of this um does that give you like a data integration challenge like how do you manage all the different steps of this together um yes uh def definitely data is challenging and I think working for international uh business where we are not only uh supporting one market but many different markets such as Canada and Mexico um there are challenges around you know how data resides in different sources how it's captured how it's generated and then how it's captured uh but then uh over time we are building robust uh uh Del Lake strategies where we are able to bring um most of the data if not all into our um data lakes and we are able to then build AIC Solutions on top of it now this is all done in a very um uh very scientific way uh in in terms of not just ensuring the data is the Integrity of the data but then how we follow all the uh all the policies around data how we keep it very transparent and explainable at all times\n"