Brick and Mortar
Let's imagine a beauty products manufacturing company with more than 50 stores and, in turn, that each store uses its sales representatives to perform the store's distribution manually and calculate the store's inventory quota (this for more than 1000 references); consequently, only 10 minutes are dedicated for each working hour in active sales or solving customers' doubts.
Photo by Clay Banks at Unsplash
This use case is focused on helping companies to improve their distribution of references for all stores and thus avoid running out of stock or having an over-supply of some products in certain stores, also to predict when and how to supply each store.
One of the ways to solve these problems is by using demand prediction algorithms, combining internal data, customer profiling and public data such as weather and holidays, to estimate demand volumes in different locations where supply is needed.
In addition, sales reps while working with the customer are collecting data with devices that allow the company in real-time to answer some questions such as: Are the references correctly distributed in each category and group, which reference has not been correctly distributed, is the distribution correctly executed in each store?
With these methods it would be possible to increase up to 150% the activity of the representatives so that they are available in active sale and fidelizing the client, also the time of audit that is used for the checking of the inventory and the collection of data is reduced up to 50%.
Data collection allows us to analyze products that have great sales potential, calculate replacement orders and increase or decrease your inventory; in addition to correcting errors to improve distribution, increase revenue, create artificial intelligence models that make these decisions on their own, and thus increase efficiency in your company.
Photo by Clay Banks at UnsplashThis use case is focused on helping companies to improve their distribution of references for all stores and thus avoid running out of stock or having an over-supply of some products in certain stores, also to predict when and how to supply each store.
One of the ways to solve these problems is by using demand prediction algorithms, combining internal data, customer profiling and public data such as weather and holidays, to estimate demand volumes in different locations where supply is needed.
In addition, sales reps while working with the customer are collecting data with devices that allow the company in real-time to answer some questions such as: Are the references correctly distributed in each category and group, which reference has not been correctly distributed, is the distribution correctly executed in each store?
With these methods it would be possible to increase up to 150% the activity of the representatives so that they are available in active sale and fidelizing the client, also the time of audit that is used for the checking of the inventory and the collection of data is reduced up to 50%.
Data collection allows us to analyze products that have great sales potential, calculate replacement orders and increase or decrease your inventory; in addition to correcting errors to improve distribution, increase revenue, create artificial intelligence models that make these decisions on their own, and thus increase efficiency in your company.
“How to Optimize the Operation of a Retail Store”– Karim David BarraganTweet
