You wouldn't elect to buy all your groceries at the convenience store because the grocery store (with more competitive priced goods) was right across the street. The 2 stores at that time weren't considered to be direct competitors because there was such a large price differential.
Those were simpler days - long before the homogenization of the shopping experience and much earlier than online shopping. Inventory control was managed exclusively by walking the aisles and counting, and computerized inventory planning - much less EDI ordering - didn't exist. Pricing was based on local demand and local competition.
As we collectively move our pricing, distribution, planning, delivery, and consumer purchases to a constant online model, demand is seen differently and in much more complex patterns. Retailers need to be able to visualize the entire cycle of the products they sell, and manage the processes involved at a very granular level. And the larger the footprint of the retailer, the more complex and varied the calculations.
The only way a retailer can successfully meet the demands with appropriate pricing is to make use of advanced systems that aggregate mountains of data from multiple sources to make short term predictions and react to changing customer preferences. Even monitoring store traffic doesn't produce accurate enough data as consumers shop online and order for delivery at the store. Or walk the aisles scanning products and ordering online for delivery to their homes.
It's time for big data and advanced analytics to guide supply chain decision making. If you haven't begun to understand the significance and at least some of the implications of these technologies, the time to start is now.