Stop guessing what happened. Start deciding what will happen.
AI and Machine Learning find the perfect balance between a positive customer experience and more profit. You don’t have to choose one or the other.
More Profit from Existing Customer Base
Effective supply chain planning improves both the customer experience and the bottom line. This requires a supply chain solution powered by machine learning (ML) and artificial intelligence (AI).
Compared to traditional retailers, C-store chains face similar challenges, but those challenges are multiplied by complex business, cultural, and demographic factors. Most C-stores are focused on having enough product in the supply chain, increasing foot traffic, and hoping promotions are effective. While it is appropriate to focus on getting enough inventory in each store location on time, profit is likely at risk if that’s the sole focus.
Profit Starts with the Customer
There’s no question that a customer-centric business model is what helps companies win. The customer is the one who matters. However, being customer-centric doesn’t only mean having the products on hand that the customer wants. Being truly customer-centric means the customer feels their expectations were met. A major part of that is ensuring customers can find and purchase the products they want. Making that happen is a bit more complex. It begins with the supply chain.
The stock in each location should be determined by customer demand. The quantities of each SKU varies by location, by season, and numerous other demographic data. Adding to the complexity is the need for localization. If your chain has two locations a few miles apart on the same street and in the same city, the SKU assortment and quantities will likely be different for each of those locations. Rather, if you want to drive increased revenue, that assortment should be different.
Ensuring a positive customer experience depends on each location having the right product in stock that the customer wants. That demand varies by location, even if the overall demographics of your chain are the same. Understanding customer demand is key to building
an effective supply chain plan.
Profit Doesn’t End with the Customer
Historically, the most common way C-store chains address customer experience is to overstock most of the SKUs. This is a short-term solution that creates a long-term problem. The hope is that overstocking will increase sales, but the most likely result is having stranded inventory. It’s one thing to have a dozen air fresheners on hand that won’t sell. It’s another issue altogether when you have too much fruit that hasn’t sold or other product with expiration dates. Overstocking creates additional profit risks. One overstocked product might be taking up space for another product that would sell if you stocked more of it. Overstocking is the fastest way Cstores lose.
You don’t need to overstock to deliver a positive customer experience. Further, you don’t need
to overstock to prevent missed sales.
A Better Customer Experience = Better Profit
AI and Machine Learning find the perfect balance between a positive customer experience and
more profit. You don’t have to choose one or the other. A supply chain that is built on ML and AI
considers granular details. It can include:
- Sales history for each product
- Customer demographics
- Individual customer purchases
- How well a SKU sells in one location versus other locations
- Demand substitutability
If “Product A” is out of stock, a demand substitutability algorithm can determine the likelihood of customers purchasing “Product B,” a similar product, but maybe a different brand. This might sound too complicated, but it’s easy and automated with the right technology. Making use of this data can help your C-store gain more profit.
A profitable supply chain solution can not only provide granular data, it can also help you find substitutable products and vendors when your existing supply chain encounters roadblocks. This is one more way ML and AI can help you achieve a positive customer experience and
Machine learning models not only tell you what happened in the past, but what will happen, and what to do about it. These functions are what separate traditional forecasting methods and modern, AI-powered supply chain technology. It’s the difference between knowing historical data, versus predictive data, versus prescriptive data. Prescriptive data is the most valuable because it actually tells you what decisions to make to maximize profit. Prescriptive data always results in a better customer experience.
When a C-store chain fine tunes its supply chain, customers in each location should always find the products they want in stock. This results in more sales. An accurate forecast ensures the retailer or C-store chain is neither out-of-stock nor overstocked. Lost sales and stranded
inventory are nearly eliminated.
More foot traffic and better promotions are always helpful, but you can find more profit from your existing traffic inside your supply chain. When your supply chain is built on AI and machine learning, you can discover hidden profit by:
- Eliminating out of stocks
- Preventing overstocks
- Creating a better customer experience.
To learn more about how 4R can help your c-store find hidden profit, call us at (610) 644-1234 or email email@example.com.