Digital Operations in Retail

  • March 13, 2018
  • Tom

Here’s a common scenario, at least at my house. I come home from work. Maybe my wife makes it home before I do (not usually). The kids are already home and everybody has one question on their mind. “What are we going to do for dinner?” Now, my family likes food. We like to eat good food, we like to cook good food. But I gotta tell ya. At 6:30 or 7:00 at night, the last thing I want to figure out is, what’s for dinner? Now, I’m terrible at planning ahead, at least when it comes to planning out what we’re going to eat the for the next week. So, planning out all our meals for the next week just doesn’t happen. In the moment, it’s usually more about what can we scrounge together based on what’s in the house, than what we really feel like eating.

Ideally, what we really want to prepare (and eat) is based on a lot more factors.

  • How late did we get home?
  • What did we all eat for lunch?
  • Is it cold outside?
  • Do I have to get up at the crack of dawn early in the next morning to fly to Austin?

Ideally, the answers to these questions would all factor in to the most important decision of day, “what’s for dinner?”

Now let’s see how an innovative grocery retailer with Digital Operations chops (sorry couldn’t resist the pun ;), might operationalize this, through a service we’ll call “What’s for Dinner?”.

As a subscriber to “What’s for Dinner?” the retailer provides a digital concierge service in the form of a chatbot, accessible from the subscriber’s phone. While on his way home, the subscriber simply asks his phone “what’s for dinner?”

Back on the retail side the “What’s for Dinner?” service is:

  • Virtually looking in the subscriber’s refrigerator, pantry, and even their freezer.
  • Curating recipes from the subscriber’s “family favorites”, Bon Appetit, Cooks Illustrated, and all the episodes of Iron Chef.
  • Looking at its store inventory for fresh fish it needs to move before it gets stinky, produce that is good today, but may not be great tomorrow, and any other ingredient that they may be trying to move, due to freshness, oversupply or that it’s just something that they know is going to delight its customer.
  • Finally the service brings all this information together, feeds it into a decisioning component that has been customized and trained specifically for this subscriber.

An instant after the subscriber asks the concierge service “what’s for dinner”? The chat bot responds with three options:

  1. Risotto with diver scallops and fennel fronds
  2. Ramen with pancetta, green garlic and ginger
  3. Chicken tacos with chipotle salsa, shredded cabbage and lime

The subscriber, decides on the Risotto.

While he’s still driving home, the retailer packages up 5 diver scallops, cuts the tops off a bulb of fennel, wraps them up and gives them to their delivery driver who delivers the remaining ingredients to the subscriber’s home before he even pulls into the driveway.

Now this problem is totally solvable. We have inventory tracking technology that we could use to keep track of what ingredients we have in the house. We have decision management techniques, such as machine learning algorithms that we can train to curate recipes and look for new potential favorites for the customer, business rules that we can use to ensure we don’t have the same thing every night. We even have optimization algorithms to determine optimal recipes that will minimize our food costs, maximize our enjoyment or even make sure we use up that leftover chicken from Sunday before it goes bad.

All these techniques are available to us to answer that persistent question of “what can I make to feed my family that I have time for, I want to eat, and that I have the ingredients? Or, if the world is my pantry… that I can have the remaining ingredients delivered from my local grocery store, and waiting for me when I get home from work.

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