Imagine a world in which you finally step out of back-to-back meetings at 1PM — your stomach is loudly grumbling — and immediately as your mind locks in on “shrimp tacos”… a delivery rolls up with freshly-made shrimp tacos awaiting your arrival. In fact, the truck had anticipated your order last week, and had already loaded up shrimp, salsa, and tortilla ingredients earlier in the day.
Today’s world is not there yet, but the growing importance of machine learning will play an important role in food consumption, with delivery robots and trucks anticipating when, where, and what you want to eat.
In Mountain View, Zume Pizza uses robots, humans, and machine learning to get you fresher, delicious pizza more quickly. Humans and robots work together to shape pizza dough and add toppings before they are put into a delivery truck, with pizzas cooked en-route to finish baking right before arriving at its destination. 
[Video Source: TechCrunch]
Delivering what you want to eat…before you know you’re even hungry
Re-routing in real-time ensures that your hot pizza stays hot:
Zume’s Baked On The Way™ Food Delivery Vehicles identifies the optimal delivery route while its pizzas bake en-route to the consumer ,. The machine algorithm responds to changes by re-routing or turning on-and-off the 56 ovens in each truck — this capability to flexibly integrate signals (e.g., traffic, historical data, future ordering patterns, weather, delivery truck GPS) is fundamental in automation of lightning-fast speed. Many delivery companies today rely on similar intelligence; for example, UberEats optimizes by calculating meal arrival based on predicted ETAs, historical data, and various real-time signals at the restaurant, and leverages spatio-temporal forecasting models to predict shortages in driver availability to incentivize drivers to log in during specific times. 
Predicting demand unlocks personalization at scale:
More data points on time, place, and type of consumption translates to smarter demand planning for hungry consumers. Unlocking the ability to predict creates opportunity to (a) pre-make food that you will want to purchase, and (b) position trucks in the right area to further minimize delivery time. For Zume, people tend to order pizza on the weekday around the same time and have it delivered to the same location; its algorithms further predicts density of orders in an area using weather, “zeitgeisty events like a Game of Thrones premiere”, and more.  Based on those factors, delivery trucks are loaded with pizzas that the algorithm thinks customers will want, and send them in that area in anticipation of future demand.
Forward deploying food inventory reduces supply chain waste:
Zume’s intelligence has additional effects on reducing ingredient inventory costs — the ability to better predict consumer eating patterns helps to stock the right number of ingredients, resulting in fresher food with lower cost and less spoilage risk. With this improved predictability, fresher food can become more accessible and less expensive over time.
Robots evolving to make more complex meals:
Expanding assortment is a gateway for many food possibilities, with CEO Alex Garden floating “coffee, steamed buns and frozen yogurt as potential ideas” — pizza was just the prototype . In the future, robots can become smarter to take on more complex actions at a quicker pace. Zume has partnered with Welbilt to build version 2.0 for Zume’s trucks, with hyper-efficient, custom-built appliances to service a broader assortment of food.  It is critical for “the integrated robots [to have] the ability to operate in dynamic, unpredictable environment” to better anticipate growing needs. 
What’s next to help hungry consumers?
Zume has many challenging topics to consider, ranging from internal hiring to food safety to product investments. Some key steps to consider are:
- Capital investments: Due to capital intensiveness of the business, Zume should further evaluate how they want to efficiently allocate spend — there is a long-term tradeoff between investing in expanding density and geographic scope of current trucks versus investing in expansive technologies beyond the existing pizza prototype. ,
- Organizational design: As Zume continues to hire, leaders should evaluate whether current reporting lines paint a clear picture on internal priorities (e.g., data science report to CEO signals fundamental importance of analytics whereas having food safety report to CEO stresses a different priority).
- Integrating consumer preference data: In expanding outside pizza, Zume should dig into how to best understanding consumer intent without wide range of behavioral data and eating patterns.
As we think about evolving food technology, there are several key open questions we anticipate Zume and similar businesses to face: What are the biggest risks — is it in the technical build (e.g., building the predictive model or dataset itself), organizational design, or food and safety? Do you believe that machine learning may displace human activities in food production, and distribution?
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