Use Cases







Optimizing meal kit delivery services with weather awareness

5.29.2019//Opinion, API & Mapping, Food and beverage

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Ross Machurick

Lead Account Executive

Meal kit services have changed the lives of many over the last decade — from reducing the stress of a single parent needing a quick, fresh, healthy dinner to an amateur home chef researching a new and exciting meal. Once just a novel concept born in Sweden, the industry evolved into a global force with players such as HelloFresh and Blue Apron leading the way. By 2017, the global market was flooded with over 150 services competing for the then-$2.2 billion pie and on track to surpass $10 billion by 2020.

Meal kits meet technology

Meal kits not only allow entrepreneurs to share their passion for food with millions, but have also emboldened them to push the boundaries of technology in the preparation and delivery of said food. Unlike traditional food industry constituents who have a long history of standardized operations, meal kit startups evolved from scratch in a world of cutting-edge technology. Case in point: We saw these food pioneers leverage machine learning (ML) and artificial intelligence (AI) to optimize operations - from procurement to distribution - long before these terms became buzzwords in American business jargon.

One area where these meal kit services have traditionally lacked control is in their transportation. It’s in that “final mile” in particular that meal kit services have often relied on manual planning to ensure that ingredients are maintaining top shape as they travel from distribution center to countertop. Xweather has set out to change that for clients in the meal kit and perishables transportation space by enabling them to harness detailed weather data to streamline their supply chains and packing logic. Our advanced weather API and weather mapping layers inform and support logistics efforts, generating greater efficiency and profitability in the process. Today, we’ll be sharing examples of how our clients leverage weather data in their transportation & packaging logic to optimize meal kit solutions.

AI & weather mapping in last-mile delivery

As described by Business Insider, last-mile delivery is arguably the most complex challenge in the transportation ecosystem — and by far the most costly, accounting for roughly 53% of the overall shipping costs. Why? Generally, only 1-2 packages need to be delivered at each stop, and stops are separated by miles of rural landscape, bustling urban settings, and... weather.

Tornadic events, intense rain, and winter flurries may not be entirely avoidable, but their effect on your meal kit logistics can be minimized. By modeling historical storm reports and observed weather conditions with other external stimuli in machine learning algorithms, AI logic can leverage those models to deploy additional resources to particular routes, change transportation modes, or adjust routes to deliver to areas before inclement weather hits or after it has passed.

By leveraging the AI tools available today, leaders in the meal kit business can greatly minimize the effects of weather on their supply chain and delivery efforts. Transportation organizations can model the impact of external stimuli and optimize their routes to meet demand while keeping costs low and drivers safe.

Bundling your meal kit for the weather

Even with just-in-time delivery, weather is a combative factor in the quality of the product received by the customer. As highlighted in a Rutgers-Tennessee State study of meal kit food safety, box temperatures can often vary widely by the time kits are opened by the consumer, with time on the doorstep in warm environments often the major culprit.

Meal kit companies have made some advances on this front, pioneering unique packaging algorithms to help them optimize cost with optimal quantities of ice and insulation. When applying these algorithms across a multitude of regions and microclimates, the result brings about a strong appetite for localized weather data. With the correct weather integrations, you can train your machine learning algorithms with historical weather data and run that logic forward with current observations and forecasts to keep your customers coming back for more.

Weather influences menus

Often, we find ourselves varying our diet based on the seasons, whether it’s the natural changes in produce availability or the unconscious effects current & forecasted weather conditions have on our decision-making. For instance, consider the most recent summer day you experienced. You may have indulged in a fresh garden salad, fish, fresh fruit, or greens. However, it’s doubtful that you took this opportunity to enjoy the combo of a warm bowl of chili, a baked potato, and a slice of cornbread.

Aside from major seasonal changes, short-term volatility can present opportunities to both delight your customers and receive off-peak pricing from your vendors. Through intuitive weather mapping & localized forecasting technology, you can predict the cravings of customers based on environmental conditions and reflect those needs in your menu offerings.

Optimize meal kit logic with weather awareness

As the food delivery network continues to grow, companies are being challenged to play smarter. In addition to traditional competitors, meal kit companies are now competing with the likes of DoorDash (delivering meals from your favorite restaurants) and Peapod (delivering groceries from your local market). This dynamic and highly competitive landscape demands innovation to pull ahead and succeed. With the Xweather API and weather mapping layers, we can help you create and maintain that competitive, winning edge with your meal kit logistics.

Ross Machurick

Lead Account Executive



Querying weather conditions along the path of totality for the solar eclipse on April 8th