#13: Planting Seeds of Organic Farming, Designing Flawless Supermarkets, and Refining the Barista's Clock
Hey there, AI enthusiasts! 🌅
Welcome to the 13th edition of Good AI Vibes - your trusted guide to AI's business magic. Stay updated, and never run out of innovative AI use cases to level up your business game.
In this issue, we're spotlighting: 👇
☕ Perfecting the Coffee Rush: AI’s Visual Insights into Shift Assignments
🛒 Checkouts & Aisles: The AI Blueprint for Perfect Supermarkets
🌱 Cultivating Organic Farming Success with AI Insights
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Cultivating Organic Farming Success with AI Insights
What's the critical hurdle?
Meet Anika, a seasoned manager at a massive agricultural company in the Netherlands that prides itself on owning vast stretches of lush farmlands, grappling with a question: If organic farming is so beneficial for food quality, health, and the environment, why is it adopted by only 1.5 percent of the world's farms? It's not just about leaving chemicals behind. Transitioning to organic farming often means facing a dip in yield, increased costs, and unpredictable market forces. For managers like Anika, strategizing this shift without jeopardizing revenues is a daunting task. The risks? A potential double whammy of falling yields and uncertain market prices during the transition.
How was this initially tackled?
To navigate this tricky terrain, managers like Anika depended on traditional forecasting. They'd pore over historical data, discern trends, and estimate land allocations for crops during the organic shift. Some even engaged experts to guide their decisions, hoping their insights would lead to sustainable choices.
Why did the initial approach fall short?
While these methods offered a foundational strategy, the world of agriculture is far from static. Nature has its whims, and market dynamics are ever-evolving. Standard forecasting didn't capture the granular challenges of each year in the organic transition. Even if data showed a certain crop thriving for years, it wasn’t a guarantee for success during the organic transition. Another overlooked factor was the interrelation of crop prices and yields. They aren’t standalone figures; they impact one another, and this crucial aspect was missing in the traditional tactics.
How did AI revolutionize the solution?
Recognizing the shortcomings of the old ways, the entry of AI brought a fresh perspective. A multi-period optimization model, designed especially for organic transition, came into play. This AI system didn't merely rely on past patterns; it actively considered the interconnectedness of crops and their market prices. For instance, if it foresees that a bountiful corn yield could depress its prices, it adjusts the land allocation strategy. This ensures a balance, preventing an oversupply of potentially low-priced yet high-cost organic corn.
For decision-makers like Anika, this is revolutionary. No longer reliant on guesswork, she has a digital strategist by her side. With this AI model, the uncertainties of transitioning to organic farming are mitigated, ensuring that her company's profits remain in the green. As we gaze into the horizon, with AI as a partner, the path to organic farming appears more achievable and economically viable than ever before.
Main reference: Jahantab, M., Abbasi, B., Le Bodic, P., et al. (2023). Farmland allocation in the conversion from conventional to organic farming. European Journal of Operational Research, 311(3), 1103-1119.
See other references at the end.
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Checkouts & Aisles: The AI Blueprint for Perfect Supermarkets
What's the critical hurdle?
Meet Maurice, the manager of a bustling supermarket in the centre of Paris. Maurice faces an interesting dilemma. His customers come in for their regular shopping, but sometimes, the placement of products can either entice them into impulse purchases or make their shopping experience tedious. The catch is, both of these situations are affected by how the store layout is designed.
Imagine this: A customer comes in to buy their weekly groceries but ends up buying that fancy wine bottle or gourmet cheese they spotted while walking around, even if it wasn't on their list. That's an impulse purchase. But at the same time, Maurice doesn't want Mrs. Dubois to walk three extra aisles just to find her favorite tea. This challenge is unique because finding the sweet spot between impulse buying and shopping convenience is like a game of tug-of-war. If the store layout heavily leans towards impulse buying, it might inconvenience the customers, leading to decreased loyalty and potential revenue loss. But if it's too convenient, then the potential extra revenue from those unplanned purchases goes untapped. Imagine if Maurice's store was losing out on potential impulse profits just because of the store layout. That's a significant hurdle for business!
How was this initially tackled?
Traditionally, Maurice relied on his intuition and years of experience. He would observe customer behavior, gather feedback, and even resort to trial and error by changing the store layout and gauging its impact. Additionally, feedback from staff, some customer surveys, and insights from other similar businesses played a role. So if Maurice noticed that customers were taking longer to find staple products, he might move them closer to the entrance, assuming it would enhance convenience. This approach was more of an art than science.
Why did the initial approach fall short?
The traditional approach Maurice used had its limitations. First, relying solely on intuition could be hit or miss. It lacked a data-driven perspective which meant that opportunities for optimization were left on the table. While Maurice could make educated guesses based on experience, there was no guarantee that these changes would always lead to increased sales or better customer satisfaction. Also, because these changes were often made based on isolated feedback or individual observations, they didn't necessarily represent the bigger picture or overall customer sentiment. This meant that even with all the effort put into changing layouts and sourcing feedback, the core problem still persisted.
How did AI revolutionize the solution?
Instead of relying on trial and error or intuition, a new approach was introduced: a store-wide shelf space planning framework, designed with AI's touch. Imagine a digital twin of Maurice's store, where AI plays around with the placement of products, taking into account two objectives: increasing impulse buying and ensuring shopping convenience.
The AI gathers data from various sources: customer surveys, store visit observations, even public data. It then simulates different layouts, predicting outcomes for each change. The results? Stunning!
In one scenario, the AI found a layout that could potentially increase impulse profit by a whopping 82% while reducing the customer's walking time by 11%. Maurice was looking at a store layout that not only made customers buy more on impulse but also made their shopping journey smoother. It's a win-win situation.
Main reference: Abdelaziz, F. B., Maddah, B., Flamand, T., Azar, J., Bianchi-Aguiar, T., et al. (2024). Store-Wide space planning balancing impulse and convenience. European Journal of Operational Research, 312(1), 211-226.
See other references at the end.
Perfecting the Coffee Rush: AI’s Visual Insights into Shift Assignments
What's the critical hurdle?
Imagine Eliana, a diligent manager of a bustling coffee chain in New York City. Every morning, Eliana grapples with the challenge of ensuring her customers receive the best service in the least amount of waiting time. She also wants to ensure her staff aren’t idling around when they could be delivering value. Eliana's ultimate goal? Keep her cafe's reputation golden and her customers coming back for more. The intricate dance of shift scheduling for her baristas is pivotal. Get it wrong, and it leads to long queues during the morning rush or idle baristas during slower hours. For Eliana, such inefficiencies can mean lost sales, wasted payroll, and disgruntled customers.
How was this initially tackled?
Before diving into the high-tech world, Eliana relied on a mix of her intuition, past sales data, and customer footfall predictions to assign shifts. For example, she'd staff up during what she thought would be rush hours, say between 7-9 am when office goers wanted their caffeine fix. During the afternoons, she'd keep a leaner crew, based on the presumption that people rarely desired coffee post-lunch. It was a tried-and-tested method, a blend of gut feeling and experience.
Why did the initial approach fall short?
Eliana's method was decent, but it wasn't foolproof. The city's dynamics, customer preferences, and even weather changes could lead to unexpected surges or lulls. For instance, during rainy days, there might be a sudden rush of customers seeking shelter and a hot cuppa. Or, on certain sunny days, Eliana might find her staff idle, while potential customers avoided the warm drink. Predicting these patterns based solely on intuition was akin to shooting in the dark. This inconsistent approach meant Eliana's store sometimes missed potential sales opportunities or wasted resources.
How did AI revolutionize the solution?
Enter the brilliance of computer vision. Instead of guessing, Eliana's coffee chain started gathering video footage from different store locations. Without delving into the intricacies, computer vision, much like our human eyes, can "watch" and "understand" these videos. It observed the baristas, noting when they were busy serving customers, when they were tidying up, and when they were, well, just waiting.
By analyzing these visual patterns, the computer could spot trends. Maybe on Tuesdays at 3 pm, there's a surprising mini-rush because of a nearby office's break time? Or perhaps on Sundays, the 11 am-1 pm slot is slow, and baristas end up wasting time?
With such insights, Eliana could now fine-tune her staffing strategy. If she noticed baristas were overwhelmed during specific hours, she could adjust shifts to have more hands on deck. The AI-backed data helped her reconfigure staff assignments and enhance operational efficiency.
The icing on the cake? Stores using this approach experienced smoother customer service, resulting in increased satisfaction. Think about it: Less waiting, more efficient service, and a happier customer who'd most likely return and perhaps even spread the word.
Reference: Good AI Vibes research.
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That’s all, another issue of Good AI Vibes comes to a close.
We were already floating from the joy of hitting 1K subscribers 💃🏻 🕺🏻, and now we have another reason to be on cloud nine ☁️: we've grown by an extra 20% since the last issue. Wow, our community is truly on the rise! Thanks for being a part of this journey with us. 🧡 💛
Until the next issue, keep the good vibes flowing. See you soon!
In case you missed our last edition, catch up on all the insights from Good AI Vibes #12 right here!
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