Digital Transformation in Coffee Retail: Reducing Waste and Improving Customer Focus with AI-Powered Forecasting
Technology Category
- Functional Applications - Inventory Management Systems
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Recycling & Waste Management
- Retail
Applicable Functions
- Warehouse & Inventory Management
Use Cases
- Demand Planning & Forecasting
- Inventory Management
Services
- Data Science Services
About The Customer
The customer is a multinational coffee roaster and retailer, with a network of over 30,000 coffee houses around the world. The company is committed to reducing waste and improving customer service, and as part of this commitment, it initiated a large digital transformation program. The company has invested in data science teams and developed proprietary algorithms to predict the impact of weather on demand and store traffic. However, it was facing challenges in terms of significant food waste due to inaccurate forecasting and baristas spending too much time on administrative tasks rather than customer service.
The Challenge
A multinational coffee roaster and retailer, with a network of over 30,000 coffee houses worldwide, was facing significant challenges in its operations. The company's baristas were spending around six hours a day on administrative tasks such as ordering, inventory management, and forecasting, which was detracting from their ability to focus on customer service. Additionally, the company was grappling with a significant food waste problem due to inaccurate forecasting. This issue was complex, as each store stocked between 500 and 5,000 SKUs, and demand volatility was influenced by factors such as weather, assortment, pricing, and local events. The company had invested in data science teams and developed proprietary algorithms to predict the impact of weather on demand and store traffic, but these were not being utilized to their full potential.
The Solution
The company initiated a large digital transformation program with o9, aiming to reduce administrative work for baristas and decrease waste through AI-powered forecasting, assortment planning, and replenishment on a single platform. The o9 platform was able to automate forecasting, replenishment, inventory management, and assortment planning, using analytics and minimal manual input. The company was also able to forecast at a Store-SKU level, incorporating leading indicators such as weather and local events. For local events, an app was developed to support baristas in inputting their local knowledge about the market. Changes in weather or local events would drive auto-replenishment based on o9’s digital brain. The company's proprietary algorithms, developed in Python, were incorporated into the o9 platform, allowing the company to run AI algorithms at scale.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Improving Production Line Efficiency with Ethernet Micro RTU Controller
Moxa was asked to provide a connectivity solution for one of the world's leading cosmetics companies. This multinational corporation, with retail presence in 130 countries, 23 global braches, and over 66,000 employees, sought to improve the efficiency of their production process by migrating from manual monitoring to an automatic productivity monitoring system. The production line was being monitored by ABB Real-TPI, a factory information system that offers data collection and analysis to improve plant efficiency. Due to software limitations, the customer needed an OPC server and a corresponding I/O solution to collect data from additional sensor devices for the Real-TPI system. The goal is to enable the factory information system to more thoroughly collect data from every corner of the production line. This will improve its ability to measure Overall Equipment Effectiveness (OEE) and translate into increased production efficiencies. System Requirements • Instant status updates while still consuming minimal bandwidth to relieve strain on limited factory networks • Interoperable with ABB Real-TPI • Small form factor appropriate for deployment where space is scarce • Remote software management and configuration to simplify operations
Case Study
How Sirqul’s IoT Platform is Crafting Carrefour’s New In-Store Experiences
Carrefour Taiwan’s goal is to be completely digital by end of 2018. Out-dated manual methods for analysis and assumptions limited Carrefour’s ability to change the customer experience and were void of real-time decision-making capabilities. Rather than relying solely on sales data, assumptions, and disparate systems, Carrefour Taiwan’s CEO led an initiative to find a connected IoT solution that could give the team the ability to make real-time changes and more informed decisions. Prior to implementing, Carrefour struggled to address their conversion rates and did not have the proper insights into the customer decision-making process nor how to make an immediate impact without losing customer confidence.
Case Study
Digital Retail Security Solutions
Sennco wanted to help its retail customers increase sales and profits by developing an innovative alarm system as opposed to conventional connected alarms that are permanently tethered to display products. These traditional security systems were cumbersome and intrusive to the customer shopping experience. Additionally, they provided no useful data or analytics.
Case Study
Ensures Cold Milk in Your Supermarket
As of 2014, AK-Centralen has over 1,500 Danish supermarkets equipped, and utilizes 16 operators, and is open 24 hours a day, 365 days a year. AK-Centralen needed the ability to monitor the cooling alarms from around the country, 24 hours a day, 365 days a year. Each and every time the door to a milk cooler or a freezer does not close properly, an alarm goes off on a computer screen in a control building in southwestern Odense. This type of alarm will go off approximately 140,000 times per year, equating to roughly 400 alarms in a 24-hour period. Should an alarm go off, then there is only a limited amount of time to act before dairy products or frozen pizza must be disposed of, and this type of waste can quickly start to cost a supermarket a great deal of money.
Case Study
Supermarket Energy Savings
The client had previously deployed a one-meter-per-store monitoring program. Given the manner in which energy consumption changes with external temperature, hour of the day, day of week and month of year, a single meter solution lacked the ability to detect the difference between a true problem and a changing store environment. Most importantly, a single meter solution could never identify root cause of energy consumption changes. This approach never reduced the number of truck-rolls or man-hours required to find and resolve issues.
