With retail undergoing a massive transformation, machine learning has become an innovative asset, changing the way organizations deliver customer experiences.
As companies adopt advanced technologies that are easily implemented and show worthwhile return on investment, there is a massive opportunity to help customers take and complete this digital journey – not just to innovate, but to scale as a digital business. With artificial intelligence (AI) and machine learning platforms, organizations’ front and back-office processes are evolving.
Many food and beverage companies are using AI, machine learning, and automation to revolutionize critical aspects of their businesses. Food and beverage retailers who stand out are leveraging a suite of machine learning platforms to guarantee products are maintained properly. With this technology, retailers can ensure their beverages, refrigerated food, and frozen food products are always stored at the right temperature and safe for consumption.
In addition, the technology allows retailers to accurately track inventory, monitor maintenance needs, and reduce losses due to spoilage or theft. Here’s why there’s never been a better time to adopt machine learning and achieve retail success:
Tracking every move
The increasingly complex supply networks and the need to coordinate logistics flows across multiple suppliers, present major challenges across the supply chain when tracking inventory. Slow reactions within the supply chain cause inefficient use of fleet, wasting both time and money, and failing customer expectations for on-time delivery.
Additionally, negligence during transportation can also result in damaged goods leading to more unsatisfied customers. These challenges prompt the need to monitor delivery as well as shipment status – and the involved connected logistics equipment (like trucks) – to have a real-time view of the current location of products. By compares the planned and current logistic flows, retailers can quickly react to unexpected conditions and deviation from plans.
Furthermore, to guarantee the on-time availability of components for the efficient processing of orders, retail companies need to implement technologies that control and monitor their complete supply and delivery chain. With an IoT-optimized tracking system, production, and delivery logistics are improved, inventory levels are increased, logistics costs are reduced, and on-time delivery rate is enhanced. This makes it possible for transportation management companies to increase customer satisfaction as products are received on-time, and in working condition.
If it’s broke, fix it
Cold chain monitoring, especially for companies with large global operations, represents both significant investment and maintenance challenges. Refrigerators, coolers, and freezers all see frequent use by restaurants and retailers, and while they are built to withstand wear and tear, asset failure not only comes with the cost to repair the appliances, but the cost of incurred losses from spoilage. Machine learning adapts the data provided through connected devices to practical applications. In this way, retailers can monitor and adjust average ambient temperatures and temperature variations from opening and closing of doors, ensuring consumers receive satisfactory products.
Additionally, today’s IoT connected appliances generate massive volumes of data from sensors and present a greater opportunity for continuous machine learning to turn this data into value-creating assets. With this data, retailers can establish a plan for predictive maintenance in advance of asset failure. By maximizing equipment uptime and ensuring consistent temperatures within pre-set tolerances, machine learning technology makes it possible for retailers to deliver the highest quality and full shelf-life products to their customers.
Stop shop loss
Retail companies selling beverages, refrigerated products, and frozen foods need to manage freezers, coolers, and other refrigeration units in their stores. These cooling units do present a significant ongoing investment in assets, maintenance, and inventory. These appliances need to be monitored to minimize or eliminate lost revenue due to spoilage or product expiration.
Machine learning, along with AI platforms, have also helped food and beverage retailers automate inventory management. By initiating machine-learning processes where employees take photos of store shelves, sensors within the platforms can identify which items are missing or incorrectly displayed. With this technology, store managers and warehouses can automatically be notified to organize or restock the shelves properly, ensuring that customer demand is met. Shelf management is an important part of reducing product loss, whether it’s from vandalism, damage, or theft.
In the food and beverage industry, retailers, producers, and restaurants are rapidly changing their business strategies to incorporate new, innovative technology to stay ahead of competition, meet consumer demands, and provide an enhanced experience.
Through the implementation of AI and machine learning technologies that provide a 360-degree view of both the consumer and their everyday operations, retailers can transform business processes and directly improve the customer journey. By investing in platforms that produce beneficial insights to facilitate this process, while also optimizing production and inventory, retailers within the food and beverage industry will see significant ROI and increase customer engagement.