End-to-end: Machine Learning Benefits the Whole Supply Chain

As organizations begin to strategize for the year ahead, retailers and consumer product companies looking to thrive in the digital era know it’s pivotal to incorporate new technologies into their business. For many organizations, key technologies like artificial intelligence (AI) and machine learning, can revolutionize critical aspects of their businesses, facilitating these organizations to redefine the consumer experience.

Not only do these technologies play an important role in helping users embark on a digital journey, but by reshaping both front and back-office processes, retailers are better able to meet consumer demands and see worthwhile returns on investment. Through the adoption of AI and machine learning platforms, organizations now have a 360-degree view of the supply chain and are experiencing lasting impact on the following processes:

Procurement. With more variety of products on the market, supply networks have become increasingly complex and the need to coordinate with multiple suppliers presents major challenges when tracking inventory. Many companies are now adopting AI technologies to improve their procurement systems, to ensure the on-time availability of products, and to efficiently process orders. With automated systems that scan databases to find the exact, or nearest equivalent, product, the ordering process is reduced to a few simple clicks.

Delivery Logistics. To guarantee the on-time delivery of undamaged goods, retail companies need to implement technologies that control and monitor their complete delivery chain. Challenges like the inefficient use of fleet and negligence during transportation prompt the need to monitor delivery as well as shipment status. This helps to avoid wasting both time and money – and failing customer expectations. By utilizing connected logistics equipment (like trucks) and IoT-optimized tracking systems, retailers have a real-time view of the current location of products and can compare the planned and current logistic flows to quickly react to unexpected conditions and deviation from plans. This real-time monitoring makes it possible for transportation management companies to increase customer satisfaction as products are received on-time, and in working condition more frequently.

Inventory Management. Using AI platforms, retailers can automate shelf and inventory management, which is an important part of reducing product loss, whether it’s from vandalism, spoilage, or theft. By using machine-learning processes where employees take photos of store shelves, sensors can identify which items are missing or incorrectly displayed and initiate corrective actions. This way, store and warehouse managers will automatically be notified to organize or restock the shelves properly, ensuring that customer demand is met.

Maintenance. Additionally, today’s IoT connected appliances generate massive volumes of data from sensors and present an 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. Especially for retail companies selling beverages, refrigerated products, and frozen foods, the need to manage freezers, coolers, and other refrigeration units in their stores is essential. However, cooling units do present a significant ongoing investment in assets, maintenance, and inventory as these appliances need to be monitored to minimize or eliminate lost revenue due to spoilage or shelf-life expiration. 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 through proper maintenance.

Consumer Engagement. Organizations can use machine learning to provide automated customer service solutions through technologies that combine natural-language processing with consumer shopping data and history. With systems that allow consumers to ask questions and receive accurate and timely answers, response time is lowered and employees can shift their focus elsewhere, while consumer satisfaction is enhanced. Additionally, food and beverage producers, retailers and restaurants now employ AI technology to monitor social media conversations. The platforms can analyze consumer sentiment, along with additional data provided, to help companies make decisions on the best products to create and stock in their stores.

To stay ahead of competition, meet consumer demands, and provide an enhanced experience throughout the supply chain, it is imperative for companies to take the right steps to innovate when strategizing for the year ahead. With the right digital strategy coupled with the right technologies, such as AI and machines learning, these companies can be more successful than ever.

Lori Mitchell Keller is Global General Manager, Consumer Industries of SAP

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