Digitization doesn’t only improve online retail. Bricks-and-mortar businesses can also benefit from such technical innovations. Artificial intelligence is accredited with a key role in this. Big data and predictive analytics assist with demand forecasting and supply chain optimization and provide numerous benefits for logistics processes.
Smart systems learn from data from real-life examples. They analyze and evaluate patterns and rules and use them to make decisions. This capability enables retailers to pool data from customers, suppliers, and inventory levels. Artificial intelligence (AI) is capable of analyzing these masses of data to gain a better overview of offers and demand or to counteract supply issues at an early stage. As a result, the entire supply chain is structured more efficiently, whereby logistics specialists can compile better sales forecasts and reduce storage costs.
Keeping Sales on Track
The possibility of analyzing records is clearly a not new phenomenon. However, with the help of AI, the increasingly huge amounts of data can now be generalized more quickly and to a higher standard. In this way, the technology makes billions of automated decisions on a daily basis. Karlsruhe-based company Blue Yonder offers a solution of this type. By integrating new technology, probabilistic forecasts can be performed. This involves different variables, including the weather, public holidays, vacations periods, or discount campaigns.
The multichannel provider Otto (German language only) already utilizes an AI solution from Blue Yonder to create accurate sales forecasts for the upcoming days. Their own products as well as partner products are offered on an online platform. But delivering third-party brands often took a long time. Using automated order decisions reduced delivery times by up to two days – without accumulating deadwood on the shelves. As the system predicts a high probability of which products will be ordered in which amount over the next few days.
The supermarket chain Kaiser’s Tengelmann (German language only) also uses this type of solution to plan sales and reduce the costs of planning store replenishment. As customer demand is different in each store, the primary focus was on demand-based goods sourcing at different locations. Through precise sales forecasting, Tengelmann was able to minimize many shortages of stock. This leads to greater customer satisfaction by reducing the frequency of products selling out.
Intelligent Market Analysis
When integrating such systems, it is important that AI is trained with the correct data. Only then can the technology draw the right conclusions from new data records and minimize inaccurate analysis. At the same time, forecasts must continuously be compared with the actual sales figures in order for the system to perfect recommendations. The ultimate goal is completely design the control of goods flows to be automated and performed in advance so that it can be better adapted to any future market changes.