The challenges of traditional inventory optimization
Since there have been supply chains and warehouses, one of the greatest challenges to achieving inventory optimization has been the balancing act between “just enough” and “not too much.” Demand forecasting has traditionally been a backward-looking practice. Even though inventory optimization and demand forecasting experts are very skilled, there is only so much that human analysis and prediction can accomplish. Therefore, linear supply chains that are powered by legacy systems will always be vulnerable, no matter how much expertise is applied. Some of the most common challenges include:
Legacy systems that can neither gather nor manage big data
Manual and non-connected technologies cannot handle volumes of disparate and unstructured data. From this data – through the application of smart technologies like AI, machine learning, and advanced analytics – some of the greatest accuracy is achieved from risk prediction to demand forecasting.
Fast-moving customer demands
Every year, consumer demand for speedy delivery and customized products is growing. Also, product lifecycles are shorter than ever. It’s expensive for companies to ramp up their logistics and supply chain networks to meet these demands, so greater precision is being asked for from inventory optimization.
An implication of Industry 4.0 and intelligent, connected supply chain technologies is that businesses can set up and grow faster than ever – all managed from a central hub. This has led to an unprecedented level of competition and consumer choice. Inventory optimization solutions are increasingly sought after to help provide a competitive edge.
Weather events and natural disasters
Every year, we are seeing more debilitating storms and destructive wildfires. There is no way to accurately predict such events, but with the use of advanced analytics and cloud-connected solutions, inventory managers can give themselves a fighting chance during the resulting periods of wavering demand.
Source: SAP insights