We found that across every industry surveyed, these companies are significantly outperforming Others in overall financial performance, as measured by enterprise value and EBITDA (earnings before interest, taxes, depreciation and amortization). These Leaders give us a window into what human and machine collaboration makes possible for all companies.
The keys to simultaneously addressing relevance, resilience and responsibility are advanced analytics and AI. Our study shows that Leaders are adopting these powerful tools at scale and, in the process, getting a head start in capitalizing on the significant opportunities created by human and machine collaboration.
For example, at least half of the Leaders said they are investing more than $5 million USD in AI-embedded connected products, AI virtual assistants, advanced data analytics, intelligent automation, Industrial Internet of Things sensors and AI-embedded connected products. Just under half said the same about ML/deep learning and sentiment monitoring analytics.
Furthermore 90% or more of both Leaders and Others agree that generating a return on this investment will require engaging with and scaling ecosystem partnerships with a wide range of players, acquiring and retaining analytics and AI-related skills and embracing key digital platforms.
Analytics and AI: Three critical use cases to drive immediate and significant value
Myriad use cases for supply chain analytics and AI exist, and the number continues to grow. But all use cases aren’t created equal. Some are more difficult to scale than others, and the impact on key business priorities can differ across use cases. This is why companies that are looking to increase their spending on and use of these technologies should focus their initial efforts to get the biggest return on their investment. We think three use cases, in particular, make the most sense as starting points—all of which can play a significant role in helping companies maximize relevance, resilience and responsibility.
1. Advanced scenario modeling
One use case that’s becoming increasingly important in the wake of COVID-19 is scenario modeling, often done with the help of a digital twin.
A digital twin is a virtual supply chain replica that represents assets, warehouses, logistics and material flows, and inventory positions—basically, an online, living version of a company’s backbone that can be used to simulate supply chain performance, including all the complexity that drives value loss and risks. A digital twin can be created for the end-to-end supply chain or for specific functional areas for targeted improvements.