These enterprise systems don’t come cheap. This is even more unsettling when considering they’re meant to interact with different deployments of ERP systems and with underlying systems that are themselves subject to regular modification and upgrades. Fortunately, a cognitive layer with a consistent data model can at least be a way to bring these disparate systems under one data roof.
And to address financial concerns, careful buyers will do well to ensure that the cost of these systems is directly connected to the benefits they are meant to provide. Ultimately, proper demand planning, for example, can reduce waste in the supply chain and help cover the costs, along with providing reduced working capital, better servicing, increased margins, and other direct benefits that justify the investment in digital transformation.
On the more human side, these analytical technologies tend to be a good fit with modern-style natural language processing technology, allowing humans to interact in time-saving, conversational ways, similar to the way they interact with Siri, Cortana or Alexa, which they do so at home as regular consumers.
Looking to 2019, the impending trade wars and other uncertainties will mandate increased agility and flexibility in supply chain operations. Technology must enhance human ability to perform analyses based on better data access and anomaly detection. Ultimately, the development of AI and machine learning will result in a future in which machines handle the lion’s share of analysis and action, with human involvement limited to supervision and value-adding input.