The evolution of planning is an ecosystem of three components: cloud technologies, algorithms, and data. Currently, however, the technology and the use of algorithms are advancing faster than the data.  Further, many essential data points are not being captured. Examples include external competitive information and macroeconomic variables. To capitalize on this new approach to AI-driven supply chain planning, companies cannot wait to begin capturing and preparing this data. The deep science and algorithms behind AI systems necessitate—and excel with—a variety of data to deliver usable results.  This data must be captured, cleansed, and managed in order for AI to deliver the expected value.

Due to existing systems, most current planning is highly clerical. Traditionally, planners input a single number into their ERP or planning systems, which was never more than an educated guess. These numbers were often wrong due to insufficient data and incomplete science. AI can augment the role planners play in the business because they will be informed with science-driven information. Because planners have deep knowledge of the business, their new role will include solving problems that the analytics engines will inform, but that cannot be solved by a machine, especially when considering cross-functional collaboration.

Introducing AI into the planning function is not a matter of “human vs. machine.” Rather, AI provides the ability to automate mundane tasks and let the planner role perform more value-added activities. Essentially, as the role of the planner evolves strategically, many of the clerical tasks may indeed be obsolete. Further, because supply chains are becoming increasingly complex, AI has the opportunity to elevate the planner role through the ability to make smarter, more strategic decisions.

Supply chains cannot operate with autonomy. The ability of AI technologies to make projections in real-time provides companies the ability to make quicker, yet more strategic decisions across all functional areas, such as marketing, pricing, and finance. This transformation towards more nimble, collaborative companies—albeit drastic—is long overdue. More importantly, the value is amplified where touch points between these functions are connected. We talked about this in a recent blog post, “Demystifying the Unified Demand Signal.”

Further, the S&OP cycle can be greatly reduced. The monthly cadence around the S&OP cycle will be reduced to real-time coordination between functions and drive towards more strategic integrated business planning. In these scenarios, planning will be facilitated by AI models that incorporate inputs from across the organization.