Planning software selection: optimize implementation risk, not the analyst ranking

Source attribution: Horizon Solutions podcast with Martijn Lofvers, “IT vs Supply Chain, Magic Quadrants, Market Trends and Selecting Planning Software.” This Radar entry curates and interprets the original conversation; it does not imply any partnership, sponsorship or endorsement. Planning software selections often devote disproportionate effort to comparing product functionality while underestimating implementation. One of the strongest lessons from the discussion is that buyers need to define their own planning requirements rather than allowing analyst classifications or vendor demonstrations to define the problem for them. That begins with understanding the current planning process and its white spots. The organization should identify which decisions are slow, poorly supported or disconnected from financial outcomes, then test vendors against those requirements. Implementation should be evaluated as part of the product decision. Partner capability, time to value, references, change requirements and the organization’s own capacity to absorb a new planning process can outweigh incremental differences in feature coverage. The business case also matters. Framing planning technology primarily as a way to reduce planner headcount understates its potential value and may produce the wrong design objective. Working capital, service, responsiveness and growth are more direct measures of whether better planning decisions matter to the company. Decision intelligence, probabilistic planning and AI add new possibilities, but they do not remove these selection disciplines. They increase the need to understand where recommendations come from, how they enter the planning cycle and whether the organization can act on them. Dataleo interpretation: the best planning system is not necessarily the product with the highest theoretical functional fit; it is the system that can reliably improve the company’s important decisions after implementation. The condition for value is a business case tied to operational and financial outcomes. The main failure mode is optimizing the RFP score while underweighting implementation complexity and adoption.