Each plant reports differently. Metrics are defined inconsistently. Comparing Site A to Site B requires manual normalization that your team does not have time for — so comparisons are approximate at best. When the board asks which plant is performing best on quality, the honest answer is often "it depends on how we define quality" — because each facility measures and reports differently.

When evaluating automation, capacity expansion, or quality systems, the business case depends on data that is scattered across spreadsheets and tribal knowledge. The ROI projections you present to the board are built on assumptions because the operational data that would validate them does not exist in an accessible, trustworthy format.

Continuous improvement programs are presented in quarterly reviews with anecdotal wins. Actual impact on cost, throughput, and quality is difficult to isolate because the data is not connected to execution. McKinsey estimates that only 30% of CI initiatives deliver sustained, measurable financial impact (McKinsey, "The Lean Management Enterprise," 2014). Without execution-level data, you cannot distinguish the 30% from the 70%.

Your best plant manager retires, and that facility's performance degrades within a quarter. A key planner leaves, and the S&OP process deteriorates. When operational discipline depends on individuals rather than systems, personnel changes become operational risks — and succession planning cannot solve a systems problem.

When demand planning, production scheduling, and material availability are managed in separate systems by separate teams, the gaps show up as expediting costs, overtime, missed shipments, and OTIF penalties. For a mid-market manufacturer, these planning-related costs typically represent 3-7% of COGS — a figure that is large enough to matter but distributed enough to remain invisible without integrated data.