A 4-month, data-driven process excellence engagement with a large, diversified shared services organisation's order-to-cash function.
The client operates a multi-site, multi-function order management division whose order-to-cash team processes thousands of customer orders each month across a broad portfolio of global accounts. Facing mounting escalations driven by manual order entry errors — incorrect shipments, excess inventory, avoidable air-freight costs, and third-party quality audit fees — leadership engaged Stat Modeller to diagnose systemic root causes and restore order accuracy to benchmark levels.
Stat Modeller deployed a structured, phase-based consulting engagement combining rigorous process analysis, data-driven root cause identification, and targeted solution implementation to address the systemic drivers of order inaccuracy.
Scoped the improvement area and mapped the end-to-end order management process.
Quantified the baseline error rate and calculated the process sigma level.
Categorised 25+ error types, established Pareto priority, and identified 11 validated root causes through structured analysis.
Designed and piloted 13 targeted solutions, then verified their financial impact.
Deployed SOPs and institutionalised digital master data governance to sustain the gains.
Deep functional understanding of order-to-cash processes enabled Stat Modeller to diagnose root causes with precision and design solutions contextually relevant to shared services operations.
Stat Modeller owned the engagement from diagnostic design through solution implementation and control — ensuring continuity, accountability, and measurable handover to the process owner.
Every intervention was tied directly to a measurable business outcome. Financial impact was independently validated, and the engagement concluded only when sustained improvement was verified.
Let's talk about what a structured, data-driven improvement programme could look like for your order-to-cash function.
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