FBN's order-to-cash operations had become highly fragmented. Service-level agreements were siloed by function and disconnected from customer outcomes. Teams lacked visibility into downstream timing dependencies, making it difficult to prioritize work in a way that protected the customer delivery promise. As a result, speed to customer was inconsistent and promise-to-delivery performance had fallen below 80%.
Order execution was governed by generic, time-based SLAs (e.g., "within 24 hours") that failed to reflect the actual sequencing flow required to deliver on customer commitments. As a result, the organization did not understand how delays impacted others and compounded across functions in the value chain. Teams optimized locally, reacted late, and relied on recovery heroics to protect customer outcomes. This fostered a blame culture with finger pointing and defensiveness getting in the way of root cause and continuous improvement.
I mapped the full order-to-cash value stream, clearly identifying ownership for each process step. Working backward from customer delivery and required transit time, we defined the latest allowable cutoff time for each upstream activity to ensure downstream steps could be completed on time, fulfilling an aggressive customer promise.
This reframed SLAs from fixed-duration targets to explicit daily cutoffs aligned to customer promise. Combined with real-time team-specific dashboards, orders approaching or missing their cutoff became immediately visible, enabling objective prioritization based on customer impact rather than intuition.
When an order missed a cutoff, the responsible team captured the reason in near real time, distinguishing between controllable and uncontrollable factors. Downstream teams were empowered to fast-track at-risk orders to recover performance when possible.
In a short period of time, this created a rich dataset highlighting hidden defects, friction points, and systemic constraints. In creating a Perfect Order measure we highlighted errors that did not always cause customer impact but were representative of serious systemic defects. Improvement efforts shifted from anecdotal and blame-focused problem-solving to targeted process and technology changes.
Most importantly, the organization transitioned from reactive firefighting to disciplined, customer-centered execution, blameless accountability, and meaningful continuous improvement. This case illustrates how clarity of flow, ownership, and input metrics transforms customer performance more effectively than effort or escalation alone.
During a period of rapid growth, FBN had accumulated significant inventory through opportunistic buying and an expanding SKU portfolio. Over time, a large portion of working capital became tied up in inventory that was no longer productive. While the company had made prior attempts to manage inventory, efforts were largely focused on high-velocity and high-value items. The long tail of slow-moving inventory remained poorly understood and largely unmanaged.
There were no formal routines, governance, or shared framework to identify non-productive inventory, challenge forecast assumptions, and drive consistent cross-functional action. As a result, inventory decisions were reactive, inconsistent, and ineffective at reducing structural working capital exposure.
I created a decision-focused inventory dashboard that visualized inventory value against two critical dimensions: a 12-month rolling inventory turn ratio and forecasted sales velocity. Interactive filters and sliders allowed leadership to quickly isolate where working capital was concentrated in slow-moving inventory.
The dashboard also enabled direct comparison between forward-looking forecasts and trailing 12-month consumption, flagging items where projected demand diverged materially from historical behavior.
These insights were embedded into a monthly operating routine where cross-functional leaders reviewed inventory risk, reduced SKU complexity, liquidated non-productive inventory, and challenged forecast assumptions.
At FBN, the Customer Experience and Support teams had formed organically. From sales support to order entry to ongoing customer care, they had become a catch-all for tasks and responsibilities that either did not fit elsewhere or no one else wanted. Over time this produced an everybody-does-everything model where cases and calls were routed and queued with no thought given to prioritization or who was best positioned to solve them.
Customers complained about long waits on urgent cases. Productivity was difficult to measure because cases varied significantly in complexity but were treated as equal when evaluating performance. SLAs were tied to broad categories like "new order" or "order issue" rather than the urgency or complexity of the actual issue, producing KPIs that were meaningless to the people doing the work.
The team itself was disengaged. Without clarity on what they were there to do, purpose was hard to find.
Upon taking ownership of the organization as an expansion of my responsibilities, I spent three months observing, learning the routines, and understanding the work and its challenges before making changes.
I started by defining a clear identity for the team: the voice of the customer, and the people responsible for understanding and measuring the customer journey from acquisition through delivery and beyond. That clarity of purpose was the foundation everything else was built on.
I then restructured the organization into four focused roles:
In parallel, I rebuilt the case management system around a severity and prioritization framework independent of case category. A farmer with a real-time delivery issue on his farm was treated as urgent. A routine account balance inquiry was not. Front-line staff could now triage and route accurately, and every team could manage their workload against SLAs that reflected both customer impact and their actual performance.
The team's engagement shifted meaningfully as well. With a defined purpose and data they could act on, they moved from reactive case handling to actively shaping the customer journey, using their insights to influence prioritization and drive improvement across the organization.