Supply Chain VELOCITY KPIs Use Case
Removing speed bumps to growth
A precision custom metal stamping and machined components manufacturer in Greenfield, Indiana, has been family-owned for nearly six decades. They have add-on plants in Columbus, Ohio, and Marietta, Georgia, completed in 18 months. A third acquisition is under due diligence.
Two acquisitions in 18 months had proven the business could grow. What hadn't been proven was whether it could move — cash, inventory, and capacity — fast enough to keep up.
Fast growth, slow cash
CASH-TO-CASH CYCLE TIME | INVENTORY TURNOVER | OEE | SUPPLY CHAIN CYCLE TIME | CAPACITY UTILIZATION
Inventory Turns at 3.9x, cash-to-cash cycle time at 124 days, Columbus capacity utilization at 58%. None of these numbers were where they needed to be, and none of them were telling the full story. The 124-day C2C was the most urgent problem, but not primarily because of inventory.
The fastest path to cash improvement ran through the DSO/DPO gap — 60 days collecting from customers, 30 days paying suppliers, a net 30-day drag before inventory was even considered. That gap was addressable. Inventory improvement takes longer and requires fixing the planning process underneath it first.
Decomposing the 124 Days
Decompose the 124-day C2C and the components are straightforward: DIO of 94 days at 3.9x turns, DSO of 60 days, DPO of 30 days. The inventory level was the largest single contributor, and the cause wasn't demand volatility or a deliberate safety stock policy. It was a set of purchasing behaviors that had developed, plant by plant across Greenfield, Columbus, and Marietta, in the absence of a reliable demand forecast.
Minimum order quantities were sized to capture unit price breaks — saving pennies per component while locking weeks of working capital into parts that might not move for months. Suppliers with long or unpredictable lead times pushed buyers toward large advance orders to protect their own schedules. Production runs were stretched to spread setup time across more units, leaving stacks of WIP at each step waiting for the next operation. Each decision was defensible in isolation.
The aggregate effect was a cash position that looked like a strategic inventory build but was actually the accumulated result of planning without a reliable signal.
Order management adding to the AP burden
A separate problem was compounding the AP bottleneck already identified in the cash analysis.
Several commodity categories carried quarterly price adjustments — a standard commercial arrangement. Procurement had correctly implemented blanket purchase orders to reduce transaction volume and administrative overhead. The discipline broke down on purchase order maintenance: when prices adjusted quarterly, the blankets weren't being updated to reflect the new rates. Invoice discrepancies that should have been resolved upstream at the blanket level were instead landing in AP for manual reconciliation, on top of the approval delays already in the queue.
The AP team was absorbing a problem that originated in order management
Track the right supply chain KPIs
Supply chain excellence is not confined to warehouses and logistics—it radiates outward, transforming every corner of the enterprise. When supply chain operates at its peak, the entire organization elevates.
Get the executive guide to the top supply chain KPIs, including formulas.
Supply chain is not a department—it is a discipline that permeates every function, influences every decision, and determines every outcome. To master supply chain is to master business itself.
Shortages and excess
The same planning process that was generating excess inventory on well-understood SKUs was chronically short on others. When a component ran out, production scrambled — expediting fees, partial builds, unplanned changeovers. Marietta compounded this further: a pattern of accommodating customer reschedules and accepting small orders outside the normal planning cycle was feeding irregular demand into a scheduling process that had no reliable way to absorb it. The result was more variation, more setups, and less throughput across the network than the utilization numbers suggested was possible.
Columbus's 58% utilization wasn't spare capacity available for new work. It was capacity being fragmented daily by interruptions the planning process had created and couldn't resolve.
Dave, the Columbus plant manager, was direct: "Don't assume we have excess capacity based on these utilization numbers. In fact, I'm confident utilization will go down as we add more variations to the mix we haven't run before. Changeovers in final assembly are killing us."
Three Plants that weren't planning as one
Having three plants doesn't automatically mean three plants' worth of flexible capacity.
Greenfield, Marietta, and Columbus were each serving their own customer sets and running their own product mix with no integrated planning logic between them. A volume spike at one plant couldn't be absorbed by another without significant coordination that the two-ERP environment made impractical. The capital plan for the datacenter opportunity had assumed the operations could deploy capacity flexibly across sites if needed. The lack of a coordinated planning process proved that assumption invalid.
The COO was reconciling spreadsheets from both ERP systems by hand the day before every planning meeting. Three plants should produce one operational picture. What existed was three versions, assembled once a week manually
Why this made the datacenter opportunity difficult
A Tier 1 datacenter contractor's RFQ asked for a credible 12-month ramp plan. What existed was a utilization figure Dave had already explained couldn't be taken at face value, produced by an S&OP process with no shared item master and no common scheduling logic across the three plants. A capacity commitment built on that foundation would not have survived the first question from a procurement team experienced in supplier qualification.
Technology considerations
No OEE baseline existed at any of the three plants.
Instrumenting the Columbus lines manually to establish that baseline was the first step — before any sensor-level or IIoT investment could be justified. IIoT and digital twin tools are the right long-term answer for line-level visibility and predictive maintenance, but they require a measurement foundation to deliver value.
Integrated demand planning and APS deployment is the right medium-term call, sequenced after the ERP rationalization is resolved. Deploying planning software across two separate ERP instances compounds the complexity it is meant to reduce.
What moved
Inventory Turns improved from 3.9x to 4.6x as minimum order quantities were renegotiated and production run lengths were set by demand signal rather than setup cost avoidance. Blanket POs were brought current on pricing, eliminating the invoice reconciliation work that had been accumulating in AP. A single S&OP cadence replaced three independent planning processes across Greenfield, Columbus, and Marietta. The cash-to-cash cycle time compressed from 124 days to 109 days as inventory levels came down and payables discipline improved. Dave's team established an OEE baseline of 61% for the first time and was using it to prioritize changeover reduction in final assembly — the specific constraint standing between the current utilization number and a ramp plan the datacenter customer could trust.