"We were about to hire 12 people to handle the same volume. We didn't need more people."
A Southeast US building materials manufacturer was losing bids on turnaround time and couldn't understand why. The answer wasn't headcount — it was a quoting process that nobody had ever mapped.
Tier
Tier 1 + Tier 2
Interviews
19
Duration
4 weeks
Organisation
480 employees · 2 plants
■The Situation
A building materials manufacturer in the Southeast US was growing in revenue, but not in margin. Their sales team was losing commercial construction bids — particularly on time-sensitive projects — and had attributed it to pricing. The COO suspected the real issue was turnaround time: competitors were returning custom quotes same-day. His team was taking three to four days.
The company had 2 production plants and 14 distribution centers across six states. They had added 8 people to the quoting team in the past three years, and the sales director was pushing to add 12 more. Before committing to the headcount, the COO wanted an independent view of whether the bottleneck was people or something else entirely.
Kerf was brought in to map the quoting workflow first. If the findings were material, the brief would expand to cover Supply Chain, Distribution, and Production Planning.
■What We Did
One week inside the sales and quoting operation, then three additional weeks across Supply Chain, Distribution, and Production Planning. 19 interviews across every role that touched order flow from enquiry to delivery:
Sales & quoting (n=8)
We mapped the end-to-end custom quote workflow from inbound enquiry to quote delivery. We documented every system accessed, every manual lookup, and every step that required an email or phone call to complete. We timed median completion time per step with the team present.
Supply chain & inventory (n=6)
We mapped the inventory replenishment and demand forecasting process across all 14 distribution centers — every manual input, every spreadsheet dependency, every decision made from experience rather than from data.
Distribution & freight (n=5)
We mapped the daily load planning process at three representative distribution centers. The freight cost variance between centers for equivalent loads was significant and entirely attributable to manual, unoptimised planning.
The quoting findings were presented at the end of week one. The 3–4 day turnaround was not a people problem. It was a data-assembly problem: producing a quote required pulling current stock levels, production availability, freight costs, and customer pricing tiers from four disconnected systems — accessed manually, in sequence, by the quoting rep. Representatives were spending 2–3 hours per quote on data gathering before writing a single number.
The COO commissioned the full engagement immediately. 'If we've been solving the wrong problem for three years, I need to know what the right problem is across the whole business.'
■What We Found
19 opportunities worth $2.1M annually. The 12 planned hires were never needed.
The custom quoting workflow was the single largest opportunity. A unified quoting interface with automated data integration would cut quote preparation from 2–3 hours to under 20 minutes, enabling same-day turnaround on 80% of enquiries. Estimated impact: $680,000 per year in recovered deals based on a conservative 12% improvement in close rate on time-sensitive bids.
Inventory was the second major finding. The 14 distribution centers had no centralised demand signal — each location was replenished based on a local manager's manual review of historical patterns. At any given time, 23% of SKUs across the network were overstocked beyond 60 days. Demand forecasting automation would reduce overstock below 8%, saving an estimated $420,000 per year in carrying costs and working capital.
Freight routing was the third significant finding. Daily load planning was performed manually at every distribution center without optimisation tools. The variance in freight cost per mile between the best- and worst-performing centers for equivalent loads was 31%. AI-assisted load optimisation across the network was estimated to save $310,000 annually.
The Opportunity Matrix flagged an $800,000 ERP integration project the COO had been planning to approve as 'Deprioritise.' The integration would have addressed exactly one of the 19 identified opportunities — at 40 times the cost of the targeted solution for that single problem.
“We've been quoting the same way for eleven years. I assumed changing it would take 18 months. It took six weeks.”
— COO, Southeast US Building Materials Manufacturer
■What Changed
The 12 additional quoting hires were put on hold. Instead, the team received an AI-assisted quoting interface that pulled live data from all four systems automatically — built and deployed in six weeks. Same-day quote turnaround went from 12% of enquiries to 78% within the first month of deployment.
Three further Quick Wins were deployed within 90 days: AI-driven demand forecasting across all 14 distribution centers (overstock reduced from 23% to 9% of SKUs), automated freight routing optimisation (freight cost per mile down 19% across the network), and AI-assisted production scheduling (manual planning time cut from 6 hours to 45 minutes per week per plant).
The $800,000 ERP integration was declined. In its place, a targeted API integration — one of the identified Quick Wins — was completed for $18,000 and addressed the specific bottleneck the ERP project would have targeted at a fraction of the cost.
$1.1M in annualised savings realised within 90 days. Quote turnaround cut from 3–4 days to same-day for 78% of enquiries. The 12 planned headcount additions were not made. Two Strategic Bets representing an additional $1M in annual savings entered the roadmap.
Decision Impact
4-week engagement · 19 opportunities identified · $2.1M savings identified · $1.1M realised in 90 days · 12 planned hires avoided · $800K ERP project deprioritised
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