Customer Story: US Based Steel Manufacturer

With a limited costing process in place, this US Based steel company was unable to calculate accurate and timely product cost answers at either the product or location level. Limitations with their ERP costing module, led the team to a process heavily burdened with spreadsheets. With constantly shifting market conditions, the lack of visibility into actual cost performance and quick access to cost analytics impeded their ability to evaluate profits for both products and customers.

By leveraging ImpactECS, the finance team established an end-to-end costing and profitability process that allowed them to calculate standard and actual costs, allocate direct and indirect
overhead spending at the product level, value inventories and generate forecasts for all locations using varied assumptions on price, volume, and sales mix.

Company Type
Large US-Based Metals Company

Annual Revenue
$750 Million

Year Founded

Saukville, WI

Oracle (ERP)

By reducing the time to calculate product costs from 4 days to a few hours a month, the team was able to shift their focus from managing spreadsheets to supporting their commercial teams. They were able to generate margin reports daily, so they could keep their eyes on profits and quickly adjust to changing demands and market dynamics.

Benefits of ImpactECS

  • Calculate detailed billet, green coil, processes coil and packaging costs by location, and determine cost in both standard and alternative units of measure in local currencies on both a daily and hourly timeframe
  • Build overhead rates from general ledger direct and indirect spending accounts and allocate costs to products by work center or routing step
  • Value raw materials, WIP, semi-finished goods and finished goods, and calculate weighted material cost from purchase receipts
  • Analyze margins and net profits by customer, product hierarchy, or finished goods and generate P+L views for products and customers
  • Convert ‘Customer + Quality’ sales forecasts to ‘Location + Product’ forecasts using historical production data

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