Project Overview – Charter Steel

About Charter Steel:

Large US-Based Metals Company
Annual Revenue: $750 Million
Year Founded: 1975
Headquarters: Saukville, WI
ERP: Oracle
ImpactECS customer since 2015

Before ImpactECS:

Existing Product Costing Process

Oracle in place, but limited costing capabilities available to finance team

• Existing General Ledger account structure not defined at cost or work center level

Spreadsheet models used as primary tool to calculate product costs

• Unable to allocate direct and indirect overhead at the product level
• No visibility into actual cost performance
• Unable to build forecasts at the ‘Product + Location’ level
• Unable to calculate accurate costs for new products in a timely manner

How Charter Steel uses ImpactECS:

Standard Costs (Frozen and Current)

• Calculates detailed billet, green coil, processed coil and packaging costs by location
• Determines cost in both standard UOM (lbs.) and alternate UOM ($/ton) in local currency
• Calculates new product costs on daily or hourly time frame

Actual Costs

• Builds overhead rates from direct and indirect spending stored in General Ledger accounts
• Allocates General Ledger spending to products by cost or work center at the specific routing step
• Analyzed variances to standard at multiple levels: BOM, routing, heat (batch), or actual material quantity used

Inventory Valuation

• Value raw materials, work-in-process, semi-finished goods and finished goods
• Calculate weighted material cost from purchase receipts

Profitability
• Margin and net profit analysis by customer, product hierarchy, finished good item, or any relevant dimension
• Profit & loss visibility by individual customer and item

Simulations

• Adjust any cost driver variable (production volume, throughput rates, raw material prices) to determine effect on costs and profits
• Maintain unlimited scenario versions for planning and comparison

Forecasting

• Convert ‘Customer + Quality’ sales forecast to a ‘Location + Product’ forecast using historical production data
• Generate forecasts for multiple locations using varied assumptions on price, volume, and sales mix.

 

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