Should-Cost Modelling is an analytical process used to determine the optimal, fact-based cost of a product or service. This methodology serves as a strategic tool for procurement and engineering teams, providing an independent estimate of an item’s true value, distinct from any price quoted by a supplier. It shifts the focus from accepting a market price to understanding the underlying economic structure of a good. By establishing this internal benchmark, organizations can assess the fairness of vendor proposals and identify opportunities for optimization across the supply chain.
Defining Should-Cost Modeling
Should-Cost Modeling is a structured, bottom-up approach that decomposes a product into its fundamental elements to calculate the most efficient cost achievable under optimal production conditions. This calculated figure, the “should cost,” represents a theoretical, optimized price point, not necessarily the current market price or the price a supplier quotes. The analysis requires breaking down every step of the manufacturing process, from raw material consumption to final delivery, to build the cost back up from scratch.
The “should cost” differs from the “actual cost,” which is the price a supplier presents, reflecting their specific operational efficiencies, overhead structure, and desired profit margin. The model’s purpose is to uncover the gap between the quoted price and the optimized cost, providing a data-driven foundation for negotiations. This technique moves beyond historical pricing data, focusing instead on what the product could cost if best-in-class manufacturing practices and competitive input prices were applied. The output is an objective financial target that reflects an achievable price, assuming reasonable production efficiency and a competitive profit for the manufacturer.
The Core Components of a Should-Cost Model
The should-cost model relies on summing distinct cost categories that represent the full expenditure required to produce an item. Each category must be meticulously calculated using specific, measurable data points to ensure the model’s accuracy. This breakdown provides the granular detail necessary to pinpoint specific cost drivers within a supplier’s quote.
Direct Material Costs
Direct Material Costs include the outlay for all raw materials and purchased components incorporated into the final product. Analysts calculate this cost using the net weight of each material, factoring in the current commodity market price. Material scrap rates, which account for unavoidable manufacturing waste, are also included to reflect the total input volume required.
Direct Labor Costs
Direct labor covers the wages and benefits paid to personnel who physically work on the product during manufacturing or assembly. Calculation relies on detailed time studies to determine the cycle time for each production step. These cycle times are multiplied by regionally adjusted wage rates, accounting for local economic factors, including mandated benefits and taxes. The resulting figure represents the labor expenditure for a single unit.
Manufacturing Overhead and Burden Rate
Manufacturing overhead encompasses all indirect costs associated with operating the production facility that cannot be traced to a specific product unit. This includes fixed costs like rent, property taxes, and equipment depreciation, alongside variable costs such as utilities and indirect materials. Total overhead is converted into a burden rate, often expressed as a cost per machine or labor hour, which is then allocated to the product based on the time it spends in the facility.
Research, Development, and Tooling Costs
This category captures one-time expenses necessary to bring a product into production. Research and development (R&D) costs cover the initial engineering design and testing required to create the product specifications. Tooling costs, such as molds, dies, or specialized fixtures, are also included and must be amortized across the expected total lifetime production volume of the part. This amortization ensures the model reflects a portion of this capital investment in every unit’s cost.
General and Administrative (G&A) Costs and Profit Margin
General and Administrative (G&A) costs cover the expenses of running the overall business, separate from the production facility, such as executive salaries, sales and marketing, and corporate accounting. These costs are generally applied as a percentage of the total manufacturing cost. Finally, a reasonable profit margin is added to the total cost base to reflect a sustainable and competitive return on investment for the supplier. This margin is based on industry benchmarks and the product’s strategic nature.
Strategic Value and Benefits of Should-Cost Analysis
Should-Cost Analysis provides a foundation for fact-based negotiations, transforming discussions from price haggling into a structured conversation about cost drivers and efficiency. With a detailed cost breakdown, the buying organization can challenge inflated pricing and propose specific cost reduction targets. This data-driven approach fosters transparent supplier relationships focused on mutual value creation.
The analysis also identifies inefficiencies and waste within the product and supply chain. Comparing the should-cost model to the quoted price often reveals suboptimal supplier processes or design choices. These discrepancies point toward opportunities for process re-engineering or material substitution that benefit both parties.
The model’s output serves as an objective internal benchmark for Design-to-Cost initiatives during product development. Engineers use the should-cost data to evaluate the cost impact of design decisions before they are finalized. This ensures product specifications are aligned with achievable cost targets from the earliest stages, preventing costly redesigns later in the lifecycle. The model provides the framework to continuously monitor and manage cost performance over the product’s lifespan.
Step-by-Step Methodology for Building a Should-Cost Model
Building a reliable should-cost model begins with a rigorous data gathering phase. This requires collecting all technical specifications, including engineering blueprints, the Bill of Materials (BOM), and detailed process documentation outlining manufacturing steps. Market intelligence is simultaneously collected to determine current pricing for raw commodities, labor rates in relevant regions, and prevailing overhead rates for similar manufacturing operations.
Once data is compiled, the model construction phase translates physical specifications into financial outputs. Specialized software or spreadsheets perform the bottom-up calculation, linking material quantities, process times, and labor rates to market cost data. Engineers must simulate the manufacturing process, calculating machine run times, tooling wear, and energy consumption to accurately determine the manufacturing overhead allocation. This simulation establishes the theoretical efficiency of the “should-cost.”
The model then moves into a validation and refinement stage to test its accuracy against real-world data and industry standards. The calculated “should cost” is benchmarked against comparable industry data or similar parts produced internally to ensure the assumptions are realistic and defensible. Any significant variance necessitates a review of the input parameters, such as the assumed scrap rate or regional labor cost, until the model reflects an achievable, competitive cost.
The final step involves scenario analysis, using the model as a dynamic tool to explore the financial impact of changing variables. Analysts can test different production volumes, alternative materials, or various manufacturing locations to determine the most cost-efficient path forward. This analysis allows the organization to build a negotiating strategy resilient to potential changes in market conditions or supply chain configurations.
Key Applications Across the Business Lifecycle
Should-Cost Modeling is applied at strategic junctures throughout a product’s lifecycle, providing timely cost intelligence.
During New Product Introduction (NPI), the model establishes a target cost before supplier quotes are solicited. This initial cost objective allows design and sourcing teams to work collaboratively toward a commercially viable product from the outset, preventing later pricing issues.
The model is useful during major contract negotiations, especially with sole-source suppliers where competitive bidding is not possible. In these scenarios, should-cost analysis provides the only objective measure to ensure the quoted price is fair and not subject to price domination. The detailed breakdown facilitates a non-adversarial discussion on the elements driving the price.
Should-cost analysis is also a mainstay in ongoing cost reduction initiatives and Value Engineering projects for mature products. By continuously measuring a part’s cost against its optimal should-cost, organizations can systematically identify components with the largest cost deviation. This information then directs engineering efforts toward redesigning or simplifying those specific components to align the actual cost with the targeted efficiency.
Common Challenges and Limitations
Implementing Should-Cost Modeling is difficult due to practical and organizational hurdles.
One significant challenge is obtaining accurate and transparent data from suppliers, necessary to validate the model’s assumptions against actual operations. Without open collaboration, the model relies on market estimates, reducing its precision as a negotiating tool.
Building and maintaining these complex models requires technical expertise in cost engineering, manufacturing processes, and commodity markets. The time and resources required to construct a detailed model for every component can be substantial, limiting the number of parts analyzed. Therefore, the model is typically reserved for high-spend or strategically important items.
Internal resistance can arise if the calculated “should cost” is significantly lower than current purchase prices, leading to pressure to achieve unrealistic targets. Overcoming this requires securing buy-in from procurement and engineering, and clearly communicating that the model’s value lies in providing a strategic benchmark for continuous improvement, not just a tool for immediate price reduction. Market fluctuations in raw material prices and labor rates also require the model to be regularly updated to prevent cost estimates from becoming outdated.