The should-cost model is a tool used in strategic procurement to establish cost transparency. It represents an organization’s independent, internal estimate of what a product or service should cost a supplier to produce. This technique moves beyond simply accepting a supplier’s quote and establishes a baseline price target based on an analysis of production inputs. Creating this detailed estimate provides organizations with an informational advantage for strategic decision-making in the supply chain.
Defining the Should-Cost Model
The should-cost model expands upon basic historical pricing or market comparisons by conducting a thorough, bottom-up analysis of a supplier’s internal cost structure. This process involves systematically breaking down a product into its constituent elements, calculating the expected cost for each, and aggregating them to reach a final target price. The model is built on objective data regarding the processes, materials, labor, and overhead required for manufacturing. The resulting figure acts as a fact-based price target, helping organizations understand the economic reality of the item being sourced.
Creating this breakdown is a proactive approach, shifting the dynamic from reacting to supplier quotes to establishing a price ceiling. It provides detail that allows for line-item comparisons, identifying where a supplier’s quoted price deviates from the established baseline. This understanding of the cost drivers enables procurement teams to engage in informed, data-backed discussions with supply partners. The goal is not to dictate a price but to collaboratively reach a fair and sustainable price that reflects efficient production.
Why Should-Cost Modeling is Essential
Employing a should-cost model is instrumental in achieving cost savings across the supply base. By analyzing the fundamental inputs of production, procurement professionals can pinpoint and challenge hidden inefficiencies or excessive pricing built into a supplier’s quote. This analytic capability provides leverage in supplier negotiations, transforming discussions from haggling over a number to a fact-based review of specific cost elements. The transparency gained promotes a shared understanding of cost drivers, leading to productive outcomes.
The modeling process facilitates strategic sourcing decisions by providing a clear metric for evaluating supplier performance and efficiency. Organizations use the models to identify which suppliers are most efficient and offer the greatest long-term value, moving beyond simple sticker price comparisons. The analysis helps in identifying opportunities for value engineering—modifying product specifications or processes to reduce cost without compromising quality. This data-driven approach ensures that pricing decisions are grounded in economic reality rather than market fluctuation or historical precedent.
The Core Components of a Should-Cost Model
Building a should-cost estimate requires collecting and calculating the four main categories of expense that contribute to the final price of a product. Understanding these elements forms the analytical foundation of the model. These components include all direct production costs, the indirect costs associated with running the business, and the necessary financial returns for the supplier.
Direct Material Costs
The calculation for direct materials begins with identifying the specific raw material inputs needed for the product (e.g., grade of steel, plastic resin, or electronic components). This analysis requires precise measurements of the material’s weight or volume in the final product. Accounting for material waste, known as the scrap rate, is incorporated into the calculation. Current commodity pricing, tracked through public indices or specialized market data, is applied to the net required material volume to determine the total material expense.
Direct Labor Costs
Direct labor costs are calculated by determining the precise time standards required for each step of the manufacturing process, measured in standard minutes or hours per unit. This time standard is multiplied by the specific hourly wage rate for the operators performing the task, which varies based on geography, skill level, and union agreements. The model must account for efficiency factors, which represent the actual output versus the theoretical maximum. Specific skill sets or specialized training required for production often result in a higher calculated wage rate.
Manufacturing Overhead and SG&A
Manufacturing overhead includes all indirect production costs, such as utilities, factory supervision, maintenance, and the depreciation of machinery and tooling. These expenses are categorized as either fixed costs (constant regardless of production volume) or variable costs (fluctuating with output). The cost of specialized tooling is amortized over the expected life or total volume of the product run. Selling, General, and Administrative (SG&A) expenses cover non-production costs like sales commissions, corporate salaries, and administrative support, calculated as a percentage of the total direct costs.
Profit Margin
The final component is the profit margin, which represents the supplier’s required return on investment. The model seeks to define a fair and sustainable margin based on objective factors like the industry standard, the level of capital investment required, and the inherent risk of the product or service. Margins can range significantly, from low single digits for high-volume commodity parts to 15% or more for highly engineered products. This calculated margin ensures the supplier can maintain quality operations and invest in necessary improvements.
Methods for Developing a Should-Cost Model
The method chosen for developing a should-cost model depends on the available data, the complexity of the product, and the stage of the sourcing process. The two primary techniques are the bottom-up engineering approach and the top-down parametric modeling. Each technique offers different depths of analysis and requires different inputs, serving distinct purposes in the procurement lifecycle.
The most accurate technique is the Bottom-Up approach, often called the “engineering build-up.” This method starts with the product blueprints and manufacturing process flow, calculating every physical input required to create the final item. Engineers determine the exact machine cycle times, material utilization rates, and operator movements, creating a zero-based calculation of cost. This technique is most effective for established products where detailed manufacturing data is accessible and justifies the time investment needed for the analysis.
A faster, less data-intensive technique is Top-Down or parametric modeling, used when detailed engineering data is unavailable or when speed is a priority. This method relies on statistical relationships between cost and specific product characteristics, such as weight, power output, or surface area. This approach provides a quick, directional estimate, making it valuable for early-stage sourcing decisions or for analyzing complex systems where a full engineering build-up is impractical.
A rigorous application of the bottom-up approach is the Clean Sheet method. This zero-based modeling technique assumes no prior history with the supplier and builds the cost from the ground up, demanding that every cost element be justified and verified. It is used for high-spend, strategically procured items where the organization is willing to invest substantial resources to gain complete cost visibility. The clean sheet analysis is a tool for validating the efficiency of an incumbent supplier or for establishing a new price target with a potential partner.
Applying the Model in Negotiation and Sourcing
Once the should-cost model is complete, it transitions into a practical tool for transforming the procurement process. The model provides the data necessary to set an aggressive yet achievable target price, shifting the negotiation focus from the supplier’s initial quote to the organization’s independently verified cost baseline. This allows the procurement team to enter discussions with confidence in their valuation of the product.
The model is used to challenge specific supplier assumptions by pointing to calculated efficiencies in areas like cycle time, material scrap rates, or labor utilization. Instead of requesting a price reduction, the team can ask the supplier to explain the discrepancy between the model’s calculated time and the supplier’s quoted time. This fact-based dialogue facilitates discussions around value engineering, where the focus shifts to mutually identifying design or process changes that reduce the cost without sacrificing performance.
Applying the should-cost model is the final step in validating the fairness and competitiveness of final quotes received from suppliers. If a supplier’s quote is significantly higher than the should-cost target, the model provides the evidence to push for justification or price adjustments. Conversely, if a quote is substantially below the calculated cost, it prompts investigation into potential quality risks or unsustainable business practices. Ultimately, the model ensures that the final price reflects an efficient and sustainable cost structure for both parties.
Common Challenges and Best Practices
Implementing should-cost modeling involves navigating several practical challenges, primarily centered on data access and supplier relationships. A major hurdle is obtaining accurate, granular data from suppliers regarding their manufacturing processes, wage rates, and overhead allocations, as this information is often considered proprietary. Developing a detailed model requires a significant investment of time and specialized engineering expertise, which can delay the sourcing cycle. Supplier resistance is common, as they may view the model as an intrusive attempt to dictate pricing rather than a tool for collaboration.
To overcome these challenges, organizations must adopt several best practices. Maintaining collaborative supplier relationships is paramount; the model should be presented as a mechanism for joint cost reduction and efficiency improvement, not as a combative weapon. Ensuring the accuracy of the underlying data is a continual process, requiring regular validation against industry benchmarks and market intelligence. Models should not be static; they must be regularly updated to reflect changes in commodity prices, labor rates, and manufacturing technology to remain a relevant tool for procurement.