Is Your Should Cost Model Truly Data-Driven Or Just An Educated Guess?

Key Takeaways:

• Traditional costing methods rely heavily on known factors from internal expertise and supplier data generalizations
• Cost models too often rely on internal expertise or past understanding of a part’s true cost, resulting in an arduous, inaccurate costing process, and little room to negotiate with suppliers
• A should cost model solution like aPriori’s provides a detailed analysis of what a product or part should cost if produced under the most optimal conditions

The Full Article:

Scenario: You’re a manufacturer who needs to determine the cost of a part. How do you go about it?

Too many manufacturers rely heavily on the “three bids and a buy” approach.

This cost model approach uses three supplier quotes, often relying on data from their sourcing and procurement teams. They also conduct a factory audit, recording the number of machines used to make that part and its cycle times. Then, they compare the parts and associated costs among the three supplier quotes they received from sourcing and procurement to arrive at the “expected” cost.

This expected cost model is built on known factors and generalizations based on supplier information. Accurate data can be difficult to acquire and even more challenging to leverage in supplier negotiations. Ensuring that you’ve also selected the “best fit” supplier based on cost, timing, location, quality, and capacity is lengthy and vulnerable to errors.

The Unseen Speedbumps of Costing

Sure, you can leverage the historical data in your ERP system to estimate. However, it likely is static and unreliable due to ever-changing market trends. Furthermore, it is a time-consuming task, since building accurate models, leveraging expert knowledge, and using cleansed data is a long-term effort. Cleansed data refers to the process of identifying and correcting (or removing) errors and inconsistencies in data to improve its quality. Validating data to ensure it meets specific criteria is one example. Moreover, you could end up with disparate cost quotes.

It is especially true if you rely on data from various sources, including the suppliers, who typically won’t share all the information that goes into their cost estimates. Additionally, relying solely on internal expertise and limited supplier data is not conducive to an unbiased baseline. This limited information will impede your negotiating power. Finally, benchmarks built by consultants create urgency to determine costs quickly, which can present a challenge for many. What you are actually creating is an expected cost model.

What if your supplier’s manufacturing process capabilities aren’t right for the job? What if the region you chose as a production site isn’t the most cost-effective? What if the machines your chosen supplier uses cause quality issues when ramping up production, adding waste, additional time and ultimately driving up costs?

It begs the question: What cost method is efficient and accurate in determining part costs?

Where Does Your Current Cost Model Fit?

To determine whether you’re using a should cost model or an expected cost model, you can look at the following key differences:

What cost model should replace the three bids and a buy methodology?

aPriori Challenges Your Expected Cost with Should Cost Analysis

aPriori’s solution incorporates the digital product, digital process and digital factory in its should cost analysis tool. Based on unbiased third-party insights, aPriori’s should cost capability utilizes simulation-driven analysis to identify the most efficient and cost optimum process, allowing them to pinpoint the right supplier in the right location with the right amount of capacity to do the work. As a result, the RFQ process is more targeted and accelerated, reducing the need for “three bids and a buy”.

It makes it an ideal process, facilitating detailed should cost breakdowns and an understanding of cycle times. Now, commodity teams, and buyers are in the driver’s seat. These detailed should cost breakdowns help them understand what is driving cost, assist in challenging their current expected cost with should cost, and empower supplier negotiations with fact-based, defensible data.

This should cost analysis provides advanced process automation tools that streamline cost analysis and improve efficiency. Here are a few important features:

1. Bulk Costing: This tool allows users to cost hundreds of components in a single batch by analyzing 3D CAD models and simulating the manufacturing process. It also considers material costs and labor costs, reducing the time and effort required for manual cost analysis.
2. Matrix Costing: This approach helps evaluate different production location and batch size scenarios and their impact on costs, enabling teams to make informed decisions about where to produce and how much for ultimate cost efficiency.
3. aP Analytics: This business intelligence tool interprets cost data to identify trends and outliers, helping users pinpoint areas for cost reduction and improve profit margins.
4. aP Workspace: This collaboration enables teams to integrate supplier feedback to design teams as well as pinpoint ways to reduce cost further with DFM feedback directly to design engineers

A Newly Found Advantage of Should Cost Analysis

Product development teams need to consider both manufacturability and cost aspects and align them with supplier feedback to ensure faster, more informed decision-making. Moreover, they should use a solution that all product development teams can leverage, from design through production, in a more efficient, collaborative method that centralizes this data.

aPriori’s should cost analysis solution offers several compelling benefits:

1. Robust Cost Estimates: aPriori’s AI-powered software generates precise cost estimates using advanced cost models, which helps in identifying cost-saving opportunities and making informed “make vs. buy” decisions
2. Automation and Efficiency: The solution automates the cost analysis process, allowing engineers to quickly evaluate the costs of various parts and assemblies. This reduces the reliance on manual, time-consuming methods like Excel spreadsheets
3. Accelerate the RFQ Process: By providing detailed insights, aPriori enables teams to optimize product development and manufacturing costs, aligning with the right supplier and accelerating the RFQ process faster and in a more targeted manner.
4. Enhanced Collaboration: The software integrates with other systems, facilitating better information sharing and collaboration among design, engineering, and sourcing teams
5. Supplier Negotiations: aPriori’s should cost estimates serve as benchmarks for supplier negotiations, helping to achieve cost-effective decisions, improved supplier relationships, and substantial savings
6. Scenario Analysis: Engineers can use aPriori to simulate different manufacturing scenarios and identify the most cost-effective options, further enhancing decision-making

aPriori streamlines the RFQ process, improves accuracy, and supports better decision-making, leading to significant cost savings and efficiency gains.

Should Cost Puts Control in Your Wheelhouse

By leveraging these methodologies, manufacturers can achieve more accurate cost estimates, streamline workflows, and ultimately reduce the cost of goods sold, improving cost management and the bottom line.

Now, commodity teams and the entire product development team have greater control over costs. The result? More optimal, cost-effective designs, more savings, fewer costly manufacturing issues, and stronger supplier negotiating power.