Should Cost Modeling: Ways to Implement your Price Negotiation Initiatives

Written by: Medha Banerjee

Introduction to Should Cost Analysis Modeling

Businesses tend to gravitate towards value analysis, value engineering, strategic sourcing, and other benchmarking data methods to identify new cost reduction efforts. Given the rapid pace of development across industries, businesses can no longer rely on traditional should cost analysis methodologies but adopt newer should cost modeling software approaches.

Businesses are constantly seeking effective strategies to optimize costs and enhance negotiation outcomes. One such powerful tool gaining prominence should cost modeling software. This methodology involves a comprehensive analysis of the various cost components associated with a product or service, providing businesses with a strategic advantage during price negotiations. In the dynamic landscape of procurement processes and supply chain organizations, the integration of AI-powered should-cost modeling software has become a game-changer. This article will delve into the challenges and benefits associated with this technique, shedding light on how it transforms the negotiation landscape.

Advancements in technology and analytics have made it possible for businesses to adopt structured approaches. Moreover, advanced should cost modeling tools also enable companies to identify and optimize cost targets. Therefore, this technique is a widely accepted approach that helps the procurement processes and sourcing teams gain actionable insights that aid price negotiation. It also helps designers and product developers achieve their cost targets by offering detailed cost and pricing insights early, i.e., in the initial product development phase.

What is Should Cost Analysis?

It is an advanced analytics methodology that helps determine a product’s price based on different factors that impact pricing. It includes the raw materials prices, manufacturing and process overheads, and other expenses contributing to the overall price.

It is a strategic and data-driven procurement approach that systematically evaluates and estimates the anticipated cost of a product or service. It leverages AI-powered technology to conduct a comprehensive should cost modeling software analysis of key price drivers, including raw materials prices, manufacturing costs, and market costs conditions. The objective is to provide organizations with an accurate and transparent view of the true cost of goods, enabling informed negotiation efforts and identification of cost-saving opportunities. The should cost analysis software facilitates cross-functional collaboration, aligning product development, cost engineering, and supply chain teams for effective decision-making. Through a detailed price breakdown analysis and clean sheet costing, the should cost modeling tools empowers businesses to enhance their negotiating position, foster long-term supplier relationships, Supplier profit margins and achieve a competitive edge in various industries.

What is the Purpose of Should Cost Model Template?

Often, product designers do not analyze the cost products themselves but instead bank upon sourcing and procurement teams for pricing information. The lack of accurate models leads to sourcing teams negotiating with suppliers with minimal information, resulting in price mismatches and high margins. These models empower businesses to analyze design concepts focusing on the target cost during the development phase, i.e., before the negotiation phase, thereby establishing a practical fail-proof approach to sourcing.

Calculating Should Cost Model Template: A Comprehensive Analysis

This Calculation is a meticulous process that plays a pivotal role in procurement and supply chain strategies. It involves leveraging advanced technology, including AI-powered algorithms, to objectively estimate and analyze the various components contributing to the overall cost of a product or service costs.

Key Drivers:

The foundation of this calculation lies in identifying and analyzing key cost drivers and the should cost methodology negotiations. These include raw materials, manufacturing orders, labor, and conversion costs, overheads, logistics, and profit margins. Each element is scrutinized to provide a comprehensive understanding of the factors influencing the final price of the product.

Data Collection:

A crucial aspect of this calculation is the collection of data from diverse sources using Top-down approach. This encompasses both internal and external data, integrating information from enterprise resource platforms, invoices, and open data sources. The use of historical and real-time data ensures a robust foundation for accurate cost projections.

AI-Powered Technology:

The integration of artificial intelligence in the calculation sets this approach apart. AI-powered algorithms analyze vast datasets with minimal manual input, significantly enhancing the accuracy and efficiency of the calculation process. This technology brings objectivity and precision to the estimation, mitigating potential errors associated with manual calculations.

Cleansheet Costing:

A fundamental should cost methodology employed in the calculation is the cleansheet approach. This involves a detailed breakdown of all costs associated with the production process, and production factors, offering transparency and visibility into each component. Cleansheet facilitates a granular understanding of cost structures, allowing for a more nuanced and informed negotiation strategy.

Projections for Costs:

It doesn’t merely focus on the current state of costs; it extends to projecting future costs. Market-driven projections, taking into account factors such as market conditions and changes in the price of raw materials like steel, provide organizations with a forward-looking perspective. This proactive approach along with Top-down approach, enables businesses to adapt to market fluctuations and make strategic decisions.

Minimum Manual Input:

One of the key advantages of AI-powered calculation is the minimum manual input required. Automation streamlines the process, reducing the likelihood of errors and increasing the efficiency of the calculation. This allows procurement teams to focus on strategic decision-making rather than being bogged down by manual data entry.

Effective Supplier Negotiations:

Armed with a comprehensive understanding of the true cost of goods and incremental-improvement mindset, organizations engaging in should cost calculation are better equipped for supplier negotiations. The insights gained from the analysis enable fact-based negotiations, identifying opportunities and fostering long-term relationships with suppliers.

Challenges faced while implementing Should Cost Modeling

Even though the advantages or benefits of the service makes Should Cost an absolute choice with incremental-improvement mindset, it comes with few challenges of its own. Some of them are as mentioned below.

Data Accuracy and Availability:

• Obtaining accurate and comprehensive data for all cost components can be challenging.
• Addressing this challenge involves leveraging advanced data analytics and sourcing strategies.

Dynamic Market Conditions:

• The volatility of market conditions poses a challenge for accurate scenario modeling.
• Overcoming this challenge requires continuous monitoring and agile adaptation to changing market dynamics.

Cross-Functional Collaboration Barriers:

• Ensuring seamless collaboration across diverse business functions can be a hurdle.
• Implementing effective communication channels and fostering a culture of collaboration is essential.

Modeling Complexity:

• These models can become intricate due to the complexity of cost structures.
• Simplifying models without compromising accuracy involves leveraging advanced analytics and modeling tools.

Benefits of incorporating Should Cost Analysis in procurement

Before moving further after going through an understanding of the should cost model meaning, let us also take a quick scan of the benefits that should cost analysis brings to the table for you.

Cost Optimization:

• Should Cost Analysis empowers businesses to identify cost-saving opportunities across the supply chain.
• By optimizing costs, organizations can enhance their profitability and competitiveness.

Negotiation Leverage:

• Armed with comprehensive cost insights, businesses gain a strategic advantage in price negotiations.
• Enables informed and data-driven negotiations, leading to favorable outcomes.

Supplier Relationship Management:

• Building transparent and collaborative relationships with suppliers is facilitated through Should Cost Modeling.
• Organizations can work closely with suppliers to achieve mutual cost efficiencies.

Risk Mitigation:

• Identifying potential risks in the supply chain becomes more effective with Should Cost Analysis.
• Proactive risk mitigation strategies can be implemented to ensure continuity.

Strategic Decision-Making:

• It provides a foundation for strategic decision-making.
• Businesses can align their procurement strategies with overall organizational goals.

Continuous Improvement:

• Regular analysis allows for ongoing refinement of cost models.
• Continuous improvement becomes a key aspect of cost management strategies.

Should Cost Analysis Model: Quantzig’s Approach

Since should cost modeling primarily focuses on analyzing the elements that make up the price of a product or service costs, businesses must have a robust analysis model or cost breakdown analysis model to price their products competitively.

To help our clients address this issue, we’ve developed a comprehensive approach to should cost modeling, which involves the following phases –

Phase 1: Data Acquisition

The ‘data acquisition’ phase revolves around gathering data from various sources, including ERP platforms, bill of materials database, spend data, purchasing invoices, and other information on raw materials.

Phase 2: Methodical Data Expansion

The second phase focuses on analyzing the collected data sets and cost drivers, including raw materials, manufacturing orders overheads, logistics, and labor costs.

Phase 3: Classification of Cost Drivers

Once all the data has been analyzed, cost drivers identified in the previous steps are classified into homogenous groups.

Phase 4: Cost Modeling

The cost modeling phase revolves around outlining annual volumes, batch quantities, and unit of measure. In this phase, processes and materials are selected and parameters are defined with respect to geographical locations.

Phase 5: Generation of Should Costing Insights

By leveraging the data obtained from the mentioned sources, we develop crucial insights using our proprietary cost analysis models, cost breakdown analysis and price analytics platforms.

Phase 6: Reporting

The final stage in this modeling revolves around generating reports with a detailed breakdown of processes, materials, non-recurring engineering (NRE), and authorization costs.

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Quantzig’s Impactful Intervention: A Should Cost Analysis Success Story

In the complex landscape of procurement and supply chain optimization, organizations often face challenges in accurately estimating and negotiating product prices. The integration of AI-powered Should Cost Analysis has proven to be a game-changer, and Quantzig, a leading analytics and advisory firm, exemplifies its expertise in transforming businesses through a success story with a global client.

Client Challenge:

The client, a prominent player in the automotive industry, was grappling with the escalating costs associated with manufacturing orders highly intricate machined metal part designs. The fluctuating market conditions, coupled with the complexities of sourcing specialist suppliers, posed a significant challenge. The client sought to optimize price without compromising on quality and sought our expertise to conduct a comprehensive analysis.

Quantzig’s Approach for should cost modeling example:

Our team initiated the project by understanding the intricate details of the client’s product portfolio, focusing on machined metal part designs, and identified key cost drivers, including raw materials, labor, conversion, and overheads. Leveraging advanced AI-powered algorithms, historical and real-time data, we conducted a cleansheet analysis to provide a granular view of the cost structure.

Data Integration and Analysis:

One of the key strengths of Quantzig lay in its ability to seamlessly integrate data from various sources, including enterprise resource platforms, invoices, and third-party databases. This comprehensive data based on should cost model procurement pool ensured an accurate representation of the cost components, minimizing the need for manual input and reducing the risk of errors.

Market-Driven Projections:

Understanding the client’s concerns about market fluctuations, our experts incorporated market-driven projections into the Should Cost Analysis. This involved a meticulous examination of factors influencing the automotive industry, such as changes in the price of steel and global market conditions. The forward-looking perspective, using should cost analysis formula allowed the client to adapt strategies to future expense dynamics.

Cleansheet Costing and Detailed Analysis:

Our application of should cost analysis formula for cleansheet costing was pivotal in providing the client with a detailed breakdown of costs associated with each machined metal part design. This transparency not only facilitated an in-depth understanding of cost structures but also empowered the client to make informed decisions in negotiations and supplier collaborations.

Automating Quoting Processes:

Recognizing the client’s need for agility in responding to market demands, our should cost analysis software solution incorporated the use of AI-powered technology to automate key cost driver analyses. This should cost negotiation not only expedited the quoting process but also enhanced the client’s responsiveness to market changes, providing a competitive edge in the automotive industry.

Impact Delivered by Quantzig:

Quantzig’s solution had a transformative impact on the client’s operations. By accurately estimating the true cost of machined metal part designs, the client was able to engage in fact-based negotiations with suppliers. This not only fostered stronger supplier relationships but also resulted in substantial savings, positioning the client for a competitive advantage in the automotive market.

Our success in assisting the client with Should Cost Analysis underscores the transformative power of advanced analytics in procurement and supply chain optimization. By leveraging AI-powered technology, integrating diverse datasets, and conducting a detailed cleansheet analysis, we not only addressed the client’s immediate challenges but also paved the way for sustained cost optimization and strategic supplier collaborations. This success story serves as a testament to the tangible benefits that organizations can achieve through the implementation of Should Cost Analysis, particularly when guided by the expertise of analytics and advisory partners like Quantzig.

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Conclusion

Quantzig’s team of 550+ seasoned analytics experts have the expertise and skill it takes to design and build should cost data models tailored to your business’s needs and equip you with data-driven, actionable insights for prudent decision-making. Our approach also helps clients reverse engineer product costs to accurately estimate the price of components. Equipped with detailed pricing insights, businesses will be well-positioned to forge better, more effective contractual agreements and boost overall market competitiveness.