The Art of Clean Sheeting: How My Client Saved $10M USD

In the world of cost estimation and financial strategy, clean sheeting—also known as should costing—is a game-changing approach that gives businesses a clear picture of what their products or services should ideally cost to produce. Coined by McKinsey, this technique has become a crucial tool for companies aiming to streamline their production processes and negotiate better with suppliers.

What is Clean Sheeting?

Clean sheeting involves breaking down a product or service into its most basic components and determining the cost of each element using the most efficient and cost-effective methods available. This bottom-up approach not only helps in understanding the true cost of production but also in identifying areas where savings can be achieved without compromising on quality.

Why is it Important?

1. Cost Transparency: Clean sheeting provides a transparent view of the costs involved, allowing companies to make informed decisions about pricing and sourcing.

2. Negotiation Power: Armed with detailed cost information, businesses can negotiate better terms with suppliers, leading to potential cost savings.

3. Process Optimization: By identifying inefficiencies in the production process, companies can implement changes that reduce costs and improve overall efficiency.

4. Competitive Advantage: Understanding the true cost of production enables businesses to price their products competitively while maintaining profitability.

   
   

How Does Clean Sheeting Work?

The clean sheeting process typically involves the following steps:

1. Component Breakdown: Identify all the components and materials required to produce the product.

2. Cost Analysis: Evaluate the cost of each component using data on market prices, labor, and production methods.

3. Efficiency Evaluation: Assess the production process to identify any inefficiencies or areas for improvement.

4. Benchmarking: Compare the calculated costs with industry benchmarks to ensure accuracy and competitiveness.

5. Implementation: Use the insights gained to negotiate with suppliers, optimize processes, and adjust pricing strategies.

   
   

Real-World Application

While working with a previous client, I created a comprehensive software solution that automated should-costing and provided deep analytics, including machine learning capabilities. To achieve this, I created a mathematical model of the entire manufacturing process using R, and provided a UI in R Shiny to allow the user to view hyper-detailed should cost analytics results. This work led to the discovery of $10 million in cost savings and a greater understanding of risk factors in the manufacturing process. Ultimately, this custom software suite enabled the client to optimize their production processes and enhance their negotiation strategies significantly.

Challenges and Considerations

While clean sheeting offers numerous benefits, it's not without its challenges. Accurate data collection and analysis are critical, and companies must be prepared to invest time and resources into the process. Additionally, maintaining updated cost information is essential to ensure ongoing accuracy and relevance.

Conclusion

Clean sheeting is more than just a cost estimation tool—it's a strategic approach that empowers businesses to make better decisions, optimize processes, and stay competitive in a challenging market. By understanding the true cost of production, companies can unlock significant savings and drive long-term success.

For those interested in learning more about Cleansheeting, the following resources can provide a more in-depth insight into the concepts and techniques used, along with a broad overview of the "Should Costing" strategy.

1. The Cleansheet cost engineering function: Introduction - McKinsey: Read more

2. The Science Behind Cost Savings - Future of Sourcing: Read more

3. A Guide to Should Cost Analysis and Negotiation - aPriori: Read more

4. How to Perform Warehouse Costing Using Cleansheet Analysis: Read more

5. McKinsey: Is “Should Costing” The Future of Procurement?: Read more

6. Machine learning for financial forecasting, planning and analysis - Springer: Read more

7. A machine learning approach for cost prediction analysis in urban environmental governance - Springer: Read more