Recommending the Lowest Cost Mix for a Leading Auto Manufacturer – A SpendEdge Case Study
Engagement Overview: A leading player in the auto industry wanted to build a comprehensive list of all cost factors and their associated benefits across the supply chain network. To do so, and identify the lowest cost mix, the auto industry client approached SpendEdge to devise a comprehensive approach that would help them develop an appropriate […]READ MORE >>
A leading player in the auto industry wanted to build a comprehensive list of all cost factors and their associated benefits across the supply chain network. To do so, and identify the lowest cost mix, the auto industry client approached SpendEdge to devise a comprehensive approach that would help them develop an appropriate cost model. A detailed quantitative analysis of various spend categories within the supply chain was carried out to identify the lowest cost mix would help them improve spend efficiency.
About the Client:
The auto industry firm was facing several financial challenges as a result of which they were looking at leveraging cost model analysis to identify the lowest cost mix and develop a model that reflects their organizational structure.
How did SpendEdge Help the Client?
- Step 1: Gathered and filtered raw data into distinct categories
The first step revolved around the process of gathering raw spend data from across the supply network. As the objective was to devise a suitable cost model, the collected data was segmented into various spend categories to distinguish its source.
- Step 2: Identification of cost drivers for each of the spend categories
Once the data is segmented into categories, it is essential to gain a detailed understanding of the driver behind each category. A detailed correlation analysis helped the client to determine the relationship between input factors and cost drivers, which further assisted them by offering an intuition on the best suitable cost model.
- Step 3: Identification of price forecasting trends in the auto industry that need to be integrated into the final cost modeling approach
To devise an effective pricing model, it is essential to differentiate variable cost factors from the ones that more or less remain constant over a certain period. Also, the implementation of price forecasting trends into the final model helps auto manufacturers to develop a robust cost model that keeps track of the fluctuations in prices of raw materials.
Key questions answered in this cost model analysis engagement include:
Benefits of the Engagement:
The cost model analysis experts at SpendEdge recommended the lowest cost mix that helped the auto manufacturer to develop a robust cost model. The developed model enabled the auto industry player to effectively map the financial relationship between their spend categories and other factors within the organization. This relationship further helped them reveal the entities that drove the cost of other entities and those to which cost was passed.
Why incorporate cost model analysis?
Cost model analysis is a key factor that promotes cost control and cost containment. It enables organizations to stay well-informed via precise cost reporting, forecasting of future costs, and enhancement of pricing strategies.
Cost model analysis also enables auto manufacturers to develop winning sourcing strategies for current spend and helps determine when and where to seek alternative supply options and materials. It is equally important to the planning and development stages of new product portfolios.
The auto industry is an ever-evolving sector where cutting-edge cost models are introduced to the market on a regular basis. Although the industry is currently experiencing accelerated growth, the auto manufacturers are facing a continuous challenge owing to the adoption of incessant innovation in order to excel in the market. Therefore, devising an effective cost model can help them drive category growth across the supply network by analyzing historic spend data to identify key cost factors and develop a forecasting model to predict future costs.
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