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Effective materials cost management has become a critical capability for CPG procurement teams navigating a volatile environment. As global supply chains face unprecedented price fluctuations driven by geopolitical tensions, climate events, and market speculation, organizations need ever more sophisticated approaches to predict future supply chain costs, contract positions, commodity movements, and hedging impacts to protect their margins. Here I outline how leading companies are addressing these challenges and explain the standardization of cost model approach developed by the Keyrus procurement team.
This article examines why different procurement categories require distinct forecasting strategies, and how leading organizations structure their planning processes to accommodate category-specific nuances while maintaining unified oversight.
A fundamental principle in modern materials management is that a one-size-fits-all approach fails to address the unique characteristics of different spend categories. Attempting to forecast packaging materials the same way you forecast commodity-based materials leads to inaccurate predictions and poor decision-making.
The variation stems from how different materials are sourced, priced, and supplied. Commodities trade on global markets with published indices and futures forecast and actual rate changes directly impact on the P&L. A diverse international supplier base drives different supply chain costs, and the volatile duty landscape adds further complexity. For packaging materials, procurement teams tend to plan by choosing a base price period and applying various escalation mechanisms to forecast future prices, and comparative analysis is made harder by different suppliers using different units of measure.
Indeed, supplier relationship dynamics shape materials management needs. Categories with one-to-one relationships between suppliers and production sites are easier to manage and model. Categories with many-to-many relationships, where multiple suppliers serve different production sites add complexity and require more granular, location-specific forecasting.
With commodities we see weekly, if not daily price volatility needing frequent model updates and the ability to easily run real time what if scenarios. At the other end of the scale, stable categories with annual contracts locking in pricing only require periodic reviews and inflation adjustments.
Leading organizations manage diverse procurement portfolios and Keyrus have developed a four cost model framework, each suited to different category characteristics and sourcing strategies, in order to simplify and standardize the way of working for procurement teams.
The Flat Price model represents the simplest forecasting approach, used when cost driver visibility is unavailable or when products are truly unique and stable. In this framework, forecast prices come directly from supplier quotes or manual estimates without decomposition into underlying component drivers.
Organizations typically use the Flat Price model for stable materials, single source single plant sourcing, and low volumes / less important categories when viewed as a percentage of total spend.
The Cost Breakdown Model builds forecast prices from predefined cost drivers organized into logical clusters. This framework works well for simple commodities and processed ingredients where underlying costs can be identified and tracked separately.
Key cost clusters typically include sourcing and supplier strategy, market benchmarks, supply chain costs such as export duties and freight, packaging and delivery, insurance and commodity premiums, customs and handling, conversion costs, and conversion costs. Each cluster contains specific drivers that can be monitored and updated independently.
This model provides complete cost transparency. Teams can see exactly how changes in individual component costs e.g., freight rates or commodity prices, will impact on the final future price. The Cost-Plus approach creates one-to-many relationships with cost models and materials, meaning a single cost model can be used to generate forecasts for multiple materials. Dynamic forecasting with minimal efforts becomes possible because changes to any underlying driver automatically flows through to the price predictions.
The BOM Cost Breakdown Model extends the cost-plus concept to complex formulations requiring bill-of-materials calculations. This framework suits those materials that have third party production and also processed ingredients with multiple input components that need individual tracking.
Instead of treating a finished ingredient as a single material, this model breaks it down into constituent parts, each with its own pricing dynamics. For example, a chocolate supplier's price might be modeled as cocoa butter, cocoa solids, sugar, emulsifiers, and other components, plus packaging, delivery costs, conversion expenses, and margin.
This granular approach reveals how changes in individual raw materials impact the finished product price. If cocoa prices spike but sugar remains stable, the model quantifies the specific effect rather than applying a blanket escalation.
The Escalation model forecasts prices using weighted percentage increases applied to specified baseline prices. This simplified approach works particularly well for packaging categories where there are multiple SKU’s covering similar requirements with, for example, different colors and thicknesses where detailed cost driver data may be unavailable or where supplier relationships don't support full transparency.
Rather than modeling every cost component, the Escalation model applies industry indices or negotiated escalation percentages to known starting prices. For example, for certain material groups (for example corrugated packaging this might mean taking the previous year's contracted price and applying a weighted combination of paper index movements and inflation factors.
The weighting mechanism allows for nuanced forecasting. A specific agreement with a packaging supplier might (for example) pass through 70% of paper cost changes and 50% of general inflation, reflecting their actual cost structure without revealing proprietary details. This methodology provides cost estimates rather than detailed breakdowns, accepting less transparency in exchange for simplicity and lower maintenance requirements.
By understanding that different procurement categories require distinct forecasting approaches, implementing appropriate cost model frameworks, and maintaining disciplined processes, companies can significantly improve their ability to predict future costs and make informed strategic decisions. In our next post we will look at how these models support addressing the additional complexities of commodity cost variance and hedging.
Want to know more? Contact the Keyrus procurement team or write me.
Effective materials cost management has become a critical capability for CPG procurement teams navigating a volatile environment. As global supply chains face unprecedented price fluctuations driven by geopolitical tensions, climate events, and market speculation, organizations need ever more sophisticated approaches to predict future supply chain costs, contract positions, commodity movements, and hedging impacts to protect their margins. Here I outline how leading companies are addressing these challenges and explain the standardization of cost model approach developed by the Keyrus procurement team.
This article examines why different procurement categories require distinct forecasting strategies, and how leading organizations structure their planning processes to accommodate category-specific nuances while maintaining unified oversight.
A fundamental principle in modern materials management is that a one-size-fits-all approach fails to address the unique characteristics of different spend categories. Attempting to forecast packaging materials the same way you forecast commodity-based materials leads to inaccurate predictions and poor decision-making.
The variation stems from how different materials are sourced, priced, and supplied. Commodities trade on global markets with published indices and futures forecast and actual rate changes directly impact on the P&L. A diverse international supplier base drives different supply chain costs, and the volatile duty landscape adds further complexity. For packaging materials, procurement teams tend to plan by choosing a base price period and applying various escalation mechanisms to forecast future prices, and comparative analysis is made harder by different suppliers using different units of measure.
Indeed, supplier relationship dynamics shape materials management needs. Categories with one-to-one relationships between suppliers and production sites are easier to manage and model. Categories with many-to-many relationships, where multiple suppliers serve different production sites add complexity and require more granular, location-specific forecasting.
With commodities we see weekly, if not daily price volatility needing frequent model updates and the ability to easily run real time what if scenarios. At the other end of the scale, stable categories with annual contracts locking in pricing only require periodic reviews and inflation adjustments.
Leading organizations manage diverse procurement portfolios and Keyrus have developed a four cost model framework, each suited to different category characteristics and sourcing strategies, in order to simplify and standardize the way of working for procurement teams.
The Flat Price model represents the simplest forecasting approach, used when cost driver visibility is unavailable or when products are truly unique and stable. In this framework, forecast prices come directly from supplier quotes or manual estimates without decomposition into underlying component drivers.
Organizations typically use the Flat Price model for stable materials, single source single plant sourcing, and low volumes / less important categories when viewed as a percentage of total spend.
The Cost Breakdown Model builds forecast prices from predefined cost drivers organized into logical clusters. This framework works well for simple commodities and processed ingredients where underlying costs can be identified and tracked separately.
Key cost clusters typically include sourcing and supplier strategy, market benchmarks, supply chain costs such as export duties and freight, packaging and delivery, insurance and commodity premiums, customs and handling, conversion costs, and conversion costs. Each cluster contains specific drivers that can be monitored and updated independently.
This model provides complete cost transparency. Teams can see exactly how changes in individual component costs e.g., freight rates or commodity prices, will impact on the final future price. The Cost-Plus approach creates one-to-many relationships with cost models and materials, meaning a single cost model can be used to generate forecasts for multiple materials. Dynamic forecasting with minimal efforts becomes possible because changes to any underlying driver automatically flows through to the price predictions.
The BOM Cost Breakdown Model extends the cost-plus concept to complex formulations requiring bill-of-materials calculations. This framework suits those materials that have third party production and also processed ingredients with multiple input components that need individual tracking.
Instead of treating a finished ingredient as a single material, this model breaks it down into constituent parts, each with its own pricing dynamics. For example, a chocolate supplier's price might be modeled as cocoa butter, cocoa solids, sugar, emulsifiers, and other components, plus packaging, delivery costs, conversion expenses, and margin.
This granular approach reveals how changes in individual raw materials impact the finished product price. If cocoa prices spike but sugar remains stable, the model quantifies the specific effect rather than applying a blanket escalation.
The Escalation model forecasts prices using weighted percentage increases applied to specified baseline prices. This simplified approach works particularly well for packaging categories where there are multiple SKU’s covering similar requirements with, for example, different colors and thicknesses where detailed cost driver data may be unavailable or where supplier relationships don't support full transparency.
Rather than modeling every cost component, the Escalation model applies industry indices or negotiated escalation percentages to known starting prices. For example, for certain material groups (for example corrugated packaging this might mean taking the previous year's contracted price and applying a weighted combination of paper index movements and inflation factors.
The weighting mechanism allows for nuanced forecasting. A specific agreement with a packaging supplier might (for example) pass through 70% of paper cost changes and 50% of general inflation, reflecting their actual cost structure without revealing proprietary details. This methodology provides cost estimates rather than detailed breakdowns, accepting less transparency in exchange for simplicity and lower maintenance requirements.
By understanding that different procurement categories require distinct forecasting approaches, implementing appropriate cost model frameworks, and maintaining disciplined processes, companies can significantly improve their ability to predict future costs and make informed strategic decisions. In our next post we will look at how these models support addressing the additional complexities of commodity cost variance and hedging.
Want to know more? Contact the Keyrus procurement team or write me.
Effective materials cost management has become a critical capability for CPG procurement teams navigating a volatile environment. As global supply chains face unprecedented price fluctuations driven by geopolitical tensions, climate events, and market speculation, organizations need ever more sophisticated approaches to predict future supply chain costs, contract positions, commodity movements, and hedging impacts to protect their margins. Here I outline how leading companies are addressing these challenges and explain the standardization of cost model approach developed by the Keyrus procurement team.
This article examines why different procurement categories require distinct forecasting strategies, and how leading organizations structure their planning processes to accommodate category-specific nuances while maintaining unified oversight.
A fundamental principle in modern materials management is that a one-size-fits-all approach fails to address the unique characteristics of different spend categories. Attempting to forecast packaging materials the same way you forecast commodity-based materials leads to inaccurate predictions and poor decision-making.
The variation stems from how different materials are sourced, priced, and supplied. Commodities trade on global markets with published indices and futures forecast and actual rate changes directly impact on the P&L. A diverse international supplier base drives different supply chain costs, and the volatile duty landscape adds further complexity. For packaging materials, procurement teams tend to plan by choosing a base price period and applying various escalation mechanisms to forecast future prices, and comparative analysis is made harder by different suppliers using different units of measure.
Indeed, supplier relationship dynamics shape materials management needs. Categories with one-to-one relationships between suppliers and production sites are easier to manage and model. Categories with many-to-many relationships, where multiple suppliers serve different production sites add complexity and require more granular, location-specific forecasting.
With commodities we see weekly, if not daily price volatility needing frequent model updates and the ability to easily run real time what if scenarios. At the other end of the scale, stable categories with annual contracts locking in pricing only require periodic reviews and inflation adjustments.
Leading organizations manage diverse procurement portfolios and Keyrus have developed a four cost model framework, each suited to different category characteristics and sourcing strategies, in order to simplify and standardize the way of working for procurement teams.
The Flat Price model represents the simplest forecasting approach, used when cost driver visibility is unavailable or when products are truly unique and stable. In this framework, forecast prices come directly from supplier quotes or manual estimates without decomposition into underlying component drivers.
Organizations typically use the Flat Price model for stable materials, single source single plant sourcing, and low volumes / less important categories when viewed as a percentage of total spend.
The Cost Breakdown Model builds forecast prices from predefined cost drivers organized into logical clusters. This framework works well for simple commodities and processed ingredients where underlying costs can be identified and tracked separately.
Key cost clusters typically include sourcing and supplier strategy, market benchmarks, supply chain costs such as export duties and freight, packaging and delivery, insurance and commodity premiums, customs and handling, conversion costs, and conversion costs. Each cluster contains specific drivers that can be monitored and updated independently.
This model provides complete cost transparency. Teams can see exactly how changes in individual component costs e.g., freight rates or commodity prices, will impact on the final future price. The Cost-Plus approach creates one-to-many relationships with cost models and materials, meaning a single cost model can be used to generate forecasts for multiple materials. Dynamic forecasting with minimal efforts becomes possible because changes to any underlying driver automatically flows through to the price predictions.
The BOM Cost Breakdown Model extends the cost-plus concept to complex formulations requiring bill-of-materials calculations. This framework suits those materials that have third party production and also processed ingredients with multiple input components that need individual tracking.
Instead of treating a finished ingredient as a single material, this model breaks it down into constituent parts, each with its own pricing dynamics. For example, a chocolate supplier's price might be modeled as cocoa butter, cocoa solids, sugar, emulsifiers, and other components, plus packaging, delivery costs, conversion expenses, and margin.
This granular approach reveals how changes in individual raw materials impact the finished product price. If cocoa prices spike but sugar remains stable, the model quantifies the specific effect rather than applying a blanket escalation.
The Escalation model forecasts prices using weighted percentage increases applied to specified baseline prices. This simplified approach works particularly well for packaging categories where there are multiple SKU’s covering similar requirements with, for example, different colors and thicknesses where detailed cost driver data may be unavailable or where supplier relationships don't support full transparency.
Rather than modeling every cost component, the Escalation model applies industry indices or negotiated escalation percentages to known starting prices. For example, for certain material groups (for example corrugated packaging this might mean taking the previous year's contracted price and applying a weighted combination of paper index movements and inflation factors.
The weighting mechanism allows for nuanced forecasting. A specific agreement with a packaging supplier might (for example) pass through 70% of paper cost changes and 50% of general inflation, reflecting their actual cost structure without revealing proprietary details. This methodology provides cost estimates rather than detailed breakdowns, accepting less transparency in exchange for simplicity and lower maintenance requirements.
By understanding that different procurement categories require distinct forecasting approaches, implementing appropriate cost model frameworks, and maintaining disciplined processes, companies can significantly improve their ability to predict future costs and make informed strategic decisions. In our next post we will look at how these models support addressing the additional complexities of commodity cost variance and hedging.
Want to know more? Contact the Keyrus procurement team or write me.
Effective materials cost management has become a critical capability for CPG procurement teams navigating a volatile environment. As global supply chains face unprecedented price fluctuations driven by geopolitical tensions, climate events, and market speculation, organizations need ever more sophisticated approaches to predict future supply chain costs, contract positions, commodity movements, and hedging impacts to protect their margins. Here I outline how leading companies are addressing these challenges and explain the standardization of cost model approach developed by the Keyrus procurement team.
This article examines why different procurement categories require distinct forecasting strategies, and how leading organizations structure their planning processes to accommodate category-specific nuances while maintaining unified oversight.
A fundamental principle in modern materials management is that a one-size-fits-all approach fails to address the unique characteristics of different spend categories. Attempting to forecast packaging materials the same way you forecast commodity-based materials leads to inaccurate predictions and poor decision-making.
The variation stems from how different materials are sourced, priced, and supplied. Commodities trade on global markets with published indices and futures forecast and actual rate changes directly impact on the P&L. A diverse international supplier base drives different supply chain costs, and the volatile duty landscape adds further complexity. For packaging materials, procurement teams tend to plan by choosing a base price period and applying various escalation mechanisms to forecast future prices, and comparative analysis is made harder by different suppliers using different units of measure.
Indeed, supplier relationship dynamics shape materials management needs. Categories with one-to-one relationships between suppliers and production sites are easier to manage and model. Categories with many-to-many relationships, where multiple suppliers serve different production sites add complexity and require more granular, location-specific forecasting.
With commodities we see weekly, if not daily price volatility needing frequent model updates and the ability to easily run real time what if scenarios. At the other end of the scale, stable categories with annual contracts locking in pricing only require periodic reviews and inflation adjustments.
Leading organizations manage diverse procurement portfolios and Keyrus have developed a four cost model framework, each suited to different category characteristics and sourcing strategies, in order to simplify and standardize the way of working for procurement teams.
The Flat Price model represents the simplest forecasting approach, used when cost driver visibility is unavailable or when products are truly unique and stable. In this framework, forecast prices come directly from supplier quotes or manual estimates without decomposition into underlying component drivers.
Organizations typically use the Flat Price model for stable materials, single source single plant sourcing, and low volumes / less important categories when viewed as a percentage of total spend.
The Cost Breakdown Model builds forecast prices from predefined cost drivers organized into logical clusters. This framework works well for simple commodities and processed ingredients where underlying costs can be identified and tracked separately.
Key cost clusters typically include sourcing and supplier strategy, market benchmarks, supply chain costs such as export duties and freight, packaging and delivery, insurance and commodity premiums, customs and handling, conversion costs, and conversion costs. Each cluster contains specific drivers that can be monitored and updated independently.
This model provides complete cost transparency. Teams can see exactly how changes in individual component costs e.g., freight rates or commodity prices, will impact on the final future price. The Cost-Plus approach creates one-to-many relationships with cost models and materials, meaning a single cost model can be used to generate forecasts for multiple materials. Dynamic forecasting with minimal efforts becomes possible because changes to any underlying driver automatically flows through to the price predictions.
The BOM Cost Breakdown Model extends the cost-plus concept to complex formulations requiring bill-of-materials calculations. This framework suits those materials that have third party production and also processed ingredients with multiple input components that need individual tracking.
Instead of treating a finished ingredient as a single material, this model breaks it down into constituent parts, each with its own pricing dynamics. For example, a chocolate supplier's price might be modeled as cocoa butter, cocoa solids, sugar, emulsifiers, and other components, plus packaging, delivery costs, conversion expenses, and margin.
This granular approach reveals how changes in individual raw materials impact the finished product price. If cocoa prices spike but sugar remains stable, the model quantifies the specific effect rather than applying a blanket escalation.
The Escalation model forecasts prices using weighted percentage increases applied to specified baseline prices. This simplified approach works particularly well for packaging categories where there are multiple SKU’s covering similar requirements with, for example, different colors and thicknesses where detailed cost driver data may be unavailable or where supplier relationships don't support full transparency.
Rather than modeling every cost component, the Escalation model applies industry indices or negotiated escalation percentages to known starting prices. For example, for certain material groups (for example corrugated packaging this might mean taking the previous year's contracted price and applying a weighted combination of paper index movements and inflation factors.
The weighting mechanism allows for nuanced forecasting. A specific agreement with a packaging supplier might (for example) pass through 70% of paper cost changes and 50% of general inflation, reflecting their actual cost structure without revealing proprietary details. This methodology provides cost estimates rather than detailed breakdowns, accepting less transparency in exchange for simplicity and lower maintenance requirements.
By understanding that different procurement categories require distinct forecasting approaches, implementing appropriate cost model frameworks, and maintaining disciplined processes, companies can significantly improve their ability to predict future costs and make informed strategic decisions. In our next post we will look at how these models support addressing the additional complexities of commodity cost variance and hedging.
Want to know more? Contact the Keyrus procurement team or write me.
Effective materials cost management has become a critical capability for CPG procurement teams navigating a volatile environment. As global supply chains face unprecedented price fluctuations driven by geopolitical tensions, climate events, and market speculation, organizations need ever more sophisticated approaches to predict future supply chain costs, contract positions, commodity movements, and hedging impacts to protect their margins. Here I outline how leading companies are addressing these challenges and explain the standardization of cost model approach developed by the Keyrus procurement team.
This article examines why different procurement categories require distinct forecasting strategies, and how leading organizations structure their planning processes to accommodate category-specific nuances while maintaining unified oversight.
A fundamental principle in modern materials management is that a one-size-fits-all approach fails to address the unique characteristics of different spend categories. Attempting to forecast packaging materials the same way you forecast commodity-based materials leads to inaccurate predictions and poor decision-making.
The variation stems from how different materials are sourced, priced, and supplied. Commodities trade on global markets with published indices and futures forecast and actual rate changes directly impact on the P&L. A diverse international supplier base drives different supply chain costs, and the volatile duty landscape adds further complexity. For packaging materials, procurement teams tend to plan by choosing a base price period and applying various escalation mechanisms to forecast future prices, and comparative analysis is made harder by different suppliers using different units of measure.
Indeed, supplier relationship dynamics shape materials management needs. Categories with one-to-one relationships between suppliers and production sites are easier to manage and model. Categories with many-to-many relationships, where multiple suppliers serve different production sites add complexity and require more granular, location-specific forecasting.
With commodities we see weekly, if not daily price volatility needing frequent model updates and the ability to easily run real time what if scenarios. At the other end of the scale, stable categories with annual contracts locking in pricing only require periodic reviews and inflation adjustments.
Leading organizations manage diverse procurement portfolios and Keyrus have developed a four cost model framework, each suited to different category characteristics and sourcing strategies, in order to simplify and standardize the way of working for procurement teams.
The Flat Price model represents the simplest forecasting approach, used when cost driver visibility is unavailable or when products are truly unique and stable. In this framework, forecast prices come directly from supplier quotes or manual estimates without decomposition into underlying component drivers.
Organizations typically use the Flat Price model for stable materials, single source single plant sourcing, and low volumes / less important categories when viewed as a percentage of total spend.
The Cost Breakdown Model builds forecast prices from predefined cost drivers organized into logical clusters. This framework works well for simple commodities and processed ingredients where underlying costs can be identified and tracked separately.
Key cost clusters typically include sourcing and supplier strategy, market benchmarks, supply chain costs such as export duties and freight, packaging and delivery, insurance and commodity premiums, customs and handling, conversion costs, and conversion costs. Each cluster contains specific drivers that can be monitored and updated independently.
This model provides complete cost transparency. Teams can see exactly how changes in individual component costs e.g., freight rates or commodity prices, will impact on the final future price. The Cost-Plus approach creates one-to-many relationships with cost models and materials, meaning a single cost model can be used to generate forecasts for multiple materials. Dynamic forecasting with minimal efforts becomes possible because changes to any underlying driver automatically flows through to the price predictions.
The BOM Cost Breakdown Model extends the cost-plus concept to complex formulations requiring bill-of-materials calculations. This framework suits those materials that have third party production and also processed ingredients with multiple input components that need individual tracking.
Instead of treating a finished ingredient as a single material, this model breaks it down into constituent parts, each with its own pricing dynamics. For example, a chocolate supplier's price might be modeled as cocoa butter, cocoa solids, sugar, emulsifiers, and other components, plus packaging, delivery costs, conversion expenses, and margin.
This granular approach reveals how changes in individual raw materials impact the finished product price. If cocoa prices spike but sugar remains stable, the model quantifies the specific effect rather than applying a blanket escalation.
The Escalation model forecasts prices using weighted percentage increases applied to specified baseline prices. This simplified approach works particularly well for packaging categories where there are multiple SKU’s covering similar requirements with, for example, different colors and thicknesses where detailed cost driver data may be unavailable or where supplier relationships don't support full transparency.
Rather than modeling every cost component, the Escalation model applies industry indices or negotiated escalation percentages to known starting prices. For example, for certain material groups (for example corrugated packaging this might mean taking the previous year's contracted price and applying a weighted combination of paper index movements and inflation factors.
The weighting mechanism allows for nuanced forecasting. A specific agreement with a packaging supplier might (for example) pass through 70% of paper cost changes and 50% of general inflation, reflecting their actual cost structure without revealing proprietary details. This methodology provides cost estimates rather than detailed breakdowns, accepting less transparency in exchange for simplicity and lower maintenance requirements.
By understanding that different procurement categories require distinct forecasting approaches, implementing appropriate cost model frameworks, and maintaining disciplined processes, companies can significantly improve their ability to predict future costs and make informed strategic decisions. In our next post we will look at how these models support addressing the additional complexities of commodity cost variance and hedging.
Want to know more? Contact the Keyrus procurement team or write me.
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