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In our last post we highlighted how leading CPG procurement organizations use four distinct cost model frameworks, each suited to different category characteristics and sourcing strategies to simplify and standardize the way of working in material forecasting. The flat price, cost plus, cost plus BOM and escalation models. As a recap these models cater to different levels of complexity and maturity and provide standard ways of linking diverse cost drivers.
In this update we examine the additional challenges that relate to commodities and how these can successfully be addressed in budgeting, forecasting, and planning cycles. Commodity linked materials behave differently. Their prices can move daily, markets are globally traded, and hedging introduces a financial layer that must align with physical procurement activities.
This makes forecasting much more complex. The good news is that for commodities, the cost model frameworks introduced in the previous post still apply — but with one major difference: the underlying cost drivers (indices, futures curves, premiums, FX rates) move constantly. This makes dynamic modeling essential.
Forward curves represent the market’s collective expectation of future commodity prices. When you integrate them into your forecasting process, you move from “what we think will happen” and start anchoring uncovered exposure to a real reference. Done properly, this allows procurement and finance teams to apply forward pricing to open volumes, so forecasts update automatically as market conditions change.
This is where the cost models we introduced in the previous post earn their keep. In a Cost Plus model, the forward curve typically becomes the live input for the commodity benchmark component, while the rest of the model holds the non-commodity drivers constant (or moves them on their own indices). In a BOM Cost Plus model, forward curves can drive multiple inputs at once (think cocoa + sugar + dairy), with the formulation logic translating market moves into an expected supplier price.
Forward curves help you forecast where the market is heading. Hedging is what you do when you want more certainty about where you will actually land. This adds a second layer of planning complexity because procurement and finance teams now need to forecast two things at the same time:
• The cost of uncovered volumes (typically driven by forward curves/market benchmarks)
• The cost impact of covered volumes (driven by your hedge positions and hedge economics)
That’s why commodity forecasting can quickly become a constant cycle of looking at covered vs uncovered volumes, assessing risk, and deciding whether you need to adjust contracting or hedging strategy—and then reflecting all of it back into the rolling forecast.
One of the most important realities to call out (and one that often gets glossed over) is that the hedge is almost never on the exact material you purchase. This explains a lot of the pain. For example, Resin purchases are hedged via crude proxies, Sugar, wheat and corn via exchange traded futures; Energy via regional forward swaps; and aluminum cans via LME aluminum. Now you have a set of modelling requirements that spreadsheets struggle with:
Even if your hedge benchmark is directionally correct, your actual delivered cost will still include layers that don’t move in perfect lockstep with the hedge:
• Basis risk: the difference between the hedge index and your supplier’s realized price
• Premiums: Regional, capacity, sustainability attributes, and supplier-specific uplifts
• FX exposure: the commodity may trade in USD while you budget in EUR/GBP
• Freight and duties: global logistics and trade policy can move independently of the curves
This is exactly where the earlier cost model frameworks matter, because they give you a disciplined way to separate these drivers rather than bury them in a single blended number.
Once hedging enters the picture, a forward curve alone can’t be your forecast. What you need is a blended view that reflects:
• Hedged volume priced at hedge economics (plus/minus hedge costs)
• Unhedged volume priced off the forward curve (plus basis/premiums)
• Non-commodity cost drivers layered in through your cost model structure
In other words: the forecast becomes a weighted combination of what’s already locked and what remains exposed. This is the point where teams realize they aren’t running “a forecast” anymore. They’re running a live reconciliation between market movements, hedge positions, supplier contracts, and operational demand.
A second practical implication is cadence. Commodity-linked inputs can move daily, while most planning cycles run monthly. If your planning process can only absorb changes once per month, you end up with stale assumptions, reactive decision-making, and an uncomfortable gap between what the market did and what your forecast says. That’s why commodity forecasting needs to support frequent updates and fast scenario planning—especially when procurement is being asked, mid-cycle, “what does this do to our margin?” and “how much of this move are we protected from?”
And This Is How You End Up with “The Big Excel Workbook…”
At this point, most teams attempt to hold it all together with spreadsheets:
• One tab for forward curves,
• One for hedge coverage,
• another for UoM conversions,
• another for supplier pricing formulas,
• and then a fragile set of links to budget and forecast templates.
The problem isn’t that Excel can’t do the math. It’s that Excel can’t do the governance, scale, and control as versioning becomes a risk, auditability becomes painful, and scenario work becomes slow. Cross-functional alignment becomes “who has the latest file?” perpetuating trusting only my workbook and hence siloed thinking.
This is where modern procurement planning platforms come into their own. The goal is not “a nicer spreadsheet”. The goal is an integrated model that can implement the four cost model frameworks (flat price, cost plus, BOM cost plus, escalation) in one consistent structure; link materials to market benchmarks and forward curves, track supplier contract positions and uncovered exposure, layer hedging coverage and economics into the forecast, and provide unified reporting and governance across categories.
Done well, this gives procurement and finance a single view of what is covered, what is exposed, what the market is doing, and what it means for the forecast and the P&L.
As processes mature, category-specific commodity management methodologies pay dividends through improved budget accuracy, stronger supplier negotiations, enhanced risk management, and ultimately, protected profit margins in uncertain markets.
If you want to know more, our team brings deep expertise in cost modelling frameworks, platform implementation, and change management.
Contact our procurement experts today to discuss how we can help you build commodity management capabilities that drive better decisions and protect your bottom line in volatile markets.
In our last post we highlighted how leading CPG procurement organizations use four distinct cost model frameworks, each suited to different category characteristics and sourcing strategies to simplify and standardize the way of working in material forecasting. The flat price, cost plus, cost plus BOM and escalation models. As a recap these models cater to different levels of complexity and maturity and provide standard ways of linking diverse cost drivers.
In this update we examine the additional challenges that relate to commodities and how these can successfully be addressed in budgeting, forecasting, and planning cycles. Commodity linked materials behave differently. Their prices can move daily, markets are globally traded, and hedging introduces a financial layer that must align with physical procurement activities.
This makes forecasting much more complex. The good news is that for commodities, the cost model frameworks introduced in the previous post still apply — but with one major difference: the underlying cost drivers (indices, futures curves, premiums, FX rates) move constantly. This makes dynamic modeling essential.
Forward curves represent the market’s collective expectation of future commodity prices. When you integrate them into your forecasting process, you move from “what we think will happen” and start anchoring uncovered exposure to a real reference. Done properly, this allows procurement and finance teams to apply forward pricing to open volumes, so forecasts update automatically as market conditions change.
This is where the cost models we introduced in the previous post earn their keep. In a Cost Plus model, the forward curve typically becomes the live input for the commodity benchmark component, while the rest of the model holds the non-commodity drivers constant (or moves them on their own indices). In a BOM Cost Plus model, forward curves can drive multiple inputs at once (think cocoa + sugar + dairy), with the formulation logic translating market moves into an expected supplier price.
Forward curves help you forecast where the market is heading. Hedging is what you do when you want more certainty about where you will actually land. This adds a second layer of planning complexity because procurement and finance teams now need to forecast two things at the same time:
• The cost of uncovered volumes (typically driven by forward curves/market benchmarks)
• The cost impact of covered volumes (driven by your hedge positions and hedge economics)
That’s why commodity forecasting can quickly become a constant cycle of looking at covered vs uncovered volumes, assessing risk, and deciding whether you need to adjust contracting or hedging strategy—and then reflecting all of it back into the rolling forecast.
One of the most important realities to call out (and one that often gets glossed over) is that the hedge is almost never on the exact material you purchase. This explains a lot of the pain. For example, Resin purchases are hedged via crude proxies, Sugar, wheat and corn via exchange traded futures; Energy via regional forward swaps; and aluminum cans via LME aluminum. Now you have a set of modelling requirements that spreadsheets struggle with:
Even if your hedge benchmark is directionally correct, your actual delivered cost will still include layers that don’t move in perfect lockstep with the hedge:
• Basis risk: the difference between the hedge index and your supplier’s realized price
• Premiums: Regional, capacity, sustainability attributes, and supplier-specific uplifts
• FX exposure: the commodity may trade in USD while you budget in EUR/GBP
• Freight and duties: global logistics and trade policy can move independently of the curves
This is exactly where the earlier cost model frameworks matter, because they give you a disciplined way to separate these drivers rather than bury them in a single blended number.
Once hedging enters the picture, a forward curve alone can’t be your forecast. What you need is a blended view that reflects:
• Hedged volume priced at hedge economics (plus/minus hedge costs)
• Unhedged volume priced off the forward curve (plus basis/premiums)
• Non-commodity cost drivers layered in through your cost model structure
In other words: the forecast becomes a weighted combination of what’s already locked and what remains exposed. This is the point where teams realize they aren’t running “a forecast” anymore. They’re running a live reconciliation between market movements, hedge positions, supplier contracts, and operational demand.
A second practical implication is cadence. Commodity-linked inputs can move daily, while most planning cycles run monthly. If your planning process can only absorb changes once per month, you end up with stale assumptions, reactive decision-making, and an uncomfortable gap between what the market did and what your forecast says. That’s why commodity forecasting needs to support frequent updates and fast scenario planning—especially when procurement is being asked, mid-cycle, “what does this do to our margin?” and “how much of this move are we protected from?”
And This Is How You End Up with “The Big Excel Workbook…”
At this point, most teams attempt to hold it all together with spreadsheets:
• One tab for forward curves,
• One for hedge coverage,
• another for UoM conversions,
• another for supplier pricing formulas,
• and then a fragile set of links to budget and forecast templates.
The problem isn’t that Excel can’t do the math. It’s that Excel can’t do the governance, scale, and control as versioning becomes a risk, auditability becomes painful, and scenario work becomes slow. Cross-functional alignment becomes “who has the latest file?” perpetuating trusting only my workbook and hence siloed thinking.
This is where modern procurement planning platforms come into their own. The goal is not “a nicer spreadsheet”. The goal is an integrated model that can implement the four cost model frameworks (flat price, cost plus, BOM cost plus, escalation) in one consistent structure; link materials to market benchmarks and forward curves, track supplier contract positions and uncovered exposure, layer hedging coverage and economics into the forecast, and provide unified reporting and governance across categories.
Done well, this gives procurement and finance a single view of what is covered, what is exposed, what the market is doing, and what it means for the forecast and the P&L.
As processes mature, category-specific commodity management methodologies pay dividends through improved budget accuracy, stronger supplier negotiations, enhanced risk management, and ultimately, protected profit margins in uncertain markets.
If you want to know more, our team brings deep expertise in cost modelling frameworks, platform implementation, and change management.
Contact our procurement experts today to discuss how we can help you build commodity management capabilities that drive better decisions and protect your bottom line in volatile markets.
In our last post we highlighted how leading CPG procurement organizations use four distinct cost model frameworks, each suited to different category characteristics and sourcing strategies to simplify and standardize the way of working in material forecasting. The flat price, cost plus, cost plus BOM and escalation models. As a recap these models cater to different levels of complexity and maturity and provide standard ways of linking diverse cost drivers.
In this update we examine the additional challenges that relate to commodities and how these can successfully be addressed in budgeting, forecasting, and planning cycles. Commodity linked materials behave differently. Their prices can move daily, markets are globally traded, and hedging introduces a financial layer that must align with physical procurement activities.
This makes forecasting much more complex. The good news is that for commodities, the cost model frameworks introduced in the previous post still apply — but with one major difference: the underlying cost drivers (indices, futures curves, premiums, FX rates) move constantly. This makes dynamic modeling essential.
Forward curves represent the market’s collective expectation of future commodity prices. When you integrate them into your forecasting process, you move from “what we think will happen” and start anchoring uncovered exposure to a real reference. Done properly, this allows procurement and finance teams to apply forward pricing to open volumes, so forecasts update automatically as market conditions change.
This is where the cost models we introduced in the previous post earn their keep. In a Cost Plus model, the forward curve typically becomes the live input for the commodity benchmark component, while the rest of the model holds the non-commodity drivers constant (or moves them on their own indices). In a BOM Cost Plus model, forward curves can drive multiple inputs at once (think cocoa + sugar + dairy), with the formulation logic translating market moves into an expected supplier price.
Forward curves help you forecast where the market is heading. Hedging is what you do when you want more certainty about where you will actually land. This adds a second layer of planning complexity because procurement and finance teams now need to forecast two things at the same time:
• The cost of uncovered volumes (typically driven by forward curves/market benchmarks)
• The cost impact of covered volumes (driven by your hedge positions and hedge economics)
That’s why commodity forecasting can quickly become a constant cycle of looking at covered vs uncovered volumes, assessing risk, and deciding whether you need to adjust contracting or hedging strategy—and then reflecting all of it back into the rolling forecast.
One of the most important realities to call out (and one that often gets glossed over) is that the hedge is almost never on the exact material you purchase. This explains a lot of the pain. For example, Resin purchases are hedged via crude proxies, Sugar, wheat and corn via exchange traded futures; Energy via regional forward swaps; and aluminum cans via LME aluminum. Now you have a set of modelling requirements that spreadsheets struggle with:
Even if your hedge benchmark is directionally correct, your actual delivered cost will still include layers that don’t move in perfect lockstep with the hedge:
• Basis risk: the difference between the hedge index and your supplier’s realized price
• Premiums: Regional, capacity, sustainability attributes, and supplier-specific uplifts
• FX exposure: the commodity may trade in USD while you budget in EUR/GBP
• Freight and duties: global logistics and trade policy can move independently of the curves
This is exactly where the earlier cost model frameworks matter, because they give you a disciplined way to separate these drivers rather than bury them in a single blended number.
Once hedging enters the picture, a forward curve alone can’t be your forecast. What you need is a blended view that reflects:
• Hedged volume priced at hedge economics (plus/minus hedge costs)
• Unhedged volume priced off the forward curve (plus basis/premiums)
• Non-commodity cost drivers layered in through your cost model structure
In other words: the forecast becomes a weighted combination of what’s already locked and what remains exposed. This is the point where teams realize they aren’t running “a forecast” anymore. They’re running a live reconciliation between market movements, hedge positions, supplier contracts, and operational demand.
A second practical implication is cadence. Commodity-linked inputs can move daily, while most planning cycles run monthly. If your planning process can only absorb changes once per month, you end up with stale assumptions, reactive decision-making, and an uncomfortable gap between what the market did and what your forecast says. That’s why commodity forecasting needs to support frequent updates and fast scenario planning—especially when procurement is being asked, mid-cycle, “what does this do to our margin?” and “how much of this move are we protected from?”
And This Is How You End Up with “The Big Excel Workbook…”
At this point, most teams attempt to hold it all together with spreadsheets:
• One tab for forward curves,
• One for hedge coverage,
• another for UoM conversions,
• another for supplier pricing formulas,
• and then a fragile set of links to budget and forecast templates.
The problem isn’t that Excel can’t do the math. It’s that Excel can’t do the governance, scale, and control as versioning becomes a risk, auditability becomes painful, and scenario work becomes slow. Cross-functional alignment becomes “who has the latest file?” perpetuating trusting only my workbook and hence siloed thinking.
This is where modern procurement planning platforms come into their own. The goal is not “a nicer spreadsheet”. The goal is an integrated model that can implement the four cost model frameworks (flat price, cost plus, BOM cost plus, escalation) in one consistent structure; link materials to market benchmarks and forward curves, track supplier contract positions and uncovered exposure, layer hedging coverage and economics into the forecast, and provide unified reporting and governance across categories.
Done well, this gives procurement and finance a single view of what is covered, what is exposed, what the market is doing, and what it means for the forecast and the P&L.
As processes mature, category-specific commodity management methodologies pay dividends through improved budget accuracy, stronger supplier negotiations, enhanced risk management, and ultimately, protected profit margins in uncertain markets.
If you want to know more, our team brings deep expertise in cost modelling frameworks, platform implementation, and change management.
Contact our procurement experts today to discuss how we can help you build commodity management capabilities that drive better decisions and protect your bottom line in volatile markets.
In our last post we highlighted how leading CPG procurement organizations use four distinct cost model frameworks, each suited to different category characteristics and sourcing strategies to simplify and standardize the way of working in material forecasting. The flat price, cost plus, cost plus BOM and escalation models. As a recap these models cater to different levels of complexity and maturity and provide standard ways of linking diverse cost drivers.
In this update we examine the additional challenges that relate to commodities and how these can successfully be addressed in budgeting, forecasting, and planning cycles. Commodity linked materials behave differently. Their prices can move daily, markets are globally traded, and hedging introduces a financial layer that must align with physical procurement activities.
This makes forecasting much more complex. The good news is that for commodities, the cost model frameworks introduced in the previous post still apply — but with one major difference: the underlying cost drivers (indices, futures curves, premiums, FX rates) move constantly. This makes dynamic modeling essential.
Forward curves represent the market’s collective expectation of future commodity prices. When you integrate them into your forecasting process, you move from “what we think will happen” and start anchoring uncovered exposure to a real reference. Done properly, this allows procurement and finance teams to apply forward pricing to open volumes, so forecasts update automatically as market conditions change.
This is where the cost models we introduced in the previous post earn their keep. In a Cost Plus model, the forward curve typically becomes the live input for the commodity benchmark component, while the rest of the model holds the non-commodity drivers constant (or moves them on their own indices). In a BOM Cost Plus model, forward curves can drive multiple inputs at once (think cocoa + sugar + dairy), with the formulation logic translating market moves into an expected supplier price.
Forward curves help you forecast where the market is heading. Hedging is what you do when you want more certainty about where you will actually land. This adds a second layer of planning complexity because procurement and finance teams now need to forecast two things at the same time:
• The cost of uncovered volumes (typically driven by forward curves/market benchmarks)
• The cost impact of covered volumes (driven by your hedge positions and hedge economics)
That’s why commodity forecasting can quickly become a constant cycle of looking at covered vs uncovered volumes, assessing risk, and deciding whether you need to adjust contracting or hedging strategy—and then reflecting all of it back into the rolling forecast.
One of the most important realities to call out (and one that often gets glossed over) is that the hedge is almost never on the exact material you purchase. This explains a lot of the pain. For example, Resin purchases are hedged via crude proxies, Sugar, wheat and corn via exchange traded futures; Energy via regional forward swaps; and aluminum cans via LME aluminum. Now you have a set of modelling requirements that spreadsheets struggle with:
Even if your hedge benchmark is directionally correct, your actual delivered cost will still include layers that don’t move in perfect lockstep with the hedge:
• Basis risk: the difference between the hedge index and your supplier’s realized price
• Premiums: Regional, capacity, sustainability attributes, and supplier-specific uplifts
• FX exposure: the commodity may trade in USD while you budget in EUR/GBP
• Freight and duties: global logistics and trade policy can move independently of the curves
This is exactly where the earlier cost model frameworks matter, because they give you a disciplined way to separate these drivers rather than bury them in a single blended number.
Once hedging enters the picture, a forward curve alone can’t be your forecast. What you need is a blended view that reflects:
• Hedged volume priced at hedge economics (plus/minus hedge costs)
• Unhedged volume priced off the forward curve (plus basis/premiums)
• Non-commodity cost drivers layered in through your cost model structure
In other words: the forecast becomes a weighted combination of what’s already locked and what remains exposed. This is the point where teams realize they aren’t running “a forecast” anymore. They’re running a live reconciliation between market movements, hedge positions, supplier contracts, and operational demand.
A second practical implication is cadence. Commodity-linked inputs can move daily, while most planning cycles run monthly. If your planning process can only absorb changes once per month, you end up with stale assumptions, reactive decision-making, and an uncomfortable gap between what the market did and what your forecast says. That’s why commodity forecasting needs to support frequent updates and fast scenario planning—especially when procurement is being asked, mid-cycle, “what does this do to our margin?” and “how much of this move are we protected from?”
And This Is How You End Up with “The Big Excel Workbook…”
At this point, most teams attempt to hold it all together with spreadsheets:
• One tab for forward curves,
• One for hedge coverage,
• another for UoM conversions,
• another for supplier pricing formulas,
• and then a fragile set of links to budget and forecast templates.
The problem isn’t that Excel can’t do the math. It’s that Excel can’t do the governance, scale, and control as versioning becomes a risk, auditability becomes painful, and scenario work becomes slow. Cross-functional alignment becomes “who has the latest file?” perpetuating trusting only my workbook and hence siloed thinking.
This is where modern procurement planning platforms come into their own. The goal is not “a nicer spreadsheet”. The goal is an integrated model that can implement the four cost model frameworks (flat price, cost plus, BOM cost plus, escalation) in one consistent structure; link materials to market benchmarks and forward curves, track supplier contract positions and uncovered exposure, layer hedging coverage and economics into the forecast, and provide unified reporting and governance across categories.
Done well, this gives procurement and finance a single view of what is covered, what is exposed, what the market is doing, and what it means for the forecast and the P&L.
As processes mature, category-specific commodity management methodologies pay dividends through improved budget accuracy, stronger supplier negotiations, enhanced risk management, and ultimately, protected profit margins in uncertain markets.
If you want to know more, our team brings deep expertise in cost modelling frameworks, platform implementation, and change management.
Contact our procurement experts today to discuss how we can help you build commodity management capabilities that drive better decisions and protect your bottom line in volatile markets.
In our last post we highlighted how leading CPG procurement organizations use four distinct cost model frameworks, each suited to different category characteristics and sourcing strategies to simplify and standardize the way of working in material forecasting. The flat price, cost plus, cost plus BOM and escalation models. As a recap these models cater to different levels of complexity and maturity and provide standard ways of linking diverse cost drivers.
In this update we examine the additional challenges that relate to commodities and how these can successfully be addressed in budgeting, forecasting, and planning cycles. Commodity linked materials behave differently. Their prices can move daily, markets are globally traded, and hedging introduces a financial layer that must align with physical procurement activities.
This makes forecasting much more complex. The good news is that for commodities, the cost model frameworks introduced in the previous post still apply — but with one major difference: the underlying cost drivers (indices, futures curves, premiums, FX rates) move constantly. This makes dynamic modeling essential.
Forward curves represent the market’s collective expectation of future commodity prices. When you integrate them into your forecasting process, you move from “what we think will happen” and start anchoring uncovered exposure to a real reference. Done properly, this allows procurement and finance teams to apply forward pricing to open volumes, so forecasts update automatically as market conditions change.
This is where the cost models we introduced in the previous post earn their keep. In a Cost Plus model, the forward curve typically becomes the live input for the commodity benchmark component, while the rest of the model holds the non-commodity drivers constant (or moves them on their own indices). In a BOM Cost Plus model, forward curves can drive multiple inputs at once (think cocoa + sugar + dairy), with the formulation logic translating market moves into an expected supplier price.
Forward curves help you forecast where the market is heading. Hedging is what you do when you want more certainty about where you will actually land. This adds a second layer of planning complexity because procurement and finance teams now need to forecast two things at the same time:
• The cost of uncovered volumes (typically driven by forward curves/market benchmarks)
• The cost impact of covered volumes (driven by your hedge positions and hedge economics)
That’s why commodity forecasting can quickly become a constant cycle of looking at covered vs uncovered volumes, assessing risk, and deciding whether you need to adjust contracting or hedging strategy—and then reflecting all of it back into the rolling forecast.
One of the most important realities to call out (and one that often gets glossed over) is that the hedge is almost never on the exact material you purchase. This explains a lot of the pain. For example, Resin purchases are hedged via crude proxies, Sugar, wheat and corn via exchange traded futures; Energy via regional forward swaps; and aluminum cans via LME aluminum. Now you have a set of modelling requirements that spreadsheets struggle with:
Even if your hedge benchmark is directionally correct, your actual delivered cost will still include layers that don’t move in perfect lockstep with the hedge:
• Basis risk: the difference between the hedge index and your supplier’s realized price
• Premiums: Regional, capacity, sustainability attributes, and supplier-specific uplifts
• FX exposure: the commodity may trade in USD while you budget in EUR/GBP
• Freight and duties: global logistics and trade policy can move independently of the curves
This is exactly where the earlier cost model frameworks matter, because they give you a disciplined way to separate these drivers rather than bury them in a single blended number.
Once hedging enters the picture, a forward curve alone can’t be your forecast. What you need is a blended view that reflects:
• Hedged volume priced at hedge economics (plus/minus hedge costs)
• Unhedged volume priced off the forward curve (plus basis/premiums)
• Non-commodity cost drivers layered in through your cost model structure
In other words: the forecast becomes a weighted combination of what’s already locked and what remains exposed. This is the point where teams realize they aren’t running “a forecast” anymore. They’re running a live reconciliation between market movements, hedge positions, supplier contracts, and operational demand.
A second practical implication is cadence. Commodity-linked inputs can move daily, while most planning cycles run monthly. If your planning process can only absorb changes once per month, you end up with stale assumptions, reactive decision-making, and an uncomfortable gap between what the market did and what your forecast says. That’s why commodity forecasting needs to support frequent updates and fast scenario planning—especially when procurement is being asked, mid-cycle, “what does this do to our margin?” and “how much of this move are we protected from?”
And This Is How You End Up with “The Big Excel Workbook…”
At this point, most teams attempt to hold it all together with spreadsheets:
• One tab for forward curves,
• One for hedge coverage,
• another for UoM conversions,
• another for supplier pricing formulas,
• and then a fragile set of links to budget and forecast templates.
The problem isn’t that Excel can’t do the math. It’s that Excel can’t do the governance, scale, and control as versioning becomes a risk, auditability becomes painful, and scenario work becomes slow. Cross-functional alignment becomes “who has the latest file?” perpetuating trusting only my workbook and hence siloed thinking.
This is where modern procurement planning platforms come into their own. The goal is not “a nicer spreadsheet”. The goal is an integrated model that can implement the four cost model frameworks (flat price, cost plus, BOM cost plus, escalation) in one consistent structure; link materials to market benchmarks and forward curves, track supplier contract positions and uncovered exposure, layer hedging coverage and economics into the forecast, and provide unified reporting and governance across categories.
Done well, this gives procurement and finance a single view of what is covered, what is exposed, what the market is doing, and what it means for the forecast and the P&L.
As processes mature, category-specific commodity management methodologies pay dividends through improved budget accuracy, stronger supplier negotiations, enhanced risk management, and ultimately, protected profit margins in uncertain markets.
If you want to know more, our team brings deep expertise in cost modelling frameworks, platform implementation, and change management.
Contact our procurement experts today to discuss how we can help you build commodity management capabilities that drive better decisions and protect your bottom line in volatile markets.
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