Who should be in charge of sales forecasting?
Governance Across Sales, Purchasing, Pricing, and the Supply Chain

Ines Amor

Ph.D. in AI and Data Science

August 20, 2026

At most retailers, demand forecasting isn't calculated just once—it's calculated four times. The sales department forecasts what it wants to sell, the purchasing department forecasts what it dares to order, and the supply chain forecasts what it can deliver at the lowest cost.

And pricing—almost always immediately afterward—translates that same figure into a pricing or promotional decision. Four interpretations, four approaches, but only one real market to contend with.

Pricing isn't a service that checks the forecast after an order has been placed; it's the function that transforms it most quickly into a signal visible to the customer, even before a truck is loaded. Overlooking it in governance is like managing three out of four decisions and letting the one that's most visible to the end customer slip by without any oversight.

This article does not address the mechanics of calculating a forecast; a separate guide in this series already covers that topic. Instead, it addresses the issue that, in practice, causes the most silent damage within a retail organization: who should be responsible for THE forecast, and how four departments—each of which is correct within its own scope (retail operations, procurement, supply chain, and pricing)—end up collectively producing a result that no one chose.

A single forecast shared across these functions—that’s what BOOPER’s AI-powered Sales Forecasting centralizes: three explainable scenarios, not four competing figures.

Table of Contents

H2 Headings

Booper Illustration: Forecast Governance—Four Functions Connected to a Central Hub

Three services, three demand forecasts

A retailer that thinks it has only one sales forecast is almost always mistaken. In reality, three departments calculate different versions of the forecast, without necessarily realizing it.

Each service starts with a different constraint:

Sales and marketing start with a target to meet—whether it’s a revenue budget, market share, or a commitment made to senior management. Structural bias: optimism, since no one builds a sales plan based on the assumption of stagnation.
Purchasing starts with the opposite constraint: avoiding ending up with excess inventory that ties up cash and ends up as markdowns. Structural bias: caution, since when in doubt, we order a little less—never a little more.
The supply chain operates at a third level: that of physical flow, truck loading, warehouse capacity, and transportation costs. Structural bias: smoothing is done at the network level to optimize logistics, not necessarily at the store level, which reflects the fine-grained commercial reality.

Three legitimate approaches. Three different figures.

And in the vast majority of organizations, there is no mechanism in place to address these issues before they take effect—a breakdown here, excess inventory there, or a month-end meeting that turns into a settling of scores.

In addition to these three perspectives, there is a fourth factor that is all too often overlooked in discussions of governance: pricing. A demand forecast almost always translates first into a pricing or promotional decision, even before it results in an order.

Ignoring pricing in forecast governance is like managing the cause without managing its most visible consequence for the customer. The price displayed on the shelf or online is often the first place where a forecast discrepancy becomes apparent, long before inventory becomes available or a stockout is displayed.

29%

Only some supply chain organizations have developed at least three of the five capabilities deemed essential for shaping the future, including a truly cross-functional strategy at the enterprise level—the least common of the five capabilities (Gartner, survey of 579 supply chain professionals, press release, February 18, 2025).

The Symptoms of an Ungoverned Forecast

An organization that produces four competing forecasts without realizing it may not notice this right away. The problem manifests itself through symptoms that are often misdiagnosed as inventory issues rather than governance issues.

Symptom 1: Simultaneous stockouts and excess inventory across different categories

A retailer may, in the same week, experience stockouts in a category experiencing strong sales momentum (the store was correct about the trend, but purchases did not keep pace) and excess inventory in another category that the supply chain restocked based on a logistics plan that was out of touch with reality on the ground.

This isn't a problem of a forecast that is generally incorrect. It's a problem of three forecasts that were never compared with one another.

Symptom 2: End-of-month meetings that turn into disputes over numbers

Each department defends its metric by citing a different reason:

Retailers are defending their lost revenue by pointing to supply shortages.
Purchasing managers defend their cautious approach by pointing to the markdown rate from previous months.
The supply chain justifies its logistics decisions by pointing to the transportation costs it has avoided.

Everyone is right about their own metric, but no one is looking at the same baseline figure. As a result, the steering committee spends more time reconciling different interpretations of reality than deciding anything for the coming month.

Symptom 3: Forecasting becomes a political issue rather than a professional one

When there is no common framework, each department learns to defend ITS forecast rather than to challenge the most accurate figure. The debate shifts from focusing on actual demand to focusing on who is right—a self-perpetuating dynamic that makes each subsequent cycle more difficult than the last.

Symptom 4: Pricing inherits all the biases, without being at the table

Pricing often silently absorbs these three biases without ever being linked to their trade-offs. A promotion launched based on an optimistic sales forecast—without challenging the volume assumption with the purchasing department—results in eroded margins.

This erosion of profit margins does not show up in the financial statements until several weeks later, well after the pricing decision has been made. It is a problem just as costly as the first three, but one that is rarely identified as such in steering committees.

Why Every Department Is Right—and That's Exactly the Problem

The natural reaction to these symptoms is to look for someone to blame—the department that is “at fault.” This is a misdiagnosis.

Each function properly optimizes the metric on which it is evaluated. The problem is not the bias of each department; it is the lack of a mechanism that allows them to communicate with one another before a decision is made.

Driven by the goal

Sales / Retail

Assessed based on revenue and market share. There is a structural bias toward optimism; underestimating demand is tantamount to setting a limit on one’s own growth.

Driven by cash flow

Purchases

Evaluated based on inventory turnover and markdowns. There is a structural bias toward caution; when in doubt, we order the safest quantity, not the most likely one.

Driven by logistics costs

Supply Chain

Evaluated based on transportation costs and service levels. There is a structural bias toward smoothing; the delivery grid that optimizes a truck's route is not the same as the one that best reflects a store's location.

Motivated by the annual commitment

Finance / Management

Evaluated based on budget compliance. There is a structural bias toward rigidity; a figure approved in January remains the benchmark, even when conditions on the ground have already changed.

Motivated by profit margins

Pricing / Category Management

Evaluated based on margin and price-image. A structural bias toward direct translation turns a forecast into a pricing or promotional decision without always challenging the underlying volume assumption.

None of these biases is an individual fault. They are the logical consequences of incentive systems that were never designed to work together.

Forecasting governance does not seek to eliminate these biases; rather, it seeks to make them visible, to address them, and to turn them into an explicit point of decision-making rather than an implicit source of conflict.

Pricing, in particular, pays a heavy price for the lack of this dialogue. It is the function that most quickly translates a forecasting discrepancy into euros visible to the customer—even before a warehouse has made a move or inventory has become available.

3–5%

additional revenue and margin when pricing and promotional decisions are managed in a coordinated and analytical manner, rather than in isolation from one another—the performance gap that most retailers leave on the table due to a lack of common governance between forecasting and pricing (McKinsey & Company, “Pricing in Retail: Setting Strategy,” April 1, 2015).

Who is responsible for what among sales, purchasing, supply chain, and pricing?

The question “Who should lead the sales forecasting process?” has a wrong default answer (“the department with the most data” or “the one who speaks up the loudest in meetings”) and a structurally sound answer: no one should lead it alone, but someone must be responsible for overseeing it.

Function	What she focuses on first and foremost	Typical bias in the forecast	Role in a Streamlined S&OP Process
Sales / Retail	Revenue, market share	Overestimating to avoid setting a limit	Drives the business vision, challenged by the facts
Purchases	Inventory Turnover, Markdowns	Underestimate to ensure cash flow security	Consulted on supplier feasibility, not as sole decision-makers
Supply Chain	Logistics costs, service levels	Adjust demand based on the truck's capacity, not the store's	Converts the forecast into physical flow, reports constraints
Finance / Management	Budget compliance, margin	Locks in a figure approved at the beginning of the year	Final arbiter in the event of a persistent disagreement
Pricing / Category Management	Turnover, margin, price-to-earnings ratio	Converts volume into price without always challenging the assumption	Provide the translation of the forecast price, as agreed upon by the trade and finance departments

To put it this way: a single baseline forecast circulates among these five functions; each enriches it with its own perspective, and none of them recalculates it on its own in a corner. This is the difference between true governance and mere coordination based on good will—which falls apart as soon as the timeline tightens. The most common misconception at this stage—treating this operational forecast as if it had to match the budget approved in January—deserves its own discussion: see the dedicated article in this report.

What a Single, Governed Forecast Actually Changes

The principle behind this approach is not new: it is S&OP (Sales & Operations Planning), or its streamlined version,IBP (Integrated Business Planning), in its most mature form. The central idea can be summed up in one sentence: a single baseline forecast, enriched and challenged by each function based on its expertise, rather than four independent forecasts that one tries to reconcile after the fact.

It's not primarily a question of tools; it's a question of processes and roles. But the effect—which has been documented—is real:

1–2 points

on average in additional EBIT for companies with amature Integrated Business Planning process, compared to those without one, with service levels 5 to 20 points higher and missed sales 40 to 50% lower (McKinsey & Company, “The Transformative Power of Integrated Business Planning,” May 25, 2022).

The improvement does not stem from a more sophisticated statistical model; the departments involved were already using reasonable calculation methods, each on their own. The improvement comes from eliminating the delay and the disconnect between the moment a demand discrepancy arises and the moment the entire organization takes it into account.

Pricing is often the one element missing from this elimination of lead time. It is the function that most quickly reflects a forecast discrepancy in the customer's experience—through a price, a promotion, or a discount.

And yet, it is rarely invited to the table where that figure is determined. As a result, the department most exposed to the consequences of an inaccurate forecast is often the one with the least say in challenging it early on.

At Booper

GENIUS Predict: A Unique, Explainable Forecast Shared Across Departments

The GENIUS Predict module centralizes demand forecasting over several rolling weeks, using three scenarios— Conservative, Balanced, and Aggressive—rather than a single figure imposed on all departments. The “AI Explanation” section details the factors that influenced each forecast, enabling the procurement and supply chain teams to challenge the business scenario with data-driven arguments, not just intuition.

Implementing a Streamlined S&OP Process in Five Steps

A comprehensive S&OP process, with its formalized monthly committees and strict schedule, is often out of reach for a medium-sized retailer—and is not necessary to achieve the primary benefit. A streamlined version, built around five principles, is sufficient to generate the four competing forecasts, including pricing.

Appoint a cross-functional arbitrator

A person (not a department) responsible for resolving disagreements between sales, procurement, supply chain, and pricing. Neither a judge and jury nor a mere meeting facilitator: a decision-maker with the authority to set a final figure.

Building a Single Data Repository

Sales history, out-of-stock items, promotions, prices: the same database feeds all four reports. Functions based on different data cannot converge, no matter how well the meeting is conducted.

Setting a two-beat rhythm

A short preliminary meeting where each department presents its analysis and assumptions, followed by an arbitration committee that resolves significant discrepancies—not a single large meeting where everything is discussed but nothing is decided.

Document the assumptions, not just the numbers

When the sales, procurement, supply chain, and pricing teams submit an estimate, they also explain why. A discrepancy becomes a factual point of discussion, not a power struggle between departments.

Link (without confusing) to the budget

A quarterly review compares the rolling operational forecast with the finance department’s annual budget commitment, without ever forcing one to conform to the other.

20–50%

improved forecast accuracy and a 10 to 30 percent reduction in inventory for companies with a high-performance S&OP process, compared to those with a basic or non-existent S&OP process (Bain & Company, “Good Sales and Operations Planning Is No Longer Good Enough,” April 12, 2016).

Booper has published a dedicated guide— *Sales Forecasting with AI: Methodology and KPIs*—covering the computational framework that powers this shared repository, including the necessary data, the method for generating the figure, and accuracy metrics such as MAPE. This guide assumes that this figure already exists somewhere within the organization—often in multiple versions—and addresses the question of which version should be considered authoritative and who makes that decision.

Four Common Misconceptions About S&OP

“This is a process reserved for large corporations.” False: The streamlined version described above is based on five principles, not on specialized software or a full-time planning department. A medium-sized retailer can implement it in just a few monthly cycles.
“We don’t need another meeting.” The problem isn’t the number of meetings; it’s their purpose. An arbitration meeting that resolves a documented discrepancy effectively replaces, in practice, several crisis meetings that merely assess the damage after the fact.
“Consensus obscures useful signals.” Well-designed governance does not seek consensus; it seeks arbitration. Disagreement between sales and procurement regarding a particular product category remains a valuable signal; it must be visible and debated, not quietly averaged out in a spreadsheet.
“A single baseline forecast eliminates the nuances of each business unit.” On the contrary: it is the lack of a common framework that forces each function to rely on its own estimate, since it cannot challenge those of others with shared arguments.

One shared forecast, not four competing forecasts

Sales, purchasing, the supply chain, and pricing don’t make mistakes when they each produce their own interpretation of demand; they’re just doing their jobs based on the metrics by which they’re evaluated. The problem is never the existence of these four assessments; it’s the lack of a forum where they can be compared and contrasted before they have an impact on the store—whether that’s a stockout, excess inventory, a poorly calibrated promotion, or a meeting that devolves into a dispute over numbers.

A streamlined S&OP process does not replace any of the existing areas of expertise. It provides them with a common framework, a regular forum for discussion, and a mediator—and that is what transforms competing forecasts into a single, well-considered decision.

The broader topic of what happens to a forecast once it informs a decision is addressed in the flagship article of this special issue, and the most costly consequence of a lack of governance (stockouts) is discussed in the dedicated article in this special issue.

A Checklist to Determine Whether Your Organization Has a Problem with Forecasting Silos

Do the sales, procurement, supply chain, and pricing teams work from the same source database, or does each team use its own export?
Have you ever had, in the same week, a stockout in one category and excess inventory in another, with no apparent connection between the two?
Do your end-of-month meetings focus on demand variances, or do they spend most of the time reconciling discrepancies in the numbers?
Is there a specific person responsible for resolving a disagreement between two departments, or does the disagreement remain unresolved until the following month?
Are the assumptions behind each forecast documented, or is only the final figure documented?
Is pricing a factor in arbitrage, or is the forecast provided after the decision has already been made elsewhere?

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Frequently Asked Questions

Who should be responsible for sales forecasting—sales, purchasing, or the supply chain?

None of the three departments should manage it alone: each brings legitimate expertise, but with its own structural bias— sales tends toward optimism (under-planning would be tantamount to setting a ceiling), procurement toward caution (when in doubt, order the safest quantity), and the supply chain toward logistical smoothing, organized at the truck level rather than the store level.

This article introduces a fourth factor that is all too often missing from this governance process: pricing, which almost always translates forecasts into pricing or promotional decisions even before an order is placed, and thus inherits all the biases of the other three factors without being involved in their trade-offs.

The right structural solution is to appoint a cross-functional arbitrator—an individual (not a department)—who is responsible for resolving disagreements based on a common data repository shared by the four functions, in line with a pricing organization that has clearly defined roles.

It is this explicit comparison of hypotheses—not the elimination of biases themselves—that transforms four competing forecasts into a single decision embraced by the organization.

What is S&OP (Sales & Operations Planning)?

S&OP is a process that periodically brings together the sales, procurement, supply chain, and finance functions (including pricing, according to this article) around a single demand forecast, so they can compare their assumptions—based on a sufficiently deep sales history—and resolve discrepancies before they lead to stockouts, excess inventory, or inconsistent pricing decisions.

Its most sophisticated version isIBP (Integrated Business Planning): a single baseline forecast, enriched and challenged by each function based on its expertise, rather than four independent forecasts reconciled after the fact.

This article outlines a streamlined version based on five principles: the appointment of a cross-functional arbitrator, a single data repository, a two-step process (pre-meeting followed by an arbitration committee), documented assumptions, and a quarterly review that links the operational forecast to the budget without confusing the two.

A formal monthly committee meeting involving the entire group is not a prerequisite: this streamlined version is sufficient for most medium-sized retailers to reap the main benefit of the program.

Why might a retailer experience both stockouts and excess inventory at the same time across different product categories?

This is a typical symptom of an unmanaged forecast: sales may have correctly identified a trend that purchasing did not follow, leading to a stockout, while the supply chain restocks another category based on a logistics smoothing process that is disconnected from on-the-ground reality—often because the correct exogenous events were not factored into the forecast—resulting in excess inventory.

This article emphasizes a key point: the problem is not that the forecast was generally incorrect, but rather that three forecasts were never compared with one another before they were implemented in the store.

This problem often persists during end-of-month meetings: each department defends its metrics—sales defends its stockouts, purchasing defends its markdown rate, and the supply chain defends the transportation costs it has avoided—without anyone looking at the same baseline figure.

Without a common framework, the steering committee ends up spending more time reconciling different interpretations of reality than deciding anything for the coming month.

Does a medium-sized retailer need a full-scale S&OP or a streamlined version?

In most cases, a streamlined version is sufficient: a cross-functional arbitrator, a single data repository, a two-step process (submission of assumptions followed by an arbitration committee), and documented assumptions rather than a single final figure.

This article debunks the common misconception that S&OP is reserved for large corporations: the streamlined version is based on five principles, not on specialized software or a full-time planning department, and can be implemented in just a few monthly cycles.

The available data confirm the value of the system even at this scale: a high-performance S&OP process yields a 20 to 50 percent improvement in forecast accuracy and a 10 to 30 percent reduction in inventory—an issue similar to the one addressed in our guide to forecasting ending inventory and seasonality—compared to a basic or nonexistent process, according to Bain & Company.

A formal monthly committee meeting involving a large group is therefore not a prerequisite: it is the regular testing of hypotheses—not the cumbersomeness of the process—that yields the bulk of the benefits.

How can I tell if my organization has a problem with forecasting silos?

Some reliable indicators identified in this article: sales, purchasing, and the supply chain operate on different databases, due to a lack of a clear distinction between internal and external data for pricing; end-of-month meetings focus on reconciling figures rather than on demand variances; and there is no explicit arbitration mechanism for resolving disagreements between two functions.

The checklist provided at the end of this article includes a factor that is often overlooked: Is pricing factored into the decision-making process, or is the forecast provided only after the decision has already been made elsewhere? This is a symptom just as revealing as simultaneous stockouts or overstocks.

Another concrete indication: having already observed, in the same week, a stockout in one category and an overstock in another with no apparent connection between the two—a sign that forecasts are never cross-checked before they affect the store.

These issues cannot be resolved simply by switching forecasting tools; they require a common data repository and a cross-functional arbitrator, as detailed in the streamlined S&OP method described in this article.

What is the difference between S&OP and simple monthly reporting?

Monthly reporting identifies a variance after it has occurred: it measures, but it does not decide. An S&OP process—even a streamlined one—compares the assumptions of each function before an order or price is finalized, with a designated arbitrator to make the final decision.

This article attributes this difference to the role of pricing: it is the function that most quickly translates a forecast discrepancy into euros visible to the customer—through a price or a promotion—while post-hoc reporting always comes too late to prevent the resulting erosion of margins.

The difference is also evident in the pace: the streamlined S&OP process involves a pre-meeting where each function presents its analysis and assumptions, followed by a resolution committee that resolves significant discrepancies, rather than a single meeting where everything is discussed but nothing is decided.

It is this shift from measurement to decision-making that explains the documented gains: according to McKinsey, a mature IBP generates 1 to 2 additional EBIT points and results in 40 to 50 percent fewer lost sales compared to simple post-hoc monitoring—gains that are managed through a pricing KPI dashboard shared across functions.