In today’s volatile and uncertain market, companies can no longer afford to operate without a structured, data-driven approach to forecasting demand. Demand planning is more than just predicting sales—it’s about building an integrated, agile business that can respond to customer needs while managing resources efficiently. Despite its importance, many organizations still rely on outdated tools, such as spreadsheets, which can lead to bias and siloed decision-making, ultimately compromising their forecast accuracy.

The potential improvements in predictive analytics and the integrated demand planning process can significantly streamline decision-making processes, create new insights, and save several business functions a huge amount of time and money.

Understand that a business will most likely invest in a new process to solve pain points, drive quantified savings, or deliver other clearly defined improvements. To successfully build a business case, you need to both help the organization understand the need and see the benefits.

Why Focus on Demand Planning?

Most companies that decide to invest or improve their process are primarily driven by one or more of the following:

• Obvious forecast accuracy challenges
• A highly variable process that requires a dedicated process to support it
• Detail-level forecasts are needed to support a more efficient manufacturing or distribution system
• Downstream inventory problems that are clearly driven by unseen variability
• An attempt to drive more cooperation between Sales and Operations through a consensus-based planning.

At its core, demand planning acts as the foundation for synchronized operations. It allows marketing, sales, supply chain, finance, and production to operate from a common set of assumptions. Without an accurate demand plan, supply planning becomes reactive; finance struggles with forecasting revenue, and customer service deteriorates due to stockouts or excess inventory.

Consumer behaviors have become increasingly unpredictable. Economic shifts, global disruptions, and rapid product cycles mean that relying solely on historical sales is no longer sufficient. Demand planning introduces a proactive lens that incorporates both internal drivers (such as promotions and price changes) and external signals (including market trends and customer insights) to create adaptive forecasts.

Inaccurate demand forecasts result in costly outcomes, including expedited shipping, excess working capital, lost sales, and markdowns. Improved demand planning helps reduce forecast error, allowing for better inventory placement, production planning, and supplier coordination. Even a 5- to 10-percent improvement in forecast accuracy can have a significant bottom-line impact.

Potential Improvements in Demand Planning

Organizations that invest in improving demand planning benefit from:

• Reduced Inventory Costs – Through better alignment of supply and demand.
• Improved Service Levels – By placing the right product in the right place at the right time.
• Higher Forecast Accuracy – Leading to more reliable plans across finance and supply.
• Faster Decision-Making – Enabled by real-time data and scenario analysis.
• Greater Agility – Ability to adjust to shifts in demand or supply quickly.

A mountain of research today shows that a mature demand planning process helps in improving forecast accuracy and delivers a high ROI. Improved forecast accuracy, when combined with software that translates the forecast into actionable insights, will decrease inventory and operating costs, increase service and sales, enhance cash flow and gross margin return on inventory investment (GMROI), and boost pre-tax profitability. The forecasting error, no matter how small, has a significant impact on the bottom line. In our experience, a 15 percent improvement in forecast accuracy will deliver a pre-tax improvement of 3 percent or higher.

In a previous IBF study of 15 U.S. companies, we found that even a one percentage point improvement in under-forecasting at a $1 billion company results in a savings of as much as $1.52 million, and for the same amount of improvement in over-forecasting, $1.28 million.[i]

The reduction in downstream finished goods inventory resulting from a well-established process and forecast accuracy improvements provides a one-time saving, as well as recurring savings arising from reduced carrying costs. There are significant benefits in a make-to-stock or distribution company. The downstream inventory reduction could range from 10 percent to 20 percent, as forecasting inaccuracies typically account for around 75 percent of the required safety stock.

Building and Investing in Demand Planning

• Build an Unbiased, Unconstrained, Consensus-Based Forecast: Organizations often confuse the demand plan with the sales target. Sales may overestimate to push for stretch goals, while operations may buffer to protect service. Demand planning needs to separate judgment from aspiration. Instituting a formal demand consensus process ensures that all voices are heard, while forecasts remain grounded in data and are evaluated against actual performance.
• Upgrade from Static Spreadsheets to Dynamic Models: Many companies still use Excel as their primary planning tool. While familiar, spreadsheets lack scalability, version control, and real-time integration. Upgrading to a dedicated demand planning system (or enhancing existing tools with forecasting models) introduces automation, improves collaboration, and enables real-time adjustments. It also supports more advanced techniques such as decomposition models or AI-based forecasts.
• Understand and Match Models to Patterns: Not all items follow the same demand pattern. Some are seasonal, some have trends, and others are highly volatile. Applying a one-size-fits-all model can lead to overfitting or underperformance. Instead, classify SKUs by their demand characteristics and apply the appropriate model, whether that’s exponential smoothing, moving average, or more complex causal models.
• Focus on Data Quality and Forecastability: Forecasting is only as good as the data behind it. Cleanse data for outliers, missing periods, and promotions. Measure forecastability using the Coefficient of Variation (CV) or Demand Intermittency. The demand planner becomes the integrator, ensuring that inputs from various departments are translated into a structured forecast. Establish accountability through KPIs such as bias, MAPE, and forecast value added (FVA).
• Invest in training and upskilling through IBF: Empower your teams with proven forecasting and planning knowledge by leveraging IBF’s certifications, workshops, and learning resources, building internal capability that drives consistent, confident decision-making.

Many companies are leaving money on the table with lost sales or poor service levels. An integrated demand planning process can result in increased revenue of 0.5 percent to 3 percent, along with improved inventory availability and demand shaping capabilities. Total annual direct material purchases, along with logistics-related expenses arising from demand variability and lost opportunities, can see direct improvements of 3 percent to 5 percent. We can also benefit from a 20 percent reduction in airfreight costs. Figure Y illustrates the anticipated benefits from a 15 percent improvement in forecast accuracy (these averages are based on individual results, which can vary depending on other variables and may be higher or lower for specific organizations).

Fig. Y | Graphic showing typical benefits from a 15 percent improvement in forecast accuracy

It is essential to understand these average savings amounts and determine what savings you believe you can achieve with a mature predictive analytics and demand planning process. Sometimes you need to know what finance and executive leadership anticipate in terms of benefits; you need to be on the same page in terms of expectations. It is here that the Institute of Business Forecasting Advisory Services (IBF.org) can shed some light on what is realistic based on past implementations.

Demand planning is not just a supply chain function; it’s a strategic business process that empowers smarter, faster decisions. In an environment where disruption is the norm and expectations are high, companies that implement disciplined, data-driven demand planning will not only survive, they will lead.

The path forward is clear: Separate judgment from strategy, invest in tools and talent, and build a collaborative process that evolves with your business.

In a world of uncertainty, demand planning offers clarity. It’s not just about predicting the future, it’s about preparing for it. Companies that invest in robust, unbiased, and collaborative demand planning are the ones that outperform, outmaneuver, and outlast their competition.

But you don’t have to do it alone.

The Institute of Business Forecasting (IBF) has been the trusted authority in forecasting, demand planning, and Sales and Operations Planning (S&OP) for over four decades. Whether you’re just starting your planning journey or looking to refine and elevate your process, IBF offers the training, certification, tools, and global community to help you succeed.

Join IBF and take the next step:

• Get certified with globally recognized credentials
• Attend industry-leading conferences and events
• Access exclusive research, case studies, and best practices
• Learn from and connect with top planning professionals around the world

[i] Chaman L. Jain (2018). The Impact of People and Processes on Forecast Error in S&OP. IBF research report #18. August 31, 2018

In today’s volatile and uncertain market, companies can no longer afford to operate without a structured, data-driven approach to forecasting demand. Demand planning is more than just predicting sales—it’s about building an integrated, agile business that can respond to customer needs while managing resources efficiently. Despite its importance, many organizations still rely on outdated tools like spreadsheets or allow bias and siloed decision-making to corrupt their forecast accuracy.

Employing predictive analytics and integrated demand planning can significantly streamline decision-making processes, create new insights, and save several business functions a lot of time and money.

This article explains why businesses need to leverage demand planning to improve their operations and explains the quantifiable value of doing it so that it can be sold within an organization.

Why Focus on Demand Planning?

Most companies that decide to invest in demand planning or improve their process are primarily driven by one or more of the following:

• Forecast accuracy challenges
• A highly variable process that needs improvement
• Need for a more efficient manufacturing or distribution system
• Downstream inventory problems driven by unseen variability
• Desire to improve cooperation between sales and operations

At its core, demand planning synchronizes operations. It allows marketing, sales, supply chain, finance, and production to operate from a common set of assumptions. Without an accurate demand plan, supply planning becomes reactive, finance struggles with forecasting revenue, and customer service deteriorates from stockouts or excess inventory.

Consumer behaviors have become increasingly unpredictable. Economic shifts, global disruptions, and rapid product cycles mean relying on historical sales alone is no longer sufficient. Demand planning introduces a proactive lens incorporating internal drivers (promotions, price changes) and external signals (market trends, customer insights) to create adaptive forecasts.

Inaccurate demand forecasts translate to costly outcomes: expedited shipping, excess working capital, lost sales, and markdowns. Improved demand planning helps reduce forecast error, allowing for better inventory placement, production planning, and supplier coordination. Even a five to ten percent improvement in forecast accuracy can have a significant bottom-line impact.

Potential Improvements Resulting From Demand Planning

Organizations that invest in improving demand planning benefit from:

• Reduced inventory costs through better alignment of supply and demand.
• Improved service levels by placing the right product in the right place at the right time.
• Higher forecast accuracy can lead to more reliable plans across finance and supply.
• Faster decision-making is enabled by real-time data and scenario analysis.
• Greater agility because of the ability to quickly adjust to shifts in demand or supply.

A mountain of research today shows that a mature demand planning process helps improve forecast accuracy and deliver a high return on investment (ROI). Improved forecast accuracy, when combined with software that translates the forecast into meaningful actions, will decrease inventory and operating costs, increase service and sales, improve cash flow and gross margin return on inventory investment (GMROI), and increase pre-tax profitability. The forecasting error, no matter how small it is, significantly affects the bottom line. In our experience, a 15 percent forecast accuracy improvement will deliver a 3 percent or higher pre-tax improvement.

In a previous IBF study of 15 U.S. companies, we found that even a one-percentage-point improvement in under-forecasting at a $1 billion company delivers a savings of as much as $1.52 million, and for the same amount of improvement in over-forecasting, $1.28 million.[i]

The reduction in downstream finished goods inventory resulting from a well-established process and forecast accuracy improvements provides a one-time saving, as well as recurring savings arising from reduced carrying costs. There are great benefits in a make-to-stock or distribution company, the downstream inventory reduction could range from 10 to 20 percent since forecasting inaccuracies typically drive around 75 percent of the required safety stock.

Building and Investing in Demand Planning

Here are some best practices when it comes to demand planning.

• Build an unbiased, unconstrained, consensus-based forecast. Organizations often confuse the demand plan with the sales target. Sales may overestimate to push for stretch goals, while operations may buffer to protect service. Demand planning needs to separate judgment from aspiration. Instituting a formal demand consensus process ensures all voices are heard, but forecasts remain grounded in data and evaluated against actual performance.
• Upgrade from static spreadsheets to dynamic models. Many companies still use Excel as their primary planning tool. While familiar, spreadsheets lack scalability, version control, and real-time integration. Upgrading to a dedicated demand planning system (or enhancing existing tools with forecasting models) introduces automation, improves collaboration, and enables real-time adjustments. It also supports more advanced techniques such as decomposition models or AI-based forecasts.
• Understand and match models to patterns. Not all items follow the same demand pattern. Some are seasonal, some have trends, and others are highly volatile. Applying a one-size-fits-all model can lead to overfitting or underperformance. Instead, classify SKUs by their demand characteristics and apply the appropriate model, whether that’s exponential smoothing, moving average, or more complex causal models.
• Focus on data quality and forecastability. Forecasting is only as good as the data behind it. Cleanse data for outliers, missing periods, and promotions. Measure forecastability using the Coefficient of Variation (CV) or Demand Intermittency. The demand planner becomes the integrator, ensuring inputs from various departments are translated into a structured forecast. Establish accountability through KPIs like bias, MAPE, and forecast value add (FVA).
• Invest in training and improving skills with IBF. Leverage IBF’s certifications, workshops, and learning resources to empower your teams with proven forecasting and planning knowledge, building internal capability that drives consistent, confident decision-making.

Taking steps to practice demand planning optimally will increase the bottom-line benefits you gain from it.

Bottom Line Benefits for Practicing Demand Planning

Many companies leave money on the table with lost sales or poor service levels. An integrated demand planning process can translate to increased revenue of 0.5 percent to 3 percent with improved inventory availability or demand shaping capabilities. Total annual direct material purchase, along with logistics-related expenses arising from demand variability and lost opportunities, can see direct improvements of 3 to 5 percent. We can also benefit from a 20 percent reduction in airfreight costs. The figure below shows the anticipated benefits from a 15 percent improvement in forecast accuracy. (These are averages and individual results for organizations. They are dependent on many other variables and can be higher or lower.)

This illustrates the possible benefits from a 15 percent improvement in forecast accuracy

It is essential to understand that these are average savings amounts. It is up to you to determine what savings you believe you can drive with a mature predictive analytics and demand planning process. Sometimes you need to know what finance and executive leadership anticipate in terms of benefits; you need to be on the same page in terms of expectations. It is here that the Institute of Business Forecasting Advisory Services can shed some light on what is realistic based on past implementations.

Demand planning is not just a supply chain function; it’s a strategic business process that empowers smarter, faster decisions. In an environment where disruption is the norm and expectations are high, companies that implement disciplined, data-driven demand planning will not only survive but also lead.

The Benefits of Demand Planning: The Final Word

The path forward is clear: Separate judgment from strategy, invest in tools and talent, and build a collaborative process that evolves with your business.

In a world of uncertainty, demand planning offers clarity. It’s not just about predicting the future, it’s about preparing for it. Companies that invest in robust, unbiased, and collaborative demand planning are the ones that outperform, outmaneuver, and outlast their competition.

But you don’t have to do it alone.

The Institute of Business Forecasting (IBF) has been the trusted authority in forecasting, demand planning, and S&OP for over four decades. Whether you’re just starting your planning journey or looking to refine and elevate your process, IBF offers the training, certification, tools, and global community to help you succeed.

Join IBF and take the next step:

• Get certified with globally recognized credentials
• Attend industry-leading conferences and events
• Access exclusive research, case studies, and best practices
• Learn from and connect with top planning professionals around the world.

[i] Chaman L. Jain (2018). The Impact of People and Processes on Forecast Error in S&OP. IBF research report #18. August 31, 2018

Sales and Operations Planning (S&OP) is a structured, cross-functional business process that aligns all areas of an organization around a unified set of assumptions to drive coordinated decision-making. The goal of S&OP is to ensure that business plans and objectives are balanced and that financial and operational plans are synchronized. It serves as the critical integration point between strategic planning and daily execution, enabling companies to translate high-level business objectives into actionable plans.

At its core, a mature S&OP is not just a supply chain or operations process. It is a business-wide planning framework that brings together sales, marketing, finance, operations, product management, and supply chain to work collaboratively. The result is a comprehensive plan everyone supports and works toward, reducing misalignment, improving responsiveness, and increasing overall business performance.

The process typically operates on a monthly cadence, with inputs from various departments converging into a final executive review meeting where trade-offs are discussed, decisions are made, and a single, unified plan is committed to.

The Value of S&OP

Implementing and executing an effective S&OP process provides tangible value across the organization. At a high level, S&OP delivers:

• Improved forecast accuracy and demand visibility: S&OP allows companies to move from reactive to proactive planning, reducing surprises and enabling better preparedness for market changes.
• Balanced supply and demand: With cross-functional collaboration, companies can more efficiently manage supply constraints, optimize inventory levels, and meet customer service goals.
• Financial alignment: S&OP ensures that operational plans are financially viable and support the broader goals of the business. It connects demand and supply plans to financial projections.
• Increased agility: The ability to run scenarios and analyze the impact of decisions across the business improves agility in the face of disruptions or demand shifts.
• Enhanced collaboration: S&OP builds a culture of accountability and transparency. It fosters communication across silos and ensures that all stakeholders work from the same assumptions.
• Executive-level visibility: With clear insights into upcoming challenges and opportunities, executives can make informed decisions with confidence.

Organizations with mature S&OP processes often see improvements in service levels, inventory turns, working capital, and revenue growth. But the actual value lies in the enhanced decision-making capabilities and improved alignment across the business.

How to Build a Sales and Operations Plan

Developing a robust sale and operations plan requires a clear structure, defined roles and responsibilities, and a commitment to consistent execution. While tools and technology play a role, the foundation of effective S&OP lies in process discipline and cross-functional collaboration.

Here are key building blocks to consider:

• Leadership commitment: Executive sponsorship is essential. S&OP must be seen as a strategic business process, not just a supply chain activity.
• Defined ownership and governance: Each step of the process should have clear ownership, with defined roles for demand planning, supply planning, finance, product management, and executive teams.
• Calendar and cadence: A standard monthly cycle should be established, with defined inputs, outputs, and meetings for each phase. Concurrent weekly S&OP meetings help manage near-term deviations.
• Unconstrained and unbiased planning: The demand plan should be developed independently of constraints or biases, providing a true reflection of expected demand. Only then can supply plans be adjusted accordingly.
• Data and metrics: Reliable data is the backbone of S&OP. Forecast accuracy, bias, inventory health, and capacity utilization are some of the key metrics that drive accountability and improvement.
• Technology and tools: While not a prerequisite, modern planning tools can enhance collaboration, scenario planning, and automation. However, these tools must support—not replace—a sound process.
• Culture and change management: S&OP is as much about people as it is about process. Building trust, encouraging open dialogue, and reinforcing accountability are crucial for success.

Key Steps in S&OP

A typical S&OP process includes several structured review steps culminating in an executive decision-making forum. Here is an overview of each phase:

Product Review

• Purpose: Align the product and portfolio roadmap with business strategy.
• Activities: Review new product introductions, end-of-life plans, promotions, and phase-outs. Evaluate the impact of changes on demand and supply.
•  Participants: Product management, marketing, R&D, operations, and finance.

Demand Review

• Purpose: Develop an unconstrained, consensus demand plan.
• Activities: Analyze historical performance, market trends, customer input, and promotional plans. Identify risks and opportunities.
• Participants: Demand planning, sales, marketing, and finance.

Supply and Resource Review

• Purpose: Determine how to meet the demand plan with available resources.
• Activities: Evaluate capacity, inventory, procurement, logistics, and supplier capabilities. Highlight constraints and propose scenarios.
• Participants: Supply planning, manufacturing, procurement, logistics, and finance.

Pre-S&OP/Reconciliation Review

• Purpose: Identify gaps between demand and supply, align on scenarios, and prepare for executive discussion.
• Activities: Review financial implications, resolve issues, and recommend decisions.
• Participants: Cross-functional team leads, finance, and planning leadership.

Executive S&OP Review

• Purpose: Make final decisions, approve the consensus plan, and provide strategic direction.
• Activities: Review scenarios, validate financial impact, approve trade-offs, and document decisions.
• Participants: Executive leadership, heads of major functions, and finance.

Concurrent Process: Sales & Operations Execution (S&OE)

While S&OP focuses on the mid- to long-term horizon (typically 3 to 24 months), S&OE manages near-term execution (0 to 13 weeks). This weekly process addresses short-term deviations from plan and ensures agility in responding to real-time changes. Key focus areas include order fulfillment, short-term supply imbalances, and demand shifts. S&OE connects strategy to execution, ensuring that decisions made in S&OP are implemented effectively.

Sales and Operations Planning: The Final Word

In today’s complex and volatile business environment, Sales and Operations Planning is more than just a process—it’s a mindset and an essential capability. Companies that embrace S&OP gain the ability to navigate uncertainty, align cross-functional teams, and drive smarter, faster decisions.

The future of S&OP is one of greater integration, intelligent automation, and real-time visibility. But, at its heart, the success of S&OP will always depend on three things: collaboration, transparency, and consensus.

Organizations that invest in building a strong S&OP process—supported by leadership, informed by data, and aligned with strategic goals—will be better positioned to thrive. They will not only deliver superior performance but also foster a culture of shared accountability and continuous improvement.

In short, Sales and Operations Planning is the bridge that connects strategic intent to operational execution. Done right, it becomes a sustainable competitive advantage.

Do you have questions about demand planning? This guide explains everything you need to know about this complex topic in a simple and understandable way.

What is Demand Planning?

Demand planning is the process of using analytics, data, insights, and experience to make predictions and respond to various business needs. It leverages demand forecasts—not as an end in themselves—but as a tool to highlight opportunities and risks, establish business goals, and support proactive planning across functions.

While demand planning often reports into the supply chain, it is not solely a supply chain function. It is a cross-functional discipline that integrates insights from sales, marketing, finance, and operations to create a consensus plan—a unified view of what is most likely to happen in the market.

Demand planning combines historical sales analysis, market intelligence, consumer behavior trends, and business knowledge to guide actions across the organization. It enables companies to anticipate demand shifts, align resources accordingly, and avoid stockouts and excess inventory, especially in an environment where customer expectations and market conditions constantly evolve.

At its core, demand planning drives better business performance by ensuring that decisions are based on relevant, timely, and collaborative inputs, not guesswork or isolated projections.

Bottom line: An accurate demand forecast provides the information your operations and sales teams need to plan how much product to buy or manufacture to meet projected demand as efficiently as possible with limited waste.

What is the Difference between Demand Planning and Demand Forecasting?

Demand forecasting and demand planning are closely connected but serve different purposes:

• Demand forecasting is the analytical process of using data, statistical models, and judgment to predict future demand. It’s a probability-based estimate of what might happen and forms the foundation for planning decisions?
• Demand planning takes the forecast further by integrating it into the business strategy, aligning stakeholders around a shared set of expectations, and determining the actions needed to respond to that demand.

What Purpose Does Demand Planning Serve?

Demand planning aims to create a realistic and actionable view of future demand so the organization can align supply, resources, and investments accordingly. It addresses critical issues including:

• Strategic planning and assessing risk (long-term planning and S&OP/IBP)
• Finance and accounting (budgets and cost controls)
• Marketing (consumer behavior, life cycle management, pricing)
• Operations and supply chain (resource planning, production, logistics, inventory)

Why Is Demand Planning Important?

In a world of increasing uncertainty, demand planning helps companies stay ahead. Done well, it enables organizations to:

• Improve service levels and customer satisfaction
• Minimize inventory carrying costs and waste
• Respond quickly to supply chain disruptions or market shifts
• Enhance collaboration between departments
• Increase profitability and operational efficiency

Demand planning is not a one-time task but an ongoing, iterative process that requires the correct data, tools, and cross-functional collaboration. It must also be flexible enough to adapt to volatility, whether driven by global events, consumer trends, or economic shifts.

Poor demand planning leads to the outcomes businesses aim to avoid: lost sales, excess inventory, wasted capital, and disconnected teams working from different assumptions. A well-executed demand planning process, on the other hand, builds organizational alignment, reduces bias, and leads to better business outcomes.

Why is it Critical for Businesses to Practice Demand Planning?

Demand planning is not just a supply chain function; it’s a core business process that drives strategic alignment, financial performance, and customer satisfaction. Based on IBF research from 34 organizations across different industries, companies that invest in structured, data-driven demand planning realize tangible benefits across key areas of the business.

1. Improve service and protect revenue: A strong demand planning process helps businesses meet customer needs with greater reliability, ensuring on-time and in-full (OTIF) performance, even in the face of market volatility or promotional spikes. The result? Improved customer satisfaction, stronger brand loyalty, and higher top-line revenue.

Fact: A 15-point improvement in forecast accuracy has been shown to drive on to two percent in top-line sales growth and improve OTIF performance, meaning fewer stockouts and more happy customers.

Demand planning ensures your business doesn’t miss out on sales because of poor availability. It provides the early visibility needed to make smarter inventory and production decisions, keeping your shelves stocked and your customers returning.

2. Increase operational efficiency and reduce cost: Demand planning enables organizations to run more efficiently by minimizing waste, improving resource utilization, and allowing smarter scheduling of production, logistics, and labor. It transforms decision-making from reactive to proactive—letting teams plan ahead rather than scramble in response.

Fact: A 15-point improvement in forecast accuracy can deliver a 2.3 percent or more increase in pre-tax net profit, driven by better operational alignment and cost control.

By aligning cross-functional teams around a consensus forecast, organizations reduce duplication of effort, optimize capacity, and ensure the right resources are available at the right time—leading to smoother operations and lower costs.

3. Manage assets and free up cash: Effective demand planning significantly improves companies’ inventory and working capital management. With clearer insight into what’s actually needed—and when—businesses can reduce excess inventory, lower carrying costs, and avoid the pitfalls of overproduction or fire-sale markdowns.

Fact: For every 15-point improvement in forecast accuracy, companies can realize a 12 percent reduction in inventory, freeing up valuable cash and minimizing waste.

Demand planning ensures businesses are not over-invested in supply, storage, staffing, or space. It helps unlock capital tied up in inventory and directs it toward more strategic, value-added investments.

Demand planning is no longer optional. It’s a strategic necessity. Organizations that invest in robust demand planning processes not only gain greater visibility and control but also position themselves to thrive in a constantly evolving marketplace. With the proper training, structure, and leadership, demand planning becomes a competitive advantage that enables more resilient, data-driven organizations.

Where Does Demand Planning Fit Within an Organization?

Demand planning is a strategic, cross-functional process that touches nearly every part of the organization—from supply chain and operations to sales, marketing, and finance. While its reporting structure can vary, what matters most is how the function is structured, supported, and empowered, not simply where it reports.

Based on IBF research and industry benchmarks:

• 48 percent of demand planning functions report into supply chain or operations
• 23 percent report into the commercial side of the business, such as sales or marketing
• 8 percent report into finance
• 10 percent operate as an independent function or report directly to a business unit owner
• The remaining 11 percent follow other models depending on organizational design.

These variations reflect the flexibility of demand planning—it can reside within different departments, depending on the company’s structure, maturity, and strategic priorities.

But here’s the key: regardless of the reporting line, demand planning must operate as a cross-functional, collaborative, and unbiased process. Its success depends on its ability to engage multiple stakeholders, reconcile competing priorities, and drive consensus to produce a unified, realistic view of future demand.

The Complexities of Demand Planning

Finding and maintaining the perfect balance between sufficiency and surplus can prove especially tricky. It isn’t a once-and-done task. Economic conditions change, and competitive environments constantly evolve.

To address this, demand planning typically requires using demand forecasting to predict future demand trends. This has added benefits, most importantly, heightened company efficiency and increased customer satisfaction.

What are the Key Components of Demand Planning?

Here are the critical parts of demand planning:

Product portfolio management

Effective demand management requires a clear understanding of product lifecycles, from launch to phase-out. Product portfolio management supports this by tracking each product’s stage and showing how changes in demand can impact related items. It also plays a key role in planning new product introductions, helping teams anticipate demand, allocate resources, and support successful launches. With strong portfolio management, companies can better manage transitions, reduce risk, and respond more effectively to market changes.

Statistical forecasting

Statistical forecasting is based on the concept that past history best predicts future performance. It uses complex algorithms to analyze historical data to develop demand forecasts. This exacting process demands accurate data, including eliminating outliers, exclusions, and baseless or inaccurate assumptions.

Sales forecast and overrides

As a process champion, the demand planner plays a critical role in driving consistency, structure, and accountability across the forecasting process. One of the key responsibilities is managing sales inputs and overrides—ensuring that adjustments to the statistical forecast are based on valid insights rather than bias. This involves working closely with sales teams to understand market intelligence, promotions, and customer expectations while also challenging assumptions when needed. The goal is to balance collaboration with discipline, ensuring that overrides improve forecast accuracy and align with broader business objectives.

Trade promotion management

In today’s highly competitive environment, it can be challenging to spark the interest of prospective customers. That’s why sales and other promotions are becoming increasingly common. They often result in increased consumer demand. Trade promotion management helps ensure that these types of programs are properly executed, that there is adequate product supply, and that they deliver all expected benefits to a company.

Demand Planning Methods

Quantitative forecasting methods are the foundation of most forecasting processes, with approximately 74 percent of companies relying on historical data to project future demand. Standard demand forecasting methods are:

• Time series models, used by nearly half of organizations (48 percent), are the most common approach and focus on identifying patterns, trends, and seasonality in historical data.
• Cause-and-effect models, used by 17 percent of companies, link external or internal variables—like price changes or promotions—to shifts in demand behavior.
• Machine learning and AI are emerging tools in forecasting. Currently, about six percent of organizations use them, and as adoption grows, they offer the potential for more adaptive and automated insights.
• Judgmental forecasting, reported by 17 percent of companies, is a qualitative method incorporating expert knowledge, market intelligence, and human insight when data is limited or context is needed.

What is Required to Do Demand Planning Effectively?

Effective demand planning is more than just generating a forecast. It’s about creating a reliable, unbiased view of future demand that drives smarter decisions across the organization. Done right, it improves service levels, optimizes inventory, enhances collaboration, and ultimately boosts profitability. However, to achieve these outcomes, companies must establish the proper foundation. Here’s what’s truly required to do demand planning effectively:

• A clearly defined process: An effective demand planning process must be structured, repeatable, and aligned with business goals. It should define all stakeholders’ roles, responsibilities, timelines, and expectations. The process should incorporate steps for data collection, model development, consensus building, evaluation, and communication—ensuring that each cycle produces more accurate and actionable insights than the last.
• High-quality, clean data: The best forecasts are built on relevant, clean, and complete data. That includes historical sales, customer orders, promotional activity, and external factors like market trends and economic indicators. Without trustworthy inputs, even the most sophisticated models will produce unreliable outputs. Demand planners must work with IT and business teams to ensure data integrity, consistency, and standardization.
• Forecasting approach: An effective demand planning process requires selecting the right forecasting approach, whether it’s bottom-up (built from item-level inputs), top-down (driven by high-level business targets), or middle-out (a blend of both, used to reconcile plans across levels). Planners must also determine the appropriate level of aggregation, such as by item, customer, location, or time, based on how the forecast will be used and the level of noise in the data. The planning horizon must match the decision being supported—ranging from strategic (long-term capacity and investments) to tactical (monthly or quarterly planning) to operational (weekly or daily execution). Since no forecast is perfectly accurate, planners should establish acceptable and expected error thresholds and measure forecast performance to continuously improve.
• Cross-functional collaboration: Demand planning is inherently cross-functional, involving input from sales, marketing, supply chain, finance, and operations. To be effective, the process must include a consensus step, where teams align on a final, agreed-upon forecast. This collaboration minimizes bias, integrates commercial intelligence, and ensures the forecast reflects both statistical outputs and business realities.
• Skilled demand planners: The demand planner plays a critical role as a process champion and cross-functional influencer. Strong planners possess analytical capabilities, organizational awareness, communication skills, and the ability to challenge assumptions objectively. They must manage statistical models, evaluate overrides, monitor forecast accuracy, and facilitate dialogue between departments.
• Focus on continuous improvement: No forecast will be perfect, but the goal is to improve continuously. That means measuring forecast accuracy and bias, tracking value-added steps, and adjusting models and inputs over time. Each forecasting cycle should yield better insights and inform more intelligent decisions.
• Executive support and integration into business strategy: Demand planning must be embedded in the organization’s decision-making processes with strong executive support to ensure it has the visibility, tools, and authority to drive change. Gaining buy-in from key stakeholders is equally critical, as it builds alignment, promotes cross-functional engagement, and reinforces the value demand planning brings through improved customer service, operational efficiency, and business performance.

Demand Planning: Best Practices

Once the foundational elements are in place, adopting these best practices ensures demand planning becomes a value-driving process that adapts to change and supports better business outcomes:

• Understand the purpose of forecasting: Clearly define why you are forecasting—whether for financial alignment, production planning, or service optimization—to tailor the process accordingly. Anchor the planning process in key business questions and explicitly state the assumptions driving forecast changes and decision-making.
• Identify demand drivers: Analyze internal and external factors—such as seasonality, promotions, economic trends, and customer behavior—that influence demand patterns.
• Gather relevant inputs across functions: Incorporate insights from sales, marketing, finance, and operations to ensure the forecast reflects a broad and informed perspective. Cleansing and structuring data is essential to ensure accuracy and consistency, providing a reliable foundation for effective forecasting and informed decision-making.
• Track forecast performance regularly: Measure and report forecast accuracy and bias at appropriate levels of aggregation to continuously improve planning effectiveness. Forecast errors and metrics help us identify uncertainty and bias, allowing us to communicate them clearly, prioritize errors in high-value products and items, and improve forecast accuracy through better inputs and process refinement.
• Schedule timely and recurring meetings: Regular forecast review meetings enable collaboration, resolve conflicts, and build consensus around the final demand plan. Demand planning should act as a hub for cross-functional alignment, bringing together departments to drive consensus and accountability.
• Communicate and manage results: Share insights and results across the organization, highlighting successes, identifying gaps, and reinforcing the value of the demand planning process.

What Skills Do Demand Planners Need?

Effective demand planners must combine analytical expertise with business acumen to interpret data and translate it into actionable insights. They need strong communication and collaboration skills to work cross-functionally with sales, marketing, finance, and operations, facilitating alignment and consensus. A deep understanding of forecasting techniques—from time series models to causal methods and emerging AI tools—is essential to building and evaluating accurate forecasts.

Demand planners must also be adept at managing uncertainty and bias, using metrics to identify errors, and continuously improving forecast performance. Critical thinking and problem-solving abilities are key to challenging assumptions, evaluating overrides, and navigating business complexity. Equally important is the ability to act as a process champion, ensuring the planning cycle is structured, repeatable, and aligned with strategic goals. Ultimately, demand planners serve as integrators across the organization, requiring a balance of technical skills, strategic thinking, and emotional intelligence to influence without authority.

The Future of Demand Planning

The future of demand planning is rapidly evolving into a more strategic, technology-enabled, and integrated function that drives value across the entire enterprise. Fueled by advancements in artificial intelligence (AI), machine learning, and predictive analytics, demand planning is becoming more precise, automated, and responsive. These technologies allow organizations to analyze vast amounts of real-time data from sources like point-of-sale systems, distributors, and suppliers, enabling more accurate forecasts and timely decisions that reduce waste and improve customer service.

As forecasting tools become more sophisticated, the demand planner’s role will shift from generating numbers to generating insights, focusing on scenario planning, cross-functional collaboration, and business alignment. Demand planning will continue to integrate with S&OP and IBP processes, connecting operational planning to financial and strategic goals. With global supply chains becoming more complex and volatile, demand planners will be expected to manage greater uncertainty while maintaining agility and discipline.

However, as Eric Wilson of the Institute of Business Forecasting (IBF) cautions, the successful integration of advanced technologies requires more than just investment—it demands alignment with business strategy, proper implementation, and upskilling teams to interpret and act on AI-driven insights. Without these, organizations risk underutilizing powerful tools or making misaligned decisions. Done right, the future of demand planning is not just digitality becoming a central pillar of strategy and competitive advantage.

Demand Planning 101: The Final Word

The world of demand planning is rapidly evolving. However, the reality is that companies that don’t practice it must jump on board. If they don’t, they risk losing out to competitors who do. Demand planning will help you satisfy consumers, run your organization efficiently, and drive dollars to your bottom line.

Leverage the information in this guide—and the other resources available through IBF—to launch and optimize a demand planning practice at your company.

The essence of Sales and Operations Planning (S&OP) is the active participation of different departments. Participants come from varied backgrounds, each with different daily objectives and challenges, and they need to reach consensus to advance the process. Achieving this alignment in an environment of trust while balancing risks and opportunities is the main challenge.

The Demand Review is a foundational step in this process It is where a picture of future demand is created by taking a statistical forecast and adjusting it according to information about clients and the market from Sales & Marketing. It is where consensus on future demand is achieved.

The building blocks of the Demand Review include a statistical forecast, input from Sales and Marketing, and technology that supports the planning process to track the different clients, SKUs, and each person who collaborates in each stage of the S&OP process. In addition, transparency is key whereby all participants have the same information regarding past performance and future expectations.

Pre-Demand Review

Before the Demand Review, we must build a statistical forecast. I highly recommend having a quick meeting at the beginning of each month to show the results of the main KPIs, similar to the daily meetings proposed in the agile methodology. This helps us to understand the main difficulties of the last month and encourages conversation between the Sales team and Demand Planners. It is important not to focus too closely on each SKU and to keep it high level. I have been in Demand Reviews that lasted more than one hour per category, causing managers to leave the meeting. At this stage, keep discussions concise and focused.

In these pre-meetings, we can gain information about products or clients. For example, we might have a deviation from the forecast because our competitors increased their prices, or a client decided to increase shelf space, or conversely, the client decided that the product will be sold in fewer stores.

At the same time, it’s important to look at our service levels for different clients. Variations in sales could be due to internal problems. I recommend discussing Fill Rate, Market Share, and Days on Hand (DOH) for each client/category. If our colleagues have this information, we can react faster to changes in sales. For example, if we see that a retailer has increased its DOH, it is highly possible that our sales for next month will be lower. Information from the past helps us identify new sales trends and focus on products or clients that could pose risks or opportunities for our projections.

The desired level of aggregation depends on the planning time horizon. We take a more granular view the closer the horizon, and a higher level view for longer horizons. I suggest breaking it down as shown in Figure 1.

Figure 1 | Aggregation by planning horizon

Inputs We Need Before The Demand Review

We need a few different types of data for our Demand Review, which we collect from different sources.

Statistical forecast: We forecast based on historical data, assuming what happened in the past could happen again. The unconstrained demand forecast is our foundation for further discussion. This forecasting step can be improved with technology. For example, with APO or IBP, it is possible to keep a history of sales affected by out-of-stock situations or promotions, leading to a cleaner history and allowing Demand Planners to create better forecasts.

Input from Sales: Collaboration from the salesperson for each client is crucial, as they know the client’s perception best and will execute the sales plan. Therefore, they must be committed to the plan, considering it is usually their goal for the next month, with correlated financial incentives. The unconstrained forecast can be adjusted accordingly.

Input from Marketing: This area must incorporate knowledge of expected future share of the category, promotions, launches, or any product changes that could mean replacing a current SKU. At this point, the effectiveness of the Demand Review increases, as all the previous hard work will help have a decision-making meeting with managers. The unconstrained forecast can be adjusted accordingly.

During the Demand Review Meeting

During the Demand Review, I recommend starting with a one-page overview focusing on each category, showing the projections in terms of volume, price, and margin. This should be high level; only go into detail for SKUs that show significant deviations versus the last three months or have notable characteristics for the next months. In this meeting, we expect the participation of the sales manager, business manager, finance manager, or revenue manager.

The S&OP leader should encourage consensus to obtain a single plan that must be followed and executed. However, the S&OP leader also needs to create tension among the areas by asking questions such as:

• Are we considering pending orders in this demand?
• With the current projection, what could be our market share? Is it similar to what we expect?
• If we create a scenario in which competitors increase or decrease their prices, what is the expected volume difference?
• Which SKU poses the biggest risk? (If we anticipate higher sales than planned, we can project a bigger proportion of the demand at the beginning of the month, adapting the production schedule accordingly and react faster to potential out-of-stock situations.)
• Are we considering discontinued SKUs according in our revenue forecasts?
• Are we considering cannibalization between SKUs?
• Are we comfortable with this plan? What is the gap between this plan and the budget?
• What do we need to do to achieve our strategy? (We can adjust projections for a specific client, considering the sales manager’s participation in the meeting.)

One of the outputs of this meeting is a realistic yet challenging demand plan, with alerts for the next stage of the process. This will facilitate the next step in the S&OP cycle, the Supply Review. While uncertainty about the future always exists, we must take action with the best information available and prepare to react to new opportunities or risks. This is why it is essential to discuss the questions above.

Overcoming Bias & Achieving Alignment

I have been in Demand Reviews with biased behavior. We cannot forget that the demand agreed on at the end of the process will be part of the sales goal. Therefore, sales teams may try not to overcommit, thinking that selling more than planned is a good problem to have. At the same time, I have seen Marketing overpromise sales for new launches to ensure sufficient inventory, even at the risk of expiration. To promote cohesion, ensure that all participants share the same overarching goal (profitability for the business). Occasionally, let everyone adopt the CEO perspective. This approach helps identify what is best for the company and reduces siloed thinking.

Conclusion

In conclusion, the Demand Review is a critical step in the S&OP process and its success depend on the quality of the data and information used in preparation. This preparation allows for a decision-making meeting instead of an informational one. The S&OP leader must align the team through commitment, transparency, and trust, creating positive tension that addresses potential risks and opportunities.

This article first appeared in the fall 2024 issue of the Journal of Business Forecasting. To access the Journal, become an IBF member and get it delivered to your door every quarter, along with a host of memberships benefits including discounted conferences and training, exclusive workshops, and access to the entire IBF knowledge library. 

]]> https://demand-planning.com/2024/07/29/top-10-benefits-of-sop/ Mon, 29 Jul 2024 10:55:51 +0000 https://demand-planning.com/?p=10399

This article is taken from the book, Practical Guide to Sales & Operations Planning (S&OP/IBP). It’s currently available at a special introductory price. Get a copy here before the price increases.

Sales and Operations Planning (S&OP) stands as a cornerstone in the realm of Supply Chain Planning, serving as the nerve center that aligns diverse planning activities within an organization. The true potential of S&OP, however, blossoms in a mature implementation, offering a myriad of benefits that significantly elevate organizational performance.

Here are the top ten advantages intrinsic to a mature S&OP process, providing compelling reasons for any organization to embrace this transformative approach.

10) Builds Collaboration: A mature S&OP process acts as a catalyst, fostering cross-functional collaboration by dismantling silos and overcoming functional barriers. The convergence of stakeholders from sales, operations, finance, and other areas not only breaks down informational silos but also establishes a foundation for increased trust and accountability.

9) Builds Consensus: Achieving a unified vision becomes a reality with S&OP, as it ensures everyone operates from the same plan, aligning day-to-day operations with overarching business strategies. This unity reduces errors, facilitates plan reconciliation, and enhances adaptability when faced with unforeseen challenges.

8) Becomes More Agile: Contrary to the misconception that S&OP hampers agility, a mature process promotes collaboration and consensus as keystones to agility. With streamlined planning, organizations are better positioned to execute swiftly, plan buffers effectively, and strategize mitigation strategies with coherence.

7) Improved Visibility and Transparency: A mature S&OP process offers a comprehensive view of the entire business landscape, providing a forum for open discussions, conflict resolution, and informed decision-making. This enhanced visibility minimizes uncertainties and sets the stage for transparent communication across all levels of the organization.

6) Forecast and Plan Improvement: Beyond mere forecasting, a mature S&OP process elevates all planning facets, minimizing bias and incorporating diverse insights. The outcome is a set of plans that are not only more accurate but also more relevant and meaningful to all functions within the organization.

5) Resource Optimization: At its core, a mature S&OP process becomes a cost-saving engine for the company. By intelligently streamlining operations and optimizing resources, organizations enhance efficiency, eliminate bottlenecks, and fortify their supply chain, production, and logistics processes.

4) Better Customer Service: Beyond on-time, in-full (OTIF) metrics, a mature S&OP process contributes to top-line growth and revenue improvement. It enables organizations to understand customer demand intricately, leading to optimized inventory levels, timely product delivery, and enhanced customer satisfaction and loyalty.

3) Enhance Decision-Making: One of the hallmarks of a mature S&OP process is its ability to enhance decision-making. By providing a holistic view of the business, S&OP empowers organizations to assess different scenarios, evaluate risks, and develop informed contingency plans that align with their strategic objectives.

2) Higher Profitability: The ultimate goal for most organizations is maximizing shareholder value, and a mature S&OP process is the key to achieving this. It contributes to increased operating margins, enhanced capital efficiency, and sustained revenue growth, making it a cornerstone for higher profitability.

1) Competitive Advantage: In today’s dynamic business landscape, S&OP is not just a choice; it is a competitive imperative. Organizations that embrace a mature S&OP process gain a significant advantage by being more agile, responsive to market changes, and differentiated from their competitors. It is not merely a process; it’s a strategic advantage that propels companies toward long-term success.

Clearly, Sales and Operations Planning (S&OP) is not just a functional process but a strategic lever for organizational excellence. A mature S&OP process weaves collaboration, transparency, and agility into the fabric of an organization, providing a robust framework for sustained growth and competitive differentiation. If you have not started the S&OP journey, you are not just falling behind; you are falling behind a competition (that has most likely already read this book and has already embraced the transformative power of S&OP).

IBF’s new book Practical Guide to Sales & Operations Planning is a fantastic resource to learn best practices in S&OP and IBP from world-leading planning experts. You’ll learn how to start an S&OP/IBP process, progress it along the maturity curve, and use it to drive effective decision making that has a direct impact on KPIs like inventory turns, forecast accuracy, cash flow, customer service and more

In the ever-changing world of Sales and Operations Planning (S&OP), getting everyone to agree on a decision is important but hard to do. It means putting together different, and sometimes conflicting, goals and points of view into a single plan.

Consensus building helps teams and organizations work together, come up with new ideas, and be more aligned, but it can be challenging because different functions and personalities have different goals and points of view. But with the right plans, it is possible to get through the messiness of consensus building and make decisions that have wide support, leading to better strategies and company success.

The Challenge of Consensus in S&OP

There are several reasons why it’s challenging to reach agreement in S&OP. There are different priorities because each area (sales, operations, finance, etc.) has its own goals and ways of measuring success. Complex sets of data from many different sources underpin S&OP decisions, making them difficult to understand clearly. Market conditions, customer needs, and supply chain factors are constantly changing, necessitating constant adjustments and reevaluations. Furthermore, it’s important to remember that S&OP involves people, each with their own unique set of limitations, issues, perspectives, and prejudices.

This can sometimes make driving consensus and decision-making within S&OP feel like a battlefield, where each party is trying to gain ground and defeat the opposing viewpoint. In these negotiations, individuals tend to see the process as a scale, believing that by piling more reasons and facts on theirside, they can tip the balance in their favor.

However, in this warlike mentality, people search for flaws in others and arguments to bolster their own positions. Consequently, rejecting even a small idea can justify dismissing all of them, turning negotiations into a series of attacks and defenses rather than a constructive give-and-take. This adversarial mindset makes reaching a consensus challenging.

Insights to Consider

Reflecting on my experiences (and with the help of a recent book I read by Adam Grant, Think Again), I’ve found that the power of rethinking and embracing the possibility of being wrong are crucial elements in driving consensus in decision-making. Encouraging healthy differences and fostering open communication can lead to better decisions, innovative ideas, and stronger relationships. In S&OP, the harmonization of diverse opinions and objectives is crucial for achieving unified decisions.

By promoting an environment where team members feel comfortable expressing their views and challenging assumptions, we can create a culture of productive debate that enriches the decision-making process.

One of the key perspectives to consider in S&OP is collaboration, consensus, and transparency, which can mean the importance of intellectual humility and being open to new information. Encouraging team members to question their beliefs and consider alternative viewpoints can lead to more robust and flexible planning. Recognizing the limits of our knowledge and being open to new ideas helps avoid the pitfalls of overconfidence and confirmation bias, making teams more receptive to data-driven insights and collaborative solutions. By prioritizing learning and evolution over correctness, we can foster continuous improvement and adaptation in the S&OP process.

Regularly seeking and integrating feedback into planning cycles, promoting respectful discussions, and encouraging diverse perspectives can lead to valuable insights and drive consensus through mutual understanding. Embracing a mindset of continuous testing and adjustment can transform forecasts and plans into dynamic hypotheses that evolve with new data and insights, ultimately leading to more effective and resilient S&OP practices.

Applying Lessons to S&OP

Driving consensus in S&OP decision making can be challenging, but by applying these ten strategies, you can facilitate more effective and inclusive processes:

1. 1. Check Your Own Biases at the Door: Embrace the possibility of being wrong and maintain a desire to find the truth. By doubting your own judgment and remaining curious, you can adapt to new information and foster a more open-minded approach to decision-making. Clinging to outdated beliefs and opinions can be detrimental, and accepting the possibility of being wrong can be liberating. There is an importance to being willing to question and revise our thoughts, much like scientists who constantly test and refine their hypotheses.
2. Establish Clear Objectives and Guidelines: Clearly define the problem or decision at hand, outline the objectives, and ensure everyone understands the purpose and desired outcomes. Adopting a scientific mindset, where curiosity and evidence guide our thinking rather than intuition and tradition, can help us navigate complex and uncertain environments more effectively.
3. Foster Open Communication: Encourage open and honest communication among team members, creating psychological safety or a safe space where individuals feel comfortable sharing their opinions, taking risks, expressing ideas and concerns, speaking up with questions,
   and admitting mistakes—all without fear of judgment or retaliation.
4. Encourage Diverse Perspectives: Actively seek out and consider different viewpoints,
   experiences, and expertise within the group. This diversity of thought can lead to more
   innovative and well-rounded decisions, helping to identify potential blind spots and challenges.
5. Facilitate Constructive Debate: Healthy debate is critical for reaching consensus. Encourage team members to challenge assumptions, question ideas, and explore alternative solutions while ensuring discussions remain focused on issues and avoid personal attacks. Consider the importance of cognitive flexibility and the ability to switch between different modes of thinking. This includes knowing when to rely on intuition and when to seek out more data and analysis.
6. 6. Build on Common Ground: Identify areas of agreement early in the discussion, and build on these commonalities. Highlighting shared goals and values creates a foundation for collaboration and helps bridge differences.
7. 7. Seek Input and Ask Questions: Regularly check in with team members to gauge their comfort levels and gather feedback. Ensuring everyone stays engaged and promptly addressing any concerns reinforces a sense of ownership and collective responsibility.
8. Practice Flexibility and Compromise: Consensus often requires compromise. Encourage team members to be flexible and willing to adjust their positions for the greater good, finding solutions that, while not perfect for everyone, are acceptable and beneficial for the group as a whole.
9. Summarize and Confirm Agreements: Periodically summarize key points of agreement and areas that still need resolution to keep everyone on the same page. After reaching a decision, validate the agreement and delineate the subsequent steps for execution.
10. Follow Up and Reflect: After a decision has been made, follow up with the team to evaluate the outcome and gather feedback on the process. Reflecting on what worked well and what could be improved helps refine your approach to consensus-building for future decisions.

Conclusion

Driving consensus in Sales and Operations Planning (S&OP) is inherently challenging but crucial for organizational success. The process is often complicated by varying priorities, complex data sets, and the inherent biases of the individuals involved. However, by applying structure and these key insights, it is possible to foster a culture of intellectual humility, continuous learning, and collaborative problem
solving.

Embracing these principles can lead to more effective and aligned S&OP processes, ultimately enhancing the organization’s agility and responsiveness in a dynamic business environment. Creating an environment where team members feel comfortable expressing their views and challenging assumptions can lead to more robust and flexible planning, helping to avoid overconfidence and confirmation bias. Continuous learning and adaptation, prioritized over simply being right, drive ongoing improvement in the S&OP process.

IBF’s new book Practical Guide to Sales & Operations Planning is a fantastic resource to learn best practices in S&OP and IBP from world-leading planning experts. You’ll learn how to start an S&OP/IBP process, progress it along the maturity curve, and use it to drive effective decision making that has a direct impact on KPIs like inventory turns, forecast accuracy, cash flow, customer service and more.

 Many companies strive to build, enhance, and protect their culture for a variety of reasons. Through corporate culture, companies provide an experience to their internal and external stakeholders that impacts the longevity of those multi-directional relationships. These relationships carry a premium value that directly impacts the bottom line.

In this article, I will describe why and how to build a data-driven culture that serves both internal and external stakeholders with a focus on the supply chain.

What is Culture?

One of the most comprehensive definitions I have encountered was provided by Dr. Norman Doidge in his book The Brain That Changes Itself, stating that “Culture is not just produced by the brain; it is also by definition a series of experience that shape the mind…we become “cultured” through training in activities such as customs, arts, ways of interacting with people and the use of technologies and the learning of beliefs and shared philosophies and religion.”

One’s identity is difficult to contain as we evolve, experience, and learn. This makes Dr. Doidge’s definition for the concept of culture appealing. We can all be on a self-correcting path by evaluating our experiences and their resulting takeaways. Although this broadly pertains to an individual’s own domain, the corporate world has a responsibility to improve the quality of the experience that their employees can ‘life’. These experiences might slowly but surely impact their weltanschauung [world view], and directly impact how employees evaluate and respond to what is around them. This is engrained in companies that have a well-defined culture.

“The starting point is identifying the mission statement – defining the purpose of the organization and how it serves its customers.”

As more and more companies mature in their culture, that responsibility grows to have an impact both internally and externally. The starting point of, course, is identifying the mission statement – defining the purpose of the organization and how it intends to serve its customers.

Internal Impact of Corporate Culture

Similar to how people become cultured in society as a whole, employees can build, embrace and enhance the corporate culture. The more serene and positive the culture, the higher the positive impact on the bottom-line. This is mainly driven by enhanced productivity through employees’ willingness to go the extra mile and adapt their efforts to support companies in dire times.

Additionally, higher employee retention rates have a direct correlation with minimizing additional costs. Based on stats shared by recruitment site www.indeed.com, hiring a new employee for most companies ranges between $4,000 and $20,000 – excluding salary and benefits.

Just some of the many external costs include advertising and marketing expenses, background checks and eligibility to work expenses, drug testing expenses, employee referral payments, and relocation costs. There are, however, additional costs that are harder to identify such as the time existing employees spend training the new hire and as a I see it, a “calibration” cost that is associated with transitioning a specific duty to a new employee while minimizing the risk of errors as part of the learning curve.

External Impact of Corporate Culture

When a positive culture is established, it is visible to the external stakeholders through employees’ interactions as well as the company’s overall reputation. The market positioning from the direct interaction between the clients and employees can be a competitive advantage that no one else can replicate.

The quality of service that the customer facing teams provide is a direct reflection of the quality of service the support teams behind the scene present as well. Culture is at the heart of all interactions.

While culture can positivity maximize the top line through repeat business from satisfied customers and converting new opportunities thanks to a positive reputation in the marketplace, it can also enhance the bottom line through cost reduction resulting from employee retention.

Key Characteristics of a Data-Driven Culture

There are few major characteristics of data driven cultures:

Objectivity

When internal teams are aligned on the importance of the data, it helps foster a safety zone in which to openly debate different points of view. This helps gain alignment without necessarily having to reach full consensus. This is where decision makers must look beyond their own interests while accepting that the best, data driven decisions win. Amongst many other things, objectivity means:

1.) Making decisions with the quality of the final product or service in mind beyond what is easy or convenient. The best opportunity I had to witness this approach was in the CDMO industry (Contract Drug Manufacturing Organization) and in the medical devices industry. The quality and availability of an end product that would help save a patient’s life – whether it was a drug or emergency medical device – would override any other consideration.

2.) Making decisions while accounting for both the current state of the P&L and the impact on future profitability. The best opportunity I had to witness this approach in action was in a global leading performance fabric company that is family owned. Critical decisions in that organization are never made without accounting for the potential long term impact on the future generations of the family.

Trust and Humility

Trust is a prerequisite to humility. The best way to build trust is through aligning on facts, and the best way to align on facts is through data. Where there is a healthy culture, employees across the hierarchy of the organization welcome constructive debate, have the humility to seek advice and feedback, and admit mistakes.

The major advantage that data driven cultures have lies in the ability to self-correct and apply lessons learned in transformation and agility journeys. This creates a unique competitive advantage. When major change-driving decisions are made and announced with transparency, and supported by data and facts, even the most difficult decisions such as reorganizations can be supported by employees across all levels. For that to happen, the foundation of a data-driven culture needs to be established so that such transparency can be accompanied with wisdom and compassion. It is worth noting that wisdom and compassion in a healthy culture is not limited to the top down, but also from the bottom up.

The Importance Of Data Integrity

The best way to appreciate something is to realize the impact of its absence or malfunction. Poor-quality data therefore can help a company embrace a data driven culture. When there is poor data within an organization, employees always welcome data integrity enhancements.

“The best way to appreciate something is to realize the impact of its absence or malfunction.”

Accounting for the fact that there is a hierarchy of planning based on horizon, investing in data integrity should be a strategic endeavor. The reasoning behind that is simple: the longer the decision horizon, the bigger the magnitude of the negative impact that using bad data has on an organization.

Once the negative impact of using bad data is comprehended, fixing the data becomes a strategic target that gets sponsored by the senior executive team.

How Processes Can Help Build a Data Driven Culture

Once data integrity is achieved, if it is not leveraged to its fullest extent, opportunities can be missed. The philosophical question posed by Dr. George Berkeley, an Anglican Bishop and philosopher in the 1600s, comes to mind: “If a tree falls in a forest and no one is around to hear it, does it make a sound?”

To ensure that organizations benefit from available data, robust processes should be implemented. S&OP/IBP is definitely a process that leverages data to its fullest extent, bridging the gap between execution and strategy with a direct impact on the bottom line. This is done in a a variety of ways. Some of the advantages consist of proactively providing visibility to constraints through the means of scenario planning and in ensuring cross-functional alignment on the integrated forecast.

“To ensure that organizations benefit from available data, robust processes should be implemented.”

An AMR Research study indicates that more than 50% of companies that implement S&OP experience increased sales revenue, along with other benefits that impact the P&L.

In addition to nurturing the data and giving it a home in the form of a process, to get the desired results across the different decision-making horizon the process itself needs to be well defined and established. The key components in ensuring the process progresses in its maturity journey include having the right champions, participants, and cadence.

Supply Chain/Operations is the Leader in Building a Data-Driven Culture

Amongst the main planning nodes that are part of a good S&OP/IBP process, Demand Planning & Forecasting represents the key node that helps shape the identity of this unique process. Although this function can fall under any part of the organization, it is well suited to being housed within supply chain. This doesn’t mean at all that S&OP/IBP is strictly a supply chain process. As the very name of Integrated Business Planning (IBP) indicates, it is a business process necessitating the integration of all the major business plans within the organization. However, the process is best suited to this segment of the business due to the lack of bias towards the demand signals that have a concrete impact on the rest of the organization.

A survey conducted by the Institute of Business Forecasting and Planning (IBF) indicated that 50% of the surveyed organizations house the Forecasting and Planning function under Supply Chain (Operations, Logistics, Procurement).

As such, to establish a data-driven culture from the ground up, once data integrity concerns are addressed, the right process needs to be established to derive the best results from the data.

“If we want objectivity, trust, and humility at the core of a data-driven culture, what environment should we provide employees to nurture it?”

One question we should all ask ourselves as leaders is that if we want objectivity, trust, and humility at the core of a data-driven culture, what environment should we provide employees to nurture it? This is where we must be clear on how to maintain positive human interaction in serving the people entrusted to our care.

To get up to speed with the fundamentals of S&OP and IBP, join IBF for our 2- or 3-day Boot Camp in Miami, from Feb 6-8. You’ll receive training in best practices from leading experts, designed to make these processes a reality in your organization. Super Early Bird Pricing is open now. Details and registration.

As the saying goes, what gets measured gets improved. The same is the case with supply chain management. End to end supply chain operations related to planning, sourcing, production and logistics have specific metrics that help us improve processes and overall performance.

For every metric, there is a target number or goal that needs to be agreed upon. Then, each of those metrics is linked with a strategic objective or a key value driver (KVD), and is further supported by planned initiatives that help in achieving the target or goal.

Demand Planning Metrics

Demand planning metrics relate primarily to forecast error metrics. They include:

• Mean Absolute Percentage Error [MAPE]
• Weighted Absolute Percentage Error [WAPE]
• Mean Squared Error [MSE]
• Forecast Bias, and
• Forecast Value Added [FVA]

Of these, Forecast Bias and FVA are critical and should be tracked over the planning time horizon. Bias indicates the direction of the forecast errors and helps us understand whether we are under-forecasting or over-forecasting.

FVA is a measure of the value added during the forecasting cycle. It is measured by comparing forecast accuracy before and after each activity in the demand forecasting and planning process to determine if that activity improved the accuracy of the demand forecast. Tracking FVA during the forecasting cycle enables us to find the right mix of quantitative and qualitative approaches that result in fewer errors.

Supply Planning Metrics

Supply planning metrics are related to sourcing and production. They include:

• Order Confirmation to Delivery Lead Time
• OTIF % [On Time in Full]
• Material Availability for Monthly Production Schedule
• % Rejections and % Returns
• Spend Analysis [By Product Mix and Supplier]
• Direct and Indirect Category Analysis and
• Supplier Performance Index

These metrics allow us to understand supplier performance and to perform spend analysis.

• Production/Manufacturing metrics include:
• Daily schedule adherence
• Production loss
• SKUs with excess production
• SKUs with shortage in production
• Line Utilization %,
• OEE% (Overall Equipment Effectiveness)

It is essential to segregate the metrics into daily, weekly, monthly and quarterly buckets. However, the most crucial performance indicator is OEE % as it gives a snapshot of equipment productivity, product quality, utilization and speed.

S&OP Metrics

During S&OP meetings, the appropriate metrics are reviewed and discussed with the respective functional teams during each phase or step of the S&OP cycle.

Logistics & Distribution Metrics

This section covers metrics related to warehousing, transportation and distribution operations.

Warehousing metrics

•  Dock to Stock Cycle Time
• Inventory Accuracy %
• Operating Costs [Budget vs Actual]
• Detention Costs
• MHE Utilization %
•  Process Productivity Metrics
• Order-Delivery Cycle Time and
• Environment-Health-Safety Metrics

The metrics that are critical and need extra attention are the ones related to inventory management and customer order management. Accurate inventory management within the warehouse is critical to ensure correct order picking, packing, staging and loading operations. End to end order management from order receipt to fulfilment to proof of delivery are central to warehouse operations.

Transportation metrics

• Truck Placement Reliability
• Loading Time
• Unloading Time
• Transportation Lead Time
• Operating Costs
• Vehicle Capacity Utilization %

In the case of transportation, vehicle arrivals at the appointed times are critical for timely onward operations. Monitoring transportation lead times are also essential, especially for last mile deliveries.

Distribution metrics

• Despatch Schedule Adherence
• OTIF [On Time-In Full] %
• Customer Order Confirmation to Delivery Lead Time
• Product Returns %
• Customer Satisfaction Score

As an extension, customer satisfaction and product returns metrics need to be measured and monitored. Most 3PL and 4PL service providers invest in processes and systems related to customer issues. Order fill rates and customer satisfaction metrics need to be focused upon and prioritized.

Other Relevant Metrics

The advent of e-commerce and quick commerce has resulted in ever-increasing reverse logistics and material flows. Therefore, metrics related to reverse flows are as important as forward flows. Also, macro-economic factors, geopolitics, climate events, trade flows, fluctuating commodity prices, etc. have necessitated the measurement and monitoring of resiliency metrics.

Reverse logistics metrics

• Logistics Costs and Product Returns as a % of Sales

The challenge here is to minimize or reduce product returns and therefore reverse logistics costs. Just like the forward supply chain, designing the reverse supply chain is essential in ensuring that returned products can be inspected, segregated and reused, repurposed or refurbished depending on the type of product

Resiliency metrics

• Time to Survive (TTS)
• Time to Recover (TTR)
• Financial Impact (Sales and Profitability)

Resiliency metrics became extremely important for supply chains following the COVID-19 pandemic. Companies soon realized how every supply node in the chain could affect downstream activities due to material shortages and supply delays.

For firms that had a global network of suppliers, the ‘Time to Recover’ took several weeks and months. Moreover, supply related constraints had an adverse impact on demand that eventually affected sales and profitability numbers. Firms have started to invest in tools and systems that enable ‘what-if’ scenario analysis – what the financial impact might be in case a supply node is unable to perform as planned.

Linking Operational & Financial Metrics

The various operational metrics and performance indicators listed above have a direct or indirect impact on business performance – more specifically, financial performance. Therefore, it is essential to establish a link between operational metrics and financial metrics.

The key financial metrics to focus on include:

• Cash to Cash Cycle Gross and Net Working Capital
• Inventory Turnover Ratio
• Gross Margin and Net Margin %
• Return on Assets (ROA)
• Return on Capital Employed (ROCE)

Cash to Cash Cycle, Gross and Net Working Capital and Inventory Turnover Ratio are the three metrics that should be on the radar of the supply chain management team. All the demand, supply and distribution plans and their execution have a direct bearing on the cash conversion cycle and working capital levels. ROA and ROCE are impacted by supply chain operating costs. Any reduction or saving in procurement costs, inventory handling costs, production and logistics costs shall have a positive impact on ROA and ROCE. The operational metrics can be dovetailed into a relevant business/financial scorecard every month, quarter, and financial year.

People, Processes & Systems

Finally, for any performance management philosophy to work, there is a need to put in place the right team with suitable skills and understanding of the supply chain domain in addition to devising and deploying the appropriate processes and procedures for data management (data recording, reporting, monitoring and analysis) and selecting the right tools, software and systems for reporting, analysis and visual management.

Visual management tools such as dashboards, functional control towers and related systems could go a long way in monitoring key operational metrics on a real time basis to enable course correction and development of corrective and preventive action plans going forward.

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The following is the second in a two-part series covering the art and science of  demand planning. Read the first part on the art of planning here.

Why do we Need to Forecast?

Companies need to understand what the future will bring to make the right decisions: What is my optimal footprint? What is the right price based on future expected volume? How much capacity is needed, and which inventory investments will yield the best returns? Using a statistical forecast provides a good starting point to answer all these questions.

However, not everyone has the background to understand the math behind the process, the underlying assumptions used in the forecast, or the best way to integrate this information into demand planning decision making. This is why it is important to simplify the statistical forecasting principles and assumptions at a high level so everyone actively participate in the discussion and help build the demand plan.

The Science Behind Statistical Forecasting

“The only way to predict the future is to understand the present.” – Isaac Asimov

The first question that needs to be answered is: What are you trying to predict? Forecasting order entry or sales will provide different outcomes and maybe both are needed, but for different reasons. My experience is that to drive the supply chain process you are better off using order entry as an input, since sales includes variability related to supply availability. But if there is a conscious thought process behind the decision of what is being used, it should not be a problem. Let’s review a quick example to clarify this situation.

A few years ago, during a demand discussion among colleagues, we were seeing a high level of volatility in a certain SKU. The Demand Planner was not able to explain it well as the item had steady sales in the past. Then the Supply Planning Manager stepped in and mentioned that we had a recent shortage in this product and the peak in sales should be due to a big lot coming in and being shipped out in the same month.

These circumstances lead us to review the process and the data we were using, as we were trying to determine what we could sell if there was not any supply constraint. The best set of data to do this was orders entered with the requested date from the customer. Once we used these figures, the pattern was much smoother as order entry had the information of what the customer needed, and the sales information had the information of when we were able to provide it. These subtle differences in the input of the process provide a very different output.

Now we can move to discuss the process. The intention of this section is to understand from a ten thousand foot view the components of the models and the high-level assumptions behind them. There will be complex calculations involved and we will not get into the details behind the math. After all, the experts can handle this very easily, but it is the understanding of these concepts in general that helps us to drive constructive discussion and decision-making.

There are a lot of different methods that try to predict the future including a simple moving average, exponential smoothing, econometric models, linear programming methods or machine learning algorithms. We will focus on time series – a series of sequential data points ordered by time. The two main methods we will review are exponential smoothing and linear regression because they are the easiest models to understand and the most widely used.

Exponential Smoothing

The first technique is called exponential smoothing. This method uses historical information to decide how much weight to give more recent history, trend or seasonality. These are the three main factors that you want to spend some time reviewing as they will give you some insight into the current demand pattern. The math will tell you how important each of these is, but it is up to people that understand the market to explain why.

This type of modeling is very helpful as it is data driven. I remember a meeting where a person on the marketing team mentioned that a certain product family was seasonal and after reviewing the data, it turned out that this was not statistically true. After digging into the details, it turned out that some products in that category had a strong seasonality component, but at the aggregate level you could not distinguish that pattern.

Another way to predict the future is to tag demand to macroeconomic variables and see if there is some correlation between them. A good indicator used in the plumbing industry is housing starts since it is a leading indicator of economic activity that tells you how many more homes are being built. There are a lot of different economic indicators out there and probably one of them is a good fit for your industry. Finding this correlation would be very useful since there are public forecasts available that can be used to predict the demand of your products.

Underlying Assumptions & Caveats

“History never repeats itself, but it does often rhyme” – Mark Twain.

What happens in an environment where there is volatility, uncertainty and complexity? Well, some time is spent discussing the data but this is usually not enough to understand all the caveats and assumptions.

One of the most important principles in forecasting is the underlying assumption that history will repeat itself and that past information can provide a good representation of what will happen next. The math tries to find certain patterns hiding in the data, like determining if the recent past is more useful or if there is a trend or seasonality involved. Early on in my career, when forecasting a high-volume item, we got a very high forecast for the near future, which did not made sense. It turned out that we had a big promotion in the recent past that was skewing the numbers. Once we took this out, the forecast corrected itself. The lesson we learned was that it is very important to scrub history to find outliers in the data.

Another important assumption is that external factors in the environment will remain constant, allowing the forecast to be developed under similar circumstances every time. Variations in the industry outlook, regulatory changes or economic growth fluctuations provide a challenge to the models described above. An alternate way to incorporate this information into the demand plan is required in these cases. A recent example of this is that after years of economic growth, the economy is stagnating or even shrinking. While this is known and discussed in the news and at the watercooler, there is always a lag from when this starts to happen and when the forecasting model picks it up.

Finally, a few important factors to consider in the assumptions are the horizon that you are using to plan and the level of aggregation. Think about the weather for a moment – usually the forecast for tomorrow is very accurate but looking at any day next month is not worth it. The same is true for any type of industry forecast; the further your look out, the less accurate it becomes.

It is a similar situation for the level of aggregation. It might be easy to predict how much you will sell in dollars for next month but it is harder to determine how many dollars per SKU or per customer you will sell next month. This is important to understand, because I have faced situations where the business asks how much we will sell in five years at SKU level by customer. It does not make sense to do a detailed analysis in this situation.  It is better to provide a directional number that is easy to explain and that supports the business to make the right decisions.

Measuring The Output Of The Process

Some of the most popular phrases in demand planning are “If I could predict the future, I would go to Vegas” or “The forecast is always wrong” and, my favorite one, “My crystal ball is broken”.  It is a fact that no forecast will be exact, but you can get within a decent range and with a good level of confidence. This is why it is good practice to measure the accuracy of your statistical forecast to understand the reasons behind your top misses.

From my perspective, it is important to understand how good the forecast accuracy is in terms of mix and volume. A good way of measuring mix is through MAPE (mean absolute percentage error), which basically tells you by how much you missed the forecast – regardless if you missed up or down – compared to what your actuals were. The advantage of this metric is that it is easy to understand (since it is a percentage) and it does not net out negative and positive values as it uses an absolute error.

The way to measure volume is through understanding if there is a bias at the aggregated level of your forecast. For example, when aggregating all your forecasts at the total dollar level by month, if you find out that the actual sales number has been below the forecast for some time, you might want to understand the reasons behind this. Ideally you want to oscillate between being a little above and then the following period a little below over the time horizon.

Discussing The Forecast During The Demand Planning Meeting

After understanding how a computer (or a Demand Planner) creates a forecast, it should not be a surprise why this needs to be reviewed in a group setting. Even if math is not your strength, a lot of value will come from having a thorough discussion of the historical data, the process used to come up with the numbers, the actual forecast, and metrics.

Below is a checklist of things to review. During this discussion, make sure that you balance time between the items that will move the needle, but covering in enough detail that allows you to understand the current situation.

• Start with the input. Are we using the right dataset and have we looked at history to review and scrub?
• How much importance do we give to more recent history versus the past? Is there a pattern in the data to be discerned, like trend or seasonality?
• Is there an economic indicator that we could peg to a group or family of SKUs that will help us determine what the future holds?
• Is there any external factor that could deter us from using our forecasting methods to determine the future values? Examples of this are changes in price (by us or the competition), changes in the economic or regulatory environment, or even new products that could cannibalize current demand.
• What are the forecast accuracy metrics telling us? Are we better than last month? What are the top offenders and why?

In conclusion, a good statistical forecast provides a good start for your demand planning process and a solid foundation. The data, assumptions and metrics need to be understood and discussed in depth. But keep in mind that this only the beginning of the process. There is an art component of the demand planning process that incorporates changes in the environment, market intelligence and in general a consensus between areas that should be aligned with the company strategy.