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 Despite its importance, many organizations still rely on outdated tools, such as spreadsheets, which can lead to bias and siloed decision-making, ultimately compromising their forecast accuracy.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 disruptive global marketplace, more and more companies are laser focused on supply and demand planning and how to get it right.

This guide explains the two disciplines, their differences, and how they work together. Use the information to build a solid foundation for controlling your inventory in these challenging times.

What is Demand Planning?

Demand planning uses analytics, data, insights, and human 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.

There are two types of demand planning — unconstrained and constrained. With unconstrained demand forecasting, the planner focuses exclusively on raw demand potential, not factoring in possible constraints like capacity and cash flow. This method determines how much you could sell if supply were not an issue. Constrained forecasting, however, considers these factors, creating a more realistic picture.

Businesses should leverage both unconstrained and constrained demand planning to deliver the most value to consumers while keeping costs down.

Essential Considerations in Demand Planning

Businesses must focus on these four areas of demand planning to succeed during this global unrest.

• Historic product sales: What you’ve sold in the past may indicate what you can expect to sell in the future, although that may not always be true. What’s critical to getting things right is to select the correct historical periods and market and economic conditions.
• Internal trends: Using historical data, identify sales trends for one product or group of products.
• External trends: Some factors that may impact a company’s ability to efficiently meet its customers’ needs. These include competition, sociocultural issues, legal factors, technological changes, the economy, and the political environment. (The last two are particularly critical today.)
• Promotional events: When companies run sales, events, or promotions, sales often increase. Demand planning must account for this as well.

Accurately forecasting demand is complex, but businesses must master it during challenging times like today.

What is Supply Planning?

As we covered, demand planning is the process of predicting consumer demand.

Supply planning, by contrast, determines how a business will fulfill demand within the organization’s financial and service benchmarks. It must factor in things like inventory production and logistics. Specifically, it must consider factors like on-hand inventory quantities, open and planned customer orders, minimum order levels, lead times, production leveling, safety stocks, and projected demand.

The five key functions of supply planning are:

1. Business operations is where demand forecasting comes in. Once you’ve calculated the demand, you are able to decide how much inventory you need. At this step, you should know how much product must be ordered and produced.
2. Acquisition involves purchasing materials or final products. Buying supplies is a critical part of having adequate inventory on hand. It requires partnering closely with your suppliers — and their suppliers — especially during uncertain times.
3. Resource management is where companies ensure adequate resources are available and distributed to the correct locations.
4. Workflow of information keeps supply chain management on track by using standardized systems across all departments preventing disconnects.
5. Transportation and logistics pull together all the components of planning, buying, manufacturing, storage, and transportation to ensure an adequate supply of items reaches the consumer.

Practicing supply planning effectively can help keep companies successful during challenging times.

Supply Planning Versus Demand Planning

Demand planning and supply planning aren’t two completely different things. They are actually two halves of a whole.

Demand planning aims to predict how much of a product you need to have available to meet consumer demand. Supply planning determines how to meet that demand within your company’s cost and service rules.

Demand impacts supply, and supply is dependent on demand.

You cannot meet demand without sufficient supply. Similarly, you can’t ensure adequate supply without clearly understanding demand, especially in changing times. You need both to keep your business healthy.

The key difference between the two types of planning are the characteristics of the data that fuels them.

Much of the information used for supply planning is internal or comes from sources connected to the company. It involves analyzing production capacity, time constraints, supply costs, delivery times, storage requirements, and other factors. Because you have relatively easy access to — and control over — supply chain data, it is u easier to master the supply side of the supply and demand equation.

Businesses typically have less control over demand data. While some of it is internal, like historical and seasonal sales records, much is external, like economic trends. This makes demand planning less dependable and more challenging than supply planning.

In short, because supply planning uses more defined and owned data points, it is typically more concrete and reliable. It provides practical direction on how you’ll meet consumer needs. By contrast, demand planning uses less definite and owned information. While certain algorithms and data sources are more accurate than others, forecasting always involves some level of prediction. Supply planning and its practical calculations using more reliably sourced data are typically less volatile.

Another way to view supply planning versus demand planning is to compare their ultimate goals. Demand planning delivers predictions that impact supply planning and other business decisions, while supply planning pays off with inventory optimization.

• Predictions: Demand planning considers a wide array of factors to develop as accurate forecasts as possible. Demand predictions inform supply planning and support other business decisions, such as when to offer promotions or find new vendors.
• Optimization: Supply planning determines how you’ll meet projected demand within your organization’s operational constraints and business objectives. It considers available resources and other factors to develop a plan prioritizing efficiency, cost savings, and speed. The supply plan must align fully with company goals and allow it to take action to achieve them. For instance, if an organization wants to reduce costs for a project, a supply plan might recommend buying materials with a slower fulfillment timeframe. This approach wouldn’t be appropriate for a business driven by tight deadlines.

A balanced approach to demand and supply forecasting is essential for ensuring appropriate stock levels without storing extra inventory, but striking that balance looks different for every business. High-quality data is a key component of both planning types, making analytics and robust supply chain management software and systems especially valuable.

Supply Versus Demand Planning: The Final Word

Supply planning and demand planning aren’t competing factors within a company. Instead, they should be viewed as complementary functions that allow businesses to operate more efficiently and effectively. This is especially critical when operating in dynamic and challenging times like today.

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.

I cannot discuss forecasting bias without mentioning MAPE, but since I have written about those topics in the past, in this post, I will concentrate on Forecast Bias and the Forecast Bias Formula.
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What Is Forecast Bias?

Forecast Bias can be described as a tendency to either over-forecast (forecast is more than the actual), or under-forecast (forecast is less than the actual), leading to a forecasting error.

There are many reasons why such bias exists including systemic ones as discussed in a prior forecasting bias discussion. Some core reasons for a forecast bias includes:

1. Optimism bias: I have seen this primarily with the sales team who seem to have an abundance of confidence in their ability to sell and therefore inflate the end results.
2. Sandbagging bias: This is the reverse of the above and I have seen this where well-meaning executive have created a system of bonuses based on exceeding the forecasts, and this has created a culture of sandbagging.
3. Anecdote bias: I have heard so many instances where regardless of what the data is telling them, client personnel would be wary of seeing it because a terrible thing that happened in the past and is part of the company folklore. Their forecast is therefore biased based on the anecdotes.
4. Recent data bias: This is probably true for all processes where humans are involved. The more recent occurrences weigh heavier in our mind. In the case of forecasting, this can create an overreaction based on the latest events.
5. Silly bias: In a study conducted by Amor Tversky and Daniel Kahneman, they asked respondents to guess the number of countries in Africa. However, they showed them a number right before asking them to guess. What they found was on average, the estimate of some countries went up when the user was shown a bigger number and went down when the users were shown a smaller number before answering the question. This makes me think a forecast could be impacted by silly things you saw before you start doing the forecast. For example, what if they saw the temperature and it was a hot day? Does that high number skew the forecast higher? What if they called someone before forecasting and the phone number was comprised of larger digits?

How To Calculate Forecast Bias

A quick word on improving the forecast accuracy in the presence of bias. Once bias has been identified, correcting the forecast error is quite simple. It can be achieved by adjusting the forecast in question by the appropriate amount in the appropriate direction, i.e., increase it in the case of under-forecast bias, and decrease it in the case of over-forecast bias.

Rick Glover on LinkedIn described his calculation of BIAS this way: Calculate the BIAS at the lowest level (for example, by product, by location) as follows:

• BIAS = Historical Forecast Units (Two-months frozen) minus Actual Demand Units.
• If the forecast is greater than actual demand than the bias is positive (indicates over-forecast). The inverse, of course, results in a negative bias (indicates under-forecast).
• On an aggregate level, per group or category, the +/- are netted out revealing the overall bias.

The other common metric used to measure forecast accuracy is the tracking signal. On LinkedIn, I asked John Ballantyne how he calculates this metric. Here was his response (I have paraphrased it some):

• The “Tracking Signal” quantifies “Bias” in a forecast. No product can be planned from a severely biased forecast. Tracking Signal is the gateway test for evaluating forecast accuracy. The tracking signal in each period is calculated as follows:

• Once this is calculated, for each period, the numbers are added to calculate the overall tracking signal. A forecast history entirely void of bias will return a value of zero, with 12 observations, the worst possible result would return either +12 (under-forecast) or -12 (over-forecast). Such a forecast history returning a value greater than 4.5 or less than negative 4.5 would be considered out of control.

At Arkieva, we use the Normalized Forecast Metric to measure the bias. The formula is very simple.

As can be seen, this metric will stay between -1 and 1, with 0 indicating the absence of bias. Consistent negative values indicate a tendency to under-forecast whereas constant positive values indicate a tendency to over-forecast. Over a 12-period window, if the added values are more than 2, we consider the forecast to be biased towards over-forecast. Likewise, if the added values are less than -2, we find the forecast to be biased towards under-forecast.

A forecasting process with a bias will eventually get off-rails unless steps are taken to correct the course from time to time. A better course of action is to measure and then correct for the bias routinely. This is irrespective of which formula one decides to use.

Good supply chain planners are very aware of these biases and use techniques such as triangulation to prevent them. Eliminating bias can be a good and simple step in the long journey to an excellent supply chain.

Alignment of demand and supply has been the subject of extensive research but is still a pain point for many organizations, causing either lost sales on the one hand or holding excess inventory on the other. Unfortunately, under or overstocking is often viewed as binary choice that has to be made, but there is another solution – balancing supply and demand.

Let’s take a look at what under and overstocking means for different functions in the business.

From the point of view of Sales, understocking means:

• Missing sales targets
• Not being able to earn bonuses
• Empty shelves at retailer store meaning lost sales
• Risk of paying penalties to contractual retailers
• Poor customer service which can cause customers to go elsewhere

From the point of view of Supply Chain, overstocking means:

• Limited space in warehouse, causing higher inventory holding costs
• Increased cost of rent if space is not enough to hold stock
• Increased risk of product obsolesce if shelf-life is limited
• Having to offer discounts to clear excess stock, impacting profitability
• Higher labor costs to manage the stock on a regular basis.

Thus, none of the managers on the supply side want to be overstocked but salespeople do so they can take advantage of all opportunities in the market. But it doesn’t have to be either/or. Instead we can find a balance that satisfies the need to both sell as much as possible without incurring the costs of holding excess stock.

How To Find The Balance Between Over & Understocking

1 -Understand Consumer Demand

The first thing is to understand demand, i.e., what consumers want and where. To do so, companies need to learn what shoppers can afford, what products they prefer and why, and environmental and cultural factors that have an impact on consumer behavior.

For instance, if the consumer demands high-end premium products in your store or region, there is no reason to overstock brands that are cheaply made or packaged in boxes with unreadable labels. This calls for historical data to ensure that sales trends, seasonality, and validity in the market can be scanned periodically. With statistical modeling we can take this sales data and extrapolate future demand. Depending on the industry, companies need to review historical sales and update forecasts daily, weekly, monthly and quarterly, depending on the product type.

This approach is also same for suppliers who need to analyze the demand coming from each retailer and store and consumer characteristics. Overall, these actions enable a much clearer picture of what consumers want and what they don’t. When we know this, we have a foundation to start meeting this demand with the necessary supply.

2 – Invest In Your Demand/Supply Planners

A good understanding of demand cannot be achieved with historical data alone. You need good demand and/or supply planners, who are knowledgeable about product groups and categories and aware of external factors that may affect consumption, and who are equipped with knowledge of demand management and forecasting methodologies. Not everything cannot be found in the data. The impact of a competitor’s in-store promotion, cultural impacts forming shopping habits, background of expatriates in the city/region etc. are only some of the factors that planners should consider when generating forecasts, planning supply, and setting stock levels.

Demand management is a specific area that includes many techniques, methodologies and nuances unique to the role, thus making education and training crucial. Planners should know which forecasting techniques to use for which data set, how to aggregate forecasts with factors affecting demand, and when to adjust forecasts with qualitative judgment. Being knowledgeable about a particular product category is a competitive advantage for planners because it allows us to understand the likely impact of promotions and competitor activities. It also enables better communication with the sales team, who we rely on for input into the forecasts and customer information.

3 – Forecasts Feed The Supply Plan

Let me ask you the following question: Does your company look to just hit monthly sales targets or to enhance profitability in the long-run?

If the goal is to just close the month with sales targets achieved, let the sales team create and approve the forecast. This is how it works at most suppliers and distributors. Don’t get me wrong, the contribution of the sales team to any business is incredibly important but they do not have the expertise to create forecasts that represent true demand. Salespeople have the unconscious habit of being optimistic on sales targets, which must be tempered by data-focused demand planners.

When generating forecasts we need input from our colleagues in Sales and Marketing, Finance, Supply Chain, and perhaps Customer Service, and the optimal way to collaborate is through a Sales and Operations Planning process. This is the forum that allows us to align on aggregated forecast numbers. Supply Chain plays a key role here. Let’s take the example of the Tesco, the biggest retail chain in the UK: when Tesco handed the responsibility of order replenishment to their Supply Chain directors, it dramatically increased product availability and reduced inventory. This approach enabled both Supply Chain and category management teams to manage shelf space, promotions and new launch item in a more efficient way.

4 – Integrate Pareto Analysis Into Your Target Stock Level

Pareto analysis, also known as the 80/20 rule, is a statistical method used for decision making which identifies which 20% of inputs leads to 80% of the desired output. In demand planning, we’re looking to identify the 20% of products that contribute to 80% of profitability.

Pareto analysis should be the best friend of planners when it comes to managing inventory. To illustrate further, when I was working for Transmed Overseas, a full service distributor in the Middle East and Africa, we had one single number of DOS (days of stock) target per brand. This was causing massive fluctuations in stock levels and was triggering not only out of stock (OOS) but also excess inventory and obsolete stock. With Pareto analysis, we first categorized the SKUs of each brand from best to worst performing. Then we categorized SKUs that generated 75% of sales as class A and the SKUs that generated 15% of sales as class B. The final group, C, were the SKUs that generated only 5% of sales. This approach identified our most important products and the safety stock levels for each product were determined according to their category.

Using Pareto analysis, we not only reduced excess stock by 15 to 20%, but also ensured availability of class A SKUs, which improved customer service by 3%. As valuable as Pareto analysis, is, you must also consider lead time, contractual agreements, forecast accuracy, and other factors.

5 – Optimize Order & Replenishment Frequency

If we get our inventory replenishment frequency right, we reap the rewards of lower inventory. It is easy to write but difficult to apply! Of course, there are many factors affecting the right order frequency such as long-lead times, seasonality, forecast accuracy, containerization, promotions, and PIPO (Phase in Phase out) practices, but it is doable.

Your starting point is to check whether your lead times are accurate or not. Without a high level of lead time accuracy, any attempt to increase order frequency is shot in the dark that risks failure and cost. Thus, the supply chain team should work meticulously to track OTIF (On time, in full) performance of every purchase from each supplier. Once there is a reliable history on lead time with accuracy performance, then the team should check forecast accuracy at the SKU level and forecast misses, which is one of the invisible inventory costs incurred by companies. Improvement in lead time and forecast accuracy will increase the confidence in replenishing products on time and in full at DCs and stores. Following this, containerization should be analyzed which has a direct impact on logistics and transportation costs. This responsibility lies with the Supply Chain team, who should compare the cost of inventory holding, forecast misses, and obsolesce versus savings from logistics and transportation. This cost/savings ratio should inform your ordering frequency.

Amending your order frequency should consider marketing or category management teams because they run promotions that can impact the amount of inventory you need. If promotions are not factored into the lead times and not communicated to the Supply Chain and Procurement teams, plans will not include the promotional volume. This not only gives rise to missed sales/understocking, but also poor customer service, and even penalties at the downstream level depending on your agreements with retailers.

The purpose of this article is to make clear the essential requirements for sustainable S&OP process implementation. S&OP implementation has a high failure rate, with most initiatives not actually delivering any value and doing nothing to improve the balance between demand and supply. Using this S&OP kick-off guide, the chances of failure are much reduced, increasing your chances of leveraging S&OP as a growth driver that provides a real competitive edge.

Often the S&OP Process implementation initiative is carried out in a poorly structured way, especially when the implementation decision is made from the bottom up without executive sponsorship. The managers involved in this type of implementation end up not following the methodology completely, leaving out the essential pillars that are required to sustain the process. This means creating more work further down the line, day-to-day difficulties in the S&OP process, and, sometimes, damaging the credibility of the process which can result it its death. Haste and lack of capacity building are usually the key factors in the demise of the S&OP. S&OP is like building a house – it needs a strong foundation, and if the proper work is not carried out at the beginning, problems down the line can cause irrevocable damage. Here are some helpful pointers that will guide a successful S&OP, regardless of whether it is a top down or bottom up initiative.

Sponsor: This is the person who will provide the necessary support before, during and after the implementation of the S&OP process. This person must have great influence within the company, have knowledge of the process, and be able to carry out all the necessary alignments and approvals with the main managers. This person needs the necessary position and communication skills to open doors that may have been locked for years.  

S&OP in the hierarchy: Normally the S&OP area is created within the Supply Chain, however, when the process leaves stage 1 maturity it is important that the S&OP area reports to a neutral entity (free of department specific interests) in the company, for example: Finance or a senior executive. S&OP and its management need to maintain the collective interest with focus on the best result for the overall business.  

S&OP Leader: This person needs to have solid experience and knowledge in Supply Chain, as they will lead a wide range of different activities. In addition to the experience and technical expertise in the field, the S&OP leader will need to have great energy and discipline to meet the schedule, and a willingness to move people around and change processes to ensure integration. Ability to communicate at different levels is necessary.. Involvement of the human resources area is critical at this stage to find the right candidate. When choosing the right person for S&OP Leader, a junior profile will not cut it.

S&OP Team: As we are dealing with a total integration process, we absolutely must involve all areas, even if we have to demand it. An S&OP Committee is recommended for the initial phase, with all parties committing to collaboration and support of the implementation project. Choosing the right people and their backups is key to starting the process as well as maintaining it later on.

Roles and Responsibilities: Each S&OP member must have a clear role and defined responsibilities within the S&OP process. They need to be trained to contribute properly in the process – both inside and outside meetings – as facilitators and process owners. I recommend you use a matrix of responsibilities, train those involved and record all activities.

S&OP Meetings: All meetings must have: an objective, a duration, participants, inputs, discussions, outputs, attendance list, and a list of required actions. Participants at each meeting will need to be trained to ensure an effective meeting.

Meeting Schedule: All S&OP monthly and weekly cycle meetings need to be set in advance. In order for people to attend the meetings, invitations need to be sent as soon as possible. One recommendation is to keep the invitations sent for the next 3 months of meetings. It is necessary to pay attention to special dates like holidays and events that could affect attendance.

Documentation: The S&OP process must be formalized through documents like process flowcharts, procedures and operational instructions to ensure the decentralization of information and the survival of the process when a team member leaves. The is important in assisting standardization and proper management of S&OP documents. One of the most important documents is the S&OP policy which details S&OPs’ involvement in the business. In addition to containing all the requirements to establish and maintain the process, this document codifies the agreements and hierarchy of decision-making. It is a living document that must be kept up-to-date by the managers of the S&OP process.

Process Auditing: After a few monthly S&OP cycles, an S&OP audit process has to be defined to ensure that everything that was planned has been implemented in practice. This must be carried out by an independent team, who will have to be trained in S&OP to audit the process. In some companies this department already exists.

[Ed: These are the essential criteria for successful S&OP, that will both facilitate its implementation and sustainability. Only 1/3 of S&OP initiatives end up actually adding any value –  make sure you lay the appropriate foundations to ensure yours is a real growth driver.]

Question

Dear Dr. Jain,

I am am keen to know the best practices for 3 points :

1. The KRA’s (Key Result Areas) for Demand Planners regarding: areas of responsibilities, weighting % for each KRA, measurement criteria/KPIs , goals and targets, and action plans to give expected results of these KRAs.

2. ROI of a Demand Planner in terms of bottom line profit.

3.  Specific roles and responsibilities of Demand Planners as opposed to Supply Planners and other roles.

Regards,

Supply Chain Planning Manager at a multinational chemical company 

Answer

1.

a) The key responsibilities of a Demand Planner is to manage forecasts, which are often prepared through a consensus process; and manage demand, which is often done through the Sales & Operations Planning (S&OP)  process.

b) Both activities—running consensus and S&OP processes— are equally important, and should thus be weighed equally.

c) KPIs used the most are amount of inventory held, customer service, margin, total revenue, market share, etc.

d) Goals/targets vary from industry to industry, company to company. It may be expressed in terms of total profit, total revenue, forecast accuracy, inventory level, customer service, etc.

e) Manage correctly both the consensus and S&OP processes.

2. It is difficult to measure precisely ROI resulting from a Demand Planner. One can get some idea by looking at the improvement in total sales and profit, inventory level, customer service, etc.

3. Demand Planners won’t be able to do their job well unless:

• All functions work together, not in a silo, and do what is good for the company, and not for a specific function.
• Senior management fully supports the role and the S&OP process.
• There is complete transparency where everything is on a radar screen so everyone knows what is going on.
• Metrics are in place to measure the performance.

I hope this helps.

Dr. Chaman Jain,

St. John’s University

Attention Demand Planners, wouldn’t you AND your CFO both like to know how much profit your current forecast is leaving on his table? Turns out you can; all that’s required is getting a second opinion of your current forecast just like you would if you had an important medical condition. Or, another way to think about it might be as an analytic consultation of your forecast. 

How Does The Second Opinion Forecast Work?

First, a bit of background is in order. As is well understood, the most important element in developing next year’s plan is the forecast. This forecast is “translated” into next year’s profit by subtracting the income statements’ various costs from the forecast’s revenue; specifically, cost of goods sold and sales & marketing, general and administrative costs. These costs come from the budget. Thus, the forecast in concert with the budget determines for the CFO next year’s planned profit.

The Second Opinion Forecast has no dependence on next year’s budget.

The Second Opinion Forecast (SOF) is developed entirely differently; it has NO dependence on next year’s budget. This fact should  please your CFO given the current budget’s widely recognized limitations, including short life-span, how time-consuming it is, and how rapidly its assumptions are outdated.

Rather, SOF accomplishes this independence from the budget by building a model of the income statement, an Operational Income Statement (OIS).  This model integrates two analytic techniques in widespread use today.  The first is Supply Chain network design and the second is demand-sensing modeling (often referred to as marketing-mix modeling).

How To Create An SOF Model

There are three steps involved in creating an SOF model:

Step 1: Working closely with the CFO’s management accountants within FP&A, a model is built from last year’s income statement (i.e. profit and loss) in Supply Chain network design software. This model, the baseline, ensures the model has structural integrity, typically modeling last year’s results to within 1-2%. However, this model differs from network design efforts because its planning horizon is only a year and it includes SG&A costs.

Step 2: This model is then updated with next year’s planning data including:

• Replacing the sales/marketing costs in the baseline model with demand sensing functions which describe how the forecast volumes vary as a function of sales and marketing expenditures
• Reflecting any and all improvements planned for the Supply Chain in the coming year including changes in sourcing, process improvements, in/outsourcing decisions, new production lines, plant facilities, etc.

Step 3: This updated baseline model then selects the Sales & Marketing expenditures which create the most profitable forecast using the demand sensing functions. This demand-sensed forecast is the SOF forecast.

Second Opinion Forecasting allows the income statement to be updated much more quickly and accurately during the year.

Why Is The SOF Important?

The SOF allows the CFO to compliment the current financially-based enterprise planning efforts with the same planning foundation that the Supply Chain currently uses, i.e. Operations. As described below, the benefits the SOF provides for the enterprise in general and the Demand Planners in particular, are significant.

There are a variety of benefits including:

• Creation of a new maximally profitable demand-sensed forecast, the SOF
• The SOF model configures the best Supply Chain required to make and fulfil the SOF, respecting all the Supply Chain’s constraints including sustainability (e.g., energy consumption, carbon emissions
• It allows the income statement to be updated much more quickly and accurately during the year
• It improves the forecast process’s profit results going forward
• It maximizes the return on investment of  sales and marketing expenses
• It fulfils a long-held belief amongst Demand Planning experts that Demand Planning is held back by being purely a Supply Chain-focused function. There’s no real reason it should be limited to Supply Chain.

Is your warehouse full of widgets wigging you out?  Not to worry, this simple ABC guide will help you implement and maintain an effective inventory management system. It allows you to identify your most valuable items and react accordingly to avoid stock outs and reduce excess inventory. 

Analyze

A simple ABC analysis will quickly and effectively inform you which widgets have the most impact on your business. Not all widgets are created equal, and the idea here is to bucket your inventory according to expected demand or value. These buckets will allow us devote an appropriate amount of time and energy to each bucket or type.

Start by looking at your future demand plan in weekly buckets. If your firm uses monthly buckets, simply divide the monthly demand by the number of weeks in the month. Your demand horizon will depend largely on the amount of variance in it. If you are in a fairly stable demand environment with little weekly variance, then a short horizon will do while a more volatile environment will require more time. I suggest no less than 8 weeks and no more than 26.

From here, take the total demand in units per item and multiply it by each item’s inventory value to determine its extended demand value (EDV) and rank them from top to bottom. You will also want to calculate your average weekly forward demand (AWFD) over the length of your horizon at this point. (We will come back to this.)

As you move from top to bottom by EDV, calculate a cumulative value until you’ve reached 80% of your total EDV. These are your ‘A’ SKUs or the ‘important few’. Following from there, the next 15% are your ‘B’ SKUs and the last 5% are your ‘C’ SKU’s or the ‘insignificant many’. To these, it is usually necessary to add two more categories, ‘D’ for discontinued and ‘E’ for any widget that has insufficient data to develop a dependable forecast – for example, a newly introduced widget. It’s important to point out that these %’s are not hard and fast rules. If there is a reason to delineate ‘A’ from ‘B’ at 75% such as a significant drop in demand at that point, then you should do so. Tailoring these designations to your business will be important in the next step.

Build

Build an inventory strategy for each widget type. Before we get too far into this I want to point out that we are going to discuss these strategies in general terms. Exactly how we get to our plan will require collaboration with our friends in planning and procurement.

‘A’ widgets are your most valuable and they will require the most time and investment. We need these important few to be in stock!  To do that, you will need to keep enough on hand to cover your order lead time plus your review time. You will want to review these no less than every other week. Your strategy will also need to include a safety stock buffer dependent on the volatility of your demand and the reliability of your supplier. To get to a quantity we will use our AWFD calculated previously. If your order cycle time is 4 weeks, you review your inventory every other week and you decide to keep 2 weeks of safety stock then you will need 8 weeks of inventory on hand at any given time. Multiply the AWFD by 8 in this case and you have your inventory target.

Repeat for ‘B’ widgets but with a less frequent review period and zero safety stock.  ‘C’ SKUs can be managed effectively with a quarterly ‘set and forget’ review. ‘D’ widgets require no review while ‘E’ widgets should be treated as ‘A’ until they are proven otherwise.

Consistent Review And Communication

Now that you have set the framework for your inventory management system, you will need to set a schedule of consistent review and communication. Your ABC Analysis should be done monthly, changing designation and strategy accordingly. Lastly, you need a quarterly review with your Supply Chain peers, Sales, and Product Development to determine disposition on ‘D’ widgets, rationalize the existence of ‘C’ widgets and determine when ‘E’ widgets can be treated as demand dictates.

Dear Dr. Jain,

I work for a large company, a group which provides services for Oil & Gas Companies. It has several activities including :

– Air transport
– Logistics (rig-moves, location of heavy transport)
– Civil engineering,
– Catering services

How can I implement a Demand Planning process to improve forecast accuracy, service level, cash flow and reduce costs of operations whilst insuring involvement of the sales department in our service activities? Is there any best-in class process you implement in a service company?

Thank you,

Mehdi Mostefaoiu

RedMed Group

Answer

Fundamentally, there is no difference in implementing a Demand Planning process for physical products or service related products. The key to all – whether improving service level and cash flow or reducing operational costs – is forecast accuracy. When forecasts improve, everything else will improve. The best practices in preparing forecasts is the use of a consensus process, where forecasts are first generated statistically, and then in a monthly meeting all the functions including Finance, Sales, Operations and Marketing get together and review the numbers.

Where necessary, they overlay judgement over the statistically generated forecasts. Judgemental overlay is needed because there are certain elements that have a bearing on a forecast but cannot be quantified, or certain information was not available at the time forecasts were generated. Also, at times, you look at the forecast numbers for certain products and see they don’t make any sense. Based on your experience, they would do much better or worse than what were forecasted. Here again, judgemental adjustment is needed. In so doing, make sure adjustment is not politically motivated. After actuals are in, do a postmortem of forecasts to see what worked and what didn’t, and why. This will help to improve your next forecasts.

Make sure forecasts are transparent so everyone knows what happened. The problem in forecasting generally arises from the use of wrong data, wrong assumptions and the wrong models. Sometimes, they are biased. Check each one thoroughly and see if there is an opportunity to improve them further.

I hope this helps.

Dr. Chaman Jain

St. John’s University