Demand Planning in 2024 – The Simplified Essentials

March 25, 2024 · 18 minutes
demand planning guide
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Demand planning is crucial to supply chain management and aligning inventory levels to meet market demand.

When demand planning is inaccurate, the consequences can be severe, resulting in either surplus inventory that ties up capital or stock shortages that lead to lost sales and customer dissatisfaction, which can significantly impact a business’s financial performance.

The unpredictable nature of consumer behavior and market fluctuations compounds the challenge of demand planning. Supply chain managers face the daunting task of creating accurate forecasts amidst this uncertainty, with the success of their strategies hinging on the precision of their predictions.

Supply chain professionals are increasingly turning to advanced forecasting techniques and AI-driven analytics to combat these difficulties. This article will explore the various demand planning methodologies and their advantages and offer a practical guide for their implementation.

What is Demand Planning?

Demand planning is the strategic process that forecasts demand for products or services, aiming to synchronize inventory with market demand and optimize resource allocation. 

Its scope extends to ensuring product availability for customer satisfaction and achieving cost efficiencies across the supply chain, integrating sales forecasting, supply chain management, and inventory management.

Significant milestones have marked the evolution of demand planning, notably the shift towards demand-driven supply chains and the introduction of advanced analytics and AI.

These innovations have enhanced the precision of demand forecasts, enabling businesses to adapt to real-time demand signals and maintain a competitive edge.

Demand planning is exemplified by a manufacturer adjusting production schedules based on demand signals, optimizing inventory management, and enhancing operational efficiency. 

This strategic approach minimizes waste and storage costs while ensuring timely product availability, improving customer satisfaction and loyalty. 

As supply chains grow in complexity, accurate and responsive demand planning becomes increasingly critical for thriving in the global marketplace.

Why is Demand Planning Important?

Demand planning is important because it achieves strategic objectives by enabling cost reduction through accurate demand forecasts. It also enhances customer satisfaction by ensuring product availability and optimizes resource allocation to prepare for future demand without excess expenditure.

You can consider demand planning as a navigational tool that steers businesses through the unpredictable currents of market demand, impacting cost management, customer satisfaction, and competitive advantage. 

What are the Elements of Demand Planning?

The elements of demand planning are statistical analysis, collaborative planning, and market intelligence.

Demand planning is a multifaceted process underpinned by these elements that work in concert to forecast demand accurately and align supply chain operations accordingly. 

Understanding them is crucial for businesses aiming to optimize their supply chain and stay ahead in the competitive market. 

Let’s delve into each one, illustrate their application, and spice them up with examples.

1. Statistical Analysis

Statistical analysis forms the backbone of demand planning, employing historical sales data to predict future demand patterns. This quantitative approach leverages various statistical models and algorithms to analyze trends, seasonality, and cyclical behaviors inherent in sales data.

Statistical Analysis Example: 

A fashion retailer, for instance, uses statistical analysis to forecast seasonal trends and consumer preferences. 

Examining sales data from previous seasons enables the retailer to identify patterns—such as an increase in demand for specific colors or materials—and adjust its inventory and production plans accordingly. 

This proactive approach enables the retailer to meet consumer demand more effectively, reducing the risk of stockouts or excess inventory.

2. Collaborative Planning

Collaborative planning emphasizes integrating information and planning processes across different departments within a company and with external partners like suppliers and retailers. 

This element of demand planning fosters a holistic view of the supply chain, ensuring that all stakeholders are aligned and working towards the same objectives.

Collaborative Planning Example

Collaborative planning is critical in synchronizing production schedules with supplier deliveries in the automotive industry. 

Automakers work closely with suppliers to share demand forecasts and production plans, ensuring that components arrive just in time for assembly. 

This collaboration minimizes inventory holding costs and enhances supply chain responsiveness, allowing automakers to adapt quickly to changes in market demand.

3. Market Intelligence

Market intelligence involves gathering and analyzing data from external sources to inform demand planning. The market intelligence analysis includes market trends, consumer behavior, economic indicators, and competitive analysis. 

When market intelligence is incorporated into demand planning, companies can anticipate shifts in demand and adjust their strategies accordingly.

Market Intelligence Example 

A consumer electronics manufacturer uses market intelligence to stay ahead of technological trends and competitor movements. 

Keeping an eye on industry reports, consumer reviews, and tech forums helps manufacturers gauge consumer interest in emerging technologies and predict which products will likely increase demand. 

This insight allows the company to prioritize R&D efforts and align its product launch strategy with market demand, ensuring a successful product introduction.

Whether it’s a fashion retailer anticipating seasonal trends, an automaker optimizing its supply chain, or an electronics manufacturer staying ahead of technological advancements, the principles of demand planning remain the same: forecast accurately, plan collaboratively, and adapt intelligently.

What are the Best Practices for Demand Planning?

The best practices of demand planning are implementing integrated business planning, leveraging advanced AI-based analytics, fostering collaborative planning, and establishing processes for continuous improvement based on feedback.

Effective demand planning is essential for businesses to optimize their supply chain, reduce costs, and improve customer satisfaction.  Adhering to these practices enhances the accuracy of their demand forecasts, making informed decisions.

Here is a breakdown of the mentioned best practices for demand planning, along with their pros, cons, and use cases:

1. Implement Integrated Business Planning (IBP)

Integrated Business Planning (IBP) is a strategic approach that synchronizes demand planning with financial, sales, and operational planning. This alignment is essential for making cohesive decisions supporting the organization’s objectives.

The advantages of IBP include enhanced collaboration among departments, which results in more accurate forecasting and strategic alignment with the company’s goals.

This collaborative approach ensures that all parts of the organization work towards the same objectives, improving efficiency and effectiveness.

However, the implementation of IBP presents challenges, primarily due to the need for cultural shifts within the organization and the integration of various systems that may currently operate in silos. Overcoming these obstacles is crucial for the successful adoption of IBP.

A practical use case of IBP is seen in multinational corporations, where it plays a vital role in aligning global operations with the demands of local markets.

Implementing IBP ensures that these corporations’ strategic objectives are consistently met across all organizational levels, from global strategies to local execution.

2. Leverage Advanced Analytics and AI

Incorporating advanced analytics and AI technologies allows companies to analyze extensive datasets, uncover patterns, and enhance the accuracy of future demand predictions.

The benefits of employing these technologies include a marked improvement in forecasting precision, optimized inventory management, and reduced operational costs. This technological approach streamlines supply chain processes, leading to more efficient and cost-effective operations.

However, challenges such as the upfront investment in technology and the requirement for specialized personnel can pose barriers to adopting these advanced tools. An example of practical application is seen in retail chains, where AI can sift through sales data, social media trends, and weather forecasts. This analysis helps accurately predict the demand for seasonal products, ensuring that inventory levels are kept at an optimum, thereby avoiding overstocking or stockouts.

3. Foster Collaborative Planning

Collaborative planning involves engaging suppliers, customers, and internal teams to exchange insights and achieve consensus on demand forecasts.

The advantages of this method are numerous: it not only increases the accuracy of demand forecasts but also fortifies business relationships and bolsters the supply chain’s resilience.

However, coordinating among various internal departments and external partners can introduce complexity and require a significant investment of time.

In the consumer goods industry, for instance, collaborative planning with retailers is instrumental in managing promotional events and responding to seasonal demand surges, thereby maintaining consistent product availability.

4. Implement Continuous Improvement and Feedback Loops

Implementing mechanisms for ongoing improvement and feedback enables organizations to enhance their demand planning approaches progressively.

The primary benefit of this approach is the ability to learn from previous forecasts and outcomes, resulting in gradual enhancements in the accuracy of future predictions.

However, the effectiveness of feedback loops is contingent upon an organizational culture that prioritizes openness and adaptability, qualities that may only be universally present across some companies.

For example, a manufacturing firm could conduct quarterly reviews of its demand forecasting accuracy, applying insights gained to refine subsequent forecasts and minimize inaccuracies.

5. Segment Demand for Precision

Demand segmentation by product lines, geographic regions, or customer groups enables more precise and focused forecasting. This strategy’s advantage is that it allows for a more nuanced understanding of demand variability, which can enhance inventory management and customer service.

However, this approach complicates forecasting and necessitates a more thorough data analysis. For instance, a fashion brand may segment demand by region to reflect varying climate conditions and fashion trends, thereby improving inventory allocation to meet regional needs.

While these best practices offer significant benefits for demand planning, they also come with challenges. The key to successful demand planning is carefully selecting and implementing practices aligning with the organization’s needs and capabilities. 

ThroughPut AI’s demand segmentation capabilities help supply chain professionals optimize the product and customer mix to get to the profitable sweet spot for businesses.

The AI-powered platform can draw insights on the performances of SKUs and customers based on multiple variables like value, frequency, etc to recommend the best plan of action to prioritize top-performing products and customers and de-prioritize the others.

Further, the tool also comes with the capability to let one see the performance of products down to every location for more location-based customization and fewer errors in forecasting demand.

What is Demand Planning Software?

Demand planning software is a sophisticated tool designed to enhance the accuracy and efficiency of forecasting future demand for products and services.  The software helps with demand forecasting and planning the production to meet this demand.

Demand planning technology is pivotal in modern supply chains as it enables businesses to analyze vast amounts of data, predict market trends, and align their inventory and production plans accordingly. 

Companies can make better-informed decisions, reduce costs, and improve customer satisfaction by leveraging demand planning software.

What are the Key Features and Functionalities of Demand Planning Software?

The key features of demand planning software are advanced analytics and forecasting models, collaborative planning capabilities, integration capabilities with other systems, scenario planning, and real-time data processing.

Most demand planning software comes equipped with a range of features and functionalities that support businesses in their forecasting efforts.

Here, we’ve outlined the importance of each mentioned feature:

  • Advanced Analytics and Forecasting Models that utilize statistical algorithms and machine learning to analyze historical data and predict future demand.
  • Collaborative Planning Capabilities facilitate information sharing and collaboration among internal teams and external partners, such as suppliers and retailers.
  • Integration with Other Systems, such as ERP, CRM, and SCM systems, allows a unified view of data across the organization.
  • Scenario Planning enables businesses to simulate various market conditions and assess the potential impact on demand, helping in strategic decision-making.
  • Real-time Data Processing that provides up-to-date insights that reflect current market conditions.

What’s the Importance of Demand Planning Software in Modern Supply Chains?

The importance of real-time demand planning software in supply chains is that it reduces the likelihood of human error, enhances forecast accuracy, and optimizes inventory levels, minimizing waste and reducing costs due to its capabilities to automate and streamline the demand planning process

In today’s fast-paced and volatile market environment, forecasting demand accurately and responding swiftly to changes is crucial for maintaining a competitive edge.

The software empowers businesses to achieve this level of agility, offering deep insights into consumer behavior and market trends. 

What’s the Difference Between Demand Planning and Demand Forecasting?

The difference between demand planning and demand forecasting is that they serve different roles within the supply chain. Demand forecasting focuses on the ‘what’ and ‘how much,’ while demand planning addresses the ‘how’ by integrating the forecast with business planning processes.

Even though there’s a clear difference between demand planning and demand forecasting the true success of accurate forecasts and planned demand lies in their synergy.

Together, they play a pivotal role in enabling businesses to navigate market dynamics effectively, ensuring they can meet customer needs while optimizing supply chain operations.

Demand Forecasting is a Predictive Analysis

Demand forecasting estimates future customer demand over a specified period using historical data, market trends, and statistical models. It is inherently predictive, focusing on what the demand will be.

  • The primary role of demand forecasting is to provide quantitative estimates of future demand, serving as the foundation for planning activities.
  • The outcome of demand forecasting is a set of demand predictions that inform inventory management, production scheduling, and procurement activities.

Demand Planning Represents a Strategic Process

On the other hand, demand planning is a broader strategic process that encompasses demand forecasting but also involves coordinating supply chain operations to meet the forecasted demand. 

Demand planning includes aspects like inventory planning, production scheduling, and distribution planning.

Demand planning’s role is to ensure that the supply chain is prepared to meet the forecasted demand most efficiently and cost-effectively.

The outcome of demand planning is a comprehensive strategy that aligns supply chain resources with market demand, aiming to optimize inventory levels, minimize costs, and maximize customer satisfaction.

Interrelation and Contribution to Supply Chain Strategy

Demand forecasting and demand planning are intrinsically linked, with demand forecasting data as a critical input for the broader demand planning strategy. 

Demand forecasting provides quantitative estimates of future demand, while demand planning uses this information to make strategic decisions about aligning the supply chain to meet this demand.

They contribute to the broader supply chain strategy by ensuring businesses respond proactively to market demands. This alignment is crucial for maintaining operational efficiency, reducing costs, and achieving high levels of customer satisfaction.

What is the Difference Between Dependent and Independent Demand?

The difference between dependent and independent demand is that independent demand refers to the demand for finished goods or products ready for sale and use by consumers, while dependent demand is the demand for parts or subassemblies required to produce a finished product.

Understanding the distinction between dependent and independent demand is crucial for effective inventory management and resource allocation within a supply chain. 

These concepts form the basis for planning production schedules, managing stock levels, and ensuring that the supply chain operates efficiently and responsively to meet market demands.

Independent Demand

The demand for finished goods that are saleable and fit for users to purchase is called independent demand. Several factors including market situations, customer fascinations and economic conditions, among others, exert a direct control on this type of demand reflecting shifts according to seasonal variations.

Example: 

Consumers’ demand for vehicles is an example of independent demand. A car manufacturer must forecast how many cars will be sold to determine production levels.

This demand is not directly tied to the demand for the components that make up the car but is influenced by broader market trends and consumer behavior.

Dependent Demand

The need for components or subassemblies to fabricate a completed item is known as dependent demand. This type of demand is intimately linked to and based on independent demand, the call for the finished product.

Example: 

The demand for car batteries within a car manufacturing process is an example of dependent demand. The number of batteries needed depends on the number of cars produced, based on the forecasted demand for the vehicles.

If a car manufacturer plans to create 200 cars, it will need an equivalent number of batteries, assuming one per car.

What is the Difference Between Demand Planning ​​and S&OP?​​​​

The difference between demand planning ​​and S&OP is in their scope and objective. Demand planning is a tactical process focused on forecasting customer demand and meeting it, whereas S&OP is a strategic process that aligns demand forecasts with broader business objectives, ensuring cohesive decision-making across all levels of the organization.​​​ 

While demand planning and S&OP are closely related, they serve different purposes within the organization. 

Demand planning can be seen as a building block of the S&OP process, providing the necessary data and insights for making informed strategic decisions.

Demand Planning as a Forecasting Foundation

Demand planning is a critical component of supply chain management focused on using forecasts and historical data to estimate future demand for various items across the supply chain. 

It involves detailed analysis and forecasting techniques to predict customer demand, aiming to balance supply and demand efficiently.

The scope of demand Planning primarily revolves around forecasting demand based on statistical sales data and insights from various departments. It is a tactical process that directly influences inventory management, production planning, and procurement strategies.

The main goal of demand planning is ensuring that the right product is available at the right time and place, minimizing stockouts and excess inventory, and d optimizing operational efficiency and customer satisfaction.

Example:
A company might use demand planning to forecast the demand for a new product line by analyzing historical sales data of similar products and market trends.

S&OP as a Strategic Decision-making Framework 

Sales & Operations Planning (S&OP), on the other hand, is a more comprehensive process that encompasses demand planning as one of its components.

S&OP is a strategic decision-making framework that balances the organization’s supply and demand, integrating financial, supply, sales, and development plans.

The scope of S&OP involves a series of steps, including demand planning, supply planning, and the reconciliation of these plans at a strategic level. It ensures that the organization’s strategic goals are aligned with its operational capabilities, involving key stakeholders from various departments.

The primary objective of S&OP is to achieve a balance between demand, supply, mix, and volume, using inventory and order backlog as buffers. It aims to align daily operations with corporate strategy, optimizing overall business performance.

Example:
An S&OP process might involve the demand planning team forecasting increased demand for a product, followed by supply planning to ensure manufacturing capacity and materials are available to meet this demand. The process culminates in executive meetings where strategic decisions are made based on these plans.

How is Demand Planning Performed with AI?

Demand planning is performed with AI using artificial intelligence (AI) and machine learning (ML) algorithms that enhance traditional demand planning processes through improved forecast accuracy, real-time data processing, anomaly detection, and scenario planning.

These technologies enable processing complex datasets and identifying patterns beyond human analytical capabilities, significantly refining demand forecasting precision.

The benefits of optimizing demand planning with AI are manifold. AI’s ability to learn continuously from new data sets results in forecasts with accuracy rates significantly higher than traditional methods. 

This increased precision ensures efficient resource utilization, saving costs and reducing waste. Additionally, AI facilitates a deeper understanding of individual customer behaviors and preferences, allowing for more personalized demand planning strategies.

However, integrating AI into demand planning comes with its challenges. High-quality data is essential for the effective functioning of AI and ML models, and the technology’s complexity necessitates specialized skills for maintenance. 

Moreover, the initial investment and the need for a cultural shift within organizations toward embracing new technologies pose significant hurdles. 

Despite these challenges, integrating AI into demand planning offers substantial benefits, heralding a new era of efficiency and customer-centricity in supply chain management.

The future trends of demand planning are advanced AI and machine learning integration, Increased Use of Real-time Data, Greater Collaboration Through Digital Platforms, and Personalization and Customer-centric Planning.

As businesses navigate an increasingly complex and volatile global marketplace, the role of demand planning in ensuring supply chain resilience and agility has never been more critical. 

Here, we’ll explore the mentioned critical trends shaping the future of demand planning and how companies can adapt to these changes:

1. Advanced AI and Machine Learning Integration

AI and machine learning use in demand planning is set to deepen, with algorithms becoming more sophisticated in handling complex data sets and providing more accurate forecasts. 

AI models and predicts the impact of external factors, such as economic shifts, social trends, and environmental changes on demand.

2. Increased Use of Real-time Data 

Integrating IoT devices and real-time data analytics will enable demand planners to respond more swiftly to market changes. This real-time visibility into demand signals will allow more dynamic adjustments to production schedules and inventory levels.

3. Greater Collaboration Through Digital Platforms 

Cloud-based platforms and collaborative tools will facilitate seamless integration and information sharing among stakeholders across the supply chain. 

The shared information enhances the collective ability to respond to demand fluctuations and coordinate actions more effectively.

4. Sustainability and Ethical Supply Chains

Consumer demand for sustainability and ethical practices will influence demand planning. Companies must incorporate sustainability metrics into their planning processes, balancing economic goals with environmental and social considerations.

5. Personalization and Customer-centric Planning 

Demand planning must become more granular as consumer preferences shift towards personalized products and experiences. The granularity is achieved when data analytics are used to understand and predict consumer behaviors and preferences.

Demand Planning with Throughput

Throughput AI revolutionizes demand planning as it utilizes advanced AI and machine learning to enhance data analysis, enabling businesses to predict future demand accurately.

This shift from traditional, manual methods reduces operational inefficiencies and errors. The software’s deep sales data analysis, market trends, and external factors like economic indicators ensure precise demand forecasts.

Based on the forecast, ThroughPut AI also recommends the best plan of action to align inventory, production, and procurement strategies, reducing the risks of overstocking and understocking, thereby boosting operational efficiency and customer satisfaction.

One of the highlights of the tool, given the current volatile market, is its capacity to forecast demand in the near-term and the ability to make adjustments to the demand plan based on real-time data.

This gives supply chains the much-need agility to respond to changing market conditions and save on losses due to a lack of foresight.

Additionally, ThroughPut’s demand segmentation, SKU optimization and replenishment planning capabilities rooted in a demand-first approach helps businesses across industries make important decisions that directly impact the company’s bottom-line.

Book a demo to find out how to plan demand with Throughtput.

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Anita Raj - Vice President of Product Marketing at ThroughPut AI
Anita Raj
Vice President of Product Marketing