What Makes Demand Forecasting Difficult? Navigating the Supply Chain Forecasting Challenges

What Makes Demand Forecasting Difficult? Navigating the Supply Chain Forecasting Challenges

Dorota Owczarek - April 22, 2023

In today’s rapidly evolving business landscape, the ability to accurately predict demand is crucial for organizations striving to maintain a competitive edge. Demand forecasting plays a pivotal role in supply chain management, as it helps companies anticipate market trends, make informed decisions, and optimize their resources. However, navigating the complexities of demand forecasting can be a daunting and time-consuming task for most organizations. With globalization, shifting consumer preferences, and an ever-increasing reliance on data, businesses face many challenges when attempting to accurately forecast demand.

In this article, we delve into the importance of demand forecasting in supply chains and explore the factors that make it such a challenging endeavor. We will also examine various strategies to overcome these challenges, with key elements such as harnessing the power of advanced technologies, fostering collaboration, and adapting to change. By understanding and addressing these obstacles, businesses can unlock their full potential and chart a path towards successful demand forecasting.


• Demand forecasting is crucial for maintaining a competitive edge in today’s fast-paced business landscape.
Key challenges in demand forecasting include:
• Complex supply chains: Intricate webs of suppliers, manufacturers, distributors, and customers make forecasting difficult.
Globalization and geopolitical factors: Trade policies, currency fluctuations, and international relations can create uncertainties in demand forecasting.
• Multidimensional supply networks: Multiple tiers of suppliers and customers with different priorities and objectives add complexity to forecasting.
External factors: Economic fluctuations, market trends, seasonal demands, consumer behavior, and product life cycles all influence demand patterns.
• Data collection and analysis: Ensuring access to historical data, data quality and quantity, utilizing advanced technologies, and fostering collaboration are crucial for accurate forecasting.
To navigate demand forecasting challenges, businesses should embrace advanced technologies like AI-based software for improved accuracy, and foster collaboration across the supply chain for better results. Regularly reviewing forecasting models, utilizing real-time data, and partnering with experienced AI experts like nexocode can lead to increased efficiency, revenue, and competitiveness.

The Importance of Demand Forecasting in Supply Chains and Why Forecasting Accuracy Matters

Demand forecasting lies at the heart of effective supply chain management. By accurately predicting future demand, businesses can make informed decisions about production, inventory, distribution, and pricing strategies, ensuring that resources are allocated efficiently, and customer expectations are met. An accurate forecast not only helps organizations avoid costly stockouts or excess inventory, but accurate results also contributes to a more resilient and responsive supply chain that can adapt to fluctuations in demand.

Forecasting accuracy is of utmost importance, as even small errors can lead to significant consequences for the entire supply chain. Inaccurate forecasts can result in lost sales, dissatisfied customers, increased holding costs, and wasted resources. Furthermore, these inaccuracies can have a ripple effect across the entire supply chain for producers, wholesalers, transportation companies, and retailers, causing inefficiencies and disruptions that can be difficult to recover from. In an increasingly competitive business environment, companies that prioritize demand forecasting accuracy are better positioned to achieve operational excellence and maintain a competitive edge.

The 4 types of data analytics from descriptive to prescriptive that not only provide insights but also foresight that help anticipate possible results and take specific actions.

The 4 types of data analytics from descriptive to prescriptive that not only provide insights but also foresight that help anticipate possible results and take specific actions.

Demystifying Demand Forecasting Challenges and Their Impact on Supply Chains

Navigating the world of demand forecasting is no easy feat, as numerous factors can influence the accuracy of predictions. In this section, we’ll unravel the key challenges that businesses face in demand forecasting and delve into the key reasons for their impact on supply chains.

The Complex Nature of Supply Chains

Today’s supply chains are intricate webs of suppliers, manufacturers, distributors, and customers, all interacting in a dynamic global environment. This complexity makes demand forecasting and financial planning particularly challenging, as it necessitates the consideration of numerous interconnected factors.

Globalization and Geopolitical Factors

As businesses increasingly operate on a global scale, they must take into account the impact of globalization and geopolitical factors on forecasting demand. These factors can include trade policies, currency fluctuations, political instability, and international relations. Such influences can create uncertainties in business forecasting and make it difficult to accurately predict how supply chains will be affected, leading to potential disruptions and inaccuracies in demand forecasts.

For instance, the semiconductor industry experienced substantial challenges during the COVID-19 pandemic. The global disruptions caused by the pandemic led to factory shutdowns, shipping delays, and a spike in demand for electronics, all of which impacted the availability of semiconductors. This situation had far-reaching consequences, particularly for European manufacturers who were heavily reliant on these components for their production lines. As a result of several challenges, businesses had to reassess their demand forecasts and adapt their strategies to navigate the uncertain landscape.

Multidimensional Supply Networks

Modern supply chains often involve multiple tiers of suppliers and customers, with various interdependencies and relationships that need to be taken into account when forecasting demand. The sheer volume of information and the number of stakeholders involved can make demand forecasting incredibly complex. Additionally, each stakeholder may have different priorities and objectives, further complicating the process. Understanding these multidimensional networks and incorporating their nuances into demand forecasting is critical for achieving accuracy and ensuring the smooth operation of the supply chain.

Predictive analytics can be used in supply chains across various departments, including production, logistics, operations management, marketing, sales, customer service, etc. Based on BCG Analysis

Predictive analytics can be used in supply chains across various departments, including production, logistics, operations management, marketing, sales, customer service, etc. Based on BCG Analysis

Operational Challenges: Managing Inventory and Resources

Operational challenges, such as managing obsolete inventory, limited storage space, and high inventory carrying costs, can significantly impact the effectiveness of supply chain forecasting. Accurate forecasts help businesses optimize their inventory levels and better define safety stock, minimizing the risk of holding excess or obsolete stock and reducing storage and carrying costs. By addressing these operational challenges and making space for utilizing insights from advanced analytics, companies can improve their overall supply chain efficiency and ensure they meet demand.

The Impact of External Factors

Various external factors can cause significant forecasting problems, making it a complex and challenging process. Organizations must be aware of these factors and adapt their predictive models to account for their influence on demand patterns.

Economic conditions and market trends play a crucial role in shaping demand. Recessions, booms, and other economic fluctuations can affect consumer spending, which in turn influences demand for products and services. For example, during the 2008 financial crisis, consumers cut back on discretionary spending, leading to reduced demand for luxury goods and travel services.

Seasonal Demands and Consumer Behavior

Seasonal factors and consumer expectations or behavior changes can also significantly impact demand forecasting. Holidays, weather patterns, and other seasonal events can create fluctuations in demand that businesses must account for in their models. Additionally, rapidly changing consumer preferences and buying patterns can make it difficult to predict future demand accurately. Organizations must closely monitor these trends and be prepared to adapt their forecasts to reflect evolving consumer behaviors.

Product Life Cycles and New Product Introductions

The introduction of new products and the varying life cycles of existing products can complicate demand forecasting, especially in industries characterized by frequent innovation. New products can cannibalize sales of existing products, while products nearing the end of their life cycles may see a decline in demand. For example, the introduction of a new iPhone model often leads to a surge in demand for the latest version while the actual demand for previous models declines. To forecast demand accurately, companies must be mindful of these factors and incorporate them into their predictive analytics models, ensuring that they can effectively manage inventory levels and make informed decisions about product development and marketing strategies.

The Challenges of Data Collection and Analysis

Demand forecasting relies heavily on collecting and analyzing relevant data to drive accurate predictions. However, businesses often face obstacles in this process, stemming from issues such as data quality and quantity, technological limitations, and insufficient collaboration across the supply chain.

Data Quality and Quantity

One of the most significant challenges in demand forecasting is ensuring the quality and quantity of data used for analysis. Inaccurate, incomplete, or outdated data can lead to misleading forecasts, rendering them less useful for decision-making. Additionally, accessibility to historical data is crucial for understanding past trends and informing future forecasts. It is not uncommon for businesses to sit on loads of data, but it is hard to access, integrate, and unstructured, making it hard to implement advanced analytics solutions. Businesses must invest time and resources into gathering comprehensive, up-to-date, and accessible data to improve the accuracy of their forecasts.

How predictive analytics models are built and work to create predictions?

How predictive analytics models are built and work to create predictions?

Inadequate Technology and Analytical Models

Inadequate technology and outdated analytical models can also hinder effective demand forecasting. Organizations need to utilize cutting-edge tools and advanced forecasting techniques to analyze data and generate accurate predictions. This often involves embracing AI and machine learning technologies that can automatically identify patterns and trends in vast datasets, enabling more precise forecasts.

Predicting sales based on past patterns in demand to optimize the production and trasportation processes.

Predicting sales based on past patterns in demand to optimize the production and trasportation processes.

Lack of Expertise in Production Deployment of Machine Learning Models

Preparing accurate forecasting models is one thing, and model integration and deployment is another. Deploying machine learning models for sales forecasting in production environments can be challenging, especially as project management for machine learning differs from traditional software engineering. The complexity of these models requires a unique set of skills and expertise to ensure seamless integration into existing systems and processes (e.g., MLOps). Businesses must invest in training and hiring skilled partners who can effectively manage the deployment of machine learning models to overcome this challenge.

Lack of Collaboration and Information Sharing

A lack of collaboration and data sharing among different companies within the supply chain can significantly impact demand forecasting. When organizations operate in silos or are reluctant to share data due to concerns about security and confidentiality, it can lead to fragmented and inconsistent data, making it difficult to generate accurate forecasts and improve the omnichannel planning processes. To address this issue, companies should establish partnerships and develop processes for sharing data and insights across the supply chain while maintaining the necessary data privacy and security measures. By fostering stronger communication and collaboration between suppliers, manufacturers, distributors, and retailers, businesses can create a more comprehensive understanding of the market and generate more accurate and effective demand forecasts.

Overcoming demand forecasting challenges requires businesses to adapt and implement effective strategies.

Harnessing the Power of Advanced Technologies: Implementing AI-Based Software at Scale

Adopting advanced technologies, such as artificial intelligence and machine learning, can significantly improve the accuracy and efficiency of demand forecasting. AI-based software can analyze vast amounts of data, identifying patterns and trends that may not be apparent through manual analysis. Businesses can automate and enhance their demand forecasting processes by implementing AI-based solutions at scale, leading to more accurate predictions and better decision-making.

Be careful, though! As mentioned earlier, you would need experienced partners to navigate the complexities of deploying and managing machine learning models in production environments. Partnering with skilled professionals or specialized firms ensures a smooth and efficient integration of AI technologies into your existing systems and processes, ultimately unlocking the full potential of advanced forecasting techniques.

Benefits of AI on every step of the supply chain operations flow

Benefits of AI on every step of the supply chain operations flow

Collaborative Forecasting: Working Together for Better Results

Promoting collaboration across the supply chain can help improve the accuracy of demand forecasts. By sharing data and insights between suppliers, manufacturers, distributors, and retailers, or in some cases departments within one organization, businesses can develop a more comprehensive understanding of market dynamics. Collaborative forecasting involves working together to create unified and informed predictions, which can lead to better planning, inventory management, and overall supply chain efficiency.

Related case study: Developing a logistics platform offering real-time visibility and integrations with different carriers

One of our clients was seeking to improve the global supply chain optimization product

Our challenge? Providing visibility and data transmission for maximum efficiency and control. We supported solution development for end-to-end execution of logistics activities in Supply Chain Management at the PO/SKU level, including PO creation, stock management, suppliers and distributors management, consolidation and load planning, carrier allocation, documentation, and final delivery. Read more about this case study.

Regularly Reviewing and Updating Forecast Models

Demand forecasting models should be regularly reviewed and updated to ensure they remain accurate and relevant. As market conditions, consumer behavior, and external factors change, data scientists and machine learning engineers must adjust the models accordingly to maintain their forecasting accuracy. Constant monitoring of model metrics and key KPIs and refining forecasting models help companies stay ahead of market shifts and adapt their strategies proactively.

Utilizing Real-Time Data and Feedback Loops

Incorporating real-time data and feedback loops into demand forecasting processes can further enhance their accuracy. Real-time data enables businesses to identify and respond to changes in demand more quickly, while feedback loops help them continually refine their forecasting models based on the most recent information. By leveraging real-time stream processing mechanisms, companies can make more informed decisions and better anticipate fluctuations in demand.

Charting a Path to Forecasting Demand: Embracing Change and Unlocking Potential

Demand forecasting is a complex yet essential process for businesses operating in today’s dynamic and interconnected world. By understanding and addressing the challenges associated with demand forecasting, companies can embrace change and unlock their full potential. Navigating these challenges involves adopting advanced technologies, fostering collaboration, and continuously refining forecasting models, all while staying informed about the latest trends and external factors that impact supply chains.

Now is the time to take action and elevate your demand forecasting capabilities. nexocode AI experts, with extensive experience in the logistics and supply chain sectors, are here to help you on this journey. Our team is well-equipped to guide you through the implementation of AI-based solutions and to ensure seamless integration into your existing systems.

Don’t miss the opportunity to transform your supply chain and boost your competitive edge. Reach out to us today and embark on the path to improved demand forecasting, greater efficiency, and increased revenue and profitability.

About the author

Dorota Owczarek

Dorota Owczarek

AI Product Lead & Design Thinking Facilitator

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With over ten years of professional experience in designing and developing software, Dorota is quick to recognize the best ways to serve users and stakeholders by shaping strategies and ensuring their execution by working closely with engineering and design teams.
She acts as a Product Leader, covering the ongoing AI agile development processes and operationalizing AI throughout the business.

Would you like to discuss AI opportunities in your business?

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AI Product Lead

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This article is a part of

AI in Logistics
51 articles

AI in Logistics

Artificial Intelligence is becoming an essential element of Logistics and Supply Chain Management, where it offers many benefits to companies willing to adopt emerging technologies. AI can change how companies operate by providing applications that streamline planning, procurement, manufacturing, warehousing, distribution, transportation, and sales.

Follow our article series to find out the applications of AI in logistics and how this tech benefits the whole supply chain operations.

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