Over the past years, control over logistics operations and warehouses has become imperative for both A-list global retailers as well as smaller local suppliers and manufacturers.
Multiple disruptions, fluctuating demand, and price volatility—in line becoming the reason for business inefficiencies. These challenges have made logistics professionals seek more resilient and future-forward solutions, one of which is data-driven demand forecasting in supply chain management.
In this guide, we’ll redefine the demand planning concept considering the changing global dynamics and explore the most efficient demand forecasting methods in supply chain that exist so far—all with the aim to provide businesses with a hands-on understanding of this practice and useful tips to get started.
- What is demand forecasting?
- Demand forecasting in supply chain: the backbone of successful logistics
- Demand forecasting methods in supply chain
- Challenges of demand and supply forecasting
- How technology revolutionizes demand forecasting in supply chain management
- How to get started with demand forecasting in the supply chain?
What is demand forecasting?
In a nutshell, demand forecasting is a business practice that utilizes statistical models, historical data, market trends, and other factors to estimate the demand for a particular product or service over a specific period.
The aim of demand forecasting is ensuring a business can deliver the right products and in the right quantities to satisfy customer demand and fulfill their promises. Just like that, the concept of demand forecasting goes hand-in-hand with logistics and supply chains.
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Demand forecasting in supply chain: the backbone of successful logistics
Efficient supply chain management is the lifeblood of businesses, regardless of their industry or size. Whether in manufacturing, retail, healthcare, technology, or service-oriented sectors, businesses rely on a well-orchestrated supply chain to ensure the timely and cost-effective delivery of products and services to customers. And today, more than ever before, supply chains depend on strategic planning.
The changing market and global realities have made demand planning a synonym of efficient supply chain management. Predicting demand helps companies build short- and long-term business strategies, optimize their inventory costs, foster customer loyalty, and withstand market competition.
Let’s overview how demand forecasting redefines supply chain management, becoming its integral backbone.
- Strategic planning
9 out of 10 organizations achieved measurable value from data and analytics investments in 2023. NewVantage Partners
Demand forecasting provides valuable insights into future demand trends, enabling businesses to make informed strategic decisions. This information helps in aligning production, procurement, and distribution strategies with anticipated market needs.
- Inventory management
70% of consumers are likely to go to another brand once they face a stockout situation while shopping online. ERC Europe
By predicting future demand, businesses can optimize inventory levels, reducing the risk of overstock or stockouts. This ensures that products are available when needed, minimizing holding costs and improving overall operational efficiency.
- Effective communication with suppliers
45% of supply chain professionals either have no visibility into their upstream supply chain or can see only as far as their first-tier suppliers. McKinsey
Demand forecasting builds visibility into potential supply chain issues and allows organizations to operate numbers and facts while they are communicating with suppliers. This allows businesses to build stronger relationships and ensures that suppliers are prepared to meet the upcoming demand, fostering a more resilient and responsive supply chain.
- Adaptability and resilience
In 2023, supply chain resilience and agility were the priorities of 55% of medium-sized businesses with 2,500 to 5,000 employees. Fictiv
Anticipating changes in demand allows businesses to proactively adapt their supply chain processes. This adaptability enhances the resilience of the supply chain, ensuring it can quickly respond to market fluctuations, disruptions, and unforeseen challenges.
- Cost efficiency
Nearly two-thirds of small and mid-sized businesses reported supply chain delays resulted in 15% or more losses in revenue during 2022. Anvyl
Accurate demand forecasts enable businesses to allocate resources efficiently. By aligning production and distribution with expected demand, organizations can reduce unnecessary costs associated with excess inventory, storage, and expedited shipments as well as maximize their revenues.
Who benefits from supply chain demand forecasting?
Typically, companies dive into demand planning because a particular department or business unit urgently needs it. Yet, the impact goes beyond—soon enough, you see the results making waves globally, affecting and benefiting more than anticipated.
Picture a common scenario: an online electronics store faces peak demand during the Black Friday season. To satisfy this demand and prevent stockouts, the company would normally analyze customers’ behavior for the past years as well as check the most recent sales data to detect ongoing trends and try to predict the type and amount of the most desired products.
In this case, the need to implement demand planning may come directly from the sales team. At the same time, there are far more parties who will benefit from demand and supply forecasting. Let’s review the same scenario to elaborate on these parties.
Sales and Marketing
Tailor promotional strategies considering the expected customer geography, gender, interests, and income level. This way, they can highlight popular products, and align marketing efforts with anticipated customer preferences, maximizing sales opportunities.
Inventory Management and Logistics
Plan and optimize stock levels and warehouse operations. With accurate forecasts, they can ensure that sufficient inventory is available, preventing stockouts and streamlining logistics to handle increased order volumes efficiently.
Operations
Plan for product availability, strategically place inventory in distribution centers, and fulfill customer orders preserving high operational efficiency and customer satisfaction.
Finance and Budgeting
Align budgets with expected sales, reduce financial risks associated with either excessive or insufficient inventory, and efficiently allocate resources during the peak season, ensuring that the financial planning is closely tied to anticipated demand fluctuations.
Technology and IT
Prepare the e-commerce platform to handle increased website traffic and transactions, ensuring a seamless online shopping experience, and preventing website crashes or slowdowns that could negatively impact customer satisfaction and sales.
Customer Support
Anticipate potential issues related to product availability or shipment delays, provide accurate information to customers, manage expectations, and address inquiries more efficiently during the peak demand period.
In this scenario, demand planning emerges as a strategic objective that, on one hand, ensures uninterrupted operations during a holiday season, and on the other hand, secures the company’s competitive advantage in the long run, earning them a good reputation and customer trust.
For global logistics companies, transportation providers, and manufacturers, forecasting supply chain management goes beyond guaranteeing operational efficiency and market leadership, helping them secure business continuity, and making sure goods reach their destinations despite port congestion, heavy road traffic, or severe weather conditions.
Learn to overcome cost and demand fluctuations with our guide on smarter freight management
Demand forecasting methods in supply chain
Approaching supply chain management forecasting usually requires executives to have a basic understanding of the key methods that help organizations implement its mechanisms in practice.
In essence, demand forecasting methods in supply chain encompass various techniques for predicting future demand based on historical data. These techniques involve the systematic processing and analysis of different types of data, considering factors such as time series patterns, influencing variables, and market dynamics. Let’s briefly overview the most common forecasting methods that exist so far.
Qualitative forecasting
Qualitative forecasting method relies on subjective judgment, expert opinions, and market research to predict future demand. This approach is suitable when historical data is limited, and there are significant uncertainties or when entering new markets.
Time series analysis
Time series analysis uses historical data to identify patterns and trends over time. Methods such as moving averages and exponential smoothing are employed. This technique is effective when demand patterns exhibit seasonality or specific trends that can be identified through historical data.
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Causal inferences
Causal inferences establish a cause-and-effect relationship between demand and various influencing factors, such as advertising, promotions, or economic indicators. This method is used when there is a clear understanding of the factors influencing demand, and when businesses want to assess the impact of specific variables on sales.
Machine learning and predictive analytics
Advanced algorithms and machine learning models analyze large datasets, identifying complex patterns and making predictions based on historical and real-time data. This method is effective when dealing with large datasets and complex demand patterns, offering accuracy and adaptability to changing market conditions.
Collaborative planning, forecasting, and replenishment (CPFR)
CPFR involves collaboration between supply chain partners, sharing information and jointly developing forecasts to enhance accuracy. This technique is usually used in industries with complex and interconnected supply chains, where collaboration among different entities can lead to more accurate predictions.
Delphi method
The Delphi method involves soliciting opinions from a panel of experts through a series of rounds, with feedback and revisions in each round. This approach is applied in scenarios where the collective wisdom of a diverse group of experts is essential for making informed predictions, decisions, or planning for the future.
Simulation models
This technique deals with the creation of virtual scenarios to assess the impact of various factors on demand. For instance, Monte Carlo simulations consider multiple variables and potential outcomes. Such models are valuable for testing different scenarios and understanding the potential variability in demand under various conditions.
Consumer surveys and market research
This method presupposes directly engaging with customers through surveys and market research to gather information about their preferences and expectations. It is particularly useful in industries with rapidly changing consumer preferences or where customer feedback plays a significant role in shaping demand.
Challenges of demand and supply forecasting
Regardless of chosen methods and the aims companies strive to achieve with demand forecasting, building predictive models is a tedious endeavor that encompasses several industry-specific and process-related challenges.
Global supply chain complexity
Managing demand across global supply chains with multiple suppliers, varied regulations, and diverse market conditions can increase the complexity of forecasting and require a more nuanced approach.
Uncertain market conditions
Rapid changes in market conditions, influenced by factors such as economic shifts, geopolitical events, or unexpected disruptions, can be a major roadblock to accurate planning and predictions.
Seasonality and trends
Identifying and accurately predicting seasonal patterns and trends, especially in industries with distinct peak seasons, requires sophisticated forecasting models to avoid underestimating or overestimating demand.
Evolving consumer behavior
Changes in consumer preferences, shopping channels, or buying habits can significantly impact demand forecasting accuracy. Adapting to these changes requires continuous monitoring and adjustment of forecasting models.
Data quality and availability
Inaccurate or incomplete historical data can compromise the effectiveness of forecasting models. Ensuring data accuracy and addressing data gaps can be challenging, especially in industries with rapidly changing product portfolios.
Collaboration across departments
Ensuring effective communication and collaboration among different departments, such as sales, marketing, and operations, is crucial. Siloed information can lead to misalignment and inaccurate demand forecasts.
Aiming to address these challenges, businesses turn to implementing and integrating advanced forecasting technologies, such as machine learning or AI.
Today, more and more businesses evaluate the potential of machine learning and AI applied to supply chain demand forecasting. Despite holding significant implementation roadblocks, these technologies in fact shape the way supply chain management will look in the next five years. That’s why investing in their adoption is worth every penny.
How technology revolutionizes demand forecasting in supply chain management
As of 2023, 73% of supply chain leaders still use spreadsheets for planning and forecasting. At the same time, 90% of them have put technology upgrades on their agenda and are now taking steps towards innovation adoption.
Among the technologies these organizations plan to adopt, machine learning and demand forecasting are the objectives of 4 out of 5 supply chain executives.
Here at Trinetix, we systematically evaluate the capabilities of emerging technology applied to various business fields, including supply chain management. Now, isolated from the excitement the term “artificial intelligence” creates in the global corporate landscape, let’s explore the changes AI/ML can bring to supply chain demand forecasting.
Traditional demand forecasting
AI-powered demand forecasting
Types of data used for predictions
- Data from previous years (historical data)
- Historical data
- External data sets considering multiple factors (macroeconomic, promotions, etc.)
Techniques to deploy
- Classical statistical methods
- Recurrent neural nets
- Forecast frameworks
- Gradient boosting (an ML technique used to build predictive models that can effectively capture complex patterns and relationships within the data)
Usage scenarios
- Stable market performance of the product
- Calculating market rates for a brand in general
- Unstable market conditions
Both traditional and AI-powered forecasting have significant power to fuel demand and supply planning. The major difference between them however is the scale of agility and accuracy applied to different scenarios and market conditions.
The increased accuracy and reliability of technology-driven demand forecasting is explained by the fact that AI algorithms are continuously learning and adapting to the new environment. In addition, they promote cross-department contribution and help to consider a significantly broader spectrum of factors when building predictions.
Why is it so important in 2024?
As of now, 6 out of 10 global organizations recognize geopolitical instability is having a detrimental impact on their supply chains. Constantly rising fuel prices, multiple regulatory changes, growing data volumes, and heightened customer expectations—the objective reality makes accurate supply chain planning a real must-have. Along with ensuring stable operational performance, it leads organizations to improved cost efficiency, helps to build resilience to changes, and as a result, enables continuous market leadership that is so desired in today’s competitive business landscape.
How to get started with demand forecasting in the supply chain?
No matter if you go for traditional supply chain demand forecasting or choose to reinforce your strategic efforts with AI and machine learning, achieving the desired results would require developing a clear implementation roadmap. Let’s visualize how this one could look.
Leveraging demand planning is an iterative process that requires continuous result monitoring and improvement. While the above plan is just a rough representation of how the process usually looks in practice, it becomes clear that even a mature business will find it challenging to go through it on their own.
The future is demand-driven
Predict it with Trinetix
Making the most of strategic demand planning with Trinetix
At Trinetix, we recognize the transformative impact of data-driven demand planning on supply chains and keep researching the capabilities of emerging tech to provide businesses with future-forward solutions to their strategic challenges.
Acting as a dedicated technology partner, we focus on developing highly personalized and agile forecasting models that guarantee long-term value and adapt to the evolving market landscape. If you aim to maximize your logistics efforts, ramp up business productivity, and optimize operational costs with accurate supply chain demand forecasting, let’s chat about the ways to get started.
