Implementing Demand Forecasting to Upgrade Your Supply Chain

Staying ahead in the dynamic landscape of modern business requires more than just having an efficient supply chain – it demands a strategic approach that anticipates shifts in demand before they occur. 

In this dynamic environment, where consumer preferences shift continually and market conditions can change overnight, supply chain management takes center stage. Among the myriad of strategies available to organizations, demand forecasting is revolutionizing the way companies manage their supply chains.

Artificial Intelligence (AI)-based demand forecasting is not merely a tool; it's a prominent advancement in understanding and predicting customer demand for products or services. 

The end goal? To equip your organization with the foresight required to make informed decisions about production, procurement, and distribution.

Let's explore demand forecasting and see how it connects raw data to practical strategies, helping supply chains become efficient engines for long-term growth and innovation.

Why is demand forecasting important for supply chain management?

Demand forecasting is the art and science of predicting future customer demand for a product or service. It involves analyzing historical data, market trends, and various influencing factors to estimate how much of a product or service customers will want over a specified period. 

This process typically utilizes a combination of statistical models, data analysis, and expert judgment to guarantee the efficient and effective flow of the supply chain.

Whether it's orchestrating marketing campaigns or launching novel products, possessing an insightful grasp of market demand empowers you to channel resources and allocate budgets constructively – here’s why:

Outputting probability estimates and assigning probabilities to different outcomes

One of the most powerful capabilities of demand forecasting is its ability to provide rough numbers, and probability estimates for them. 

Any proficient supply chain manager can hypothesize that the demand for coats will rise as winter approaches. However, a well-developed machine learning (ML) model will offer a more detailed prediction, such as selling between 200 and 300 coats per day in the first weeks of November, then 500 per day in the second half, and peaking around 1,000 coats per day in December. 

This quantitative insight into demand can be invaluable, especially for complex products.

Moreover, demand forecasting has the ability to assign probabilities to different outcomes. For example, a model might suggest that there's a 25% chance of selling 75-150 coats per day, a 50% chance of selling 150-200 coats per day, and a 25% chance of selling 200-250 coats per day. 

This information may not seem useful at first glance. However, suppose manufacturing can only be done in batches of 100 coats, and you need to decide whether to stock enough to sell 100 or 200 coats per day. Now, you can accurately estimate the likelihood of overstocking or understocking.

Preventing stockouts and overstocking

Accurate demand forecasting can also help businesses avoid stockouts, where products are unavailable when customers want to purchase them. Simultaneously, it prevents overstocking, which ties up capital and storage space that could be used more efficiently. 

For instance, a retailer may use demand forecasts to predict exactly when people will start buying winter coats next season and to forecast precisely how demand will grow as winter approaches.

Efficient production planning and resource allocation

Forecasting demand allows manufacturers to adjust their production schedules to match expected customer orders. This prevents underutilization of production capacity and ensures that resources like labor and machinery are used effectively. 

For example, an electronics manufacturer releasing a new smartphone might use demand forecasting to predict how long demand will remain high after the initial peak of sales, to understand how to organize the production process.

Coordination along the supply chain

Accurate demand forecasts facilitate better communication and coordination between suppliers, manufacturers, and distributors. This coordination ensures that each entity in the supply chain is prepared to meet the expected demand. 

An example of this is a car manufacturer working closely with its tire supplier to ensure an adequate supply of tires as vehicle production increases.

Cost savings through efficiency

Demand forecasting reduces waste by aligning production with actual demand. This minimizes the need for rushed or expedited shipments, which can be costly. 

Imagine a fashion retailer using demand forecasts to produce the right quantity of seasonal clothing; it can avoid excess inventory that might otherwise go unsold and require markdowns.

Mitigating the bullwhip effect

The bullwhip effect is the amplification of demand fluctuations as they move up the supply chain. 

For example, a retailer sells an average of 100 units of a product per day, but one day, the retailer sells 120 units of the product. The retailer assumes that this is an increase in demand and orders 150 units from the distributor the next day. The distributor sees the retailer's order and assumes that demand is increasing for the product, so they order 200 units from the manufacturer. The manufacturer sees the distributor's order and assumes that demand is increasing even more, so they order 300 units from their supplier.

As you can see, the demand for the product has only increased by 20 units, but the order quantity has increased by 300 units. This is the bullwhip effect in action.

Further highlighted by a study published in the European Journal of Operational Research, the importance of reducing the bullwhip effect to improve supply chain efficiency cannot be overstated. By providing accurate demand information, forecasting minimizes the need for knee-jerk adjustments and helps suppliers anticipate actual demand.

Adaptation to trends

Demand forecasting can help companies adapt to changing market trends. 

For example, a beverage company can adjust production based on the forecasted rise in demand for a particular flavor during the summer month, and allocate resources to flavors that are more likely to experience high demand.

Given these fruitful use cases, leveraging the power of demand forecasting can significantly enhance business operations, making them more efficient and profitable.

Factors that affect supply chain demand

The heartbeat of supply chain demand is intricately tied to the fluctuations of consumer behavior. Consumer preferences, trends, and purchasing power collectively shape the market, influencing what products find their way from production lines to store shelves. 

The most common factors that can potentially impact supply chain demand include:

• Economic conditions: The state of the economy, including factors like gross domestic product (GDP) growth, employment rates, and consumer spending, can greatly impact demand. During economic downturns, consumer demand for non-essential goods may decrease, while demand for basic necessities may remain stable.
• Seasonality: Demand for winter clothing, for example, increases in colder months, while demand for outdoor furniture rises during the summer. While some seasonal patterns are apparent, others can be more complex, increasing at specific points due to events potentially unknown to the retailer, such as sports events or bank holidays.
• Consumer trends and preferences: Changes in consumer preferences, lifestyles, and trends can significantly alter demand patterns. A shift towards healthier eating, for instance, might increase demand for organic and natural foods. These trends and preferences often manifest over relatively long timelines.
• Competitor actions: Pricing changes, product launches, or marketing campaigns by competitors can influence demand for a particular product. Monitoring competitors' activities is crucial to anticipate shifts in demand. 
• Government policies and regulations: Regulatory changes, tax policies, and government initiatives can influence demand. For instance, subsidies for renewable energy sources can drive demand for solar panels and wind turbines. 
• Global events and disruptions: Natural disasters, geopolitical events, and pandemics can disrupt supply chains and cause sudden changes in demand. The COVID-19 pandemic, for example, led to fluctuations in demand for various goods, including personal protective equipment (PPE) and home fitness equipment. These sorts of events, known as ‘black swan events’, are impossible to predict. 
• Supply chain disruptions: Disruptions in the supply chain, such as shortages of raw materials or components, can impact the availability of finished products and influence customer demand. An interesting case study to consider here is the vinyl shortage that coincided with the pandemic. The shortage impacted the music industry, illustrating how supply chain disruptions can significantly affect demand.

By considering these factors, businesses can better anticipate demand, optimize their supply chain operations, and ultimately, enhance their competitive advantage.

Types of supply chain demand forecasting

The ability to predict future customer needs with precision can spell the difference between operational success and costly disruptions. To this end, supply chain professionals rely on two primary methodologies: quantitative and qualitative demand forecasting.

Quantitative methods

Quantitative demand forecasting methods rely on historical data and mathematical models to predict future demand. They are particularly useful when there is a substantial amount of data available for analysis. The arsenal of quantitative techniques includes:

• Time series analysis: This method involves analyzing historical data over a specific time period to identify patterns, trends, and seasonal variations. Techniques like moving averages, exponential smoothing, and decomposition help extrapolate future demand based on historical patterns.
• Seasonal decomposition: Decomposing historical data into trend, seasonal, and irregular components helps identify recurring patterns and isolate underlying trends.
• Causal models: Causal models establish a cause-and-effect relationship between demand and other factors, such as advertising expenditure, economic indicators, or competitor actions. 

While quantitative methods provide a solid foundation, they often fall short of capturing the nuances of unforeseen market shifts, emerging trends, and other qualitative factors that can impact demand. This is where qualitative demand forecasting steps in, recognizing that not all variables can be neatly quantified.

Qualitative methods

Qualitative methods rely on expert opinions, judgment, and market insights to predict future demand. These methods are useful when historical data is limited or when dealing with unique or new products.

Human expertise, industry insight, and intuition play a pivotal role in this approach, encompassing:

• Market research: This involves conducting surveys and interviews, organizing focus groups, and studying customer feedback to gather insights about customer preferences and expectations.
• Delphi method: Obtaining forecasts from a panel of experts and aggregating their opinions, the Delphi method helps in arriving at a consensus forecast.
• Scenario planning: Scenario supply chain planning is the creation of different scenarios based on potential future events, trends, and market conditions to estimate demand under various circumstances.
• Judgmental forecasting: This method relies on the judgment and experience of individuals within the organization to make predictions about future demand.
• Historical analogy: Historical analogy helps in drawing insights from similar products or markets to predict demand for a new or related product.
• Leading indicators: This approach involves identifying leading indicators or early signals that correlate with future demand changes. For example, an increase in online searches for a product may indicate rising interest.

Despite the fact that one-size-fits-all solutions in demand forecasting rarely prevail, the most effective approach often involves a fusion of both quantitative and qualitative methodologies. By marrying the precision of quantitative data analysis with the nuanced insights of qualitative expertise, organizations can create a forecasting model that accounts for the full spectrum of demand influencers.

That’s exactly where employing machine learning becomes paramount.

Identifying the need for machine learning in supply chain demand forecasting

In pursuit of precision in forecasting, the emergence of machine learning (ML) has not just offered a solution, but a transformative evolution.

Machine learning is a subset of artificial intelligence (AI) that involves the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data. Unlike traditional programming, where explicit instructions are given to perform specific tasks, ML focuses on creating systems that can learn and improve from experience.

What sets machine learning apart is its ability to decipher patterns hidden within colossal datasets that conventional approaches struggle to make sense of. By tapping into this treasure trove of information, businesses can gain insights that go beyond the surface, capturing nuances and trends that may otherwise evade detection. 

Handling complex relationships

Supply chain data is often characterized by intricate relationships between variables, such as seasonality, trends, and external factors. Machine learning algorithms can automatically identify and model these complex interactions, capturing patterns that traditional methods might miss. 

These algorithms are capable of capturing nonlinear relationships and interactions that traditional methods might overlook, which is especially important when demand patterns are influenced by convoluted and nonlinear factors.

Real-time adaptability

ML enables real-time or near-real-time forecasting, allowing businesses to respond quickly to changing market conditions, unexpected events, and disruptions. Traditional methods may require manual adjustments and may not be as agile. 

However, it's essential to note that ML methods extract patterns and assumptions from the data they are trained on. If these patterns and assumptions are substantially disrupted by unexpected events, such as the COVID-19 pandemic, they will not be able to react instantly.

Large and diverse data

ML can handle high-dimensional and heterogeneous data, making it well-suited for extracting meaningful insights from diverse information sources. This includes historical sales, promotional activities, economic indicators, and social media sentiment. 

Automated feature selection

Machine learning algorithms can automatically select the most relevant features (variables) from the data, reducing the risk of overfitting, and enhancing the quality of forecasts. They can also find patterns that drive demand, which can be incredibly valuable for growing the business.

While ML algorithms can significantly enhance demand forecasting, it's crucial to utilize the Human in the Loop methodology. Experts need to check the predictions made by the algorithm, ensuring the forecasts align with their understanding and expectations.

If you're interested in diving deeper into the world of ML-powered forecasting, check out our article titled “What is ML-Powered Forecasting?”. You’ll get even more insights into how machine learning can revolutionize your forecasting processes!

Akkio for supply chain demand forecasting

Having a tool that simplifies and improves the process of supply chain demand forecasting can be a game-changer. Enter Akkio, a user-friendly, no-code AI platform designed to significantly streamline and enhance demand forecasting.

Gone are the days of relying solely on historical data and guesswork to predict consumer preferences and demands. Akkio introduces a refreshing paradigm shift, transforming this crucial aspect of supply chain management into a well-oiled machine of informed decision-making. Seamlessly integrating cutting-edge technology with the intuitive understanding of human ingenuity, Akkio can guide businesses through the labyrinth of market demand uncertainty.

Akkio is used to create predictive ML models, including forecasting models that utilize time series analysis. You won’t need to be a data scientist or a machine learning expert to forecast demand accurately; the platform is designed to be intuitive and easy to use, making sophisticated ML models accessible to everyone.

Even better, setting up a demand forecasting model with Akkio is straightforward. You simply need to:

1. Connect your datasets, either by uploading .csv files or by integrating Akkio with data collection tools your business already uses, like Google Sheets, Google BigQuery, Snowflake, HubSpot, PostgreSQL, and Salesforce.

We are using a dataset from Kaggle with over 21,503 records about Lego sets and themes.

2. Prepare and clean your dataset, or even merge multiple datasets. You can utilize the built-in Chat Data Prep feature for this.

Alt text: Data preprocessing and cleaning using Akkio’s Chat Data Prep feature.

Akkio’s Chat Data Prep simplifies the process of cleaning and structuring datasets for further analysis by providing an intuitive interface that’s as easy as chatting with a friend.

Our dataset contains entries from the year 1949 all the way to 2023. For this tutorial, we want to exclude all data that is dated before the year 2015. All we have to do is type a prompt like “Exclude any data where the year_released is less than 2015”, and Chat Data Prep will handle the rest. It will even show you a preview of the results before applying any modifications to your dataset.

This was the range of years in our dataset before using Chat Data Prep.

This is the result after using Chat Data Prep. This dataset cleanup took less than two minutes!

You can even explore your dataset after preprocessing and cleanup to get quick insights. This is made possible via Akkio’s Chat Explore feature. This functionality will let you investigate your data as if you’re chatting with a data expert and show you the results almost immediately.

For this example, we wanted to know the average number of parts used in all the Lego kits present in our dataset. The answer was approximately 215 pieces.

3. Select the forecasting option and set up the model.

Akkio will automatically choose the right model for your use cases; you simply need to choose the right parameters. For example, for a time series analysis, you will need to select the field that corresponds to time (has a data type of “date”), and then select the fields you want the model to focus on.

4. Akkio's dynamic charts, reports, and dashboards provide valuable visual insights and real-time updates. This is a top-level view of our results:

With Akkio's ability to route insights anywhere, stakeholders can react quickly to insights, making informed decisions on the fly.

With the power of machine learning, and particularly the user-friendly features of Akkio, businesses can navigate the complexities of demand forecasting, propelling them toward strategic decisions that yield palpable real-world outcomes.

No longer will you be channeling resources into initiatives that miss their mark – Akkio’s demand forecasting hones your focus on what truly resonates with your customer base.

Ready to take your supply chain demand forecasting to the next level with Akkio? Check out Akkio’s pricing plans and get started today!