How Predictive Analytics in Supply Chain Helps with Demand Forecasting

One of the most efficient ways to ensure the highest performance of logistics companies is to adopt software-based solutions that cover key business processes, including demand forecasting. According to the latest MHI report, more than 55% of supply chain leaders have increased their investment in supply chain technologies.

Modern software can significantly boost any company's efficiency, regardless of industry or business specifics. However, this impact can be easily noticed in logistics and supply chains.

Any supply chain business depends heavily on the supply-demand ratio, accuracy of its prediction, and satisfaction. Therefore, predictive analytics is one of the most essential and beneficial features required by any company in this field.

Today, we would like to discuss the impact of predictive analytics in supply chain, its role in demand forecasting, and how to ensure such services within your supply chain business to maximize its benefits.

Why Demand Forecasting is Crucial for Supply Chain Efficiency

Let’s start with the basics and briefly discuss demand forecasting itself. The supply chain is a highly complex process that covers each phase of product delivery, starting with gathering raw materials, processing them, creating a demanded product, and delivering it.

However, due to their complexity and scale, some companies prefer to divide such supply chains into niches, covering fewer steps and stages to achieve higher efficiency.

Either way, the demand shifts impact the whole chain: if the specific goods are in high or low demand, it can affect all previous phases, starting with their production and ending with material extraction. Otherwise, businesses can lose money delivering goods without demand or undersupplying popular products.

To avoid such scenarios, supply chain predictive analytics is used.

How Does Predictive Analytics in Supply Chain Work?

Frankly, every forecasting model is just a probability calculation based on historical data and some common sense. In other words, supply chain predictive analytics won’t predict demand 100%, yet it can help define potential trends in the future, increasing your chances of meeting it perfectly.

For instance, it is easy to assume that ice cream is more prevalent in summer than winter. However, this is an oversimplified prediction. It doesn’t provide you with even approximate amounts of this delicious treat.

On the flip side, having access to historical data of your supply chain for the previous few years allows you to make a more detailed forecast and try to define a rough amount of ice cream to prepare for the next season.

Simply put, predictive analytics in supply chains involve math-driven algorithms based on historical data that suggest potential trends and help calculate the rough amount of certain goods to produce or buy to meet the potential demand.

Besides, we can define the core operations required for successful predictive analytics:

1. Historical data on previous years or seasons is used to sort goods, products, or services and define which ones might be in demand soon.
2. Data-driven calculations provide the forecasts with actual numbers and expectations, allowing businesses to match demand with appropriate supply and avoid under/over delivery.

Demand forecasting is also a very complex task that impacts other supply chain operations and defines the efficiency and cost of different processes. For instance, it affects inventory management, supplier and procurement planning, and the overall supply chain performance.

How Predictive Analytics Improves Demand Forecasting

Let’s talk more about the positive effects of predictive analytics in supply chain demand forecasting. As was mentioned above, supply chain predictive analytics helps with:

• Reducing stockouts and overstock
• Enhancing inventory management
• Enhanced supplier and procurement planning
• Higher cost efficiency thanks to improved accuracy

Reducing Stockouts and Overstock

The core goal of demand forecasting is to avoid stockouts and overstock. Thanks to the possibility of defining patterns and predicting trends, companies can correctly plan their orders and evaluate the loadout. As a result, businesses will optimize their supplies, ensuring they meet the demand perfectly.

Otherwise, if a company operates more or less goods than market expects, they start losing money:

If the storage is overstocked, the company must find a way to sell the excess. Until then, businesses will lose potential incomes as they cannot operate new products by buying, storing, and selling them.

At the same time, if they are understocked, they also lose potential income by being unable to satisfy the demand on the spot while waiting for new goods to be produced and delivered.

Stockouts and overstocks negatively impact the whole supply chain, overheating some processes, cooling down other operations, and causing the system to lose its balance.

Enhancing Inventory Management

Frankly, all of these processes are interconnected and support each other. Still, inventory management is among the first and most apparent operations affected by accurate demand forecasting.

Supply chain companies can significantly simplify and improve their inventory management operations thanks to more accurate predictions and avoiding overstock. However, it assumes not only better warehouse organization but also a more cost-efficient and performative transportation:

When ordering just enough goods, companies can significantly reduce their freight-forwarding operations, better manage parcel tracking, and save on transportation costs.

Enhanced Supplier and Procurement Planning

Predictive analytics services also enhance supply chain planning. Businesses can make more proper and accurate product supply and transportation plans by predicting future trends and demand.

If you know the exact needs and demands to satisfy, you can effortlessly make a more proper plan for delivering expected results. Thus, it becomes easier to meet deadlines, create a proper execution or delivery pipeline, etc.

Apart from that, improved planning helps to save costs and avoid expenses by allowing companies to schedule and organize the supply chain more cheaply and efficiently.

Higher Cost Efficiency Thanks to Improved Accuracy

Finally, predictive analytics is a powerful instrument to boost cost savings and resource efficiency. Once again, thanks to the possibility of predicting trends and evaluating approximate demand, businesses can avoid under or overstocking, improve inventory management, and ensure the sale of goods.

As a result, companies can prioritize the stocking and selling of the chosen products. Thus, they can maximize their profits, avoid unnecessary expenses, and achieve the highest supply chain efficiency.

The Role of Predictive Analytics in Demand Forecasting

Apart from discussing the benefits, we also have to highlight the roles of predictive analytics in supply chain, including the main processes covered by such services. Long story short, the list of core demand forecasting roles consists of:

• Analyzing historical data
• Identifying demand patterns and trends
• Improving forecast accuracy with machine learning models
• Enhancing decision-making with predictive insights

Analyzing Historical Data

First, predictive analytics are data-driven and highly depend on historical data, quality, and quantity. Still, simple access to such data is not enough. Data processing and analysis are the core processes that enable predictive analytics in general and demand forecasting in particular.

Frankly speaking, many other unrelated operations and services also rely on data analysis. Still, it doesn’t mean the same algorithms or methodology can cover them.

Simply put, proper historical data analysis methodologies and approaches are essential, regardless of whether there are other data analysis tools or services. To use predictive analytics in supply chain for demand forecasting, you must integrate appropriate data processing algorithms.

One of the easiest and most efficient ways to set up correct data analysis operations is to cooperate with a supply chain software development company with proven experience in such tasks.

Reliable software developers experienced in such tasks can cover all major aspects and partly streamline data-related processes, creating a ready-to-use toolset for the data analysis team. As a result, they will only have to use these instruments to improve their accuracy and performance.

Identifying Demand Patterns and Trends

As was mentioned above, demand forecasting is a data-based process that suggests analyzing information and finding correlations, patterns, or trends.

After figuring them out, analysts try to understand the reasons for such trends and how to define them in early stages or even far ahead. Based on their findings, data analytics teams shape their assumptions and predictions, which are used for later possible marketing and sales strategies design and planning.

Even today, such assignments are performed manually by engaging related specialists. However, there is a tendency for changes:

The emergence of open-source artificial intelligence and machine learning solutions has significantly impacted multiple software solutions. However, pattern-seeking and identification are among the most popular and powerful features in AI automation services.

AI-based software highly relies on such services and operations. Therefore, you should consider AI integration for predictive analytics and demand forecasting.

Improving Forecast Accuracy with Machine Learning Models

To be honest, machine learning models and open-source AI solutions like ChatGPT, Gemini, or their alternatives make a massive difference in the industry. They significantly increase the competitive edge and boost the overall efficiency of pattern-seeking and trend forecasting operations.

For instance, AI-driven predictive analytics in supply chain can handle much more data simultaneously, making more accurate and fast predictions based on the processed data.

Still, this doesn’t mean that you can simply replace your analytics team with a single AI integration. Instead, consider it a tremendous additional instrument that can help boost the performance of your specialists, highlighting potential misses or mistakes.

Enhancing Decision-Making with Predictive Insights

Clearly, demand forecasting and predictive analytics in supply chains have the core goal of enhancing decision-making and providing businesses with insights into potential outcomes and trends.

With such processed data, businesses can make more informed decisions, predict trends, save costs, improve their services, etc. The benefits are limitless, depending on your context, goals, and gathered intel.

How to Successfully Integrate Predictive Analytics in Supply Chains

Finally, let’s discuss the best ways to integrate predictive analytics in supply chain business. Long story short, such an analysis can be done in a few ways.

Due to the complexity of predictive analytics, such tasks traditionally required specialist involvement. Your data analysts will use different software instruments to cover some working aspects, but the core analysis and predictions are done manually.

Alternatively, modern software solutions suggest multiple methods for streamlining and automating more analysis and forecasting operations with artificial intelligence and machine learning. Such an approach requires at least four steps:

1. Gather historical data
2. Choose the most suitable predictive model
3. Test the accuracy of predictive software
4. Integrate the proven solution

Gather Historical Data

Data analysis requires having this data in the first place. Demand forecasting is impossible without historical data and its processing.

Therefore, the first and most crucial step is to ensure efficient and constant data gathering loops. Eventually, this will be used regardless of whether you are using AI-based or completely manual data analysis.

Choose the Most Suitable Predictive Model

You can choose the predictive models after establishing efficient data-related operations and processes. The modern market offers countless solutions and open-source models that can be easily integrated into your business for data handling and analysis.

Nevertheless, the variety of options makes it challenging to choose. We suggest two advice on simplifying it:

1. Cooperate with software development specialists who are experienced in such services. Therefore, based on their expertise and previous cases, they can make suggestions or even choices for you.
2. Perform your own research, finding and testing existing models by giving them similar tasks on analysis and forecasting.

Test the Accuracy of Predictive Software

Judging from our experience, you should always pay attention to testing every software solution, especially regarding AI or ML systems.

Apart from performing general tests, we also recommend making more specific testing. For instance, if you are considering adopting a certain AI model like ChatGPT for demand forecasting, you should use a customization possibility. In other words, you should use prompt engineering and other personalization instruments for advanced trials.

For instance, you can use your historical data for previous years and test the accuracy of AI predictions by comparing the forecast with the actual numbers.

Integrate the Proven Solution

Finally, after performing some advanced tests and finding the best AI model for predictive analytics in supply chain, you can proceed to integrate it into your system. Primarily, they are relatively easy to adopt. However, it might take some time and effort to adjust and set up the model properly, ensuring it shows the best performance possible.

The best way to cover the integration process is by contacting software development companies with proven experience in such projects. They will not only deliver the expected result but can suggest ways to improve the software solution and warn you about the potential challenges or issues of such solutions.