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What is the chief source of pain for corporate executives? The largest IT research enterprise in the world, Gartner, provides a simple response: demand volatility. Buyers frequently change their minds because of the excessive number of factors influencing them, including weather changes and social media influencer posts. 

Even worse, events like a competitor's store opening next door or the COVID-19 pandemic lockdown can completely change customers' intentions. Or take Greta Thunberg, a young climate activist. She started the "flight shame" movement by refusing to fly for environmental reasons, leading to Sweden's 5% drop in air travel. 

To foretell scenarios like the "Thunberg effect," there is no magic wand. However, some technologies can increase demand forecasting's precision. Although it will never be completely accurate, it can be accurate enough to support your business objectives. 

We will examine the capabilities of machine learning forecasting techniques in this article. 

What is Machine Learning in Demand Forecasting? 

Machine learning techniques can predict the number of products/services purchased in the future. A software system can learn from data in this case to improve analysis. Machine learning outperforms traditional demand forecasting methods in the following ways: 

• Improves data processing speed 
• Provides a more precise forecast 
• Updates forecast automatically based on the most recent data. 
• More data is being analyzed. 
• Detects hidden patterns in data. 
• Builds a strong system 
• Enhances adaptability to change 

With the advent of artificial intelligence and machine learning, most businesses are looking to automate processes and implement AI using big data. 

According to a 2019 report by Research and Markets, "AI in retail was $720.0 million in 2018 and is expected to grow at a CAGR of 35.4% between 2019 and 2024." 

How can Machine Learning Improve Demand Forecasting Accuracy? 

Machine learning (ML) in demand forecasting eliminates traditional planning challenges such as long delivery lead times, high transportation costs, high inventory and waste levels, and incorrect decision-making due to inaccurate forecasts. 

The goal of ML models is not only to improve demand forecasting accuracy but also to free up time for demand planners. Planners can make better use of their time by focusing on the most important products or gathering more last-minute information to advise forecasts more accurately. With enough historical data on forecasts, products, and sales, ML techniques can generate more accurate demand forecasts. 

Top Four Advantages of Using Machine Learning in Demand Forecasting 

Machine learning in demand forecasting has matured to the point where accuracy, transparency, and replicability translate into transformative results, including in the following five areas: 

Accuracy and Transparency 

Forecasting stakeholders in demand planning are concerned with accuracy. They are unlikely to accept a new forecasting method unless it has been rigorously validated against known forecasting benchmarks with proven accuracy. Kaggle competitions and modeling benchmarking studies have demonstrated that machine learning (ML) for demand forecasting is extremely accurate. 

Machine learning has stable accuracy/bias trade-offs that can be altered on an "efficient frontier" of data science workflow, allowing for the quick implementation of an accurate ML forecasting solution and subsequent long-term analysis to further refine the forecast. Furthermore, ML forecasting is not a black box; the impact of model inputs can be weighed and understood, resulting in an intuitive and transparent forecast. 

Greed for More Data 

The capacity of machine learning to ingest disparate data and utilize that information at a detailed level to enhance SKU-level forecasting is a significant factor in ML forecasting accuracy. Simply put, ML forecasting can use data that has been matched to the SKU at the point of sale or the point of distribution. 

Rapid Adaptation to Change and Supply Chain Disruption 

Machine learning forecasting also has the benefit of being "always on" in a way that it can be programmed to update itself based on the most recent data. This typically involves creating a running forecast based on the most recent actuals, updating the data warehouse with each forecast refresh, and updating the forecast on a daily or weekly basis using aggregate data. 

This "always on" forecast monitoring can be adjusted to find out which customer segments are most price sensitive, what makes up a market basket, etc. This will lay the groundwork for an online recommender system. 

Analytical Processing Speed and Accelerated Corporate Learning 

The speed at which machine learning processes data is another benefit. Modern machine learning software has been developed to take advantage of Intel and GPU chip architecture, maximizing in-memory storage and squeezing more calculations per second. 

With the analytical results showing the speed/accuracy trade-off as an effective frontier, machine learning can incorporate more predictors and Deep Learning when even higher levels of accuracy are crucial. As a result, decision-makers can make the necessary trade-offs regarding where to invest in order to obtain more data, a larger computational cluster, faster data processing, and so on. 

Machine Learning Demand Forecasting; Successful Use Cases 

Demand forecasting is frequently too complex to be performed successfully with simple tools due to the highly variable environment, dozens of factors driving buying behaviors, and many types of data involved. As a result, large corporations choose to invest in smart technologies to improve inventory management. 

Nestlé implements Demand Driven Forecasting 

Nestlé used to create 80 percent of their forecasts manually. However, they desired to understand their customers' motivations better. They were able to sense and analyze demand signals associated with sales promotions, price, advertising, in-store merchandising, and economic factors thanks to SAS forecasting and analytics technology. 

"Today, the solution generates 80 percent of Nestlé's forecasts with no human intervention. Every 1% improvement in forecast accuracy resulted in a 2% reduction in inventory safety stock. With this improved forecasting capability, they could eventually take out anywhere from 14 to 20% of their inventory safety stock, reduce it, and still meet consumer demand. If you have $100 million in inventory, that's a $20 million savings." 

Charles Chase, an industry consultant reports.  

PUMA employs an integrated inventory management strategy 

Due to disconnected business systems and fragmented tools, PUMA suffered losses and a supply-demand gap. They were able to fix and standardize their planning and analytics processes after implementing a comprehensive solution with data management, forecasting, and simulation capabilities. 

As a result, they were able to streamline procurement, reduce shortages and residual stock, and gain greater visibility into both external market conditions and internal operations. 

UK Hospitals Reduce Waste from Blood Overstocks With ML 

The significant complexity of the supply chain, short-term demand spikes, and the high cost of errors (with human lives at stake) prompted the UK's National Health System (NHS) Blood and Transport department to transition from spreadsheets and manual databases to an ML-powered planning system with enhanced predictive capabilities. It enabled hospitals to reduce blood-stock waste by 30% while maintaining service quality and responding quickly to potential shortages. 

"It's unfortunate if there's no yogurt on the supermarket shelf. The consequences are very different if there is no blood in the hospital." 

An NHS executive explained the reason for investing heavily in the advanced solution. 

Functions of Machine Learning in Demand Forecasting 

Demand forecasting powered by machine learning has indicated the following functions: 

H3: Customer Relationship Management 

Consumers expect the goods to be readily available when they plan a purchase. Imagine that people would want to purchase new or traditional clothing when a festival is approaching. Demand forecasting can determine which products should be readily available in particular store locations. This increases customer satisfaction and trust in your service, boosting the recognition of your brand. 

Supplier Relationship Management 

It becomes simple to look for new supply chains and reduce the number of suppliers once you can determine how many products to order per customer demand. 

Order Fulfillment and Logistics 

Supply chains can be made more effective by using demand forecasting. Products that are unsold or in low demand shouldn't be displayed in place of the newest, most popular items. You can achieve your company's target profit margins by optimizing this. 

Marketing Campaigns 

Forecasting is frequently used to modify marketing and advertising campaigns and can potentially affect sales volume. Marketing data can also be included in sophisticated machine-learning forecasting models. 

Accelerates Data Processing Mechanism 

The modern machine learning architecture optimizes memory storage while propelling forecasting mechanisms to lightning speed. This allows decision-makers to make well-informed investments in advanced data processing. 

Why is Machine Learning so Important in Demand Forecasting? 

A machine-learning model can predict what will happen a year from now if a company wants to know. Today, a number of important data initiatives are attracting an essential role in machine learning forecasting. Experts in supply chains, logistics, and big retailers are using machine learning forecasting to increase customer engagement and create more accurate demand forecasts than they could with traditional methods. If you think the future will be predicted, you should look into what machine learning can do for you. 

• ML for demand forecasting can assist you in anticipating changes in system volume, market size, and price points. Because the process differs from other forecasting methods, you should test and compare ideas using real-time data from your own sales and marketing plan. 
• ML aids in processing data, making predictions, and forecasting future behaviors. This machine-learning feature produces predictions, not just models, with clear human and machine-learning outcomes, such as new powerful sales tools and decision-making frameworks. 
• The most recent and powerful ML is used to eliminate black swans and direct customers to the most appropriate location with the shortest wait times. Machine learning models can analyze various characteristics of highly correlated datasets and produce simple demand forecasting tools. 
• Marketers must know that demand does not always manifest as expected in their campaigns. They can use machine learning to leverage past individual and pattern behavior to understand future demand better and optimize their marketing efforts. 
• Including machine learning (ML) in your demand planning toolkit helps marketers better understand demand and may also help your business become more profitable over time. Data and patterns are used by ML technology to predict future demand with uncanny accuracy, allowing enterprises to improve their demand forecast with minimal risk significantly. 

Future of Machine Learning in Demand Forecasting 

Demand and sales forecasting are not the only applications of machine learning. How effectively we utilize this technology will determine its potential in the future. By addressing issues like predicting customer engagement, future trends, brand development, marketing campaigns, resource usage, financial risks, etc., we work on demand forecasting technology today and comprehend the added value it can provide to modern businesses. 

As new ML trends develop, we never know what opportunities AI technology will present to us in the future. 

ML is quickly becoming a commonplace technology. The sooner we deploy a solution, the more beneficial improvements to the business can be made by fine-tuning the algorithms. More importantly than the value created by optimizing processes, saving costs, and increasing margins, it is discovered that ML can significantly improve overall customer satisfaction when the right problem is addressed with the right approach. 

Conclusion 

If a business wants to know what will occur in a year, a machine-learning model can produce this data. Here are some benefits a machine learning algorithm can provide for your company: more precisely, forecasting customer demand, understanding buyer motivation to shorten sales cycles, monitoring competitor performance and engagement, and increasing competitive advantage. 

To sum up, technologies like machine learning, artificial intelligence, and automation are truly a blessing when you want your business to achieve its goals. Everybody wants to stay updated in this quick-paced world and profit from any opportunity that comes their way. 

The bar for customer satisfaction and business efficiency is rising. Is your business ready?