In the era of modern technology, the competitive paradigm among organizations is changing at an unprecedented rate. Many organizations today have been caught off guard by supply chain disruptions and unexpected increases in demand. Meeting immediate market demand is essential for maintaining a competitive edge in the era of immediate satisfaction. Furthermore, due to the growing economic uncertainty caused by the pandemic, businesses have been compelled to devise novel approaches for accurately forecasting demand for their goods, inventory, and products. A company risks losing customers and revenue if it fails to satisfy the immediate need. New success metrics are applied to the organization’s supply chain performance to outperform the competition. However, this lead can only be obtained and sustained if the organization has an effective and efficient supply chain and an appropriate forecasting technique.

Traditional demand forecasting techniques forecast demand using past shipment and sales data. The major drawback of this method is that it does not factor in real-time changes in market conditions and other events that can likely influence demand. As a result, there is too much dependency on historical data. The traditional techniques have limitations, so they urgently need to be strategically updated to meet the most recent consumer demand trends and handle the ever-growing supply chain challenges. A series of consumer expectations, international complications, an abundance of routes to the market, and other unexpected conditions mainly cause these issues in the supply chain network. As a result, supply chain managers must develop clever business strategies to eliminate these challenges and keep everything running efficiently. Given the ongoing challenge, the most beneficial approach for organizations across any sector is to use tech-enabled supply chain forecasting and demand sensing platforms. Businesses want to choose the best action plan for their future production and demand. In contrast to traditional methods, which rely on individual preferences and bias, AI-powered supply chain forecasting tools automate the process by collecting massive amounts of data from different sources to determine future demand or events based on market statistics. Machine learning algorithms can analyze large amounts of data and identify patterns and relationships that traditional statistical methods may miss.

Forecasting is essential in many sectors, including business, economics, finance, meteorology, and supply chain management. In this article, we will explore how machine learning is used for demand forecasting in different industries. It involves predicting future events, trends, and outcomes based on past data and statistical models. The effective AI-powered demand forecast tool enables organizations to be successful in the global market. Any organization can plan their production, manage its inventory, and streamline its supply chain with accurate forecasting. This process requires planning and coordination between business stakeholders, including data engineers and scientists.

The Business Challenge:

The client, a prominent player in any industry, was interested in finding methods to create dependable market predictions and improve customer service levels. Consequently, they aimed to achieve precise and reliable forecasts for the product’s demand and ensure the appropriate supply to meet that demand. Additionally, they sought to optimize production timing and streamline delivery processes to minimize inventory expenses and address supply chain irregularities. The rapid shifts in customer demand resulting from the global pandemic pose significant challenges for demand forecasting.

Overall, the solution architecture for forecasting using machine learning involves an iterative process of data collection, feature engineering, model selection, training, evaluation, and deployment, as shown in the image. 1.

The Image shows the main stages of the solution architecture, including:

• Data Collection and Preparation: This involves collecting relevant data from various sources, cleaning and pre-processing the data, and transforming it into a format suitable for machine learning. Information such as the names of distributors, product names, sales quantities, calendar dates, product prices, promotion data, and the number of festivals, etc., can also be helpful. Before sending data to further steps, it needs to do sanity checks on the data.
• Feature Engineering: Feature engineering is using domain knowledge of the data to create features that help ML algorithms learn better. This involves selecting and transforming the relevant features from the data to create a feature matrix that can be used for training the ML model. In order to enhance the accuracy of the model,  derived features like lag, the occurrence of the last positive sales, and the number of holidays can also be used.
• Model Selection and Training: This involves selecting an appropriate ML algorithm, training the model on the prepared data, and optimizing the model parameters to minimize the prediction error. State-of-the-art models- XGBoost LSTM can also be applied.
• Model Evaluation: This involves evaluating the performance of the trained model using appropriate metrics and techniques and making necessary adjustments to improve the model’s accuracy. Forecast accuracy is measured using the Mean Absolute Percentage Error (MAPE).
• Deployment: This involves deploying the trained model into a production environment, where it can generate real-time forecasts.
• Forecast results: In this step, a dashboard displays the forecasted results, which shows historical and forecast quantities. Also, flexibility is given to end users to verify the impact of different offered discounts on products.

To efficiently execute the aforementioned steps, a popular cloud platform, Microsoft Azure, can be used.  

As an example, assume that one needs to forecast the sales quantity of Product 1 for the next three months, specifically for Distributor A. So, to make a forecast, the following historical input data is available spanning the past four years, as shown in Image 2.

Upon completing the aforementioned steps outlined in the provided architecture, a set of methods have been tried to get the following results forecasted for Distributor A and Product 1, as shown in Image 3.

There are several challenges that come with demand forecasting. These challenges include:

Machine learning algorithms require a large volume of high-quality data to produce accurate forecasts. However, data may be incomplete, noisy, or biased, which can affect the accuracy of the forecast or it can lead to inaccurate predictions. In the case of intermittent, erratic, and lumpy data patterns, reaching an accurate forecast is difficult compared to a smooth pattern. Forecasting requires expertise in data analysis, statistical modelling, and domain-specific knowledge related to the forecasted industry or sector. A lack of expertise in these areas can lead to inaccurate predictions. With appropriate CI/CD tools, the model needs to retrain and redeploy for new data continuously.

Accurate demand forecasting can greatly affect a business in numerous ways:

1. Improved financial planning: Forecasting can help businesses plan their finances more effectively by predicting future cash flows, revenues, and expenses. This can help businesses manage their working capital, secure funding, and plan for growth.
2. Better inventory management: Forecasting can help businesses optimize their inventory levels and reduce stockouts or overstocking. This can lead to lower warehousing costs, increased efficiency, and improved customer service levels.
3. Improved supply chain planning: Forecasting can help businesses plan their supply chain more effectively by predicting future demand and adjusting their procurement and distribution strategies accordingly. This can help organizations reduce lead times, improve delivery times, and avoid stockouts.
4. Enhanced labour planning: Forecasting can help businesses plan their labour needs more accurately, including staffing levels and scheduling. This can help organizations avoid overstaffing or understaffing, which can be costly.
5. Reduced waste: Forecasting can help businesses reduce waste by predicting future demand and adjusting production and procurement accordingly. This can help organizations avoid excess inventory or stockouts, reducing waste and improving sustainability.

In conclusion:

Machine learning proves to be a formidable asset for accurate forecasting. Through the analysis of extensive data sets and the detection of patterns and correlations, ML algorithms can generate precise forecasts related to demand, inventory requirements, and sales. Leveraging these forecasts, organizations can enhance their production processes, streamline supply chains, optimize inventory management, and bolster marketing strategies. Ultimately, this empowers organizations to make informed decisions, boost revenue, mitigate risks, and secure a competitive edge.