Let’s admit that one of the major challenges faced by organizations is the instability of demand. After all, the buyer regularly changes their mind. Like everything else, this has an impact on business. The effect of social media influencers, alterations in the economy like inflation or recession, and a lot of multiple factors affect the purchasers’ buying decisions. A global epidemic could cause problems similar to COVID-19 in 2020. Sadly, there is no magic wand that can foretell such events. So, here the concept of demand forecasting comes into the picture.

What is demand forecasting?

Demand forecasting is the estimation of the future demand for a product or service how much will it be needed and when. This term is interchangeable with demand planning and demand sensing.

1. Demand Planning:

Demand planning is an extensive process that commences with forecasting, but its scope exceeds that. The Institute of Business Forecasting and Planning (IBF) defines it as the process of applying both past experience and predictions to gauge the demand for various items at different stages of the supply chain. In addition to forecasting, demand planners play an essential role in optimizing inventory, ensuring the availability of necessary products, and tracking the variance between projections and actual sales.

Demand planning is fundamental to several other activities, including warehousing, shipping, price forecasting, financial planning, and most importantly, supply planning. Supply planning aims to meet the demand and requires knowledge of the expected customers’ requirements. As such, demand planning serves as the foundation for many other critical business processes.

2. Demand Sensing:

It is about creating short-term predictions. Demand sensing is a relatively novel addition to the planning process that relies on advanced analytical methods to capture real-time changes in purchasing patterns. This approach can be particularly useful for companies operating in dynamic markets.

Demand sensing solutions gather daily data from various sources such as point of sale (POS) systems, warehouses, and external sources to identify shifts in sales volume compared to historical patterns. The system automatically assesses the significance of each deviation, analyzes the factors driving it, and recommends adjustments to short-term plans.

Implementing demand sensing has reportedly led to a reduction of 30 to 40 percent in near-term forecast errors. This approach enables businesses to quickly respond to sudden changes in customer needs and develop a data-driven supply chain. However, it is not suitable for mid or long-term planning decisions. Demand sensing can serve as a valuable complement to traditional forecasting methods, but it cannot replace them entirely.

How demand forecasting is helpful?

While dealing with supply chains, businesses face a variety of inventory concerns, and the current situation is particularly problematic for the supply chain. Demand forecasting significantly enhances financial planning, capacity planning, profit margins, and risk assessment decisions, which assists organizations in lowering supply chain costs.

Retail companies rely heavily on demand and sales forecasting to maintain a healthy inventory balance. Failure to do so can lead to disruption in the supply chain, with overstocking or understocking of products during certain periods. Excess inventory can result in the need to offer discounts to sell products, while insufficient inventory leads to lost profits.

The application of demand and sales forecasting can help resolve these issues by increasing the return on inventory and predicting the buying behavior of consumers in the future, including their willingness to purchase a specific product at a particular price.

As the name implies, the discipline of demand forecasting in predictive analytics is predicting future customer demand by analyzing historical data. Organizations use demand forecasting tools to avoid inefficiencies brought on by the misalignment of supply and demand throughout business operations.

The goal of machine learning in demand forecasting:

The goal of machine learning (ML) is to analyze and comprehend statistical data using algorithms that look for patterns. Demand forecasting is a commonly utilized machine learning application in supply chain planning, according to a Gartner survey. The report also shows that 43% of businesses aim to utilize AI-powered demand forecasting within the next two years, while 45% of businesses are already using the technology.

It describes a subfield of artificial intelligence where intelligence is applied to mine useful data from enormous data sets in order to perform fresh analysis. This intelligence is made up of several Machine Learning algorithms, such as The research and implementation of AI in today’s applications has made notable strides thanks to developments in the field of machine learning.

Using demand forecasting methodologies can help businesses improve their decision-making processes for cash flow, risk assessment, capacity planning, and people planning. Machine learning algorithms aid in increasing the accuracy of forecasting techniques and optimizing replenishment procedures. These innovations aid businesses in reducing the expense of cash-in-stock and out-of-stock scenarios.

Demand Forecasting through Machine Learning: Advantages

Uncertainty in the market leads to highly volatile data. Traditional methods for demand forecasting are futile nowadays and hence the use of machine learning algorithms offers flexible results and new information in the models can be infused easily. ML methods are quite adaptive and fruitful.

The following are the main advantages of demand forecasting through ML and AI as they may offer help to the manufacturing company precisely:

• Better Accuracy: Accuracy will increase over time as machine learning algorithms gain knowledge from the data already available.
• Greater customer satisfaction: Customers will be less satisfied when things are “out of stock,” however they will be more satisfied when products are constantly accessible. This enhances brand impression and consumer loyalty.
• Enhanced Workforce Planning: Demand forecasting may help the HR department make appropriate decisions about the mix of full-time and part-time employees, maximizing HR expenses and effectiveness.
• Better markdown/discount optimization: Cash-in-stock situations, where things remain unsold for longer than anticipated, are frequent in the retail industry. Higher estimated inventory costs and the possibility of items becoming out of date and losing value are frequently the results of this. Products are offered for sale in this circumstance at lower selling prices. By using demand forecasting, this possibility may be reduced.
• Efficiency on a Holistic Basis: General effectiveness Instead of attempting to decrease or expand inventory and personnel levels, teams may concentrate on strategic concerns with the help of demand forecasts.

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Top Machine Learning-Based Saas Tools

Demand forecasting can benefit greatly from the use of machine learning-based SaaS (Software as a Service) tools. These tools employ computers to examine big databases and spot trends that can help companies more precisely predict future demand. The top SaaS technologies based on machine learning might be helpful for demand forecasting in the following ways:

1. Offer forecasts that are precise:

Machine learning-based SaaS applications can analyze a large quantity of data and offer very precise projections. In order to make more accurate predictions, these systems might use a variety of variables, including past sales data, consumer behavior, market trends, and seasonality.

 2. Faster analysis:

SaaS technologies that employ machine learning algorithms are capable of conducting in-the-moment data analysis and offering immediate insights into altering demand trends. This implies that companies can swiftly adapt their inventory and manufacturing schedules to variations in demand.

3. Scalability:

SaaS technologies can manage massive volumes of data and offer precise projections for companies of all sizes since they are extremely scalable. Also affordable and requiring less hardware and software commitment are these tools.

4. Automation:

Several of the operations involved in demand forecasting, such as data gathering, processing, and reporting, can be automated using machine learning-based SaaS technologies. This can speed up the forecasting process and lower the possibility of human error.

5. Integration:

SaaS tools can be combined with other business systems, such as CRM and ERP (Enterprise Resource Planning), to give a more thorough picture of the company and increase the precision of projections.

In general, the best machine learning-based SaaS systems can give firms precise, real-time demand predictions that can aid in better decision-making and operational optimization.

Some effective machine learning SaaS-based Tools:

Businesses can utilize a variety of SaaS machine learning solutions for demand forecasting. Here are some of the best tools and the reasons they work well:

1. ForecastRX:

Using algorithms to examine historical data and forecast future demand, ForecastRx is a machine learning-based application for demand forecasting. Insights regarding demand-shaping elements like promotions, pricing, and seasonality are also provided by the technology. To give a more complete picture of the business, ForecastRx can be coupled with other business systems and is extremely customizable.

2. Demand Works Smoothie:

It is a SaaS product that employs cutting-edge analytics to deliver precise demand estimates. The technology can analyze large data sets and find trends that can guide firms in planning their inventory and production. Demand Works Smoothie is simple to use and offers useful insights into demand trends.

 3. Blue Yonder:

Blue Yonder is a machine learning-based platform for demand forecasting that leverages algorithms to examine the information from a variety of sources, including social media, weather reports, and economic indicators. The solution offers capabilities for supply chain optimization, price optimization, and precise demand forecasting. All sizes of companies can use Blue Yonder because it is highly scalable.

 4. Lokad:

Lokad is a cloud-based solution for demand forecasting that makes precise predictions for companies of all sizes using machine learning algorithms. The tool can evaluate big data sets and offer forecasts for several products, geographies, and time frames. To help companies cut inventory costs and shorten lead times, Lokad also provides supply chain optimization services.

 5. IBM Watson:

IBM Watson is a predictive analytics tool that is based on machine learning and may be used for demand forecasting and other tasks. The program analyses data using sophisticated algorithms to find patterns that might guide business decision-making. For a more complete picture of the business, IBM Watson may be connected with various business systems and is extremely adaptable.

The combination of these SaaS machine learning technologies’ cutting-edge algorithms, real-time data processing, and scalability make them extremely useful for demand forecasting. Businesses can benefit from their precise demand projections and insights into the demand-creating variables by using this information to organize their inventories and production more effectively.

Demand Forecasting through Machine Learning Tools

Demand forecasting is becoming advanced with machine learning, which allows for improved projections based on real-time data from both internal and external data sources, including demographics, weather, online reviews, and social media. Supply chain networks can perform better than networks maintained more manually by data analysts and react to external changes with the use of external data and sophisticated machine learning techniques.

Machine learning forecasting techniques may find groups of previous products with related traits and lifetime curves and use those datasets as a substitute to create forecasts for new products that lack historical data. Machine learning is an advanced technology that enhances statistical models by incorporating various internal and external sources of data to provide accurate, data-driven predictions. These machine learning engines can work with both structured and unstructured data, which includes:

1. Historical data:

Machine learning can utilize past financial and sales reports to identify patterns and trends, which can then be used to make predictions about future performance.

2. Marketing polls:

Machine learning can analyze marketing polls to understand customer preferences and behavior, allowing businesses to tailor their marketing campaigns to specific demographics.

3. Macroeconomic indicators:

By analyzing macroeconomic indicators, machine learning can help businesses understand how economic conditions may affect their operations and sales.

4. Social media signals:

Machine learning can monitor social media signals such as retweets, shares, and spikes in followers to identify trends and gauge customer sentiment.

5. Weather forecasts:

Machine learning can incorporate weather forecasts to predict how weather patterns may impact consumer behavior, such as changes in demand for certain products.

6. News about local events:

Machine learning can analyze news about local events to predict how they may affect consumer behavior, such as changes in demand for certain products or services.

7. Competitor activity:

Machine learning can monitor competitor activity to identify trends and make predictions about how competitor actions may affect a business.

So, by using machine learning and incorporating various internal and external sources of data, businesses can make more accurate predictions about future performance and consumer behavior. This can help businesses stay ahead of the competition and make data-driven decisions that lead to greater success.

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Understanding through a case study:

A real-world illustration may be found at the French food goods firm Danone Group. To improve its demand projections, Danone built a machine learning system. Due to the short shelf-life of its fresh products and the unpredictable nature of demand, the firm needed more precise and secure demand projections. Danone frequently employs marketing and media occasions. The demand projections were rather ad hoc because more than 30% of the entire volume is sold through promotional offers like discounts and leaflets.

Not only did the machine learning system that was put in place enhance projections, but it also enhanced coordination across several departments, including sales, supply chain, finance, and marketing. By enhancing productivity and inventory harmony, this system helped Danone meet its goal service levels for channel inventors.

How Machine Learning-based SaaS Tools and Solutions are helping Brands Estimate Future Demand Based on Varied Demand Signals?

Brands can forecast future demand using a variety of demand signals thanks to machine learning-based SaaS tools and solutions. Here are a few illustrations:

1. Real-time data analysis:

SaaS technologies can analyze vast volumes of data in real-time, enabling marketers to monitor changes in demand signals and modify their forecasts as necessary. These techniques are capable of accounting for a number of variables that may affect demand, including societal emotion, climatic patterns, and economic indicators.

2. More accuracy:

Machine learning algorithms can evaluate previous sales data to find patterns and trends that might help firms create more accurate forecasts. These algorithms can enhance forecast accuracy and lower the likelihood of overstocking or understocking by recognising the most crucial demand signals.

3. Demand sensing:

As we learned earlier that it is a forecasting technique that makes use of machine learning algorithms to identify sudden changes in demand signals, such as changes in consumer behaviour or unanticipated market developments. Brands can more successfully meet demand by identifying these changes and modifying their inventory levels and manufacturing schedules.

4. Supply chain optimization:

By anticipating future demand and modifying inventory levels and production schedules accordingly, machine learning-based SaaS technologies can also assist brands in optimizing their supply chains. Brands may increase their profitability and save waste by minimizing stockouts and overstocking.

5. Personalization:

Machine learning algorithms can assist firms in personalizing their services and enhancing the consumer experience by evaluating customer data and seeing patterns in customer behavior. This may result in greater client retention and improved sales.

Machine learning-based SaaS tools and solutions can assist brands in estimating future demand based on various demand signals by evaluating large volumes of data in real time, enhancing forecast accuracy, spotting changes in demand signals, improving the supply chain, and personalizing offerings.

These technologies can give businesses a competitive edge in marketplaces that are undergoing rapid transition by enabling them to react rapidly to changes in demand and maintain an advantage over the competition. So, let’s see what leading brands are doing and which solutions are they implementing to streamline the process of demand forecasting.

Amazon Machine Learning Services

This service can determine which fields are category and which are numerical, therefore the majority of data preparation activities are carried out automatically.

While automatic preprocessing save time, there are times when the processed data won’t fit the data scientist’s intention, necessitating additional customization (or you could just auto-build a model that really doesn’t make sense). This level of automation has both benefits and drawbacks for ML use.

Using its Amazon Machine Learning services and its Amazon SageMaker IDE, Amazon provides a comprehensive selection of machine learning tools. This platform provides pre-trained AI services that may be used by less experienced teams and don’t require any coding knowledge or machine learning ability. Also, it provides a solid starting point for the more experienced teams.

Amazon Sagemaker Studio

A machine learning environment called Amazon Sagemaker Studio makes workflow easier by offering tools for rapid model development and deployment. It brings the majority of machine learning tools together in one location, making it simple to transition from model construction to scale deployment using its interface.

To make data exploration and analysis easier without the complexity of server maintenance, the platform includes a Jupyter notebook.

Also, the ML APIs that Amazon proposes and built-in SageMaker techniques mostly overlap, but data scientists are still free to experiment and utilize their own datasets. Also, you may use SageMaker’s deployment features to run models and add your own methods.

Microsoft Azure Machine Learning Studio

Both beginning and seasoned data scientists can benefit from the development environment known as Azure Machine Learning Studio. It provides capabilities for everything from data analysis to deep learning to data visualization and labeling. Most actions in Azure ML Studio may be accomplished using a graphical drag-and-drop interface, just like with Microsoft Windows (which makes it easy to use). This comprises:

• Exploring data
• Preprocessing, selecting modeling techniques, and
• Justifying modeling outcomes.

Each stage of the procedure is shown using this Azure ML graphical interface. Less experienced ML teams may experiment with Azure ML studio’s Interface to learn more about the key techniques and models. Eventually, kids will be able to comprehend some more complex data science ideas.

Google Cloud Platform

Google offers two tiers of machine learning and AI services:

• Cloud AutoML for users who are not well-versedinh tthe ech world, and
• Google Cloud Machine Learning for data professionals who are tech-savvy.

A cloud-based machine-learning platform called Cloud AutoML provides a range of machine-learning solutions for inexperienced data scientists. Clients may:

• Upload their datasets,
• Train their own models, and
• Deploy them online.

Cloud AutoML maintains data in the cloud and is completely linked with all Google services. The REST API interface allows for the deployment of trained models. It is based on cutting-edge transfer learning and neural architecture search technology from Google.

IBM Watson Machine Learning

A platform called IBM Watson Machine Learning aids data scientists and developers in hastening the adoption of AI and machine learning.

IBM Machine Learning offers data scientists freedom over model deployment and model retraining at scale with its variety of open-source tools and approaches.

It also provides a list of business-friendly AI-powered products including chatbots, sentiment analysis software, prediction tools, etc. The free IBM Watson OpenScale platform is used for managing pre-trained models and tools for dynamical re-training.

Watson Machine Learning’s built-in customizable interface also makes it easier for teams to work together inside a single modeling area. Also, it interacts well with current systems.

Some other use cases:

1. Nestle:

Previously, Nestlé used to create 80 percent of their forecasts through human intervention. However, they realized the need to better understand their customer motives. By implementing SAS forecasting and analytics technology, they were able to sense and analyze various demand signals, including sales promotions, price, advertising, in-store merchandising, and economic factors.

As a result, today, 80 percent of Nestlé’s forecasts are generated from the solution without any human judgment. Charles Chase, an industry consultant, reports that every one percent improvement in forecast accuracy translated into a two percent reduction in inventory safety stock.

By leveraging this improved forecasting capability, Nestlé was eventually able to reduce their inventory safety stock by 14-20 percent and still meet consumer demand. This reduction in inventory safety stock translates to a significant cost-saving, with a potential reduction of up to US $20 million for a company with US $100 million in inventory.

2. Puma:

To streamline their demand forecasting process. By partnering with First Insight, a technology provider that specializes in consumer data analytics, Puma was able to incorporate consumer feedback into their demand planning process.

First Insight’s technology collects real-time consumer feedback on potential products, allowing Puma to gain insights into consumer preferences and behavior. This data was then used in conjunction with machine learning algorithms to create more accurate demand forecasts. The system uses both structured and unstructured data, including point-of-sale data, inventory levels, social media metrics, and consumer feedback to develop predictions.

With this new system in place, Puma was able to make more informed decisions about their product assortment, pricing, and marketing strategies. The technology allowed Puma to forecast demand with greater accuracy, enabling them to adjust inventory levels and production plans as needed.The result was a significant improvement in their demand forecasting capabilities, with Puma reporting a 5-10 percent increase in accuracy. This improvement translated into reduced inventory carrying costs, increased sales, and improved customer satisfaction.

Puma’s success with demand forecasting using machine learning and AI highlights the importance of leveraging technology to gain a competitive edge in today’s fast-paced business environment. By incorporating consumer data into its forecasting process, Puma was able to make data-driven decisions that ultimately led to increased profitability and growth.

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Conclusion:

Forecasting demand and sales are crucial in the retail industry. Without this capability, businesses risk disrupting the inventory balance by placing too many or too few orders for a certain time period. When there is a surplus, a business must give discounts to customers in order to make sales. Otherwise, it can run into inventory problems. Profits are lost as a result of scarcity. Yet, by using demand and sales forecasting, it is possible to maximize the return on inventory and identify future consumers’ intentions to purchase a given good at a specific price.

Today, there are numerous solutions available in the market that can assist with demand planning activities. These solutions come with varying capabilities, and the choice of the appropriate one depends on your business needs. Most demand forecasting software vendors offer out-of-the-box integrations with popular ERP providers, Excel, and other business tools, so it’s essential to ensure that your chosen provider can assist you with system connections. If not, you will have to engage IT specialists to build internal integrations.

However, building the best solution for demand forecasting can pose various IT challenges. This is why it’s crucial to have industry specialists who can help define which factors should be considered in your predictive models. While machine learning models can provide accurate predictions, human logic is still necessary to evaluate the relevance of outcomes produced by these models and to make final conclusions based on deep domain expertise.