AI for Demand Forecasting: Building Businesses for Future
What if we tell you that your business can foretell what your customer will be looking for and stay prepared by matching the forecasted demand, does that blow your mind?
AI for demand forecasting brings a game changing card for businesses who depend on customer demands. The technology achieves it through the study of real-time data using latest algorithms to understand how the customer buying patterns change.
Now, business can bring down costs and completely cover customer demands. With AI based demand planning, enterprises can now become smart on ‘what’ to put ‘when’ for ‘whom’. Let’s dissect and understand how AI is used to forecast shortages and bring a competitive edge for your business!
Understanding the Benefits that AI-based Demand Forecasting brings for Businesses
Many may think that the demand forecast is just ‘divining’ about what can possibly happen in their business. But it is not so. AI based demand forecasting brings many things into picture including insights that help businesses take action to boost their sales. It has been observed that companies that bring in AI gain 50% higher accuracy and reduce overall stocking concerns.
As per a McKinsey report, AI helps enterprises bring down the supply chain cost up to 20% as well as improve inventory turnover by 35%.
Some of the benefits that AI in demand planning bring are:
- Operational Cost Savings: With the help of AI, businesses can cut down on unwanted inventory. This helps in cost reduction related to warehouse and stockouts. This is because AI helps businesses to predict demand, making sure that the inventory stays stocked with the right products on time.
- Improved User Satisfaction: As AI helps forecasting the product demand, it brings satisfaction in users and increases loyalty towards the brand. One such example is retail businesses can stock items that are predicted to go high on demand, helping customers get it on time.
- Resource Efficiency: Prevent any waste that rises due to overproduction or improperly timed product placement leading to less user purchase. Organizations that turn to AI for demand forecasting meet their sustainability goals without bringing down efficiency.
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Examples and Scenarios of how AI in Demand Forecasting helps in different Industries
AI has brought in real-life business transformation beyond thought. Some of them are listed below:
(1) Retail:
Big giants in the Retail segment such as Walmart take advantage of AI in researching user purchase history, trends shift in society, and weather patterns.
This lets them plan the demand needs in advance. By using AI, they bring down the markdowns, drive inventory stocks to be optimal, and meet peak season product demands.
(2) Healthcare:
During the recent pandemic of COVID-19, AI helps hospitals in knowing the optimal resources needed during a particular period. This includes ventilators and PPEs that bring supply risk mitigation to its best.
(3) Manufacturing:
From the manufacturing’s perspective, procuring raw material and scheduling production is a major risk factor. Especially when it comes to demand falling, such mismanagement can bring in heavy costs.
AI helps them analyse all the factors, internal and external, to build a strategy that uses the optimal resources as per the demand. This helps in lead time reduction and operational cost reduction and at the same time, the quality remains high.
(4) E-commerce:
Ecommerce entities such as Amazon, Alibaba, take advantage of AI forecasting to maintain the inventory at all times.
AI also helps them smoothen logistics to faster, well-planned deliveries. With AI, they predict the package delivery timelines and thus speed up the delivery, improving customer experience.
Through such industry-based examples, it can be seen that AI adapts quickly and presents results that enhance operations and decision-making.
Revamping Supply Chains using Capabilities of AI in Demand Forecasting
AI builds supply chains into high-speed data-informed eco structure through its capabilities:
- Real-Time Data Integration:
AI takes data from multiple sources. These cover social media, IoT devices or economic indicators. With the analysis of these dynamic market indicators, businesses get a complete view of market trends.
- Predictive Analytics:
Historical and real-time data is analyzed using machine learning models. This leads to pattern identification, trend and disruption prediction, such as possible supplier delays or possible geopolitical situations.
- Automated Insights:
AI brings insights and recommendations that businesses can take forward and implement. These AI-driven insights based on data keep pouring in without any human involvement leading to lowered resource costing.
These AI capabilities make supply chain adaptive, robust and defined as per business objectives.
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What are the critical steps involved in implementing AI for Demand Forecasting
While AI can help businesses forecast their demands, there are some major steps involved in building such AI agents as mentioned below:
Data Collection:
Data is the basis of every decision – manual or AI. This data comes from sources such as point-of-sale (POS) systems, supplier networks, and external databases. Vaster the data sources, the more accurate the forecasting will be.
Data Cleaning:
Now having data isn’t enough. It has to be properly set in a structure that is readable by algorithms. Thus, there is a need to cleanse the data to remove duplicates, check missing values and set standard formats.
Model Selection:
Businesses have to select a machine learning model that suits their needs. These include regression analysis, neural networks, and decision trees which are commonly seen demand forecasting models
Integration:
Businesses have their existing software applications. Through integration, AI can seamlessly converse with these systems without hassle. To integrate, APIs and cloud-based solutions are to be used, enabling minimum disruption.
Continuous Monitoring:
The models have to be updated on a regular basis. Continuous data is to be provided to improve accuracy. Overall monitoring is to be done to check whether the forecasts fit the changing nature of the market.
PSSPL smoothens the AI implementation process. It covers all the steps mentioned with risk checks to enable the creation of AI for demand forecasting.
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Frequently Asked Questions
While it can be used across many industries, here are some industries that take maximum advantage of AI in demand planning. Healthcare, manufacturing, e-commerce, Saas, Retail - are some of the industries.
Yes, it is possible. AI understands the supply chain data and data on external factors including weather or geopolitical events. On this basis, it forecasts any possible shortages.
The size of the business is not a concern for AI solutions. It is accessible to all organizational sizes.
With AI, forecasts can now be made accurate up to 50% and in some cases, more. This is much higher than the traditional methods existing in markets.
AI can capture data from various systems. This includes POS systems, market reports, social media, and IoT devices.
This is based on the complexity of the project involved. Thus, it can be anywhere between weeks or up to months.
Such AI projects have large initial investments. But the long-term benefits that it brings in through supply chain efficiency is something that businesses look eagerly as compared to the expenses.
AI promotes sustainable practices. It decreases overproduction, thereby leading to a decrease in waste. Thus, it helps in eco-friendly practices.
There do exist some challenges which include the need for proper quality of data, cost head of integration with existing systems, and need for skilled developers to set the entire system.
Yes, AI has such an adaptable nature to understand the shifting patterns in the market trends. With that, it always responds in real-time to any noticeable differences.
Future-proof your Business using AI-Based Demand Forecasting with PSSPL
With AI for demand forecasting, businesses can now use it as a transformation tool to present insights with high accuracy, efficiently adapting to the market changes.
But the most essential step towards a complete AI adoption is selecting the right partner for your AI transformation journey. At PSSPL, we have an Expert AI team that focuses on anticipating the best for the clients through successful tailored AI solutions.
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