Today’s global world is highly inter-connected and dynamic and that’s the reason why the supply chain industry faces a higher level of complexity in its operations. Consumers’ expectations are always high for faster and safer deliveries. In this ever-changing supply chain industry, relying on traditional methods for demand forecasting will take you behind your competitors.
That’s why emerging technologies like AI are playing a crucial role in demand forecasting. AI helps in creating a plan to satisfy projected demand which includes requirements planning, product planning, and later supply chain processing stages.
In this article, we will explore various aspects of how Enterprise AI Chatbot Development Services and AI are transforming demand forecasting in supply chain management. Let’s begin.
Do you know as per McKinsey report, applying AI-driven forecasting to supply chain management can reduce errors by 20 to 50?
Why is there a need for accurate demand forecasting in Supply Chain Management?
In the supply chain industry, forecasting is highly crucial. It helps in filling orders quickly, reduces inventory costs, and allows planning for any changes in the prices. Here are the major reasons why accurate demand forecasting is an essential need in supply chain management:
a. Planning and Scheduling Processes
Accurate forecasting makes the planning process much more precise. When the managers know the past and current demand for products, the supply chain remains at an appropriate level for maximum profitability.
b. Seasonal Changes in Demand
The managers don’t need to put a lot of effort or guesswork into identifying the changing demands in different seasons. With reliable data to support the forecasts being made, there will be no random decisions.
c. Predicting Product Demand
While no company can forecast the future with absolute certainty, identifying important patterns and making AI-based decisions on those patterns will help. It will help the company get as close to the goal as feasible.
d. Streamline the shipment
At the shipping stage, forecasting helps in assigning an appropriate number of resources for packing the goods. We then avoid staffing shortages or having too many workers who aren’t needed right now. Forecasting prevents high labour costs.
e. Price Forecasting
You can take control of the business by predicting pricing during the demand forecasting stage. It is possible to forecast and respond appropriately to the effects of price changes on a certain supply chain segment.
f. Anticipating Customer Demand
If you know the demand of customers, the lead times will get much shorter and the customer gets products on time. Furthermore, there will be increased trust between the customers and suppliers. The overall image of the company will improve and the end user will get a positive experience.
Problems with Traditional Supply Chain Forecasting Methods
In today’s thriving market, you can’t excel without involving technology in your operation or by doing everything manually or by using traditional methods. The major drawback of traditional methods is the lack of real-time data analysis. Thus, it becomes really difficult for supply chain managers to react swiftly to unanticipated demand patterns or abrupt changes in the market. There can be significant differences between forecasts and actual demand due to this rigidity.
Another major disadvantage is the inability of conventional forecasting techniques to adjust to changes in the market. They usually employ simplistic models that fail to capture the complexity of modern supply chains, ignoring factors like social trends, economic swings, and geopolitical events that could have a big influence on demand. The drawbacks of these methods are not only theoretical but also have real-world implications.
Businesses that are rigid and do not use the latest AI-based solutions may fall behind their nimbler competitors who are leveraging these cutting-edge technologies. The businesses will not just face serious financial repercussions, the sales will also be reduced, and the brand image will also deteriorate.
How is AI different from Traditional Methods in Forecasting Demand in Supply Chain Management?
It is obvious that employing traditional methods in forecasting demand will give less accurate results since these methods don’t take into account contextual elements like consumer behaviour, market trends, and external influences. The major issue is the assumption that the past patterns will remain unaltered in the future.
That’s why the traditional methods shouldn’t be applied as they are unable to handle complex patterns or enormous volumes of data.
Artificial Intelligence, on the other hand, utilizes advanced algorithms for machine learning. It is able to process enormous amounts of data and spot complex patterns and trends. AI models quickly update projections to take into consideration unanticipated circumstances, including unanticipated product increases or outages. A conventional demand forecasting solution uses a range of data types to accurately anticipate future demand.
Use Cases of AI in Demand Forecasting Across All Supply Chain Verticals
Here are the major use cases of AI in supply chain verticals:
1. Data Analytics
AI is capable of identifying even the most complex patterns and considering variables like holidays, promotions, and seasonality. The analysis of this data will help businesses to forecast future demand and understand historical consumer behaviour. The data can be generated in real-time through various sources like social media platforms, online transactions, and POS systems.
2. Optimizing Warehouse Space and Production Planning
As there are shifts in the demand for products, there is a need to optimize space usage. AI-based demand forecasting methods examine market trends, order history, and SKU-level data. This data helps in forecasting which products will see a spike in demand, the warehouse can better manage its inventory by modifying storage arrangements and allocating space appropriately.
3. Customer Behavior Analysis and Satisfaction
As we mentioned above, AI utilizes data from various sources which helps in analyzing customer behaviour. The predictive models of AI can help manufacturers obtain important insights and find patterns & trends in consumer preferences. Supply chain managers can ensure a proactive response to anticipated changes in customer demand and encourage a more adaptable and customer-focused manufacturing approach by purposefully modifying production numbers and enhancing product offerings.
4. Supplier Collaboration and Production Planning
AI allows real-time data sharing and communication between suppliers and managers. Thus, there are no gaps in communication and it fosters stronger relationships. The suppliers can adjust production schedules proactively for timely deliveries and there will be no stockouts. AI-driven insights also support demand alignment across the supply chain, allowing manufacturers to optimize production rates based on predicted needs.
5. Cost Reduction and Increased Profitability
By lowering forecasting mistakes, businesses can significantly reduce costs associated with overstocking and understocking. AI-based demand forecasting helps in saving costs, operational effectiveness, customer satisfaction, etc. Businesses can create new chances for development and innovation by cutting back on unnecessary spending and increasing return on investment.
Wrapping Up
In this article, we have gone through all the major aspects of AI in Supply chain management and understood how AI can transform demand forecasting. AI-based solutions are becoming common tools in almost every industry and the supply chain industry is no different.
At A3Logics, the enterprise AI Development company has a team of skilled AI developers with vast experience in AI-based SaaS solutions. We provide various AI development services and give a digital reality to our client’s vision. Let us know your requirements.
