Among the many interrelated processes involved in supply chain management, predicting dynamic customer demand is like mining for gold. Businesses that have the confidence to determine how much of a product there will be, and when it will be required will probably be the ones whose operations flow with ease behind the scenes or suffer very costly interruptions. According to forecasts by IHL Services, businesses lost approximately $1.77 trillion dollars in the year 2023 due to inventory distortion. Such events like stock outs and overstocks have been erosive to market sales. Due to constantly shifting customer preferences, changing economy, and uncertainties, forecasting demand is always a thorn in the flesh. This is where AI & predictive analytics come in to help businesses with the necessary tools that they need to identify better and more realistic opportunities. A McKinsey’s report shows that turning to AI-enabled supply-chain management for early adopters has helped to reduce logistics costs by 15%, inventory levels by 35%, and improve service levels by 65% as compared to the competitors?!
In this blog, we’ll explore why accurate forecasting is a priority today, and discuss how AI improves demand prediction. We’ll also look at the tangible business benefits of incorporating predictive analytics into supply chain management.
Why Supply Chains Need Predictive Analytics Now More Than Ever
The answer to the above is in understanding the importance of accurate demand forecast and how it affects supply chain management. In basic terms, businesses use demand forecasting to understand what their customer’s needs would be so that they can make changes in production, purchasing or stock levels accordingly. The picture in the demand forecasting is coined as proactive and not reactive, anticipating which goods will be needed and the time-frame for demand.
The above holds relevance in today’s era of trade. Forecasting involves predicting what, how much and when supply will be needed. Incorrect forecasting can impact the entire supply chain with over estimation of demand into assets leading to overstocking, high resource wastage and the costs of holding inventory. While, underestimation of demand is characterized by stock outs, loss of sales, unhappy customers and negative relations with suppliers.
As a matter of fact, forecasting effects more than stocking levels alone. It encompasses also planning of manpower requirements, planning of the production timetables, supplier selection, negotiation and purchasing strategies and the logistics network as a whole. While being one of the most important aspects of making a supply chain work, it happens to be one of the hardest to get right particularly using empirical forecasting techniques.
How do AI and Machine Learning Models Assist In Demand Forecasting
Just as in other domains, AI and machine learning (ML) are transforming the landscape of demand forecasting practice as well. They are doing so by giving actionable, data-driven answers in real time that are dynamic and evolve with changing conditions. Predictive analytics driven by AI does not rely only on historical sales data or standard fixed formulas. They go one step further to harness the power of sophisticated algorithms to incorporate many components that other approaches may not use. Such considerations may include current market trends, customer behaviour patterns & sentiment, seasonality, economic changes, competitor pricing and promotions, global supply chain disruptions, and more. Considering these internal and external factors, AI is able to deliver more accurate, comprehensive, and dynamic forecasts, even in the during uncertainty. Unlike static methods, which are often limited to “best guesses,” AI systems continuously learn and refine their predictions based dynamics of the market.
The Felix Solution!
With it’s robust AI-based Predictive Analytics Solutions, Felix is redefining the supply chain management sector. By employing web-based data and analytical methods, we help companies foresee demand, avert or predict disruption, assist in route planning, and manage inventory ahead of time. The advanced technology we have in place guarantees flawless communication and collaboration at all stages of the supply chain helping bring in a whole new level of perspective that ensures all decisions made are indeed comprehensive, smart, and based on data. Organizations can now easily manage their inventory, optimally allocate their fleets, lower their operational expenses, and maximize revenue potential, increasing customer satisfaction and creating a competitive edge.
Turning Insights into Impact: How Predictive Analytics Contributes to Business Outcome Enhancement
Visualize a situation where an retail outlet is preparing for what it hopes will be the peak sale period – the festive season. In the case of this retailer, an AI model, instead of simply basing on the sales made the previous year, can analyze current trends from social media platforms, research done online, buying behavior of customers, competitiveness in the market, the season and even global insufficiencies such as pandemics and so on. These coordinated efforts give rise to a prediction that is even smarter and more focused, enabling this retailer to be prepared with the right amounts of items for purchase, stocking and dispatching.
Let’s look at how predictive analytics translate into real-world benefits for businesses.
1. Reducing Stockouts and Surplus Inventory
Most critical benefit AI-powered demand forecasting is the ability to strike an optimized balance between supply and demand and avoid both understocking and overstocking! 4% (or $900 billion) of the total worldwide annual revenue in the commercial market is lost due to out-of-stock (OOS) events! When it comes to inventory shrinkage, retailers lose around $45.2 billion annually in the United States.
Inadequate inventory management has been known to cause a revenue loss of over $300 billion for businesses! To avoid such costs, major retailers from Addidas, Amazon, Walmart, and Procter & Gamble among others use AI to predict demand, automate replenishment, and manage inventory across thousands of stores and warehouses. According to a report, AI-driven demand sensing has been shown to reduce inventory errors in supply chain management by up to 50%!
By predicting demand more accurately, AI tools help businesses avoid unnecessary replenishments and minimize inventory holding costs, ensuring customers find what they need when they need it. Instead of relying on buffer stock and excess inventory to avoid stockouts, predictive analytics allows companies to fine-tune inventory levels without overstocking.
2. Optimizing the Supply Chain
AI goes a step further than just optimizing inventory—it optimizes the entire supply chain! A McKinsey report suggests, successfully incorporating AI-driven forecasting into supply chain management can reduce errors by between 20-50% leading to a reduction in lost sales and product unavailability of up to 65%. Further, it can lower warehousing costs by 5-10%, and administration costs by 25-40%.
From fine-tuning production schedules, aligning with supplier lead times, conducting quality checks, optimizing delivery routes, and enhancing overall efficiency, AI-powered predictive analytics helps streamline operations like never before.
3. Enhancing Customer Satisfaction
Ultimately, all supply chain efforts come down to one thing: customer satisfaction. By equipping businesses to meet customer demand consistently, AI-driven forecasting helps create a seamless customer experience.
Understocking results in approximately 70% of consumers or more buying the product from elsewhere. Incorporating AI-enabled supply-chain management has allowed early adopters to improve service levels by 65% compared to their competitors. This not only boosts sales but also enhances brand loyalty, the value of which can never be taken for granted in an increasingly competitive marketplace.
4. Mitigating Risks and Increasing Resilience
Predictive analytics helps companies stay resilient in today’s volatile global landscape by identifying potential risks and creating contingency plans. By being made aware of potential supply chain disruptions, companies can respond proactively with a level of agility that is critical in minimizing losses and ensuring long-term sustainability. For instance, businesses that were using AI for demand forecasting during the COVID-19 pandemic were better able to adjust to the rapidly changing conditions, whether that meant scaling down production due to reduced demand or scaling up delivery services to meet increased online orders.
Concluding Remarks
In a world where customer expectations are higher than ever, and supply chain disruptions are a constant threat, the need for accurate demand forecasting can’t be overstated. The future of demand forecasting in supply chain management is bright—and it’s powered by AI. With its ability to analyze enormous amounts of data and predict outcomes with precision, AI-driven predictive analytics is helping businesses build smarter and more resilient supply chains.
To transform your own supply chain, why in touch with us today for a demo!