$1.77 trillion! That’s the total cost of global inventory distortion IHL projected for 2023. Out of this, out-of-stocks cost $1.2 trillion and overstocks amounted to $562 billion.
Empty shelves and excess inventory – both are among the worst nightmares of a retailer. They significantly impact growth resulting from lost sales, underutilized resources, high costs and bad customer experience. That is why accurate demand forecasting is critical.
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However, Traditional Forecasting Methods Are Not Enough
Under ideal conditions, demand forecasting would be easy using historical and transactional data. But reality is far from ideal. Traditional forecasting methods simply cannot capture the intricacies of today’s. There is a long list of factors affecting demand. It includes changing consumer behavior, seasonality, weather, economic shifts, geopolitics, currency fluctuations, and the growing complexity of supply chains and their disruptions.
Traditional methods often fall short. It’s because they don’t consider the interdependencies and non-linear relationships of these factors. Additionally, these methods cannot analyze massive amounts of growing data to extract accurate and timely insights. Then there is a human error risk involved. All combined, this creates a recipe for inaccurate forecasts and poor decision-making.
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How AI Is Making Way For Future-Forward Retail
AI and machine learning are painting a bright future for the retail industry. These technologies enable demand forecasting with unparalleled accuracy, efficiency, and agility. They can analyze vast datasets and detect critical patterns using sophisticated algorithms. So, decision-makers can get invaluable insights that were hitherto inaccessible and even unknown. Let’s explore more.
Unprecedented accuracy
Already massive data is continuously growing. It is too complex to process with human intervention alone. Then there are various external factors, as this article mentions above, and their interdependencies that shape consumer demands.
Unlike traditional forecast approaches, AI does more than crunching numbers. It analyzes all demand drivers and their relationships at a granular level. It helps uncover hidden patterns that would remain undetected otherwise. It also eliminates the scope of guesswork in various cases, e.g., a new product or a product with no historical data. With no room for human judgment and errors, AI delivers unprecedented accuracy in demand predictions.
Real-time insights
When your potential competitor launches a similar product line, you need to know on the go how it will impact your demand so that you can adjust your stock, pricing and marketing accordingly. This is just one of many possible cases. The retail landscape is highly dynamic with fluctuating demands and retailers need to be agile.
That is why they need real-time insights to ensure timely execution and better coordination across the supply chain. According to Gartner, 60% of chief supply chain officers are expected to make faster, more accurate and consistent decisions in real time. AI processes real-time data in real time and provides real-time insights to make quick and informed decisions. It allows retailers to agile and adapt their strategies to demand fluctuations quickly.
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Targeted marketing strategies
Inaccurate forecasts can result in ineffective marketing and wrong targeting. It can lead to low demand, excess stock, missed opportunities and even churn risk. AI can provide powerful demand intelligence to retailers to reach the right consumers with the right message at the right time. Thus, they can personalize their campaigns to individual customers, realizing higher marketing ROI.
With better insights into demand and customer sentiments, they can determine with confidence when to launch a new product and how it will do. These insights also help increase prices for existing products without triggering customer defection risk.
Optimizing the supply chain
AI can establish a cohesive and collaborative supply chain ecosystem by facilitating seamless communication between manufacturers, suppliers, distributors and retailers. It helps optimize supply chains with accurate demand prediction at the product, store, and channel levels. AI allows them to anticipate potential disruptions and expected events. It then recommends dynamic adjustments to mitigate risks early to maintain smooth coordination and operational efficiency across the ecosystem. This ensures optimal inventory levels across their entire network.
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Answering what-if scenarios
AI lets retailers take a peek into the planned future as well as several hypothetical situations. It can help you explore different what-if scenarios and alternatives without them having to in reality. For example – how will a sudden change in transportation costs impact prices and demand? How increasing the price of a product range will affect its demand? What if you launch a new product in the following quarter, how will customers like it and how will it influence the demand for existing products? With these AI-driven what-if analyses, retailers can be prepared for unexpected changes in consumer demands and maintain optimal inventory levels.
Easy scalability
One of the major benefits of AI-powered demand forecasting is high scalability. By automating mundane tasks and streamlining forecasting processes, it saves valuable time, resources and capital. So, you can scale your business without a linear increase in labor costs and drive efficiency in cost and operations.
AI’s machine learning algorithms are constantly learning and adapting. They continue to incorporate new data and insights to improve forecast accuracy. So retailers are always prepared for the unexpected, even as market trends and customer preferences evolve. Additionally, the forecasts stay relevant even as your business expands or enters into new markets.
Accurate demand forecasting can ensure product availability as and when customers need it. For retailers, AI-powered forecasting enables data-driven decisions to optimize inventory, maximize efficiency, and drive profitability.
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AI-powered demand forecasting is not just a technological advancement. It is a new age in the retail industry. It offers retailers the much-needed agility with real-time intelligence so they can deliver seamless customer experience and drive growth and profitability. In a dynamic and complex supply chain landscape, this ability is an imperative for retailers with thousands of SKUs.
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