How to Improve Demand Planning When You Have Imperfect Sales History

Demand planning, a crucial aspect of retail merchandise planning, often rests heavily on historical sales data. But what happens when that sales data is flawed, suspect, or incomplete? What if unforeseen circumstances—like the recent global pandemic—disrupt patterns, rendering past data an unreliable predictor of future demand? This has been a common challenge for many businesses, especially in the retail and e-commerce sectors, over the past few years. Fortunately, advancements in AI retail merchandise planning software present innovative and effective solutions to these challenges.

The Post-Pandemic Curveball: Impact on Demand Forecasting

The COVID-19 pandemic sent shockwaves across the globe, causing seismic shifts in consumer behavior and business operations. In the retail sector, brick-and-mortar stores faced closures, supply chains were disrupted, and shopping habits altered significantly. E-commerce, on the other hand, witnessed a surge, as consumers turned to online shopping amidst lockdowns and social distancing norms. These sudden and drastic changes made historical sales data almost irrelevant for demand forecasting.

As businesses entered the post-pandemic era, they grappled with a new reality: erratic and unpredictable demand. Traditional demand planning methods, which relied heavily on historical data, were ill-equipped to handle this uncertainty. This emphasized the need for more sophisticated and adaptive forecasting models—enter AI.

AI: The Game Changer in Demand Planning

Artificial Intelligence, with its ability to analyze vast amounts of data and identify patterns, offers a robust solution for demand planning in the face of flawed or incomplete sales history. AI-driven demand planning software uses machine learning algorithms to analyze not just historical sales data, but also a wide array of external factors like market trends, seasonal fluctuations, promotional activities, and even macroeconomic indicators. This enables the generation of more accurate and reliable demand forecasts, even when sales history is flawed or incomplete.

Handling Incomplete or Unreliable Data

When dealing with flawed or incomplete data, AI algorithms can ‘fill in the gaps’ by analyzing patterns in the available data and extrapolating them to create a complete dataset. For example, if an e-commerce business lacked sales data for a particular product category due to a supply chain disruption, AI could analyze the sales trends of similar products, along with other influencing factors, to predict demand for the missing category. AI retail merchandising software can also identify outliers like large unusual one-time purchases and exclude or dampen their effects on the demand forecast.

Adapting to Changing Consumer Behavior

AI’s ability to continuously learn and adapt makes it invaluable in situations where consumer behavior changes rapidly. For instance, in the post-pandemic era, a travel gear retail business might observe a sudden increase in demand for carryon luggage due to loosened Covid regulations across the globe. Traditional forecasting methods, based on past data, would likely fail to predict this spike. However, AI, by analyzing current data and identifying the new trend, could adjust the demand forecast accordingly.

Incorporating External Factors

AI can integrate a variety of external factors into demand forecasting models, thus providing a more holistic and accurate forecast. For instance, an e-commerce platform might observe a surge in sales during promotional events like Black Friday or Cyber Monday. While traditional models might struggle to factor in the impact of such events, AI can easily incorporate these into its forecast, ensuring that the business is well-prepared to meet the spike in demand.

Turning Challenges into Opportunities: Real-World Examples

To better understand how AI can aid demand planning amidst flawed sales history, let’s consider some real-world examples.

Example 1: A Fashion Retailer

Let’s take the case of a fashion retail brand that had to shut down its physical stores during the pandemic, causing a significant disruption in its sales data. Post-pandemic, as the stores reopened, the brand struggled with demand planning due to the missing historical data. By deploying an AI-based demand planning solution, the brand was able to analyze real-time sales data, along with other factors like the latest fashion trends and social media sentiment, to create accurate demand forecasts. This helped the brand optimize its inventory levels and avoid both stockouts and overstocks.

Example 2: An E-Commerce Brand

Consider an e-commerce platform that saw a surge in demand for certain categories like home essentials and fitness equipment during the pandemic, while other categories like luxury goods witnessed a dip. As the market conditions changed post-pandemic, the platform found it difficult to predict demand based on the skewed historical data. However, by using AI, the platform was able to continuously monitor and learn from real-time data, adjusting its demand forecasts as consumer behavior evolved.

TL;DR – while flawed, suspect, or incomplete sales history presents significant challenges in demand planning, it also offers an opportunity to innovate and adapt. By harnessing the power of AI solutions like OmniThink.ai, businesses can transform their demand planning process, making it more robust, adaptive, and accurate. As the post-pandemic retail and e-commerce landscape continues to evolve, the ability to accurately forecast demand—despite data challenges—will be a critical factor in achieving business success.