Demand sensing is revolutionizing retail by harnessing cutting-edge technology and real-time data analysis to unveil consumer behavior with unprecedented clarity.

Recent consumer behavior shifts exposed the limitations of traditional demand forecasting methods. Even leading retailers struggled to adapt to rapid changes in consumer sentiment, first towards higher demand and at-home products, then to lower-priced goods as inflation concerns grew.

These challenges stem from inflexible sensing systems, outdated forecasting applications, and the sheer complexity of demand prediction. This has led to inventory mismatches, ineffective distribution, and missed opportunities to capitalize on peak demand periods.

Much like an experienced navigator reading the sea’s subtle cues to anticipate weather patterns and swells, demand sensing provides a dynamic, real-time view of market conditions.

Dubbed as a “prerequisite for an agile supply chain response” by Gartner, its implementation can boost forecast accuracy by 15-40% and lead to a 10% growth in sales.

What is Demand Sensing?

Demand sensing is an advanced forecasting methodology that uses real-time data and analytics to predict short-term customer demand with high accuracy. Unlike traditional forecasting methods that rely heavily on historical data and often struggle to account for sudden market changes, demand sensing incorporates a wide range of current, granular data points to create a more dynamic and responsive picture of consumer behavior.

At its core, demand sensing aims to capture and interpret demand signals as close to real-time as possible. These signals can come from various sources, including point-of-sale data, weather patterns, social media trends, economic indicators, and even IoT devices. By analyzing this diverse set of inputs, demand sensing systems can detect subtle shifts in consumer preferences, identify emerging trends, and anticipate potential disruptions in the supply chain.

The power of demand sensing lies in its ability to provide retailers with a more nuanced and timely understanding of demand. This enhanced visibility allows businesses to make more informed decisions about inventory management, pricing strategies, and promotional activities. As a result, retailers can reduce stockouts, minimize excess inventory, and ultimately improve their bottom line while meeting customer expectations more effectively.

IKEA exemplifies market success through the significant impact of demand sensing, achieving a notable 5% improvement in forecast accuracy. Parag Parekh, Global Chief Digital Officer (CDO) for IKEA Retail (Ingka Group), emphasizes the transformative role of AI, stating, “AI has been a game-changer for us, simplifying our routine tasks with the help of smart models.”

This advancement not only streamlines operations but also enhances the company’s ability to respond to customer needs effectively. In essence, demand sensing represents a shift from reactive to proactive retail management. By harnessing the power of big data and advanced analytics, retailers can move beyond simply responding to historical trends and instead position themselves to anticipate and shape future demand.

The building blocks

Effective demand sensing relies on several key components working in harmony to provide accurate, actionable insights.

Data collection and integration

The foundation of any demand sensing system is robust data collection and integration. This involves gathering data from a wide variety of sources, both internal and external to the organization. Internal data sources might include point-of-sale systems, inventory management systems, and customer relationship management (CRM) databases. External data can encompass weather forecasts, social media trends, economic indicators, and competitive intelligence.

The challenge lies not just in collecting this data, but in integrating it into a cohesive, usable format. This often requires sophisticated data management systems that can handle large volumes of data from disparate sources, clean and normalize the data, and make it available for analysis in near real-time. The ability to quickly and effectively integrate new data sources as they become available is also crucial for staying ahead in the fast-paced retail environment.

Advanced analytics and AI

Once the data is collected and integrated, the next critical component is the analytical engine that turns this raw data into actionable insights. This is where advanced analytics and artificial intelligence (AI) come into play. Machine learning algorithms can identify patterns and correlations in the data that might not be apparent to human analysts, allowing for more accurate demand predictions.

AI-powered demand sensing systems can also learn and improve over time and process vast amounts of data quickly, considering a multitude of factors simultaneously to generate nuanced, granular demand forecasts.

The use of AI also allows for the automation of many forecasting tasks, freeing up human analysts to focus on strategy and decision-making rather than number-crunching. For example, many consumer packaged goods companies are beginning to implement autonomous end-to-end planning, a more sophisticated method for managing supply chains in unpredictable environments.

Real-time monitoring and alerts

The final key component of effective demand sensing is a system for real-time monitoring and alerts. Real-time monitoring systems can track key performance indicators (KPIs) and alert relevant personnel when certain thresholds are met or when unusual patterns are detected.

For example, if sales of a particular product suddenly spike, the system could automatically alert inventory managers to increase stock levels. Similarly, if social media sentiment around a product turns negative, marketing teams could be notified to adjust their strategies. These real-time capabilities enable retailers to be proactive rather than reactive, addressing potential issues before they become major problems.

Effective real-time monitoring also requires user-friendly dashboards and reporting tools that allow stakeholders across the organization to easily access and understand the insights generated by the demand sensing system. This democratization of data helps ensure that the benefits of demand sensing are realized throughout the organization, from the C-suite to the store floor.

Implementation in retail operations

Implementing demand sensing effectively can transform various aspects of retail operations. This technology can be applied to three areas: supply chain optimization, inventory management, and pricing and promotion strategies.

Supply chain optimization

Demand sensing can significantly enhance supply chain optimization by providing more accurate short-term forecasts. This improved foresight allows retailers to fine-tune their supply chain operations, reducing inefficiencies and cutting costs.

With demand sensing, retailers can better coordinate with suppliers, adjusting order quantities and timing based on real-time demand signals. This can lead to reduced lead times and more frequent, smaller shipments, which in turn minimizes warehouse space requirements and lowers carrying costs. By anticipating demand fluctuations, retailers can also optimize transportation routes and schedules, potentially consolidating shipments and reducing overall logistics costs.

Moreover, demand sensing also enables retailers to be more responsive to unexpected events that might disrupt the supply chain. For instance, if the system detects a sudden increase in demand for a particular product, it can trigger automated reordering processes or alert supply chain managers to expedite shipments. This agility can help retailers avoid stockouts and maintain customer satisfaction even in volatile market conditions.

Inventory management

By providing more accurate short-term demand forecasts, demand sensing helps retailers strike the delicate balance between having enough stock to meet customer demand and avoiding excess inventory.

With demand sensing, retailers can implement more sophisticated inventory strategies. For example, they can dynamically adjust safety stock levels based on real-time demand patterns, reducing the risk of stockouts while minimizing carrying costs. The technology also enables more effective allocation of inventory across different store locations or channels, ensuring that products are available where they’re most likely to be purchased.

The technology can also help retailers better manage seasonal inventory and new product introductions. By analyzing early sales data along with external factors like weather patterns or social media buzz, retailers can quickly adjust their inventory strategies for these hard-to-predict scenarios. This can significantly reduce the risk of markdown losses on seasonal items or the costs associated with over-or under-stocking new products.

Pricing and promotion strategies

Demand sensing also offers powerful capabilities for optimizing pricing and promotion strategies. By providing real-time insights into consumer behavior and market conditions, it allows retailers to implement more dynamic and targeted pricing strategies.

For instance, demand sensing can help retailers identify the optimal timing for promotions based on predicted demand patterns. It can also provide insights into the effectiveness of different promotional strategies, allowing retailers to fine-tune their approach for maximum impact. Additionally, by accurately forecasting demand at a granular level, retailers can implement more sophisticated markdown strategies, optimizing the timing and depth of markdowns to maximize revenue while clearing excess inventory.

In terms of pricing, demand sensing enables retailers to be more responsive to market conditions. For example, if the system detects a sudden increase in demand for a product, it might suggest a small price increase to maximize profitability. Conversely, if demand for a product is waning, the system might recommend a temporary price reduction to stimulate sales and prevent excess inventory buildup.

Turning obstacles into opportunities

Implementing demand sensing offers clear benefits but comes with notable challenges. It relies on extensive data from multiple sources, making accuracy, timeliness, and integration critical hurdles. Many retailers face issues with siloed data systems, requiring investment in infrastructure and a shift towards greater collaboration.

Advanced analytics capabilities are essential, demanding sophisticated algorithms and machine learning. Retailers may need to partner with tech providers or build in-house data science teams to meet these requirements.

Change management is another key obstacle, as demand sensing often necessitates significant adjustments to existing workflows. Employees might resist these changes, so strong leadership and effective training are crucial for fostering acceptance.

Cost can also deter adoption, as the initial investment in technology and personnel can be substantial. Convincing stakeholders of the long-term benefits is often challenging, especially for those with tight margins.

Privacy and ethical considerations around data collection must be addressed, ensuring compliance with regulations and maintaining customer trust. This requires robust governance policies and transparent communication.

Lastly, retailers must remain adaptable to rapidly evolving technologies and techniques in demand sensing, continually updating their systems to stay competitive. The key to successful adoption lies in careful planning, a commitment to ongoing learning and improvement, and a willingness to embrace change.

The road ahead

The future of demand sensing in retail looks increasingly sophisticated and impactful.

AI-powered demand sensing systems will become increasingly adept at identifying subtle patterns and correlations in data. These systems are poised to take on decision-making roles with the autonomy of a self-driving car.

The Internet of Things (IoT) is another trend that will greatly influence the future of demand sensing. As more devices become connected, retailers will have access to an unprecedented amount of real-time data. Smart shelves that track inventory levels, connected appliances that monitor usage patterns, and even smart clothing that tracks wear and tear could all provide valuable data points for demand sensing.

While IoT sensors gather data, Edge Computing will be used to apply advanced analytics directly at the source, allowing for quicker decision-making and more immediate actions based on real-time insights. For retailers, this could mean having powerful demand sensing capabilities right in individual stores, allowing for instant adjustments to inventory, pricing, and promotions based on local conditions.

Collaborative initiatives are gaining traction, particularly in sectors like retail, logistics, and healthcare, where companies share aggregated or non-sensitive data to improve supply chain efficiency and customer insights. By providing a secure and transparent way to share data across the supply chain, blockchain technology could enable more collaborative demand sensing efforts.

Virtual and Augmented Reality, conjure new wells of consumer insight, with AR apps allowing customers to virtually try on clothes or place furniture in their homes, providing valuable data on preferences and purchase intent that can be fed into demand sensing systems to improve forecast accuracy. This trend towards personalization will grow as retailers collect more individual customer data, enabling personalized demand forecasts that could lead to highly targeted inventory management, with stock levels and assortments tailored to local customer bases or even individual high-value customers.

Sustainability considerations will also shape the future of demand sensing. As consumers and regulators place more emphasis on environmental impact, demand sensing systems will need to incorporate sustainability metrics. This could involve predicting demand not just for products, but for sustainable packaging options or forecasting the carbon footprint of different supply chain decisions.