Leveraging AI for demand planning and forecasting has proven to be a game-changing solution for the retail sector, especially when faced with rapid fluctuations and unforeseen obstacles. By swiftly forming crisis teams with authoritative decision-making abilities, companies can effectively address challenges without the hindrance of bureaucratic processes. Transitioning from intuition-based decision-making to AI-driven data analysis has been a pivotal shift for many businesses. This shift has allowed companies  to accurately predict demand trends and efficiently manage their supply chain, ensuring that essential items remain available in all stores during times of crisis. This strategic pivot not only enabled to effectively meet customer demands but also led to a significant increase in market share during challenging times, highlighting the value of AI in improving demand planning and forecasting for the retail industry. Key benefits include:

• Quickly analyzing and correlating demand insights in a user-friendly format.
• Enhancing product mix and service availability, leading to increased revenue margins.
• Accurately forecasting and responding to fluctuating margins and inventory needs.
• Identifying patterns from past events to predict future demand trends efficiently.

How Demand Sensing Enhances Traditional Forecasting in Retail

Many food retailers commonly utilize traditional forecasting techniques that employ a time-series methodology to generate a forecast based on historical sales data. However, this approach has a significant drawback. While past sales data can be reliable for predicting future plans in the mid and long term, it may lack accuracy when it comes to short-term forecasting. Recent events worldwide have highlighted the importance of short-term plans, as market conditions can change rapidly on a daily or weekly basis. This is where Demand Sensing comes into play, emphasizing that the most recent data is the most precise predictor of immediate future trends, building on the notion that the past is the best indicator of long-term outcomes. The abundance of demand planning theories that rely on traditional methods like weighted averages may seem convenient, but they only provide predictions based on historical data. These methods lack the essential real-time information and external factors that can significantly impact current demand trends. For food retailers looking to make accurate short-term demand planning decisions, utilizing Demand Sensing is crucial. Unlike traditional approaches, Demand Sensing is capable of quickly responding to immediate demand fluctuations, ensuring retailers can adapt swiftly to changing market conditions. Ultimately, Demand Sensing aims to reduce supply chain inefficiencies by minimizing the delay between events and the corresponding responses. By promptly analyzing and acting upon relevant demand signals, food retailers can effectively address fluctuations in consumer demand. In the food retail sector, one of the major hurdles has always been aligning supply with demand due to the extensive range of products and ever-changing sales patterns. Implementing Precise Demand Sensing can greatly alleviate the limitations of traditional demand forecasting methods in retail.

• The Challenge of Stock Shortages Vs Excess Inventory: Inaccurate forecasts can result in unforeseen stock shortages, leading to missed sales opportunities. Consistent stock availability is essential for meeting customer expectations and maintaining store traffic. Stockouts can result in decreased foot traffic and potential loss to competitors. On the other hand, excessive inventory ties up valuable capital and can lead to wastage or the need for markdowns to clear out old stock. Food retailers may struggle to find the optimal balance to maximize profit margins.
• Legacy Forecasting Systems in Retail: Legacy forecasting systems in retail often present challenges for food retailers. These systems are typically inflexible and do not take into account real-time changes, weather variations, and local factors. They also lack a comprehensive understanding of the various factors influencing consumer purchasing decisions. As a result, retailers struggle to promptly address their customers' evolving needs, leading to issues such as excess or insufficient inventory, missed sales opportunities, and unnecessary product wastage.
• Uncertainty of Launching New Products: Accurate forecasting for new product launches is more crucial than ever before due to the shrinking product life cycle and increasing assortment turnover. In the food retail sector, where new products are regularly introduced and restocked, these introductions account for a significant portion of sales. Any inaccuracies in demand forecasts for these new products can lead to errors in overall business forecasts.
• Sales Volume Variance and Fluctuations: Retail managers must navigate the ever-changing nature of sales volumes, particularly in sophisticated retail settings. Effective demand forecasting in retail requires a blend of statistical analysis and human expertise to accurately predict and adapt to these fluctuations. Sales variances can be influenced by broader market conditions, necessitating forecasting methods that are adept at detecting short-term shifts and drawing connections to overarching events.

Intelligent Demand Sensing

Demand Sensing is a valuable tool that can be utilized throughout various levels within the food retail sector. By implementing advanced technologies such as Artificial Intelligence and Machine Learning for rapid demand sensing, food retailers and supply chain managers can effectively manage and reduce risks associated with fluctuating demand. These innovative techniques are designed to promptly analyze and incorporate critical data to enable agile decision-making in real-time supply chain operations.

• When it comes to forecasting models, it is crucial to look beyond historic data alone. Utilizing AI-driven demand sensing models that can adapt and learn from data collected from other items and stores can lead to more accurate forecasts in the long term. These advanced algorithms have the ability to analyze detailed information from various marketing events to accurately predict the response of a new product or service to changes in prices, demand trends, and other factors. To effectively prepare for future demand fluctuations, it is essential to consider more than just historical data. By leveraging advanced forecasting models that incorporate data from multiple sources, food retailers can better assess and respond to changes across different locations, distinguishing between isolated incidents and broader trends.
• Ensuring Accurate Data for Improved Demand Sensing Achieving higher forecasting accuracy requires going beyond traditional methods of annual or quarterly demand planning. The most crucial data for accurate forecasting lies in day-to-day activities that impact daily orders and purchases at the store. Inaccuracies in these daily numbers can significantly skew forecasting accuracy and in turn, affect the performance of the supply value chain as a whole. Implementing a demand forecasting solution that can be seamlessly integrated across all sales channels can guarantee forecasting accuracy of over 100%. It is important for these models to capture granular data from all sales channels, taking into account seasonal variations, holidays, customer trends, and regularly correlating and mapping this data accordingly.
• The food retailer implemented a data-driven analysis of fulfillment and shipping practices in their extensive warehouses to optimize replenishment and allocation down to individual SKUs. Limited insights into existing siloed and legacy data systems prompted the retailer to seek an accurate flow analysis between warehouses and enhance shipment handling and transport processes to increase overall profit margins. Utilizing an AI-driven Demand Sensing solution can improve accuracy and automation in pull-based replenishment, ultimately leading to a more profitable push-based replenishment. These algorithms continuously learn and provide real-time insights to effectively respond to sudden changes in demand.
• Using a Demand Sensing platform can help to manage real-time changes in customer demand more effectively. Traditional forecasting methods may struggle to keep up with sudden shifts in customer needs, especially when looking far into the future. By implementing a more real-time approach, retailers can better address near-term changes and prevent forecasting errors. Incorporating Demand Sensing technology can lead to a significant reduction in forecasting errors, by as much as 30%-40%. This solution allows retailers to quickly adapt to sharp changes in customer demand, ultimately leading to a more data-driven and efficient supply chain.

Advantages of Demand Sensing in Retail

Demand Sensing takes forecasting to the next level by offering a range of more detailed benefits:

• Aligns demand planning and execution, providing users with a deeper understanding of customer behavior by evaluating various costs across different outcomes.
• Optimizes inventory needs, minimizes waste, and reduces costs related to logistics and transportation.
• Enables precise, timely, and well-informed decision-making on inventory, pricing, replenishment, and assortment at a more granular level.
• Enhances upstream planning by extracting relevant data directly from Point-of-Sale (PoS) systems, enhancing forecasting accuracy without relying on distributors or suppliers.
• Improves forecast accuracy from the conventional 60% to over 80% at the SKU, shelf, and location levels.
• Enhances production-planning efficiency through consistent input to supply operations, facilitating adjustments to production plans by expediting orders.
• Bridges inbound and outbound gaps between warehouses to establish a cohesive understanding of global demand.
• Optimizes inventory management and supply planning by considering inventory levels in regional warehouses and projected customer demand by location.
• Anticipates seasonal demand trends to enhance supply chain responsiveness and prevent food retailers from experiencing inventory obsolescence.
• Manages the introduction of new products with appropriate inventory levels to meet demand and maintain optimal inventory of both new and older product versions based on customer needs.
• Enhances evaluation of marketing promotions to provide planners with insight into the factors influencing promotional success and aid in planning future promotions.

Retail Demand Forecasting with Nirmalya's Retail & Distribution Platform

Nirmalya Enterprise Platform enabled with Business Intellegence serves as a data-driven planning and forecasting platform that effectively analyzes and links demand insights, while adapting to changing margins and inventory requirements. Specifically designed for the retail and distribution sectors, this  utilizes AI technology to simulate supply chain planning processes, aiding in the prediction of future demand trends and optimizing the flow of materials, production processes, and cash flow within the supply chain. Demand planning is now an essential function that serves as the cornerstone of vital business intelligence across the organization. This requires active involvement from all stakeholders, as it directly influences profit margins, revenue streams, capital flow, and resource allocation. Nirmalya Enterprise Platform addresses this collaborative necessity through its standout features:

• Connecting the Dots Between Past, Present, and Future : Using historical product/service sales data, identifies time-series patterns and analyzes short-term trends. It considers external events, holidays, and promotions to accurately predict future demand patterns, improving product and service availability and increasing revenue margins. The business outcome includes eliminating manual efforts and prioritizing operations more efficiently through accurate demand sensing.
• Gain Confidence with a Comprehensive Overview of Your Demand Plans : To effectively plan for demand, it is crucial for demand planners to have a comprehensive understanding of data ranging from detailed levels to a broader perspective. It provides you with easy access to this crucial data. The ultimate goal is to drive accurate, data-driven decisions by leveraging a multi-dimensional view, leading to improved productivity and collaboration within the organization.
• Categorize Sellable Items Using Rational Grouping : Organizing sellable products or services into segments using ABC/XYZ Segmentation, geo-location, and product mix can enhance your product optimization. This allows for a more thorough evaluation of top and bottom-performing SKUs. Resulting in a convenient, segment-focused analysis of your product assortment across various locations, product groups, and SKUs.
• Extract Demand Summary and Augmentation Views Across Variables :  This offers a comprehensive summary view that includes Throughput, Lost Sales, and OTIF/demand trends. Users have the option to add and test extra variables such as holidays, promotion events, and weather changes against demand. This enables businesses to thoroughly analyze the variables that could impact demand forecasts, while considering potential fluctuations based on market trends.
• Monitoring Changes in Market Sentiments and Customer Behaviour with Precision: By closely monitoring fluctuations in market sentiments and customer behaviour, you can accurately predict upcoming trends and adjust inventory levels and operational strategies accordingly. This proactive approach effectively manages operational costs and ensures that your products are always well-stocked, even in the face of unpredictable shifts in market dynamics. Ultimately, this strategy leads to optimal product availability and improved business outcomes.
• Calculate Procurement Efficiency to Reduce Operational: Waste Consider various external factors such as economic, geographical, and climatic conditions when evaluating procurement versus wastage. By doing so, you can effectively measure your input against output and minimize unnecessary waste. This can ultimately lead to a competitive advantage by optimizing demand sensing in the supply chain.

Future Trends in Demand Sensing and Forecasting in the Retail Industry

In looking ahead to the future of demand sensing and forecasting in the retail sector, several trends are set to transform the industry. The integration of Artificial Intelligence (AI) and machine learning is currently revolutionizing retail operations by enabling accurate, real-time predictions utilizing a variety of data sources and economic indicators. This development not only enhances inventory management but also boosts customer satisfaction through the reduction of stockouts and excess inventory. Moreover, the utilization of big data analytics allows retailers to delve deeper into understanding consumer behaviors and preferences, thereby improving their predictive capabilities. Additionally, the Internet of Things (IoT) is playing a significant role in reshaping the industry, as technologies like RFID tags, connected sensors, and smart shelves deliver continuous data on inventory levels and product movement. This enables retailers to make well-informed decisions and optimize their supply chain operations. Omni-channel integration is becoming increasingly important as consumers shop across multiple platforms. This integration ensures that inventory meets consumer demand across all channels, enhancing the shopping experience and uncovering new sales opportunities. Collaborative forecasting is on the rise, encouraging a unified approach by sharing demand data throughout the supply chain, enhancing collaboration among partners, vendors, and suppliers. Sustainability considerations are also playing a greater role in demand sensing, with retailers incorporating eco-friendly forecasting models to track demand for sustainable products and minimize waste. The progress made in this area represents a transformative change towards retail operations that are more interconnected, intelligent, and adaptable. This shift not only aims to better address consumer demands but also propel the industry forward in innovative ways.

Nirmalya Enterprise Platform, equipped with BI and AI capabilities, offers a consolidated platform for retail and distribution businesses to efficiently oversee all operations from a single source. By utilizing Nirmalya's AI-driven Supply Chain solution, a retailer experienced noteworthy cost savings in their supply chain on an annual basis. This led to decreased trucking expenses, higher revenue generation, and enhanced inventory turnover. For further information on Nirmalya SCM, please contact us.