What is Demand Sensing and Why is it Important?

Demand Sensing Explained

Demand sensing uses real-time data and advanced analytics to understand and predict customer demand. This enables companies to optimize their supply chain and inventory management.?

The concept of demand sensing differs from traditional demand planning, which relies on historical sales data and assumes that future demand will follow the same patterns as in the past.

Modern demand sensing is designed to be more agile and responsive, incorporating up-to-date information on factors like changes in consumer behavior, weather patterns, and economic conditions.

And in today's market, demand sensing is becoming increasingly important as companies face a variety of challenges that make traditional demand planning less effective. These challenges include:

Rapidly changing consumer preferences: Consumers today have access to more information and more choices than ever before, leading to constantly evolving preferences and buying patterns. The traditional concept of demand planning was not always able to keep up with these shifts, leading to inaccurate forecasts and missed opportunities.

Increasing complexity of supply chains: Supply chains are becoming more global and interconnected, with more stakeholders and variables to manage. This complexity makes it more difficult to accurately forecast demand using traditional methods.

Market Disruptions: Market disruptions can have a significant impact on the demand for products and services, and companies that are not able to respond quickly to these changes risk losing market share and revenue. Economic factors, such as changes in consumer income or shifts in global trade policies, can impact demand in unexpected ways.?

Social factors, such as changes in consumer preferences or the emergence of new technologies, can also have a significant impact on demand. Environmental factors, such as natural disasters or pandemics, can disrupt supply chains and lead to significant shifts in demand.

Why is Demand Sensing important today?

Demand sensing helps organizations better understand and anticipate customer behavior and demand for their products and services. And this means, with demand sensing analytics, organizations can identify and act on insights to improve customer service and optimize pricing, and promotions.?

Additionally, demand sensing can help organizations stay ahead of their competition by predicting market trends and adjusting their supply chain accordingly. It can also help businesses lower costs by reducing inventory, avoiding stockouts, and minimizing costly markdowns.

Today’s demand management tools can capture quantitative and qualitative demand insights from online and offline data inputs, And custom calculations can help you spot trends, identify forecast variances, and respond to other demand stream changes quickly and efficiently.

Demand sensing can support these Key Performance Indicators:

Supply Chain KPIs

Perfect Order: Improve customer service by producing the right product mix matched to actual demand.

Production Efficiency: Stabilize production schedules and avoid emergency changeovers to meet unexpected surges in demand.?

Logistics: Reduce transportation costs by avoiding transshipment and expensive emergency shipments and reduce warehouse costs with lower inventory levels.

Financial KPIs

Revenue and Profit Margins: react to upswings in demand to capture additional revenue and increase profit margins by avoiding costly supply chain inefficiencies due to demand uncertainty.

Cash-to-Cash Cycle Time: Free up cash flow and achieve a higher return on invested capital by reducing inventory levels.

How Does Demand Sensing Work?

Now that we know what demand sensing is, let’s explore how it works, the process of demand sensing, the benefits it provides to companies and some real-world examples of its implementation.

Step 1: Data Collection

The first step in the demand sensing process is data collection. Companies must collect a variety of data related to the product or service, such as sales history, inventory levels, and customer behavior.?

This data can come from a variety of sources, including point-of-sale systems, online sales platforms, and customer surveys. Data from external sources such as social media trends, weather patterns, and economic indicators can also be included to provide a more comprehensive picture of consumer demand.

Step 2: Data Integration

Once the data is collected, it must be integrated into a single database or platform to enable analysis. This step involves the extraction, transformation, and loading (ETL) of the data, which can be a time-consuming process. The process of data integration typically involves three main components: extraction, transformation, and loading (ETL):

• Extraction involves pulling data from various sources, such as point-of-sale systems, online sales platforms, customer surveys, and external sources like social media and weather data. This can be a complex process, as data may be stored in different formats or require different extraction methods.
• Transformation involves cleaning and standardizing the data to ensure consistency and accuracy in the analysis. This step may involve removing duplicate entries, correcting errors, and standardizing data formats.
• Loading involves moving the transformed data into a single database or platform. This can be done through a variety of methods, such as batch processing or real-time data streaming.

Challenges in Integrating External Datasets

Integrating external datasets into a demand sensing platform can be challenging for a variety of reasons. For example, external datasets may be stored in different formats or require specialized extraction methods. Additionally, external datasets may contain missing or incomplete data or be subject to data privacy and security concerns.

To overcome these challenges, companies can use a variety of techniques and technologies, such as

• Data Wrangling: This involves preparing data for analysis by cleaning and transforming it into a standardized format. It can be done manually or through automated data-wrangling tools.
• API Integration: Some external datasets may offer application programming interfaces (APIs) that enable companies to access and integrate the data directly into their demand sensing platform.
• Data Governance: Establishing data governance policies and procedures can help ensure the quality and security of external datasets.
• Data Integration Platforms: Using a data integration platform can help streamline the ETL process and provide a centralized repository for all relevant data.

Step 3: Data Analysis

Once the data is integrated, companies can use various statistical and machine-learning techniques to analyze the data and identify patterns in consumer demand. For example, they may use regression analysis to understand the relationship between sales and different variables such as price, seasonality, and marketing promotions.

Machine learning algorithms such as neural networks and decision trees can be used to capture complex relationships between variables. These algorithms can identify patterns in the data that may be missed by traditional statistical methods and can make more accurate predictions of future demand.

Step 4: Data Modeling

Based on the analysis, companies can develop models to predict future demand for the product or service. These models may incorporate real-time data, such as changes in inventory levels, weather patterns, or social media trends. The models can also be updated on a regular basis to incorporate new data and improve their accuracy.

Step 5: Data Reconciliation

An important factor is to map the correct data levels and time buckets for the demand sensing planning horizon and to determine how demand sensing data will interact and reconcile with any existing operational, tactical and strategic plans.

Step 6: Data Optimization

Companies can then use demand sensing models to optimize their supply chain and production processes, ensuring that they can meet demand while minimizing excess inventory or stockouts. For example, if the demand for a particular product is predicted to increase, the company can adjust its production schedule to increase output and ensure that the product is available in sufficient quantities.

Demand Sensing Benefits

Demand sensing is a powerful tool that can provide companies with a variety of benefits, including improved accuracy in demand forecasting, enhanced customer satisfaction, reduced inventory costs, and improved agility. By using real-time data and advanced analytical techniques, companies can make more accurate predictions of future demand, which can be crucial in optimizing supply chain operations and meeting customer needs.

Short-term and Mid-term Demand Accuracy

Demand sensing can improve short-term and mid-term demand accuracy by providing real-time insights into consumer behavior. This can help companies quickly adjust production schedules and inventory levels to meet changing demand. By accurately forecasting demand, companies can avoid stockouts and minimize excess inventory, which can result in significant cost savings.

Last Mile Delivery and Stockouts

Demand sensing can also help companies avoid stockouts and optimize last-mile delivery. By predicting demand for specific products, companies can ensure that they have the necessary inventory on hand to meet customer demand. In addition, by using real-time data on traffic, weather, and other external factors, companies can optimize delivery routes and schedules to ensure timely delivery.

Seasonal Events

Demand sensing can be particularly useful for events such as Halloween or Black Friday sales. By analyzing historical sales data and external demand signals, companies can accurately predict demand for specific products and adjust inventory levels accordingly. This can help companies avoid stockouts during these high-demand periods and ensure that customers have access to products.

E-commerce Store Benefits

Demand sensing can also be incorporated into e-commerce stores to improve pricing techniques and ad optimization. By analyzing real-time data on consumer behavior, companies can optimize pricing strategies and targeted advertising to increase sales and customer satisfaction.

Impact on Sales

Demand sensing can provide granular insights into sales activities, engagement, and delivery status. By monitoring late deliveries and comparing pending deliveries with current stock, companies can optimize inventory levels and reduce stockouts. Additionally, by gaining insights into accounts receivable and sales outstanding values, companies can improve cash flow and minimize financial risk.

Demand Sensing vs Demand Forecasting

In order to respond to market volatility and shifting consumer preferences, companies need to be able to adjust their production and inventory levels quickly and effectively. This requires access to real-time data and the ability to make rapid decisions based on that data.

Traditional demand business planning methods, which rely on historical data and statistical models, may not be able to provide the level of agility and responsiveness needed in today's market. But demand sensing, on the other hand, is designed to provide real-time visibility into customer demand and enable companies to adjust their demand plans and inventory levels accordingly.?

By incorporating data from a variety of sources, including social media, weather patterns, and economic indicators, demand sensing can provide a more accurate and comprehensive picture of demand. This allows companies to make more informed decisions about production and inventory levels, as well as pricing and promotional strategies.

For example, consider the impact of the COVID-19 pandemic on consumer demand. As the pandemic swept across the globe, many traditional demand planning methods were unable to keep up with the rapid changes in consumer behavior. Demand for certain products, such as toilet paper and cleaning supplies, skyrocketed while demand for other services, such as travel and hospitality, plummeted.

Companies that were able to use demand sensing to quickly adjust their supply chains and inventory levels were better positioned to weather the storm and emerge from the pandemic with minimal damage.

Demand Sensing by o9 Solutions

With o9’s Demand Sensing, companies can pick up on short-term trends and market shifts immediately to better predict what consumers want, and when, and where they want it. Our solution allows you to create multiple demand models with as much granular detail as you like.?

o9 is trusted by the world’s leading companies. Our demand sensing software approach excels in providing businesses with actionable insights, allows easy collaboration, and leverages built-in models.

BOOK A DEMO HERE.

About the author

o9 offers a leading AI-powered Planning, Analytics & Data platform called the Digital Brain that helps companies across industry verticals transform traditionally slow and siloed planning into smart, integrated and intelligent planning and decision making across the core supply chain, commercial and P&L functions. With o9’s Digital Brain platform, companies are able to achieve game-changing improvements in quality of data, ability to detect demand and supply risks and opportunities earlier, forecast demand more accurately, evaluate what-if scenarios in real time, match demand and supply intelligently and drive alignment and collaboration across customers, internal stakeholders and suppliers around the integrated supply chain and commercial plans and decisions. Supported by a global ecosystem of partners, o9’s innovative delivery methodology helps companies achieve quick impact in customer service, inventory levels, resource utilization, as well as ESG and financial KPIs—while enabling a long-term, sustainable transformation of their end-to-end planning and decision-making capabilities.

*This article was previously published @ o9Solutions.com.