Did you know that around 69% of companies do not have complete visibility of their supply chains or that 46% of small businesses don’t track their inventory or don’t have an automated method to track it? These numbers are worrying, especially now that we’ve witnessed one of the greatest pandemics in history.

If the COVID-19 pandemic has taught us anything, it’s the volatility of the market and how the demand dynamics can change almost overnight. Companies have seen customer behavior evolve rapidly in the last few months and the need to react to these changes in near real-time is now stronger than ever. 

Traditional techniques of demand forecasting in supply chain can no longer predict these changes in the demand-supply network. Almost 71% of organizations are struggling to manage and safeguard their unstructured data and nearly 80% of the enterprises have little visibility into what’s happening in their unstructured data silos

The question here is obvious. How to manage the rapidly evolving demand-supply dynamics using the data available and how to do it right?

Artificial Intelligence (AI) to the Rescue

The first step in addressing these pressing questions would be to minimize manual effort in digitizing documents as much as possible, by building an internal data ecosystem that facilitates accuracy and eliminates delays in business processes. 

AI in supply chain management fuels accuracy, efficiency, and reliability across your supply chain. Using AI and Machine Learning (ML) for demand forecasting in supply chain is one of the most promising areas of Artificial Intelligence (AI).

These solutions work together to improve forecasting accuracy by combining AI learning algorithms with Big Data to analyze an infinite number of contributing factors simultaneously. By learning from the data on past and current performances, AI-enabled approaches continuously refine and enhance the process of demand forecasting in supply chain.

Demand Forecasting vs Demand Sensing in Supply Chain

As evident from the name, demand forecasting in supply chain management is the process of estimating and forecasting customer demand by analyzing historical data. Demand forecasting in supply chain management is based on the time-series techniques that create a forecast based on prior sales history. However, past sales records can only predict forecast levels in mid and long-term planning horizons. Short-term planning needs a little more, which is why demand sensing comes into the picture. To put it simply, demand sensing helps you provide real-time responses to real-time demand.

Capture Demand Signals More Efficiently with Demand Sensing

Demand sensing uses detailed short-term demand data to improve near-future forecasts, depending on how dynamic your supply chain is. It inspects multiple demand signals and combines them to predict real-time demand with superior forecast accuracy compared to traditional time-series approaches.

AI-powered demand sensing uses machine learning to take supply chain demand forecasting to the next step by using internal and external data sources such as demographics, weather, online reviews, and social media trends. Using external data and modern Machine Learning algorithms, organizations can outperform networks that are managed manually and adapt to external changes faster. Some benefits of using a demand sensing software include:

  • AI-enabled demand sensing software can improve your forecast accuracy from the traditional 60% to more than 80%. 
  • It can enhance demand forecasting in supply chain by leveraging multiple real-time signals to sense demand shifts and respond swiftly to varying market conditions.
  • AI-driven demand sensing provides an accurate estimate of the supply required to meet market demands while keeping inventory in check.
  • It leverages Machine Learning to rapidly process all data, identify patterns, and automatically produce a near-term daily forecast.

Sense to Navigate the Ever-Evolving World of Demand

Most organizations are finding ways to reinvent demand sensing with AI technologies and using algorithms that leverage deep learning to suggest improvements through cross-learning, using internal and external data inputs to detect patterns.  

The insights generated through these algorithms provide organizations with the ability to adapt to shifting external dynamics and fast-track enterprise-wide decisions about supply chain scalability, strategies, product portfolio, and resource planning that can create a real difference between competitive supremacy and obsoleteness.

Do It Right With ELI

A customer-centric demand management process can help your organization identify the drivers of demand for your customers and to use demand-shaping levers at the right time to maximize profits.

With ThroughPut’s ELI Demand Sensing Module, you can gain an outstanding competitive edge as your go-to forecasting and planning tool, which leverages a suite of AI-driven analytical technologies. ELI’s intelligent demand sensing solution uses real-time data, automation, and machine learning algorithms to create daily forecasts that reflect current market trends and realities. It gives your organization the ability to transform raw sales data into operational insights at an exceptional speed and scale. Its guaranteed forecast accuracy helps you overcome cause and effect by testing demand scenarios and sensitivities at varying levels of granularity. 

Do you need help in moving to intelligent supply chain processes? Sign up for a demo with ThroughPut and let us make the move easier for you.

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