Demand sensing software can improve short-term forecasts using detailed demand data. It closes the gap between your supply chain forecast and what’s actually happening in your supply chain by detecting changes and patterns in downstream activities. A supply chain demand sensing solution analyzes replenishment orders, POS data and EDI transactions so you can achieve target service levels even amid demand volatility.

The ToolsGroup machine learning engine further refines your baseline demand forecast by analyzing and predicting the effect of short-term, external factors like weather and macro-economic trends. It can also incorporate data from demand shaping activities such as marketing and promotions planning and new product introductions.


"Why You Should be Using Demand Sensing to React Faster to Market Changes"

What are the benefits of a supply chain demand sensing solution?

You can cut short-term forecast error by up to 50% and raise inventory performance by up to 20% while hitting or exceeding target service levels by using a supply chain demand sensing solution. Traditional demand forecasting software and approaches can’t deliver a truly responsive and reliable forecast. This is because fast-moving products become slow-movers much quicker than in the past, and the activity across SKU-locations increases heterogeneous demand behavior. A large number of companies don’t even have the demand forecasting software for a reliable baseline forecast.

How can you use demand sensing software?

Typically forecasts look out a month to 90 days, during which planners are unable to make improvements. By sensing short-term sales history and related demand causals, demand sensing gives rapid, near real-time insights within the month to make forecast updates on a shorter-term horizon. You don’t need to be confined to the typical definition of demand sensing. There are many ways to sense demand and each new insight can speed reaction time and boost profits.

  • Start doing short-term demand forecasting using sell-in data

Often the easiest way for companies to get started with demand sensing software is to use the most granular historical data available, typically by analyzing daily sell-in/ship-to demand data using shorter time horizons and adjusting the forecast accordingly. This type of demand sensing uses shipment history which is already readily available in most supply chain planning or ERP systems.

  • Incorporate sell-out data into your demand forecasting software

Downstream data such as customer, PoS or channel data is used to identify demand trends, provide advanced warning of problems, and remove the latency between the plan and what is really happening in the supply chain.

  • Incorporate external data and demand causals

Demand sensing software should also use the wide range of demand correlated variables to create a more accurate forecast that responds to real-world events such as market shifts, promotions, social media, new product introductions, weather and other external factors.

Putting all three pieces together—the sell-in and sell-out data along with relevant demand casuals—gives you a full, connected picture of demand and enables an automated supply chain demand sensing solution.

What are some capabilities demand sensing software should have?

  • Ability to model demand at the most atomic level, such as Item/Ship-to Location/Daily: Ship-to locations can be key accounts, sell-in channels, geographical territory etc. It’s critical that the demand models use the latest demand sensing data to identify the relevance of short-term spikes, outliers, trends and patterns.

  • Ability to model demand variability: A demand confidence interval is needed to understand the latest data feeds and segregate noise from the demand signal. This is essential because noise has no statistical relevance and can result in a “nervous” supply chain.

  • Ability to use downstream data: This could be ship-to data, VMI feeds, POS data, collaborative planning, etc.

  • In advanced demand sensing software you will be able to assess the impact of external variables like weather forecast, economic conditions, oil price or similar causal factors. These can be modeled into demand forecasting software to predict short-term demand.

Promotions, media, new product introductions and other demand shaping activities cause trends to shift rapidly and dramatic increases in demand variability. Your best weapon to reduce variability and guarantee high service is inventory. Demand sensing helps you best use inventory by extracting signal from noise to improve your forecast, reduce demand variability and minimize inventory to guarantee service to customers.


"Why You Should be Using Demand Sensing to React Faster to Market Changes"

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"Why You Should be Using Demand Sensing to React Faster to Market Changes"